sha
null | last_modified
null | library_name
stringclasses 154
values | text
stringlengths 1
900k
| metadata
stringlengths 2
348k
| pipeline_tag
stringclasses 45
values | id
stringlengths 5
122
| tags
listlengths 1
1.84k
| created_at
stringlengths 25
25
| arxiv
listlengths 0
201
| languages
listlengths 0
1.83k
| tags_str
stringlengths 17
9.34k
| text_str
stringlengths 0
389k
| text_lists
listlengths 0
722
| processed_texts
listlengths 1
723
| tokens_length
listlengths 1
723
| input_texts
listlengths 1
61
| embeddings
listlengths 768
768
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gq-indo-k
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7905
- Rouge1: 22.5734
- Rouge2: 6.555
- Rougel: 20.9491
- Rougelsum: 20.9509
- Gen Len: 12.0767
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.9355 | 1.0 | 13032 | 2.8563 | 22.4828 | 6.5456 | 20.8782 | 20.8772 | 11.915 |
| 2.825 | 2.0 | 26064 | 2.7993 | 22.547 | 6.5815 | 20.8937 | 20.8973 | 12.0886 |
| 2.7631 | 3.0 | 39096 | 2.7905 | 22.5734 | 6.555 | 20.9491 | 20.9509 | 12.0767 |
### Framework versions
- Transformers 4.6.1
- Pytorch 1.7.0
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{"metrics": ["rouge"]}
|
text2text-generation
|
fadhilarkan/gq-indo-k
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
gq-indo-k
=========
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7905
* Rouge1: 22.5734
* Rouge2: 6.555
* Rougel: 20.9491
* Rougelsum: 20.9509
* Gen Len: 12.0767
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.6.1
* Pytorch 1.7.0
* Datasets 1.11.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.7.0\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.7.0\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
48,
113,
4,
30
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.7.0\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
-0.07607080787420273,
-0.013563286513090134,
-0.002247680677101016,
0.07994938641786575,
0.1962406486272812,
0.033163998275995255,
0.10877521336078644,
0.1163998693227768,
-0.10711760818958282,
0.017673837020993233,
0.10371947288513184,
0.1687660664319992,
0.023862071335315704,
0.1309463530778885,
-0.07679495960474014,
-0.29576876759529114,
-0.0008280042675323784,
0.03759877383708954,
-0.04218148812651634,
0.13654889166355133,
0.09028078615665436,
-0.14135611057281494,
0.06768882274627686,
-0.014106850139796734,
-0.20257961750030518,
0.021518399938941002,
0.020765909925103188,
-0.06761875003576279,
0.15842053294181824,
0.027224961668252945,
0.13197971880435944,
0.011888726614415646,
0.08251263946294785,
-0.19842588901519775,
0.012603750452399254,
0.0598558634519577,
0.019190028309822083,
0.07126925140619278,
0.07685298472642899,
-0.015832826495170593,
0.1336849182844162,
-0.08887696266174316,
0.06175364926457405,
0.01854928955435753,
-0.1282978504896164,
-0.20807531476020813,
-0.061591751873493195,
-0.0065307882614433765,
0.07455620169639587,
0.10586176812648773,
-0.01863286830484867,
0.1488603949546814,
-0.10817937552928925,
0.10851150006055832,
0.2256767749786377,
-0.26996684074401855,
-0.06986203044652939,
0.0005381411174312234,
0.020425764843821526,
0.0996936559677124,
-0.0964500904083252,
-0.007268013898283243,
0.04748305305838585,
0.06347391754388809,
0.12492667883634567,
-0.03128143027424812,
-0.10387174040079117,
0.009323234669864178,
-0.14908798038959503,
-0.027623314410448074,
0.10812649875879288,
0.02125287428498268,
-0.027765896171331406,
-0.03237587958574295,
-0.07236281782388687,
-0.1446608603000641,
-0.05165869742631912,
-0.02580839954316616,
0.03365766257047653,
-0.04086707904934883,
-0.07330246269702911,
0.0028982022777199745,
-0.09215620160102844,
-0.061086542904376984,
-0.06641647219657898,
0.17244480550289154,
0.046910010278224945,
-0.0044032372534275055,
-0.043922342360019684,
0.10297852009534836,
-0.023699427023530006,
-0.13006220757961273,
0.03016970492899418,
0.015935540199279785,
-0.027956383302807808,
-0.056021712720394135,
-0.07906804978847504,
-0.09941922128200531,
-0.0003716709034051746,
0.08574439585208893,
-0.06362020969390869,
0.06955870240926743,
-0.004513680003583431,
0.022767115384340286,
-0.08706248551607132,
0.1864331066608429,
-0.01789449341595173,
-0.02823759987950325,
0.005278685595840216,
0.04316416755318642,
-0.004165001679211855,
-0.020567385479807854,
-0.09242549538612366,
-0.002207375131547451,
0.12857021391391754,
0.02232552506029606,
-0.07196874171495438,
0.07221497595310211,
-0.03610645979642868,
-0.025497866794466972,
-0.047607384622097015,
-0.10744781047105789,
0.037943266332149506,
-0.013207556679844856,
-0.06869842857122421,
0.025413978844881058,
0.012579834088683128,
0.02238752879202366,
-0.05181414261460304,
0.14467011392116547,
-0.07215702533721924,
0.039199259132146835,
-0.10925044864416122,
-0.13186310231685638,
0.00578654371201992,
-0.041588690131902695,
0.011022612452507019,
-0.10970354825258255,
-0.16077539324760437,
-0.010735590010881424,
0.04557819664478302,
-0.03635116666555405,
-0.04767296835780144,
-0.05533077195286751,
-0.06225980073213577,
0.026354044675827026,
-0.029204199090600014,
0.17786213755607605,
-0.060243360698223114,
0.11486490070819855,
0.06508604437112808,
0.06773161143064499,
-0.028826704248785973,
0.05884050950407982,
-0.08404890447854996,
-0.0019536754116415977,
-0.20131640136241913,
0.068593829870224,
-0.03304286301136017,
0.07266727834939957,
-0.0741066187620163,
-0.11483284085988998,
-0.00023794779554009438,
0.0029904500115662813,
0.09994425624608994,
0.10812212526798248,
-0.1537753939628601,
-0.08445598185062408,
0.16975262761116028,
-0.06791899353265762,
-0.10505039244890213,
0.12421246618032455,
-0.05531168356537819,
0.030290061607956886,
0.08172094076871872,
0.16380546987056732,
0.0489153116941452,
-0.07348296046257019,
0.03791258484125137,
-0.03975621238350868,
0.058985576033592224,
-0.0274979081004858,
0.04976091906428337,
0.018220070749521255,
-0.0315130241215229,
0.02686489373445511,
0.01014813780784607,
0.06819139420986176,
-0.11517205834388733,
-0.07518503069877625,
-0.04952400177717209,
-0.08433584123849869,
0.05249844118952751,
0.06359162926673889,
0.08331246674060822,
-0.1211950033903122,
-0.07325581461191177,
0.06603802740573883,
0.06255317479372025,
-0.060141485184431076,
0.04062121361494064,
-0.052310019731521606,
0.051597096025943756,
-0.03372154384851456,
-0.0022867873776704073,
-0.20549526810646057,
-0.025324424728751183,
0.00902040209621191,
0.060037486255168915,
0.03235387057065964,
0.00182810437399894,
0.07587983459234238,
0.06485828757286072,
-0.06514746695756912,
-0.025454988703131676,
-0.021265888586640358,
-0.005591833032667637,
-0.1425565481185913,
-0.1770988255739212,
-0.013433491811156273,
-0.01563936285674572,
0.0971536710858345,
-0.20473721623420715,
0.022957218810915947,
-0.04061286523938179,
0.0820440948009491,
0.0004541338130366057,
0.004167607054114342,
-0.05541536584496498,
0.1006525382399559,
-0.031140610575675964,
-0.0364094041287899,
0.07606244832277298,
-0.004807071760296822,
-0.07765941321849823,
-0.037030044943094254,
-0.13079489767551422,
0.16717205941677094,
0.13472452759742737,
-0.16641607880592346,
-0.08519388735294342,
-0.0040425932966172695,
-0.05255390331149101,
-0.029362859204411507,
-0.052545785903930664,
0.026308616623282433,
0.1892772763967514,
-0.0035416653845459223,
0.1557813286781311,
-0.06653407216072083,
-0.03443305566906929,
0.016880691051483154,
-0.03173324465751648,
0.04075681045651436,
0.11439239233732224,
0.09415413439273834,
-0.0904109850525856,
0.11145012080669403,
0.14806704223155975,
-0.09204049408435822,
0.1351369172334671,
-0.025263752788305283,
-0.08780592679977417,
0.0051727681420743465,
-0.024997880682349205,
-0.000048502901336178184,
0.056069668382406235,
-0.14208070933818817,
-0.0037871194072067738,
0.01895764470100403,
0.04047369584441185,
0.027547260746359825,
-0.21967709064483643,
-0.028490634635090828,
0.03835808485746384,
-0.03057045117020607,
-0.030191613361239433,
-0.013832617551088333,
0.03682759404182434,
0.12776102125644684,
0.005045967176556587,
-0.05765659734606743,
0.01973467320203781,
-0.004107972607016563,
-0.08765760809183121,
0.20988212525844574,
-0.08350735157728195,
-0.17302699387073517,
-0.0911867544054985,
-0.10941782593727112,
-0.043793320655822754,
0.008701781742274761,
0.06394117325544357,
-0.12099174410104752,
-0.0346207432448864,
-0.04287249222397804,
0.05585462227463722,
-0.01973063126206398,
0.04353219270706177,
-0.004395082592964172,
-0.0017035488272085786,
0.05823780596256256,
-0.09233561903238297,
-0.02267676405608654,
-0.04870017245411873,
-0.06351854652166367,
0.07412707805633545,
0.03571870177984238,
0.10442601889371872,
0.16629460453987122,
-0.036748405545949936,
0.02052999474108219,
-0.0399746298789978,
0.2208734154701233,
-0.0692954882979393,
-0.036689020693302155,
0.13879935443401337,
-0.0131599185988307,
0.05472249165177345,
0.11439375579357147,
0.051720332354307175,
-0.09341534972190857,
0.03700665757060051,
0.03676788881421089,
-0.03257877007126808,
-0.22207345068454742,
-0.041555002331733704,
-0.06831363588571548,
-0.032549671828746796,
0.08151500672101974,
0.012202536687254906,
0.04332621395587921,
0.05800096318125725,
0.032429974526166916,
0.07159488648176193,
-0.0293281227350235,
0.06299678981304169,
0.1471487432718277,
0.044699665158987045,
0.13826905190944672,
-0.04314250126481056,
-0.08637522161006927,
0.03440821170806885,
-0.040654849261045456,
0.22385208308696747,
0.008975345641374588,
0.06449242681264877,
0.03805546462535858,
0.14445602893829346,
0.013655347749590874,
0.10073043406009674,
0.012605218216776848,
-0.0526735819876194,
-0.010164620354771614,
-0.03577004745602608,
-0.049588222056627274,
0.014949580654501915,
-0.0413145087659359,
0.04088209196925163,
-0.14998121559619904,
-0.026400556787848473,
0.05494518205523491,
0.24264273047447205,
0.034901201725006104,
-0.3164387345314026,
-0.07559730857610703,
0.015182435512542725,
-0.06897559016942978,
-0.027896638959646225,
0.012905371375381947,
0.07298360019922256,
-0.11956188082695007,
0.05578659847378731,
-0.07464677095413208,
0.10992030799388885,
-0.021929273381829262,
0.05887080356478691,
0.032347194850444794,
0.11595527827739716,
-0.004129953682422638,
0.07738212496042252,
-0.3596982955932617,
0.27405744791030884,
0.0015335573116317391,
0.07097331434488297,
-0.0838790237903595,
-0.004336530342698097,
0.038455091416835785,
0.04717012122273445,
0.01620294712483883,
-0.022460870444774628,
-0.05810136720538139,
-0.19317001104354858,
-0.026287654414772987,
0.04270930588245392,
0.12822304666042328,
-0.0069524734281003475,
0.12239804118871689,
-0.03889455646276474,
0.010047074407339096,
0.07546644657850266,
-0.004015667364001274,
-0.10011547058820724,
-0.07978228479623795,
-0.010091722942888737,
0.019107811152935028,
-0.006117724347859621,
-0.06066302955150604,
-0.11525771766901016,
-0.10895603150129318,
0.1618519425392151,
0.047423411160707474,
-0.027112113311886787,
-0.12732788920402527,
0.1007549911737442,
0.07487193495035172,
-0.08238699287176132,
0.03383644297719002,
0.01440745685249567,
0.06696892529726028,
0.027481023222208023,
-0.08084505051374435,
0.11695490777492523,
-0.07023915648460388,
-0.15322592854499817,
-0.04961223900318146,
0.11156388372182846,
0.015626350417733192,
0.07338780164718628,
-0.017096534371376038,
0.012958195060491562,
-0.02426782436668873,
-0.08463835716247559,
0.01972782425582409,
-0.01949136331677437,
0.06283775717020035,
0.05517655983567238,
-0.06787718832492828,
0.00325418240390718,
-0.075074203312397,
-0.043522775173187256,
0.22474262118339539,
0.2302720546722412,
-0.06675481051206589,
0.009693212807178497,
0.02415817603468895,
-0.06863187253475189,
-0.1815946102142334,
0.0521828792989254,
0.07987102121114731,
0.012899959459900856,
0.03312297910451889,
-0.17453500628471375,
0.09634537249803543,
0.07993146032094955,
0.011095317080616951,
0.08894208818674088,
-0.31595221161842346,
-0.13440482318401337,
0.11172487586736679,
0.15892916917800903,
0.14685370028018951,
-0.14238792657852173,
-0.013032792136073112,
-0.046770043671131134,
-0.10964725911617279,
0.10903562605381012,
-0.07699589431285858,
0.1293439269065857,
-0.017563126981258392,
0.10871568322181702,
0.018058007583022118,
-0.048779405653476715,
0.09632205218076706,
-0.01143107283860445,
0.08505997806787491,
-0.07426762580871582,
0.00536193884909153,
0.012625986710190773,
-0.036951933056116104,
-0.0009951175889000297,
-0.05663459748029709,
0.01770624704658985,
-0.09446720033884048,
-0.031206009909510612,
-0.09181880205869675,
0.022126635536551476,
-0.02322087064385414,
-0.06467698514461517,
-0.01207242626696825,
0.009748827666044235,
0.05603098124265671,
-0.02095085196197033,
0.10521657019853592,
-0.023703686892986298,
0.17230664193630219,
0.08580638468265533,
0.11108118295669556,
-0.08330242335796356,
-0.015640661120414734,
-0.0052645327523350716,
-0.015564103610813618,
0.03442130237817764,
-0.12281113862991333,
0.031124956905841827,
0.14833059906959534,
0.005594105459749699,
0.13695460557937622,
0.0986805409193039,
-0.016949264332652092,
0.014850330539047718,
0.07738561928272247,
-0.16155901551246643,
-0.09432064741849899,
-0.01757706329226494,
-0.04687740281224251,
-0.09793063998222351,
0.035780102014541626,
0.10011844336986542,
-0.07115678489208221,
-0.012766349129378796,
-0.02133234217762947,
-0.003692388301715255,
-0.0709046721458435,
0.21515506505966187,
0.040707480162382126,
0.053735844790935516,
-0.10108621418476105,
0.06901324540376663,
0.045506689697504044,
-0.09438309818506241,
0.017114808782935143,
0.13212352991104126,
-0.07520478963851929,
-0.0361882820725441,
0.10244724154472351,
0.18074773252010345,
-0.06377161294221878,
-0.03159986063838005,
-0.14073224365711212,
-0.14335575699806213,
0.08635811507701874,
0.16872578859329224,
0.08807425945997238,
-0.006385116372257471,
-0.06608612835407257,
0.026346854865550995,
-0.13707803189754486,
0.07749714702367783,
0.06682582199573517,
0.0721951425075531,
-0.1336086392402649,
0.19982565939426422,
0.00321986828930676,
0.05656345561146736,
-0.03482448309659958,
0.012366396375000477,
-0.10981608182191849,
0.03445470705628395,
-0.15110331773757935,
-0.05646749213337898,
-0.005905001424252987,
-0.0076272208243608475,
-0.006309965625405312,
-0.06854155659675598,
-0.0609305240213871,
-0.003204388078302145,
-0.12139254808425903,
-0.026103591546416283,
0.017092758789658546,
0.0385056734085083,
-0.1014077216386795,
-0.03681290149688721,
0.028659475967288017,
-0.06588670611381531,
0.05880991369485855,
0.06141796335577965,
-0.006174610927700996,
0.08013321459293365,
-0.14503128826618195,
-0.020556790754199028,
0.06871206313371658,
0.005420076195150614,
0.07251329720020294,
-0.08397012948989868,
0.008496426977217197,
0.008649192750453949,
0.09068343788385391,
0.03417453169822693,
0.07776031643152237,
-0.12850549817085266,
-0.0032674504909664392,
-0.03680875152349472,
-0.10640500485897064,
-0.058615852147340775,
0.029285764321684837,
0.04976441711187363,
0.01579468883574009,
0.17184758186340332,
-0.09933202713727951,
0.05802683159708977,
-0.2214149683713913,
-0.004396536853164434,
-0.022442299872636795,
-0.11633988469839096,
-0.08860976994037628,
-0.07871846109628677,
0.08648845553398132,
-0.047548532485961914,
0.11532234400510788,
0.02340509369969368,
0.1020892933011055,
0.035239871591329575,
-0.03546001762151718,
0.010719145648181438,
0.02809467352926731,
0.22820837795734406,
0.04959890991449356,
-0.04607502743601799,
0.05613522604107857,
0.06498904526233673,
0.10839855670928955,
0.12735390663146973,
0.23662839829921722,
0.14962057769298553,
-0.0005288260290399194,
0.09580673277378082,
0.014903565868735313,
-0.05161033198237419,
-0.1419733166694641,
0.0078056505881249905,
-0.04371722415089607,
0.09975649416446686,
-0.03392507880926132,
0.20798291265964508,
0.0788794755935669,
-0.1580137461423874,
0.054178059101104736,
-0.06973464787006378,
-0.09293940663337708,
-0.11438030004501343,
-0.010256335139274597,
-0.0921550765633583,
-0.16385915875434875,
-0.00019251070625614375,
-0.12390564382076263,
0.047855399549007416,
0.10554711520671844,
0.027487408369779587,
-0.02766057848930359,
0.16929876804351807,
0.049069128930568695,
0.00534017151221633,
0.08513611555099487,
-0.007051735185086727,
0.003669401630759239,
-0.07142508774995804,
-0.06960798799991608,
-0.01654333807528019,
-0.011231575161218643,
0.04055968299508095,
-0.04114683344960213,
-0.10745253413915634,
0.03871801868081093,
-0.03592865541577339,
-0.1091340184211731,
0.024984613060951233,
0.02526981383562088,
0.07350527495145798,
0.06694076210260391,
0.009120501577854156,
-0.004397101700305939,
-0.02361970953643322,
0.24364489316940308,
-0.0977468341588974,
-0.11200183629989624,
-0.09787199646234512,
0.3091495633125305,
0.0493117980659008,
0.000599670922383666,
0.02331003174185753,
-0.058761097490787506,
-0.014990278519690037,
0.25348979234695435,
0.17558199167251587,
-0.10588985681533813,
-0.014249047264456749,
0.0005126685136929154,
-0.007214350625872612,
-0.011025166139006615,
0.14586010575294495,
0.15076535940170288,
0.055865172296762466,
-0.10436509549617767,
-0.015284767374396324,
-0.04914744943380356,
-0.012965478003025055,
-0.04620613530278206,
0.08249904215335846,
0.06355299055576324,
0.003478531027212739,
-0.029941746965050697,
0.07703514397144318,
-0.0726843774318695,
-0.05242672935128212,
-0.007071784697473049,
-0.20342248678207397,
-0.15985189378261566,
-0.018796350806951523,
0.09130074828863144,
-0.0025267861783504486,
0.06673802435398102,
-0.026007594540715218,
-0.0011326191015541553,
0.05488181114196777,
-0.01837734691798687,
-0.05266289785504341,
-0.08499737083911896,
0.10097026079893112,
-0.11689341068267822,
0.145405113697052,
-0.03935663774609566,
0.05458074063062668,
0.12970709800720215,
0.0648510605096817,
-0.05610905587673187,
0.08224319666624069,
0.03750820830464363,
-0.08729379624128342,
0.04444955289363861,
0.13464896380901337,
-0.03956779092550278,
0.029197324067354202,
0.04701937362551689,
-0.1501840054988861,
0.0315573550760746,
-0.09314462542533875,
-0.03978608921170235,
-0.019829092547297478,
-0.04298949986696243,
-0.05840228870511055,
0.11600907146930695,
0.23502860963344574,
-0.023503722622990608,
0.030781293287873268,
-0.0951702892780304,
0.006374203134328127,
0.033558204770088196,
0.06834197044372559,
-0.08632484823465347,
-0.26237720251083374,
-0.004776547662913799,
0.09469445794820786,
-0.025876225903630257,
-0.2591387629508972,
-0.08726963400840759,
-0.00011639032163657248,
-0.06218493729829788,
-0.1200968474149704,
0.11077622324228287,
0.08319942653179169,
0.03613196685910225,
-0.04883745685219765,
-0.14060921967029572,
-0.06534074991941452,
0.19298294186592102,
-0.14952266216278076,
-0.07542966306209564
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qa-indo-k
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4984
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2537 | 1.0 | 8209 | 1.9642 |
| 0.943 | 2.0 | 16418 | 2.2143 |
| 0.6694 | 3.0 | 24627 | 2.4984 |
### Framework versions
- Transformers 4.6.1
- Pytorch 1.7.0
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{}
|
question-answering
|
fadhilarkan/qa-indo-k
|
[
"transformers",
"pytorch",
"albert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #albert #question-answering #endpoints_compatible #region-us
|
qa-indo-k
=========
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4984
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.6.1
* Pytorch 1.7.0
* Datasets 1.11.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.7.0\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #albert #question-answering #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.7.0\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
30,
98,
4,
30
] |
[
"passage: TAGS\n#transformers #pytorch #albert #question-answering #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.7.0\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
-0.0883120596408844,
-0.0411439947783947,
-0.0017433665925636888,
0.09196717292070389,
0.21109025180339813,
0.039637867361307144,
0.10200946033000946,
0.08227881789207458,
-0.1256236881017685,
0.0024050259962677956,
0.1143365353345871,
0.1560308039188385,
-0.021831264719367027,
0.04647960141301155,
-0.06986591964960098,
-0.25180360674858093,
-0.0248868428170681,
0.060372497886419296,
-0.12145556509494781,
0.14141960442066193,
0.08088656514883041,
-0.16576507687568665,
0.059084806591272354,
-0.00418064696714282,
-0.2767196595668793,
0.0329463928937912,
0.025159861892461777,
-0.06716614961624146,
0.15801464021205902,
-0.002302268985658884,
0.19737441837787628,
-0.004670789465308189,
0.08968020975589752,
-0.17184779047966003,
0.007306316401809454,
0.05066155642271042,
0.02118353731930256,
0.06222253292798996,
0.0448404960334301,
-0.012796380557119846,
0.09044971317052841,
-0.11276738345623016,
0.07897993177175522,
0.01866939477622509,
-0.1543487161397934,
-0.2825738489627838,
-0.06332560628652573,
-0.01417645625770092,
0.07457824051380157,
0.11943558603525162,
-0.009530048817396164,
0.18147480487823486,
-0.15730494260787964,
0.10790086537599564,
0.28776049613952637,
-0.2533194422721863,
-0.08287931233644485,
0.03828039392828941,
0.008416548371315002,
0.06960279494524002,
-0.11463844776153564,
-0.027564316987991333,
0.07377727329730988,
0.06260266900062561,
0.1027630865573883,
-0.044519420713186264,
-0.13927416503429413,
0.043963123112916946,
-0.1534949690103531,
-0.03254033252596855,
0.1063452959060669,
0.014484052546322346,
-0.01713091880083084,
0.02351946011185646,
-0.07181355357170105,
-0.12736831605434418,
-0.038012344390153885,
-0.035304803401231766,
0.06755156069993973,
-0.07847410440444946,
-0.10980038344860077,
0.04457823559641838,
-0.10894569754600525,
-0.0831608846783638,
-0.06431610137224197,
0.24931637942790985,
0.05540728569030762,
0.031614962965250015,
-0.08388592302799225,
0.10995253920555115,
-0.018564729019999504,
-0.15074589848518372,
0.023461557924747467,
0.038525309413671494,
-0.033967357128858566,
-0.06087813153862953,
-0.09410785138607025,
-0.03603333234786987,
-0.0010350161464884877,
0.12024831771850586,
-0.09977725893259048,
0.05564117431640625,
0.06320486217737198,
0.02806149795651436,
-0.0953279659152031,
0.24023550748825073,
-0.021230489015579224,
0.023389603942632675,
-0.017000213265419006,
0.013005733489990234,
-0.03274323418736458,
0.010816438123583794,
-0.05994651839137077,
-0.012485047802329063,
0.12168198078870773,
0.030073637142777443,
-0.09118108451366425,
0.040151920169591904,
-0.03366038203239441,
-0.0016265071462839842,
-0.058104828000068665,
-0.10209614038467407,
0.048576124012470245,
-0.004610384348779917,
-0.08294180780649185,
0.048753879964351654,
-0.0032929955050349236,
0.010141821578145027,
-0.011341696605086327,
0.16699479520320892,
-0.07900408655405045,
0.07016103714704514,
-0.12639647722244263,
-0.10630521178245544,
-0.006308265030384064,
-0.028092198073863983,
0.043955400586128235,
-0.09329626709222794,
-0.11793262511491776,
-0.006466494873166084,
0.06104668229818344,
-0.02143467403948307,
-0.011757736094295979,
-0.02279965579509735,
-0.10586241632699966,
-0.0124696409329772,
-0.023056723177433014,
0.1950351893901825,
-0.04980246722698212,
0.12177189439535141,
0.09290888160467148,
0.08493449538946152,
-0.0077930595725774765,
0.04628753289580345,
-0.08294280618429184,
-0.005051724147051573,
-0.25911402702331543,
0.020977964624762535,
-0.047599855810403824,
0.06471812725067139,
-0.06052906811237335,
-0.15387585759162903,
0.058834582567214966,
-0.004050701390951872,
0.09319315105676651,
0.100801981985569,
-0.1408371925354004,
-0.08121129125356674,
0.13798066973686218,
-0.07034510374069214,
-0.09589419513940811,
0.09700609743595123,
-0.061902593821287155,
0.018148625269532204,
0.07404503226280212,
0.14296062290668488,
-0.01273888535797596,
-0.11477314680814743,
0.02113782986998558,
-0.03072747029364109,
0.030255192890763283,
-0.03941540792584419,
0.0418446809053421,
0.029470961540937424,
-0.002844220958650112,
0.02915235608816147,
-0.05958247184753418,
0.058453042060136795,
-0.14508309960365295,
-0.07132600992918015,
-0.05628114938735962,
-0.08722943812608719,
0.050571806728839874,
0.09515061229467392,
0.09039371460676193,
-0.1289043128490448,
-0.06080714613199234,
0.13361071050167084,
0.045656461268663406,
-0.04641353338956833,
0.031730473041534424,
-0.04835185408592224,
0.030671529471874237,
-0.06287459284067154,
-0.037764716893434525,
-0.21557600796222687,
-0.03474308177828789,
-0.0024974681437015533,
0.04903523623943329,
0.004321074113249779,
0.050550732761621475,
0.0942171961069107,
0.04234607145190239,
-0.08502476662397385,
-0.030894124880433083,
-0.08638186752796173,
-0.009812239557504654,
-0.13301996886730194,
-0.1821964979171753,
-0.025634855031967163,
-0.0065161376260221004,
0.06094798073172569,
-0.15331728756427765,
0.018690794706344604,
-0.033341407775878906,
0.0755615085363388,
-0.012000628747045994,
0.008820923045277596,
-0.07835625112056732,
0.10205569118261337,
0.006198180373758078,
-0.038125645369291306,
0.05076233297586441,
-0.021104084327816963,
-0.05169016122817993,
-0.07342991232872009,
-0.0625791922211647,
0.20388802886009216,
0.14266088604927063,
-0.19635963439941406,
-0.10209152102470398,
0.05944192036986351,
-0.07503889501094818,
-0.02156536839902401,
-0.06067214906215668,
0.05806436017155647,
0.24145591259002686,
-0.012385230511426926,
0.13421568274497986,
-0.0750066339969635,
-0.05226202309131622,
0.0018093293765559793,
-0.055878181010484695,
0.08157894760370255,
0.12173622101545334,
0.09790250658988953,
-0.059388525784015656,
0.10453875362873077,
0.15278840065002441,
-0.13375091552734375,
0.08604792505502701,
-0.06919226050376892,
-0.08207147568464279,
-0.026379486545920372,
-0.02685566619038582,
-0.007303209509700537,
0.12650911509990692,
-0.12004538625478745,
0.005551520735025406,
0.004777290392667055,
0.04367746412754059,
0.03237568959593773,
-0.25449660420417786,
-0.059885408729314804,
0.027799135074019432,
-0.012905944138765335,
-0.046526405960321426,
-0.020267004147171974,
0.05127422511577606,
0.1169748455286026,
-0.016831936314702034,
-0.062115274369716644,
0.010605322197079659,
0.0022981923539191484,
-0.06431857496500015,
0.24460269510746002,
-0.06081400439143181,
-0.07858270406723022,
-0.07018567621707916,
-0.05424674600362778,
-0.025049639865756035,
-0.014664909802377224,
0.06972144544124603,
-0.15853360295295715,
-0.026264015585184097,
0.009269203059375286,
0.06240328401327133,
-0.014180989935994148,
0.050079718232154846,
0.01996248960494995,
0.005889063701033592,
0.05727481096982956,
-0.12430860847234726,
-0.010961903259158134,
-0.08351729810237885,
-0.09436338394880295,
0.09132068604230881,
0.056380871683359146,
0.10731352865695953,
0.15400385856628418,
-0.04141310974955559,
0.016572412103414536,
-0.015846744179725647,
0.26676470041275024,
-0.08650434017181396,
-0.07395181059837341,
0.12174657732248306,
0.0018092724494636059,
0.03893469646573067,
0.08595576882362366,
0.08078686892986298,
-0.11994569003582001,
0.003304045647382736,
0.02839907445013523,
-0.03747139871120453,
-0.24483899772167206,
-0.021513469517230988,
-0.040771305561065674,
-0.0819714292883873,
0.04425205662846565,
-0.006145555060356855,
0.02169724926352501,
0.05043656751513481,
0.054193828254938126,
0.06815555691719055,
-0.0709419846534729,
0.050065454095602036,
0.10688889026641846,
0.04047117009758949,
0.11908674240112305,
-0.04792068526148796,
-0.10659387707710266,
0.0023876202758401632,
-0.07456861436367035,
0.2837046980857849,
0.003952550236135721,
0.02309657819569111,
0.06246671453118324,
0.14353543519973755,
0.013598203659057617,
0.11355767399072647,
0.009360426105558872,
-0.08713974058628082,
0.002518625929951668,
-0.030129604041576385,
-0.02940700761973858,
0.001872764085419476,
-0.05121953785419464,
0.07250405848026276,
-0.13980966806411743,
-0.04019041359424591,
0.0640445128083229,
0.22710900008678436,
0.012689827010035515,
-0.2859035134315491,
-0.0648653581738472,
-0.01052081398665905,
-0.04782174155116081,
-0.0049751377664506435,
0.0104749146848917,
0.10723999887704849,
-0.10792344063520432,
0.012392114847898483,
-0.04965515807271004,
0.09496954828500748,
0.010626695118844509,
0.055827464908361435,
0.04373637214303017,
0.09036222845315933,
0.005964008159935474,
0.0667971521615982,
-0.3511998951435089,
0.3327435851097107,
0.004444397985935211,
0.0940890982747078,
-0.05781504511833191,
-0.025976672768592834,
0.015052233822643757,
0.050387777388095856,
0.026456372812390327,
-0.017954863607883453,
-0.014400963671505451,
-0.2127961814403534,
-0.01632896065711975,
0.0702609047293663,
0.13643471896648407,
0.009076647460460663,
0.10438038408756256,
0.004460004158318043,
0.0061179837211966515,
0.09002076089382172,
-0.019167376682162285,
-0.08233829587697983,
-0.03146517276763916,
-0.04699849337339401,
-0.02531301975250244,
-0.052027761936187744,
-0.06881240755319595,
-0.12164720892906189,
-0.09629584848880768,
0.10506681352853775,
0.06819866597652435,
-0.034163013100624084,
-0.12001992762088776,
0.10990212857723236,
0.10688308626413345,
-0.07545854896306992,
0.03167523816227913,
0.03576970845460892,
0.027035102248191833,
0.046332649886608124,
-0.04900439828634262,
0.10106486827135086,
-0.06754334270954132,
-0.15509873628616333,
-0.03536611795425415,
0.09811966866254807,
0.04243512079119682,
0.08259912580251694,
-0.019837362691760063,
0.03147127479314804,
-0.030141707509756088,
-0.1062123030424118,
0.008085637353360653,
-0.03787270560860634,
0.07475419342517853,
0.05961262434720993,
-0.025385204702615738,
0.06204204261302948,
-0.06234594061970711,
-0.00044113234616816044,
0.18624922633171082,
0.25901666283607483,
-0.07743518799543381,
-0.039284247905015945,
0.004379536956548691,
-0.04951426386833191,
-0.15971477329730988,
0.1076236367225647,
0.09858015179634094,
-0.006741650402545929,
0.02504735253751278,
-0.13706037402153015,
0.14892295002937317,
0.09710042923688889,
0.00809232983738184,
0.06004556268453598,
-0.3229343295097351,
-0.12575304508209229,
0.08388122171163559,
0.19046568870544434,
0.1849527657032013,
-0.15182386338710785,
0.006359577178955078,
-0.018108289688825607,
-0.16348296403884888,
0.059656932950019836,
-0.07783011347055435,
0.11463853716850281,
-0.03904099762439728,
0.10165382921695709,
0.003314174944534898,
-0.08138134330511093,
0.12499688565731049,
0.029054081067442894,
0.12194716930389404,
-0.03355379030108452,
-0.03521426394581795,
0.05742743983864784,
-0.023315034806728363,
-0.014574284665286541,
0.003933507949113846,
0.027225656434893608,
-0.07800142467021942,
-0.014774962328374386,
-0.12276527285575867,
0.0283038392663002,
-0.04999028891324997,
-0.05429333075881004,
-0.018511643633246422,
-0.006106802728027105,
0.03326614201068878,
-0.031033771112561226,
0.09797212481498718,
0.02903483621776104,
0.1762275993824005,
0.02866189368069172,
0.07586620002985,
-0.09946262836456299,
-0.10010920464992523,
0.012403441593050957,
0.007968549616634846,
0.058807406574487686,
-0.16436238586902618,
0.02050558105111122,
0.16156525909900665,
0.05131901800632477,
0.109932541847229,
0.11430659145116806,
-0.008735301904380322,
0.0014650074299424887,
0.05346853658556938,
-0.15613293647766113,
-0.08884218335151672,
0.006397351156920195,
-0.07514729350805283,
-0.0831233337521553,
0.057871829718351364,
0.0604986734688282,
-0.07450179010629654,
-0.009665235877037048,
-0.0289651770144701,
-0.03963281586766243,
-0.07941816002130508,
0.2349262237548828,
0.0849708840250969,
0.06512892246246338,
-0.1209036186337471,
0.04942905530333519,
0.030961384996771812,
-0.09463614225387573,
-0.007542853709310293,
0.07777027040719986,
-0.05789697542786598,
-0.017405832186341286,
0.15559327602386475,
0.214459627866745,
-0.08608435839414597,
-0.0170490350574255,
-0.17636609077453613,
-0.12505598366260529,
0.0705755278468132,
0.19710060954093933,
0.13411056995391846,
-0.012164415791630745,
-0.0346548892557621,
0.03889112547039986,
-0.1539580076932907,
0.055543363094329834,
0.03629142791032791,
0.07645496726036072,
-0.12278091907501221,
0.193842351436615,
0.025699838995933533,
0.060143034905195236,
-0.030128877609968185,
0.030694296583533287,
-0.1390962153673172,
0.06560713797807693,
-0.12775465846061707,
-0.06567773222923279,
0.028690215200185776,
-0.017079150304198265,
0.000038001187931513414,
-0.09795887768268585,
-0.06869334727525711,
0.023127367720007896,
-0.14134997129440308,
-0.005015361588448286,
0.04937044158577919,
0.01790572889149189,
-0.12656550109386444,
-0.04482497647404671,
0.056676313281059265,
-0.07685768604278564,
0.03540617600083351,
0.08415783941745758,
0.008606253191828728,
0.10249247401952744,
-0.14744654297828674,
-0.05780837684869766,
0.07978285104036331,
-0.00197802996262908,
0.10978688299655914,
-0.09066993743181229,
0.0023233871906995773,
-0.002250376157462597,
0.13824434578418732,
0.039788227528333664,
0.035380080342292786,
-0.13411147892475128,
0.01537796389311552,
-0.03956986963748932,
-0.11753575503826141,
-0.061497513204813004,
-0.016043415293097496,
0.08035430312156677,
0.01879570633172989,
0.1567326933145523,
-0.07322447746992111,
0.07637818902730942,
-0.2258312851190567,
-0.024043066427111626,
-0.02186008170247078,
-0.0959283635020256,
-0.08626385778188705,
-0.048003289848566055,
0.0885954275727272,
-0.05540398880839348,
0.11896247416734695,
0.012536438181996346,
0.13712821900844574,
0.04412170499563217,
-0.02546296827495098,
0.01714279316365719,
0.03516397997736931,
0.2227407544851303,
0.04369674250483513,
-0.02574237622320652,
0.09394972026348114,
0.09446952491998672,
0.10571005195379257,
0.07226774096488953,
0.25484246015548706,
0.18753457069396973,
-0.038948290050029755,
0.095436692237854,
0.011136847548186779,
-0.048055220395326614,
-0.130842924118042,
-0.022871874272823334,
-0.02260647527873516,
0.06771973520517349,
-0.009586011059582233,
0.1859195977449417,
0.06754276156425476,
-0.16594350337982178,
0.06119813397526741,
-0.07995697855949402,
-0.09772709757089615,
-0.09903238713741302,
0.12294083088636398,
-0.08078353852033615,
-0.17717047035694122,
0.03830756992101669,
-0.1303921490907669,
0.017518840730190277,
0.1491554081439972,
0.018595049157738686,
-0.00424342742189765,
0.20677727460861206,
0.08124113082885742,
0.0533704049885273,
0.04274703562259674,
-0.008431194350123405,
-0.012654410675168037,
-0.07381236553192139,
-0.02288188599050045,
-0.03264077007770538,
-0.038464076817035675,
0.03055606596171856,
-0.042001109570264816,
-0.14887821674346924,
0.021593905985355377,
-0.023896094411611557,
-0.10602870583534241,
0.02289225533604622,
0.029951484873890877,
0.08153917640447617,
0.01991010271012783,
-0.005239290650933981,
0.03788240626454353,
-0.03598333150148392,
0.2467544823884964,
-0.09101299196481705,
-0.13013312220573425,
-0.0910852700471878,
0.29968559741973877,
0.06951171159744263,
-0.0032831125427037477,
0.03358267992734909,
-0.05642562359571457,
-0.03350335732102394,
0.24448201060295105,
0.1301925778388977,
-0.08876974135637283,
-0.0027487885672599077,
0.006884511094540358,
-0.01411850843578577,
-0.03648895025253296,
0.12200479954481125,
0.1606128066778183,
0.058231282979249954,
-0.1426057517528534,
-0.05698694288730621,
-0.06918694078922272,
-0.025473883375525475,
-0.03626508638262749,
0.031806256622076035,
0.07333742827177048,
-0.006175338290631771,
-0.04601702094078064,
0.06967554986476898,
-0.052684441208839417,
-0.10622843354940414,
0.07518208026885986,
-0.22487719357013702,
-0.15021869540214539,
-0.007413913495838642,
0.14597631990909576,
-0.007780879735946655,
0.09327567368745804,
-0.02508401870727539,
-0.029349694028496742,
0.07374635338783264,
-0.025082789361476898,
-0.07551302015781403,
-0.11256611347198486,
0.12492407858371735,
-0.13154104351997375,
0.14152644574642181,
-0.029389506205916405,
0.13302429020404816,
0.12108806520700455,
0.0614306703209877,
-0.06479649990797043,
0.058832842856645584,
0.05114053562283516,
-0.15822574496269226,
-0.012263866141438484,
0.13455995917320251,
-0.026248138397932053,
-0.006845658645033836,
0.026968641206622124,
-0.18612687289714813,
0.029352298006415367,
-0.040603939443826675,
-0.008020303212106228,
-0.05385932698845863,
-0.05639544501900673,
-0.058049771934747696,
0.07700762152671814,
0.24346238374710083,
-0.01406620629131794,
0.042190711945295334,
-0.08333182334899902,
0.027994539588689804,
0.05651956796646118,
0.04110085219144821,
-0.10300634056329727,
-0.2522828280925751,
0.021142663434147835,
0.0883435606956482,
-0.06298784166574478,
-0.18377375602722168,
-0.09916441887617111,
0.042873967438936234,
-0.0961863100528717,
-0.07339596003293991,
0.054582852870225906,
0.07034119218587875,
0.06988485902547836,
-0.04121842235326767,
-0.15904501080513,
-0.07667579501867294,
0.1815049946308136,
-0.15509097278118134,
-0.09926120191812515
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qa-indo-math-k-v2
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9328
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 80 | 0.7969 |
| No log | 2.0 | 160 | 0.7612 |
| No log | 3.0 | 240 | 0.7624 |
| No log | 4.0 | 320 | 0.7424 |
| No log | 5.0 | 400 | 0.7634 |
| No log | 6.0 | 480 | 0.7415 |
| 0.9241 | 7.0 | 560 | 0.7219 |
| 0.9241 | 8.0 | 640 | 0.7792 |
| 0.9241 | 9.0 | 720 | 0.7803 |
| 0.9241 | 10.0 | 800 | 0.7666 |
| 0.9241 | 11.0 | 880 | 0.7614 |
| 0.9241 | 12.0 | 960 | 0.7616 |
| 0.6373 | 13.0 | 1040 | 0.7673 |
| 0.6373 | 14.0 | 1120 | 0.7818 |
| 0.6373 | 15.0 | 1200 | 0.8030 |
| 0.6373 | 16.0 | 1280 | 0.8021 |
| 0.6373 | 17.0 | 1360 | 0.8025 |
| 0.6373 | 18.0 | 1440 | 0.8628 |
| 0.5614 | 19.0 | 1520 | 0.8616 |
| 0.5614 | 20.0 | 1600 | 0.8739 |
| 0.5614 | 21.0 | 1680 | 0.8647 |
| 0.5614 | 22.0 | 1760 | 0.9006 |
| 0.5614 | 23.0 | 1840 | 0.9560 |
| 0.5614 | 24.0 | 1920 | 0.9395 |
| 0.486 | 25.0 | 2000 | 0.9453 |
| 0.486 | 26.0 | 2080 | 0.9569 |
| 0.486 | 27.0 | 2160 | 1.0208 |
| 0.486 | 28.0 | 2240 | 0.9860 |
| 0.486 | 29.0 | 2320 | 0.9806 |
| 0.486 | 30.0 | 2400 | 1.0681 |
| 0.486 | 31.0 | 2480 | 1.1085 |
| 0.4126 | 32.0 | 2560 | 1.1028 |
| 0.4126 | 33.0 | 2640 | 1.1110 |
| 0.4126 | 34.0 | 2720 | 1.1573 |
| 0.4126 | 35.0 | 2800 | 1.1387 |
| 0.4126 | 36.0 | 2880 | 1.2067 |
| 0.4126 | 37.0 | 2960 | 1.2079 |
| 0.3559 | 38.0 | 3040 | 1.2152 |
| 0.3559 | 39.0 | 3120 | 1.2418 |
| 0.3559 | 40.0 | 3200 | 1.2023 |
| 0.3559 | 41.0 | 3280 | 1.2679 |
| 0.3559 | 42.0 | 3360 | 1.3178 |
| 0.3559 | 43.0 | 3440 | 1.3419 |
| 0.3084 | 44.0 | 3520 | 1.4702 |
| 0.3084 | 45.0 | 3600 | 1.3824 |
| 0.3084 | 46.0 | 3680 | 1.4227 |
| 0.3084 | 47.0 | 3760 | 1.3925 |
| 0.3084 | 48.0 | 3840 | 1.4940 |
| 0.3084 | 49.0 | 3920 | 1.4110 |
| 0.2686 | 50.0 | 4000 | 1.4534 |
| 0.2686 | 51.0 | 4080 | 1.4749 |
| 0.2686 | 52.0 | 4160 | 1.5351 |
| 0.2686 | 53.0 | 4240 | 1.5479 |
| 0.2686 | 54.0 | 4320 | 1.4755 |
| 0.2686 | 55.0 | 4400 | 1.5207 |
| 0.2686 | 56.0 | 4480 | 1.5075 |
| 0.2388 | 57.0 | 4560 | 1.5470 |
| 0.2388 | 58.0 | 4640 | 1.5361 |
| 0.2388 | 59.0 | 4720 | 1.5914 |
| 0.2388 | 60.0 | 4800 | 1.6430 |
| 0.2388 | 61.0 | 4880 | 1.6249 |
| 0.2388 | 62.0 | 4960 | 1.5503 |
| 0.2046 | 63.0 | 5040 | 1.6441 |
| 0.2046 | 64.0 | 5120 | 1.6789 |
| 0.2046 | 65.0 | 5200 | 1.6174 |
| 0.2046 | 66.0 | 5280 | 1.6175 |
| 0.2046 | 67.0 | 5360 | 1.6947 |
| 0.2046 | 68.0 | 5440 | 1.6299 |
| 0.1891 | 69.0 | 5520 | 1.7419 |
| 0.1891 | 70.0 | 5600 | 1.8442 |
| 0.1891 | 71.0 | 5680 | 1.8802 |
| 0.1891 | 72.0 | 5760 | 1.8233 |
| 0.1891 | 73.0 | 5840 | 1.8172 |
| 0.1891 | 74.0 | 5920 | 1.8181 |
| 0.1664 | 75.0 | 6000 | 1.8399 |
| 0.1664 | 76.0 | 6080 | 1.8128 |
| 0.1664 | 77.0 | 6160 | 1.8423 |
| 0.1664 | 78.0 | 6240 | 1.8380 |
| 0.1664 | 79.0 | 6320 | 1.8941 |
| 0.1664 | 80.0 | 6400 | 1.8636 |
| 0.1664 | 81.0 | 6480 | 1.7949 |
| 0.1614 | 82.0 | 6560 | 1.8342 |
| 0.1614 | 83.0 | 6640 | 1.8123 |
| 0.1614 | 84.0 | 6720 | 1.8639 |
| 0.1614 | 85.0 | 6800 | 1.8580 |
| 0.1614 | 86.0 | 6880 | 1.8816 |
| 0.1614 | 87.0 | 6960 | 1.8579 |
| 0.1487 | 88.0 | 7040 | 1.8783 |
| 0.1487 | 89.0 | 7120 | 1.9175 |
| 0.1487 | 90.0 | 7200 | 1.9025 |
| 0.1487 | 91.0 | 7280 | 1.9207 |
| 0.1487 | 92.0 | 7360 | 1.9195 |
| 0.1487 | 93.0 | 7440 | 1.9142 |
| 0.1355 | 94.0 | 7520 | 1.9333 |
| 0.1355 | 95.0 | 7600 | 1.9238 |
| 0.1355 | 96.0 | 7680 | 1.9256 |
| 0.1355 | 97.0 | 7760 | 1.9305 |
| 0.1355 | 98.0 | 7840 | 1.9294 |
| 0.1355 | 99.0 | 7920 | 1.9301 |
| 0.1297 | 100.0 | 8000 | 1.9328 |
### Framework versions
- Transformers 4.6.1
- Pytorch 1.7.0
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{}
|
text2text-generation
|
fadhilarkan/qa-indo-math-k-v2
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
qa-indo-math-k-v2
=================
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9328
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.6.1
* Pytorch 1.7.0
* Datasets 1.11.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 100\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.7.0\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 100\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.7.0\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
48,
113,
4,
30
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 100\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.7.0\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
-0.08156592398881912,
-0.012361937202513218,
-0.002191622043028474,
0.0805610716342926,
0.1962430328130722,
0.03247357904911041,
0.10526630282402039,
0.11685120314359665,
-0.10524746775627136,
0.018731512129306793,
0.10433346778154373,
0.16635513305664062,
0.02452840842306614,
0.13050977885723114,
-0.07545632869005203,
-0.29224729537963867,
-0.003891986096277833,
0.040468789637088776,
-0.04690833017230034,
0.1362561136484146,
0.08868353813886642,
-0.14323222637176514,
0.0669264942407608,
-0.013661333359777927,
-0.20484478771686554,
0.020347634330391884,
0.02354174107313156,
-0.06574802100658417,
0.15930257737636566,
0.029096899554133415,
0.13415977358818054,
0.008522158488631248,
0.0828954428434372,
-0.1953328549861908,
0.012613357976078987,
0.06161973997950554,
0.020888937637209892,
0.07415274530649185,
0.07515265047550201,
-0.014040946029126644,
0.13339315354824066,
-0.08832243084907532,
0.057688068598508835,
0.019544832408428192,
-0.12891989946365356,
-0.21175411343574524,
-0.06238570809364319,
-0.007512605283409357,
0.07336979359388351,
0.10613157600164413,
-0.01782912015914917,
0.14746402204036713,
-0.10643308609724045,
0.10897105187177658,
0.2229255586862564,
-0.26820623874664307,
-0.06963095813989639,
0.00046356074744835496,
0.01664808951318264,
0.0965929627418518,
-0.0982825830578804,
-0.0062537300400435925,
0.04712412506341934,
0.062132008373737335,
0.12536299228668213,
-0.030444344505667686,
-0.11150761693716049,
0.010179899632930756,
-0.1461651772260666,
-0.027119984850287437,
0.10410107672214508,
0.022851448506116867,
-0.02783772721886635,
-0.028728580102324486,
-0.07634850591421127,
-0.1475607007741928,
-0.05342481657862663,
-0.02339308336377144,
0.035308837890625,
-0.04027707874774933,
-0.0747775286436081,
0.003829027758911252,
-0.0903463214635849,
-0.0645487904548645,
-0.06897576153278351,
0.17286139726638794,
0.045211274176836014,
-0.003030786057934165,
-0.041944943368434906,
0.10197316855192184,
-0.025500716641545296,
-0.13121312856674194,
0.03008628822863102,
0.019737817347049713,
-0.029657404869794846,
-0.053955160081386566,
-0.07975015044212341,
-0.10121779143810272,
-0.001701170695014298,
0.08316102623939514,
-0.07129357010126114,
0.06722857058048248,
-0.0023449051659554243,
0.02441696636378765,
-0.09042230248451233,
0.1876072734594345,
-0.019580435007810593,
-0.027687927708029747,
0.007115843705832958,
0.039987243711948395,
-0.004189804196357727,
-0.020267726853489876,
-0.09183340519666672,
-0.005950891878455877,
0.1269233226776123,
0.019225839525461197,
-0.07440506666898727,
0.07029393315315247,
-0.037363201379776,
-0.023065347224473953,
-0.04705582186579704,
-0.10606907308101654,
0.0389242060482502,
-0.0165330208837986,
-0.06944584101438522,
0.023399153724312782,
0.015182996168732643,
0.019811121746897697,
-0.05178304389119148,
0.1462029367685318,
-0.07439347356557846,
0.038686878979206085,
-0.11298705637454987,
-0.1318408101797104,
0.005205955822020769,
-0.039169102907180786,
0.011351092718541622,
-0.10780920088291168,
-0.16051797568798065,
-0.010871108621358871,
0.045229896903038025,
-0.037699904292821884,
-0.04843717813491821,
-0.05365730822086334,
-0.06260155141353607,
0.029357904568314552,
-0.028793780133128166,
0.17843802273273468,
-0.058726757764816284,
0.11773480474948883,
0.06659951061010361,
0.06951174885034561,
-0.026033323258161545,
0.05881397798657417,
-0.08543605357408524,
-0.001155498088337481,
-0.20454782247543335,
0.06925611197948456,
-0.03247334808111191,
0.07224316895008087,
-0.07318670302629471,
-0.11403539776802063,
-0.00399272795766592,
0.0035342148039489985,
0.10355202853679657,
0.1094234511256218,
-0.15285441279411316,
-0.085750512778759,
0.17397509515285492,
-0.06677883863449097,
-0.10422054678201675,
0.1271226406097412,
-0.05578520894050598,
0.02744918502867222,
0.08166985958814621,
0.16023744642734528,
0.04621736332774162,
-0.07214579731225967,
0.03694041073322296,
-0.035749997943639755,
0.05911632627248764,
-0.03237204998731613,
0.04780024662613869,
0.018219366669654846,
-0.03258119150996208,
0.025079814717173576,
0.01430481020361185,
0.07161345332860947,
-0.11695604026317596,
-0.07452653348445892,
-0.05153312161564827,
-0.08623193204402924,
0.05347554013133049,
0.0660325139760971,
0.0834190770983696,
-0.12051711231470108,
-0.076043039560318,
0.06546886265277863,
0.06306762248277664,
-0.0582939088344574,
0.03946337476372719,
-0.05180769041180611,
0.05153338983654976,
-0.03536981716752052,
-0.0016059649642556906,
-0.20587171614170074,
-0.022857746109366417,
0.008073410950601101,
0.06461566686630249,
0.0320754274725914,
0.002276958432048559,
0.07737171649932861,
0.06627040356397629,
-0.06631135940551758,
-0.024310344830155373,
-0.023790691047906876,
-0.007458725478500128,
-0.14541374146938324,
-0.17559818923473358,
-0.013363081030547619,
-0.013602374121546745,
0.09527606517076492,
-0.20535700023174286,
0.02246628701686859,
-0.0418417789041996,
0.08131337910890579,
0.0006479981821030378,
0.0035715799313038588,
-0.055536482483148575,
0.1002596914768219,
-0.030548367649316788,
-0.03677939251065254,
0.07458896189928055,
-0.004014624282717705,
-0.07757394015789032,
-0.036191634833812714,
-0.12376689165830612,
0.17084944248199463,
0.13602393865585327,
-0.16954867541790009,
-0.08788222819566727,
-0.0034156637266278267,
-0.050604816526174545,
-0.02920519933104515,
-0.05492391064763069,
0.027170097455382347,
0.18945646286010742,
-0.002452743472531438,
0.15765012800693512,
-0.06701381504535675,
-0.03473101183772087,
0.01755576767027378,
-0.031234977766871452,
0.04630566015839577,
0.11674298346042633,
0.09423884004354477,
-0.0867689996957779,
0.11391671746969223,
0.1414593756198883,
-0.09452817589044571,
0.13421489298343658,
-0.02713855542242527,
-0.08908320963382721,
0.005498530808836222,
-0.025136081501841545,
0.0006174388690851629,
0.05773322284221649,
-0.1436210423707962,
-0.003704630769789219,
0.01955839805305004,
0.03744697943329811,
0.025616459548473358,
-0.21941712498664856,
-0.028770050033926964,
0.03919560834765434,
-0.031240349635481834,
-0.028557509183883667,
-0.01514813769608736,
0.03664287552237511,
0.12837722897529602,
0.003918725065886974,
-0.05507150664925575,
0.018461063504219055,
-0.002038399688899517,
-0.08561477065086365,
0.2109488546848297,
-0.08126039057970047,
-0.16944155097007751,
-0.09494645893573761,
-0.103628970682621,
-0.04107386991381645,
0.010040915571153164,
0.05998431518673897,
-0.12355157732963562,
-0.03333575651049614,
-0.03980891406536102,
0.058443840593099594,
-0.019900057464838028,
0.04467008262872696,
-0.005247924476861954,
-0.002472796244546771,
0.05938917398452759,
-0.09313619881868362,
-0.021917514503002167,
-0.048436105251312256,
-0.06351713091135025,
0.07294513285160065,
0.03793459013104439,
0.1062745675444603,
0.16727639734745026,
-0.04113749787211418,
0.01954019069671631,
-0.03783256560564041,
0.2226545810699463,
-0.07065949589014053,
-0.035232726484537125,
0.1406080573797226,
-0.008954760618507862,
0.054412245750427246,
0.10985829681158066,
0.054513540118932724,
-0.0936233326792717,
0.036178432404994965,
0.038142867386341095,
-0.03299715369939804,
-0.2217046022415161,
-0.04062091186642647,
-0.06997639685869217,
-0.03572576865553856,
0.08096108585596085,
0.011047260835766792,
0.041876211762428284,
0.06021096557378769,
0.030059678480029106,
0.07647974789142609,
-0.03563937172293663,
0.06057292968034744,
0.1473267525434494,
0.04598378390073776,
0.13854357600212097,
-0.04329238086938858,
-0.08807298541069031,
0.034942325204610825,
-0.043948352336883545,
0.22426560521125793,
0.006810232065618038,
0.06963241100311279,
0.033503033220767975,
0.14727959036827087,
0.015234394930303097,
0.10349060595035553,
0.0098769785836339,
-0.05304459482431412,
-0.00964740477502346,
-0.03613941743969917,
-0.05434631183743477,
0.014518837444484234,
-0.0464152917265892,
0.041090235114097595,
-0.14836284518241882,
-0.02575421892106533,
0.05242691561579704,
0.24290722608566284,
0.03418606147170067,
-0.3162679076194763,
-0.07888150215148926,
0.0135786859318614,
-0.06766806542873383,
-0.03204919397830963,
0.011413341388106346,
0.07646040618419647,
-0.11742173880338669,
0.051994238048791885,
-0.0747532770037651,
0.1123824268579483,
-0.025330480188131332,
0.05627720430493355,
0.028550388291478157,
0.1185828447341919,
-0.005157695151865482,
0.07827578485012054,
-0.35539141297340393,
0.27662745118141174,
0.003073939587920904,
0.07218938320875168,
-0.084047831594944,
-0.004576345905661583,
0.03810352832078934,
0.04700513184070587,
0.01563114859163761,
-0.01973857916891575,
-0.05847560241818428,
-0.1972339004278183,
-0.024963529780507088,
0.0395720899105072,
0.12441221624612808,
-0.00569147989153862,
0.12137434631586075,
-0.03730965778231621,
0.009678706526756287,
0.07638825476169586,
-0.005298185627907515,
-0.09682627022266388,
-0.07865706086158752,
-0.015056869015097618,
0.023821765556931496,
-0.005598831456154585,
-0.06038588285446167,
-0.11441786587238312,
-0.10203856974840164,
0.1606806516647339,
0.04484165087342262,
-0.027772976085543633,
-0.12828269600868225,
0.10487665981054306,
0.07655799388885498,
-0.08315619826316833,
0.034379743039608,
0.013411624357104301,
0.0685739815235138,
0.025124525651335716,
-0.07911702245473862,
0.11827168613672256,
-0.07032951712608337,
-0.15252719819545746,
-0.05069190636277199,
0.11002770066261292,
0.016784830018877983,
0.07432560622692108,
-0.02002001367509365,
0.01427633035928011,
-0.026097998023033142,
-0.085694819688797,
0.02153901755809784,
-0.024332324042916298,
0.064873106777668,
0.05520246922969818,
-0.0674532949924469,
0.010582173243165016,
-0.07259780168533325,
-0.042910926043987274,
0.22096148133277893,
0.22793251276016235,
-0.06865843385457993,
0.005219974555075169,
0.027579961344599724,
-0.06761012971401215,
-0.18253099918365479,
0.05185053125023842,
0.08090239018201828,
0.016011467203497887,
0.029556017369031906,
-0.17877168953418732,
0.0994090735912323,
0.08057235926389694,
0.010586963966488838,
0.09373987466096878,
-0.3127830922603607,
-0.13418908417224884,
0.11231516301631927,
0.15691860020160675,
0.15425118803977966,
-0.14217087626457214,
-0.014217986725270748,
-0.04887180030345917,
-0.10892941802740097,
0.10569969564676285,
-0.07586206495761871,
0.12929503619670868,
-0.01870008371770382,
0.11115100979804993,
0.018936390057206154,
-0.049237996339797974,
0.09512273967266083,
-0.011688520200550556,
0.08750584721565247,
-0.07402250915765762,
0.00809576828032732,
0.011871110647916794,
-0.03558581322431564,
0.00037999535561539233,
-0.05780060589313507,
0.020197534933686256,
-0.09738274663686752,
-0.03161368891596794,
-0.09057449549436569,
0.023804016411304474,
-0.02189425565302372,
-0.06508973240852356,
-0.015404540114104748,
0.009467998519539833,
0.05248389393091202,
-0.021461782976984978,
0.10415682941675186,
-0.02090083248913288,
0.17442187666893005,
0.08974908292293549,
0.10973232239484787,
-0.08662267029285431,
-0.018044037744402885,
-0.0017621275037527084,
-0.014233981259167194,
0.03821919858455658,
-0.12258588522672653,
0.029137101024389267,
0.14938685297966003,
0.009007656015455723,
0.1342206597328186,
0.09763464331626892,
-0.018834056332707405,
0.012871709652245045,
0.0764993354678154,
-0.16056770086288452,
-0.09515730291604996,
-0.01899721287190914,
-0.045431021600961685,
-0.09813666343688965,
0.03270506486296654,
0.09861964732408524,
-0.07407747954130173,
-0.011483022011816502,
-0.021264906972646713,
-0.0053487191908061504,
-0.07002084702253342,
0.21686112880706787,
0.04307674616575241,
0.05537967011332512,
-0.10159841179847717,
0.06625688821077347,
0.04767652228474617,
-0.09188994020223618,
0.016364287585020065,
0.13092873990535736,
-0.07336020469665527,
-0.035060815513134,
0.10443607717752457,
0.1845375895500183,
-0.06773792952299118,
-0.031146278604865074,
-0.1392481029033661,
-0.1427164375782013,
0.08739054203033447,
0.16955256462097168,
0.0883423388004303,
-0.006321068853139877,
-0.0665694996714592,
0.024472352117300034,
-0.1371605545282364,
0.07752097398042679,
0.06678934395313263,
0.07164173573255539,
-0.13135965168476105,
0.2021605372428894,
0.003968580160290003,
0.056564804166555405,
-0.03535480424761772,
0.013903305865824223,
-0.10855726897716522,
0.035105057060718536,
-0.14875178039073944,
-0.05501783266663551,
-0.0034675851929932833,
-0.008127414621412754,
-0.009434674866497517,
-0.07008369266986847,
-0.06011364609003067,
-0.0031191587913781404,
-0.12063869088888168,
-0.025064054876565933,
0.017074469476938248,
0.03485650196671486,
-0.10202792286872864,
-0.03674716502428055,
0.030778029933571815,
-0.06472188979387283,
0.05608314648270607,
0.060030899941921234,
-0.004469986539334059,
0.07949493080377579,
-0.1449567675590515,
-0.017428968101739883,
0.06697534769773483,
0.00617951201274991,
0.07314978539943695,
-0.08183480054140091,
0.011369144544005394,
0.0065465401858091354,
0.09233494848012924,
0.03192931413650513,
0.07652485370635986,
-0.1305561512708664,
-0.006189081352204084,
-0.03633757308125496,
-0.10643675178289413,
-0.060500603169202805,
0.031185446307063103,
0.05436583235859871,
0.016342543065547943,
0.16953518986701965,
-0.09850378334522247,
0.05601150169968605,
-0.22634932398796082,
-0.003723207861185074,
-0.022142795845866203,
-0.1167202740907669,
-0.0893765389919281,
-0.0788145512342453,
0.08820559829473495,
-0.04641098156571388,
0.11538023501634598,
0.026614811271429062,
0.10001445561647415,
0.03447217866778374,
-0.038344334810972214,
0.009740661829710007,
0.025112878531217575,
0.23006615042686462,
0.052194949239492416,
-0.04572294279932976,
0.056419409811496735,
0.06660132855176926,
0.10920874029397964,
0.1302766352891922,
0.2349407821893692,
0.1472669243812561,
0.0006001877482049167,
0.09709389507770538,
0.014617946930229664,
-0.05236945301294327,
-0.13932691514492035,
0.006854372099041939,
-0.04571027681231499,
0.09714233875274658,
-0.03378656134009361,
0.21044933795928955,
0.08045510947704315,
-0.1572306752204895,
0.05856654793024063,
-0.06850942969322205,
-0.09400800615549088,
-0.11519379168748856,
-0.007769725751131773,
-0.09153199195861816,
-0.16575485467910767,
-0.0007444829097948968,
-0.12391283363103867,
0.04834705591201782,
0.10508774220943451,
0.025463063269853592,
-0.029229411855340004,
0.17155587673187256,
0.051755890250205994,
0.00717551214620471,
0.0854274183511734,
-0.006061000283807516,
0.005276612006127834,
-0.07177067548036575,
-0.07419288903474808,
-0.013107351027429104,
-0.011485196650028229,
0.041581008583307266,
-0.03996804356575012,
-0.10823087394237518,
0.03990282118320465,
-0.030823061242699623,
-0.10875604301691055,
0.02335517667233944,
0.025248456746339798,
0.07503625005483627,
0.06686077266931534,
0.0068625397980213165,
-0.001210031216032803,
-0.026529155671596527,
0.24266797304153442,
-0.09753766655921936,
-0.10921866446733475,
-0.09486725926399231,
0.3055872917175293,
0.0506800152361393,
0.0005355795728974044,
0.022392744198441505,
-0.05889357626438141,
-0.015314276330173016,
0.2544131577014923,
0.17033807933330536,
-0.10679995268583298,
-0.01644803397357464,
-0.0007447610842064023,
-0.008161580190062523,
-0.010371278040111065,
0.14622755348682404,
0.1500340849161148,
0.05860152468085289,
-0.10464649647474289,
-0.01653124764561653,
-0.05119522288441658,
-0.014489850029349327,
-0.04641348123550415,
0.08325956016778946,
0.06123735383152962,
0.004324223846197128,
-0.03106127493083477,
0.07620450109243393,
-0.0699843168258667,
-0.058380771428346634,
-0.0010427181841805577,
-0.2040269672870636,
-0.15942607820034027,
-0.018468867987394333,
0.08725828677415848,
-0.00019703646830748767,
0.0657811313867569,
-0.025293251499533653,
0.0027136430144309998,
0.053168922662734985,
-0.019575338810682297,
-0.05437298119068146,
-0.08814610540866852,
0.09856393188238144,
-0.11538822948932648,
0.14458170533180237,
-0.038550201803445816,
0.053230997174978256,
0.1296432763338089,
0.06444915384054184,
-0.05597954988479614,
0.08393774926662445,
0.036558568477630615,
-0.08875875920057297,
0.045524775981903076,
0.135824054479599,
-0.038439106196165085,
0.02681538835167885,
0.04449934884905815,
-0.15154066681861877,
0.030040839686989784,
-0.09587044268846512,
-0.04032942280173302,
-0.023374559357762337,
-0.0437486469745636,
-0.05869390442967415,
0.11631328612565994,
0.2372196763753891,
-0.022701885551214218,
0.030273662880063057,
-0.09549014270305634,
0.004634181968867779,
0.03354795649647713,
0.06481315195560455,
-0.08534902334213257,
-0.2617630660533905,
-0.003882673801854253,
0.10097938030958176,
-0.027140099555253983,
-0.2597610056400299,
-0.08477798849344254,
-0.00033813866320997477,
-0.06229758262634277,
-0.11699606478214264,
0.10955744981765747,
0.08228383213281631,
0.03763625770807266,
-0.04802930727601051,
-0.14179600775241852,
-0.06352081894874573,
0.1925448179244995,
-0.1494217813014984,
-0.07428309321403503
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qa-indo-math-k
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8801
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 127 | 0.7652 |
| No log | 2.0 | 254 | 0.7520 |
| No log | 3.0 | 381 | 0.7681 |
| 0.9618 | 4.0 | 508 | 0.7337 |
| 0.9618 | 5.0 | 635 | 0.7560 |
| 0.9618 | 6.0 | 762 | 0.7397 |
| 0.9618 | 7.0 | 889 | 0.7298 |
| 0.6652 | 8.0 | 1016 | 0.7891 |
| 0.6652 | 9.0 | 1143 | 0.7874 |
| 0.6652 | 10.0 | 1270 | 0.7759 |
| 0.6652 | 11.0 | 1397 | 0.7505 |
| 0.6174 | 12.0 | 1524 | 0.7838 |
| 0.6174 | 13.0 | 1651 | 0.7878 |
| 0.6174 | 14.0 | 1778 | 0.8028 |
| 0.6174 | 15.0 | 1905 | 0.8154 |
| 0.5733 | 16.0 | 2032 | 0.8131 |
| 0.5733 | 17.0 | 2159 | 0.8278 |
| 0.5733 | 18.0 | 2286 | 0.8308 |
| 0.5733 | 19.0 | 2413 | 0.8433 |
| 0.5378 | 20.0 | 2540 | 0.8303 |
| 0.5378 | 21.0 | 2667 | 0.8352 |
| 0.5378 | 22.0 | 2794 | 0.8369 |
| 0.5378 | 23.0 | 2921 | 0.8518 |
| 0.5095 | 24.0 | 3048 | 0.8749 |
| 0.5095 | 25.0 | 3175 | 0.8533 |
| 0.5095 | 26.0 | 3302 | 0.8547 |
| 0.5095 | 27.0 | 3429 | 0.8844 |
| 0.4856 | 28.0 | 3556 | 0.8752 |
| 0.4856 | 29.0 | 3683 | 0.8804 |
| 0.4856 | 30.0 | 3810 | 0.8801 |
### Framework versions
- Transformers 4.6.1
- Pytorch 1.7.0
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{}
|
text2text-generation
|
fadhilarkan/qa-indo-math-k
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
qa-indo-math-k
==============
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8801
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.6.1
* Pytorch 1.7.0
* Datasets 1.11.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.7.0\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.7.0\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
48,
113,
4,
30
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.7.0\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
-0.07876863330602646,
-0.01118527539074421,
-0.002109619090333581,
0.07825209200382233,
0.19553090631961823,
0.032103851437568665,
0.10814165323972702,
0.11652316153049469,
-0.10729686915874481,
0.01780310645699501,
0.10366617888212204,
0.16803176701068878,
0.02419506199657917,
0.13096101582050323,
-0.07742803543806076,
-0.29511120915412903,
-0.0031211788300424814,
0.040202006697654724,
-0.04192832484841347,
0.13761933147907257,
0.08973608911037445,
-0.1427420973777771,
0.06687559187412262,
-0.013555490411818027,
-0.20429635047912598,
0.021050872281193733,
0.022349048405885696,
-0.0672602429986,
0.15907897055149078,
0.027496013790369034,
0.133317768573761,
0.009572228416800499,
0.08380139619112015,
-0.19640658795833588,
0.012924011796712875,
0.0608113557100296,
0.020188763737678528,
0.07305281609296799,
0.07516076415777206,
-0.015222141519188881,
0.13574670255184174,
-0.09046994149684906,
0.059141673147678375,
0.018280280753970146,
-0.1289244294166565,
-0.2091543972492218,
-0.060662250965833664,
-0.006863149348646402,
0.07351328432559967,
0.10733332484960556,
-0.019104091450572014,
0.1511935144662857,
-0.10684298723936081,
0.10891487449407578,
0.22520487010478973,
-0.2704365849494934,
-0.06931246072053909,
-0.0010000319452956319,
0.019069069996476173,
0.0984329879283905,
-0.0989292562007904,
-0.008349441923201084,
0.047672536224126816,
0.061330847442150116,
0.12495031952857971,
-0.031139299273490906,
-0.10566065460443497,
0.009305061772465706,
-0.14810679852962494,
-0.027635810896754265,
0.10665923357009888,
0.0228547565639019,
-0.026945047080516815,
-0.03195560351014137,
-0.07433097809553146,
-0.14445677399635315,
-0.05325973033905029,
-0.02413390576839447,
0.034055471420288086,
-0.04041131213307381,
-0.07392889261245728,
0.0037195072509348392,
-0.09156434237957001,
-0.06280460208654404,
-0.06759657710790634,
0.17305676639080048,
0.04644401744008064,
-0.004960101563483477,
-0.04530971497297287,
0.102129727602005,
-0.026440078392624855,
-0.1327335089445114,
0.03100740537047386,
0.01854841411113739,
-0.028268402442336082,
-0.05621477961540222,
-0.07949574291706085,
-0.10145706683397293,
-0.002201434690505266,
0.08486118167638779,
-0.06595933437347412,
0.0686219334602356,
-0.0046417429111897945,
0.022951442748308182,
-0.08805018663406372,
0.18793125450611115,
-0.01805627904832363,
-0.030170176178216934,
0.005847490392625332,
0.041790835559368134,
-0.004707553889602423,
-0.01940382830798626,
-0.09194616228342056,
-0.0042986199259757996,
0.12737056612968445,
0.019466040655970573,
-0.07306783646345139,
0.07119468599557877,
-0.03561023622751236,
-0.02426142618060112,
-0.04677235335111618,
-0.10654240101575851,
0.0380493625998497,
-0.014527968131005764,
-0.07104900479316711,
0.023492496460676193,
0.012723921798169613,
0.021770179271697998,
-0.052795276045799255,
0.14552190899848938,
-0.07377798855304718,
0.0383957140147686,
-0.11174752563238144,
-0.13185952603816986,
0.0051524443551898,
-0.041671060025691986,
0.011724812909960747,
-0.10709357261657715,
-0.16323287785053253,
-0.011656043119728565,
0.044669486582279205,
-0.035028569400310516,
-0.04749147221446037,
-0.05554370582103729,
-0.06220083683729172,
0.0276790801435709,
-0.02870737388730049,
0.17928069829940796,
-0.05932903662323952,
0.11711394786834717,
0.06464777886867523,
0.06769675761461258,
-0.029711009934544563,
0.05947006493806839,
-0.08614356070756912,
-0.003532569156959653,
-0.20089121162891388,
0.06833444535732269,
-0.031513381749391556,
0.07322089374065399,
-0.07372424006462097,
-0.11486988514661789,
-0.0027698096819221973,
0.0031008822843432426,
0.1016671434044838,
0.10893285274505615,
-0.15406276285648346,
-0.0851905569434166,
0.17325355112552643,
-0.06567980349063873,
-0.10360556840896606,
0.12323974817991257,
-0.05668551102280617,
0.03108334168791771,
0.08252289146184921,
0.1639144867658615,
0.046836014837026596,
-0.0717591941356659,
0.03769942745566368,
-0.03828994184732437,
0.05839695781469345,
-0.02997707948088646,
0.04829609394073486,
0.01824900321662426,
-0.02904541976749897,
0.02497623674571514,
0.012657364830374718,
0.06894680112600327,
-0.11548791825771332,
-0.07461008429527283,
-0.050603192299604416,
-0.0853206142783165,
0.050738830119371414,
0.06622011214494705,
0.08422867953777313,
-0.12040264159440994,
-0.07442088425159454,
0.06767305731773376,
0.06356798112392426,
-0.0590076744556427,
0.03966306149959564,
-0.05130447447299957,
0.048981454223394394,
-0.03299710154533386,
-0.0018452215008437634,
-0.2081061750650406,
-0.022803502157330513,
0.008322203531861305,
0.06050723046064377,
0.03398400917649269,
0.002262207679450512,
0.07644705474376678,
0.06347277015447617,
-0.06481567770242691,
-0.025098245590925217,
-0.022292930632829666,
-0.0070825666189193726,
-0.1446281522512436,
-0.17657770216464996,
-0.011708182282745838,
-0.01491472963243723,
0.09658606350421906,
-0.2046498954296112,
0.022991668432950974,
-0.04177366942167282,
0.08296536654233932,
0.0009277924546040595,
0.003080383175984025,
-0.05500268563628197,
0.1001628190279007,
-0.03044954128563404,
-0.036704014986753464,
0.07532984018325806,
-0.004049880430102348,
-0.07779916375875473,
-0.037809908390045166,
-0.130279541015625,
0.16704949736595154,
0.13638633489608765,
-0.16811947524547577,
-0.08595427870750427,
-0.003644476179033518,
-0.051512956619262695,
-0.029033977538347244,
-0.05355062708258629,
0.027530645951628685,
0.18899258971214294,
-0.003246389562264085,
0.15735004842281342,
-0.06785834580659866,
-0.03514726459980011,
0.016476228833198547,
-0.031193530187010765,
0.04501413553953171,
0.11609791964292526,
0.09208162873983383,
-0.09214381873607635,
0.11298881471157074,
0.14585518836975098,
-0.09198582917451859,
0.137017160654068,
-0.026428723707795143,
-0.08952922374010086,
0.005474883131682873,
-0.026261046528816223,
-0.0007120826048776507,
0.05729485675692558,
-0.1421336978673935,
-0.004480747506022453,
0.019788028672337532,
0.038589902222156525,
0.026784725487232208,
-0.2208707630634308,
-0.02801772952079773,
0.03870905563235283,
-0.030216138809919357,
-0.03234752267599106,
-0.013272705487906933,
0.03645464777946472,
0.12847653031349182,
0.004791411105543375,
-0.05543389171361923,
0.017735959962010384,
-0.00408752728253603,
-0.08629681915044785,
0.21056696772575378,
-0.08204662799835205,
-0.17084093391895294,
-0.09209942817687988,
-0.10644176602363586,
-0.04110458120703697,
0.009203934110701084,
0.062009118497371674,
-0.1220102459192276,
-0.03355121612548828,
-0.03954797238111496,
0.05460671707987785,
-0.01765640825033188,
0.043570660054683685,
-0.005220597144216299,
-0.0013380522141233087,
0.05839388445019722,
-0.09272065758705139,
-0.022626671940088272,
-0.049613550305366516,
-0.060997433960437775,
0.07169077545404434,
0.036196645349264145,
0.10512620210647583,
0.16664142906665802,
-0.03881903365254402,
0.020228387787938118,
-0.03908130154013634,
0.22121353447437286,
-0.07247936725616455,
-0.03641403093934059,
0.14082512259483337,
-0.012494832277297974,
0.05452388525009155,
0.11216384172439575,
0.05315442383289337,
-0.0935191810131073,
0.03729270398616791,
0.03763268142938614,
-0.03213666379451752,
-0.22205857932567596,
-0.04181166738271713,
-0.06894627958536148,
-0.036467574536800385,
0.08193187415599823,
0.011406484991312027,
0.04489988833665848,
0.05819597467780113,
0.03105510026216507,
0.07324272394180298,
-0.03312277793884277,
0.0625709816813469,
0.1453876793384552,
0.04614708945155144,
0.137979194521904,
-0.044579047709703445,
-0.08791477978229523,
0.03379705175757408,
-0.043594811111688614,
0.22316862642765045,
0.009052499197423458,
0.06775124371051788,
0.03524775430560112,
0.14466173946857452,
0.014886675402522087,
0.10130161046981812,
0.012133649550378323,
-0.053587429225444794,
-0.009611531160771847,
-0.03620981052517891,
-0.05174442380666733,
0.01616973988711834,
-0.043848760426044464,
0.04193682223558426,
-0.14833800494670868,
-0.028023600578308105,
0.053442325443029404,
0.24277950823307037,
0.03504035994410515,
-0.3167230188846588,
-0.07740586996078491,
0.014264498837292194,
-0.06962832063436508,
-0.031134039163589478,
0.012710651382803917,
0.07129327952861786,
-0.11860466003417969,
0.05499131605029106,
-0.07449901103973389,
0.1122848391532898,
-0.02418886497616768,
0.059329207986593246,
0.029202785342931747,
0.11627335101366043,
-0.0054195839911699295,
0.0778505727648735,
-0.3592117726802826,
0.2773599326610565,
0.002596428617835045,
0.07161357998847961,
-0.0851825475692749,
-0.0054798731580376625,
0.039303719997406006,
0.046973809599876404,
0.017061395570635796,
-0.022403230890631676,
-0.05542779341340065,
-0.19086360931396484,
-0.02479315921664238,
0.041543927043676376,
0.1281253546476364,
-0.005674374755471945,
0.12206733971834183,
-0.03844480589032173,
0.009721781127154827,
0.07662937045097351,
-0.008707261644303799,
-0.09916238486766815,
-0.0778525248169899,
-0.012062202207744122,
0.021892059594392776,
-0.00532707991078496,
-0.06053885444998741,
-0.11409419029951096,
-0.1068732962012291,
0.16056524217128754,
0.04692252352833748,
-0.026480166241526604,
-0.1273612082004547,
0.10440802574157715,
0.07482150197029114,
-0.08271586894989014,
0.03378454968333244,
0.01415498461574316,
0.06804186850786209,
0.027633367106318474,
-0.07919403910636902,
0.11750800907611847,
-0.06849021464586258,
-0.1514797955751419,
-0.04970039427280426,
0.11043215543031693,
0.016461100429296494,
0.07366374880075455,
-0.018195753917098045,
0.013671470806002617,
-0.027064483612775803,
-0.08436831086874008,
0.018874261528253555,
-0.024589385837316513,
0.06373821198940277,
0.05546676740050316,
-0.06699778884649277,
0.00342717207968235,
-0.07407218217849731,
-0.04332590848207474,
0.22369380295276642,
0.2282133400440216,
-0.06685593724250793,
0.0072219595313072205,
0.026319945231080055,
-0.06797492504119873,
-0.18254981935024261,
0.05392242595553398,
0.07965775579214096,
0.015052535571157932,
0.030512971803545952,
-0.17652861773967743,
0.09649205207824707,
0.07844321429729462,
0.01097642071545124,
0.08785057812929153,
-0.3141491413116455,
-0.13472677767276764,
0.11397648602724075,
0.1570948213338852,
0.14769893884658813,
-0.1418079137802124,
-0.014046653173863888,
-0.048879753798246384,
-0.10915666073560715,
0.10874573141336441,
-0.07755222171545029,
0.12886720895767212,
-0.01770738884806633,
0.11096123605966568,
0.01899007521569729,
-0.04952535778284073,
0.09445612877607346,
-0.011570974253118038,
0.08743184804916382,
-0.07423962652683258,
0.005915384739637375,
0.013622797094285488,
-0.03578644618391991,
-0.000345397274941206,
-0.05652567744255066,
0.01852705329656601,
-0.09529750794172287,
-0.03044995106756687,
-0.09198551625013351,
0.022906744852662086,
-0.022243676707148552,
-0.06543852388858795,
-0.013437158428132534,
0.010213010013103485,
0.054171372205019,
-0.020774396136403084,
0.1065211147069931,
-0.024160070344805717,
0.17379212379455566,
0.08348633348941803,
0.11166571825742722,
-0.08395060151815414,
-0.015066372230648994,
-0.003373929066583514,
-0.013991122134029865,
0.03620266169309616,
-0.12715421617031097,
0.03016088530421257,
0.14939597249031067,
0.008462121710181236,
0.13676954805850983,
0.09718445688486099,
-0.01667632721364498,
0.013158882036805153,
0.07679673284292221,
-0.16140426695346832,
-0.09392596781253815,
-0.01800397038459778,
-0.04573892056941986,
-0.09688390791416168,
0.03459654748439789,
0.09848678857088089,
-0.07178056240081787,
-0.012116230092942715,
-0.021209780126810074,
-0.006546559743583202,
-0.07079477608203888,
0.21574640274047852,
0.0424991101026535,
0.0538104809820652,
-0.10194025933742523,
0.06748706102371216,
0.04684314504265785,
-0.0940212681889534,
0.01693706028163433,
0.13224664330482483,
-0.07534690946340561,
-0.03531958907842636,
0.10422228276729584,
0.18343600630760193,
-0.06690070033073425,
-0.032071467489004135,
-0.14087915420532227,
-0.1414538472890854,
0.08756560832262039,
0.17176619172096252,
0.08850182592868805,
-0.007789907045662403,
-0.0676017478108406,
0.025897067040205002,
-0.13761784136295319,
0.0772528126835823,
0.06637606024742126,
0.07156918942928314,
-0.13103629648685455,
0.20184725522994995,
0.003901261370629072,
0.05458878353238106,
-0.034796472638845444,
0.013195662759244442,
-0.10919912904500961,
0.03520360216498375,
-0.15008951723575592,
-0.057858679443597794,
-0.0049168881960213184,
-0.008362747728824615,
-0.008758909069001675,
-0.06918269395828247,
-0.06037374213337898,
-0.0036914634983986616,
-0.12150952965021133,
-0.026148997247219086,
0.017488569021224976,
0.0368213877081871,
-0.10185164213180542,
-0.03558943420648575,
0.02946418710052967,
-0.063367560505867,
0.05680811032652855,
0.061044223606586456,
-0.005007303785532713,
0.07997987419366837,
-0.14413779973983765,
-0.018743077293038368,
0.06763740628957748,
0.006634070072323084,
0.0725112035870552,
-0.08153211325407028,
0.010029017925262451,
0.007931656204164028,
0.09207667410373688,
0.03278215974569321,
0.07755714654922485,
-0.1287730187177658,
-0.004098436329513788,
-0.03611160069704056,
-0.10549700260162354,
-0.06002266705036163,
0.030441591516137123,
0.05137162655591965,
0.01500660553574562,
0.1700609028339386,
-0.09723303467035294,
0.05626573786139488,
-0.22277745604515076,
-0.0038097237702459097,
-0.022070005536079407,
-0.11837439239025116,
-0.08936530351638794,
-0.08051665872335434,
0.08756139874458313,
-0.04719655588269234,
0.11412665247917175,
0.024728020653128624,
0.10232948511838913,
0.03570408746600151,
-0.03322488069534302,
0.00867508165538311,
0.026464471593499184,
0.23052021861076355,
0.05121311917901039,
-0.046333424746990204,
0.05697467550635338,
0.0667804554104805,
0.1081833690404892,
0.12855179607868195,
0.23529623448848724,
0.15058447420597076,
0.0022088689729571342,
0.09543383866548538,
0.014691184274852276,
-0.051394931972026825,
-0.14220473170280457,
0.00985025055706501,
-0.04455934464931488,
0.09802530705928802,
-0.03325482830405235,
0.2107568383216858,
0.07913272827863693,
-0.15714013576507568,
0.05677848309278488,
-0.06859923899173737,
-0.09297452867031097,
-0.11404907703399658,
-0.008485482074320316,
-0.09175989031791687,
-0.16298571228981018,
-0.00028748606564477086,
-0.12374600768089294,
0.049446962773799896,
0.10445382446050644,
0.025904176756739616,
-0.028795646503567696,
0.1725260317325592,
0.04714955762028694,
0.0038057868368923664,
0.0866909921169281,
-0.006385760847479105,
0.004316847305744886,
-0.07485149800777435,
-0.07029129564762115,
-0.0140297282487154,
-0.012321440503001213,
0.04060230776667595,
-0.042173322290182114,
-0.10754071176052094,
0.0389433279633522,
-0.033644452691078186,
-0.10733957588672638,
0.024757128208875656,
0.025782348588109016,
0.07536797970533371,
0.06643086671829224,
0.0082040224224329,
-0.0022186622954905033,
-0.025076836347579956,
0.24439741671085358,
-0.09872090071439743,
-0.11318129301071167,
-0.09628134965896606,
0.3084511458873749,
0.05032582953572273,
0.00008416866330662742,
0.02290303073823452,
-0.058724042028188705,
-0.012801283970475197,
0.25486746430397034,
0.1721808910369873,
-0.10686954855918884,
-0.014573848806321621,
0.0006379300612024963,
-0.0075448183342814445,
-0.012077447958290577,
0.14406535029411316,
0.15137916803359985,
0.055778659880161285,
-0.1040058583021164,
-0.015980413183569908,
-0.05043524503707886,
-0.014255673624575138,
-0.04489685222506523,
0.0837947428226471,
0.0633363425731659,
0.0023136876989156008,
-0.029685938730835915,
0.07722613215446472,
-0.07069322466850281,
-0.055732402950525284,
-0.006284026429057121,
-0.20524843037128448,
-0.16018922626972198,
-0.017862524837255478,
0.08984879404306412,
-0.0011949760373681784,
0.06471744924783707,
-0.02525509148836136,
0.0007457917672581971,
0.053742095828056335,
-0.018164418637752533,
-0.0521109439432621,
-0.08707653731107712,
0.09971737116575241,
-0.12061189115047455,
0.1441032886505127,
-0.03940131887793541,
0.05355242267251015,
0.13011714816093445,
0.06421755254268646,
-0.05495550483465195,
0.08338558673858643,
0.03737889975309372,
-0.0877002477645874,
0.04449997842311859,
0.13620701432228088,
-0.03863288462162018,
0.028394071385264397,
0.04553099349141121,
-0.15231412649154663,
0.03136765956878662,
-0.09422779828310013,
-0.0406910739839077,
-0.021820032969117165,
-0.042969848960638046,
-0.057832617312669754,
0.1169750988483429,
0.2347845435142517,
-0.022342246025800705,
0.03157990798354149,
-0.09524141997098923,
0.005760520696640015,
0.03463857248425484,
0.06728789210319519,
-0.08652747422456741,
-0.2612270712852478,
-0.004600421525537968,
0.09688504040241241,
-0.0265514999628067,
-0.2590712010860443,
-0.0858839750289917,
-0.0003910810628440231,
-0.06332722306251526,
-0.11792726069688797,
0.10883645713329315,
0.08287663757801056,
0.03603976219892502,
-0.04823977127671242,
-0.14183050394058228,
-0.06360667198896408,
0.1928228735923767,
-0.1490124613046646,
-0.07526174187660217
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum-2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9536
- Rouge1: 28.8137
- Rouge2: 9.1265
- Rougel: 26.0238
- Rougelsum: 26.0217
- Gen Len: 13.854
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.2142 | 1.0 | 8760 | 1.9994 | 29.007 | 9.2583 | 26.2377 | 26.2356 | 13.4546 |
| 2.1372 | 2.0 | 17520 | 1.9622 | 29.1077 | 9.445 | 26.3734 | 26.3687 | 13.6995 |
| 2.0755 | 3.0 | 26280 | 1.9536 | 28.8137 | 9.1265 | 26.0238 | 26.0217 | 13.854 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "metrics": ["rouge"], "model_index": [{"name": "t5-small-finetuned-xsum-2", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "dataset": {"name": "squad", "type": "squad", "args": "plain_text"}, "metric": {"name": "Rouge1", "type": "rouge", "value": 28.8137}}]}]}
|
text2text-generation
|
fadhilarkan/t5-small-finetuned-xsum-2
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
t5-small-finetuned-xsum-2
=========================
This model is a fine-tuned version of t5-small on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9536
* Rouge1: 28.8137
* Rouge2: 9.1265
* Rougel: 26.0238
* Rougelsum: 26.0217
* Gen Len: 13.854
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 10
* eval\_batch\_size: 10
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.9.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
73,
113,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 10\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
-0.08584735542535782,
0.0992618054151535,
-0.0039571598172187805,
0.08623979985713959,
0.11087407171726227,
0.0004456715832930058,
0.15910284221172333,
0.15556558966636658,
-0.12500765919685364,
0.05796042084693909,
0.1311701387166977,
0.14513522386550903,
0.051048099994659424,
0.16598615050315857,
-0.06923911720514297,
-0.26250365376472473,
0.049879107624292374,
0.052217334508895874,
-0.015311752445995808,
0.12592639029026031,
0.0877671092748642,
-0.12380092591047287,
0.08618072420358658,
0.028240257874131203,
-0.17921704053878784,
-0.012511014938354492,
-0.000050127506256103516,
-0.08607012778520584,
0.10974336415529251,
0.027102872729301453,
0.09626840800046921,
0.04033678025007248,
0.0478006973862648,
-0.16680806875228882,
0.010498058050870895,
0.06413140147924423,
0.0061685568653047085,
0.09899240732192993,
0.058520928025245667,
-0.013298665173351765,
0.11170542240142822,
-0.07651378959417343,
0.076536625623703,
0.020918095484375954,
-0.125277578830719,
-0.25574591755867004,
-0.11440207809209824,
0.04122365638613701,
0.06734079122543335,
0.07584256678819656,
-0.0031482314225286245,
0.180698424577713,
-0.026563124731183052,
0.11293216049671173,
0.2618125379085541,
-0.3148324489593506,
-0.05607132241129875,
-0.014175349846482277,
0.0589849017560482,
0.07862930744886398,
-0.07280569523572922,
-0.03154456615447998,
0.01828784868121147,
0.047912344336509705,
0.1504959911108017,
-0.016264567151665688,
-0.045072805136442184,
-0.021194126456975937,
-0.14061525464057922,
-0.06773552298545837,
0.14933256804943085,
0.025803014636039734,
-0.038806576281785965,
-0.06873161345720291,
-0.08341214805841446,
-0.18671859800815582,
-0.05343848466873169,
-0.007737587206065655,
0.048285018652677536,
-0.035266660153865814,
-0.08438678830862045,
-0.02556983008980751,
-0.07218629121780396,
-0.03390216827392578,
-0.06615693122148514,
0.13964545726776123,
0.05284208431839943,
0.018876811489462852,
-0.06592272222042084,
0.06860506534576416,
-0.03651143237948418,
-0.15702681243419647,
-0.015369389206171036,
0.010739562101662159,
0.00959298387169838,
-0.0387917198240757,
-0.04111267998814583,
-0.12884770333766937,
0.014756615273654461,
0.1699063777923584,
-0.0955289825797081,
0.08000864088535309,
-0.04501046985387802,
0.04022569954395294,
-0.0786084532737732,
0.18434150516986847,
-0.018353721126914024,
0.012541214935481548,
0.018726371228694916,
0.06008005514740944,
0.05746745318174362,
-0.03217723220586777,
-0.11079777032136917,
0.038743842393159866,
0.11495918035507202,
0.027590835466980934,
-0.03407713770866394,
0.07437902688980103,
-0.03749508038163185,
-0.027149278670549393,
0.040330152958631516,
-0.10956737399101257,
0.036902546882629395,
-0.013723715208470821,
-0.06374974548816681,
0.0036392011679708958,
0.015418927185237408,
0.003991646226495504,
-0.0504332035779953,
0.09285221993923187,
-0.07740925997495651,
0.02241150476038456,
-0.08132917433977127,
-0.1412046253681183,
0.03839689865708351,
-0.11353690922260284,
-0.0037248479202389717,
-0.09890389442443848,
-0.14033867418766022,
-0.004914071410894394,
0.06028631329536438,
-0.043599121272563934,
-0.04298204183578491,
-0.04754870757460594,
-0.09732627123594284,
0.04314708709716797,
-0.028114542365074158,
0.0693347230553627,
-0.07230019569396973,
0.08213768154382706,
0.026003697887063026,
0.07720588892698288,
-0.05128736421465874,
0.050149817019701004,
-0.08823229372501373,
0.03609832748770714,
-0.223740816116333,
0.05778861418366432,
-0.054258812218904495,
0.07845111936330795,
-0.1030733734369278,
-0.10311351716518402,
0.029652070254087448,
-0.02577129378914833,
0.1003960520029068,
0.08211269229650497,
-0.1729101985692978,
-0.07116984575986862,
0.2051159292459488,
-0.09201522916555405,
-0.13488523662090302,
0.12827950716018677,
-0.0494302473962307,
0.0008552625658921897,
0.056424736976623535,
0.22372590005397797,
0.05271419882774353,
-0.10327542573213577,
-0.007333851885050535,
-0.04673563316464424,
0.04165162891149521,
-0.050133850425481796,
0.06427457183599472,
0.007720343768596649,
0.09158088266849518,
0.008887630887329578,
0.023798078298568726,
0.02751709148287773,
-0.08409322053194046,
-0.07387498021125793,
-0.05834991857409477,
-0.06400258839130402,
0.017810912802815437,
0.03473624959588051,
0.06544459611177444,
-0.1299087107181549,
-0.10357885807752609,
0.06800329685211182,
0.0685114711523056,
-0.08334056288003922,
0.06322578340768814,
-0.10994572192430496,
0.11112672090530396,
-0.04751935973763466,
0.002247440628707409,
-0.18743503093719482,
-0.02926953136920929,
0.037997275590896606,
-0.016801534220576286,
0.02372104860842228,
-0.04145417734980583,
0.06769006699323654,
0.06365665048360825,
-0.03200269490480423,
-0.024696961045265198,
-0.015580186620354652,
0.004510323982685804,
-0.11369217932224274,
-0.19971975684165955,
-0.03748781606554985,
-0.037388961762189865,
0.07381036877632141,
-0.14373011887073517,
0.048551030457019806,
0.06798010319471359,
0.12317453324794769,
0.04060181975364685,
-0.01621297001838684,
-0.01532784290611744,
0.07327544689178467,
-0.05188262090086937,
-0.0732225552201271,
0.06072100251913071,
0.03047906793653965,
-0.08648740500211716,
0.00816005002707243,
-0.1646048128604889,
0.14234235882759094,
0.14540410041809082,
-0.03020465560257435,
-0.049013905227184296,
-0.0010310993529856205,
-0.054885007441043854,
-0.022003334015607834,
-0.008748465217649937,
0.021760093048214912,
0.16160090267658234,
0.020493432879447937,
0.16294832527637482,
-0.09895725548267365,
-0.054053571075201035,
0.04822308570146561,
-0.032314106822013855,
-0.003117491491138935,
0.0955914780497551,
0.03787332400679588,
-0.10416106134653091,
0.13678915798664093,
0.14353537559509277,
-0.051781896501779556,
0.1256266087293625,
-0.06384119391441345,
-0.06364995241165161,
-0.027903785929083824,
-0.008136012591421604,
0.027310913428664207,
0.08405937254428864,
-0.12846976518630981,
-0.01792714186012745,
0.038734350353479385,
0.038314059376716614,
0.008446644060313702,
-0.1921273171901703,
0.014373007230460644,
0.03473970666527748,
-0.058291930705308914,
-0.03207993507385254,
-0.005939110182225704,
0.02467060275375843,
0.10723304748535156,
0.022331412881612778,
-0.06491817533969879,
0.03166753053665161,
0.009291766211390495,
-0.06174449250102043,
0.17484484612941742,
-0.11326184123754501,
-0.17679230868816376,
-0.10910230129957199,
-0.11330094188451767,
-0.06235586479306221,
-0.0032830270938575268,
0.08885188400745392,
-0.06933203339576721,
-0.052024900913238525,
-0.1017429530620575,
-0.017926404252648354,
-0.007135421968996525,
0.0272233784198761,
0.04718980938196182,
-0.014160910621285439,
0.06382004171609879,
-0.11778134852647781,
-0.029722701758146286,
-0.025526298210024834,
-0.006628426723182201,
0.065660260617733,
0.02170683816075325,
0.10985083878040314,
0.13023130595684052,
-0.04030542075634003,
0.040066782385110855,
-0.045241110026836395,
0.21593201160430908,
-0.07063885033130646,
-0.008991808630526066,
0.1321907341480255,
-0.01855345442891121,
0.08965563029050827,
0.12355485558509827,
0.04486452788114548,
-0.09430581331253052,
-0.005254560150206089,
0.01177634671330452,
-0.048406220972537994,
-0.2252243310213089,
-0.023432815447449684,
-0.05436677485704422,
0.008713411167263985,
0.09815286844968796,
0.03560304641723633,
0.04321134462952614,
0.044423554092645645,
0.01331339217722416,
0.05988559499382973,
-0.003022992517799139,
0.11837897449731827,
0.1418503224849701,
0.05664079263806343,
0.1461828649044037,
-0.05652656778693199,
-0.030071545392274857,
0.05420089140534401,
0.00928149838000536,
0.2221190631389618,
0.004486629273742437,
0.18958432972431183,
0.054600201547145844,
0.1494414508342743,
0.029652554541826248,
0.06346410512924194,
-0.019211966544389725,
-0.0014566711615771055,
-0.016836069524288177,
-0.042371951043605804,
-0.036324407905340195,
0.014274864457547665,
-0.07030358165502548,
0.025723746046423912,
-0.10830756276845932,
0.010771289467811584,
0.05993590131402016,
0.30586931109428406,
0.025444351136684418,
-0.3642767369747162,
-0.10124993324279785,
0.009906662628054619,
-0.06144833192229271,
-0.040566813200712204,
0.017601393163204193,
0.07767143845558167,
-0.0710521936416626,
0.08816220611333847,
-0.08762599527835846,
0.10790145397186279,
-0.051124729216098785,
0.04176494851708412,
0.057455919682979584,
0.09804605692625046,
-0.003246966516599059,
0.04446382820606232,
-0.3008970320224762,
0.2578900158405304,
0.020112482830882072,
0.06479835510253906,
-0.07419470697641373,
0.020817061886191368,
0.011699755676090717,
0.05578368157148361,
0.05271891504526138,
-0.01828804239630699,
-0.11687621474266052,
-0.14542485773563385,
-0.08585039526224136,
0.014835935086011887,
0.10382066667079926,
0.026034008711576462,
0.12175910174846649,
-0.02340715378522873,
0.00001889447412395384,
0.045328449457883835,
-0.03463023900985718,
-0.04271881654858589,
-0.1048237532377243,
0.029057275503873825,
0.04858400672674179,
-0.006675299257040024,
-0.06619148701429367,
-0.09657350182533264,
-0.08297896385192871,
0.18054789304733276,
0.006546925753355026,
-0.06793729215860367,
-0.12496135383844376,
0.03700164705514908,
0.08069810271263123,
-0.08217740803956985,
0.041545987129211426,
-0.016656124964356422,
0.12017178535461426,
0.008048992604017258,
-0.08867453783750534,
0.11610760539770126,
-0.050188783556222916,
-0.1764776110649109,
-0.044868819415569305,
0.1120874434709549,
0.0061345347203314304,
0.05747169628739357,
-0.00743403984233737,
0.04256541654467583,
-0.029870079830288887,
-0.07346749305725098,
0.018096154555678368,
0.010271218605339527,
0.0844101831316948,
-0.03381235525012016,
-0.023971086367964745,
0.009976040571928024,
-0.06435319036245346,
-0.012968857772648335,
0.1843469887971878,
0.25417473912239075,
-0.09093844145536423,
0.07966423779726028,
0.03831318020820618,
-0.05901720002293587,
-0.15645919740200043,
0.009830444119870663,
0.04843428358435631,
-0.007907376624643803,
-0.007883596234023571,
-0.17160673439502716,
0.03032444790005684,
0.09990144520998001,
-0.012646126560866833,
0.07677630335092545,
-0.3162672221660614,
-0.12832903861999512,
0.08752918988466263,
0.12865646183490753,
0.08142311125993729,
-0.16679829359054565,
-0.041788700968027115,
-0.029016777873039246,
-0.13646340370178223,
0.14222262799739838,
-0.11504282802343369,
0.11791214346885681,
-0.02852950617671013,
0.10047561675310135,
0.009007461369037628,
-0.055813081562519073,
0.1062621921300888,
-0.0037888132501393557,
0.08078280091285706,
-0.07774589955806732,
0.02892182767391205,
0.12109271436929703,
-0.07973743230104446,
0.0468827486038208,
-0.08960741758346558,
0.029462357982993126,
-0.09827804565429688,
-0.01566763035953045,
-0.061323631554841995,
0.002853203099220991,
-0.026543309912085533,
-0.03669626638293266,
-0.05091514438390732,
0.0001469973212806508,
0.08250035345554352,
-0.02100486308336258,
0.20391900837421417,
0.017019888386130333,
0.14874738454818726,
0.1502220332622528,
0.10269059240818024,
-0.1336606740951538,
-0.04413376376032829,
0.007945524528622627,
-0.028026504442095757,
0.0425548292696476,
-0.15550273656845093,
0.04560641199350357,
0.1417471319437027,
-0.0012136929435655475,
0.11905777454376221,
0.07156790792942047,
-0.05679392069578171,
0.03227810189127922,
0.04944806545972824,
-0.1679626852273941,
-0.10858434438705444,
0.02219724841415882,
0.033401135355234146,
-0.10890822857618332,
0.06786797195672989,
0.13391335308551788,
-0.05324294790625572,
-0.01857275888323784,
0.007918319664895535,
0.020238593220710754,
-0.01512724906206131,
0.16522081196308136,
0.020157191902399063,
0.055987220257520676,
-0.11122720688581467,
0.08421118557453156,
0.05369129776954651,
-0.12086380273103714,
0.05267513915896416,
0.12532466650009155,
-0.10499317198991776,
-0.028030438348650932,
0.04443185403943062,
0.1545441895723343,
-0.056778356432914734,
-0.05270526558160782,
-0.15398940443992615,
-0.1292145848274231,
0.10387812554836273,
0.20231951773166656,
0.05972826108336449,
0.011184469796717167,
-0.03943886235356331,
0.005111428443342447,
-0.12161858379840851,
0.09033931791782379,
0.03927061706781387,
0.08104190975427628,
-0.12342159450054169,
0.13409295678138733,
-0.0054840692318975925,
0.038233447819948196,
-0.018565760925412178,
0.01699814945459366,
-0.10654328018426895,
0.004654191434383392,
-0.14900833368301392,
0.006811130791902542,
-0.052186135202646255,
-0.002777061192318797,
-0.013120804913341999,
-0.04233861714601517,
-0.0631592869758606,
0.013951645232737064,
-0.11282175034284592,
-0.03142080828547478,
0.00874464213848114,
0.026623938232660294,
-0.12297465652227402,
-0.019318372011184692,
0.0016779868165031075,
-0.08493781834840775,
0.0896947979927063,
0.04939866438508034,
-0.01781505160033703,
0.02897464856505394,
-0.046712812036275864,
-0.013085570186376572,
0.06142320856451988,
0.010536909103393555,
0.07778161019086838,
-0.10124333947896957,
-0.018649574369192123,
0.01866067759692669,
0.020440224558115005,
0.02809942699968815,
0.1025506779551506,
-0.11672069132328033,
0.006309335120022297,
-0.00689778383821249,
-0.06154801324009895,
-0.06614469736814499,
0.05717695131897926,
0.0977000966668129,
0.018264537677168846,
0.18445689976215363,
-0.07653816044330597,
0.03190300986170769,
-0.19306421279907227,
-0.003111821599304676,
0.013428931124508381,
-0.14872470498085022,
-0.06443072855472565,
-0.04477577656507492,
0.06599319726228714,
-0.0679950937628746,
0.1333918422460556,
0.006247237790375948,
0.03318145498633385,
0.05076105147600174,
-0.04036763310432434,
-0.041453517973423004,
0.021109988912940025,
0.17160408198833466,
0.031115621328353882,
-0.04875050485134125,
0.06877001374959946,
0.01392285618931055,
0.08430175483226776,
0.08954999595880508,
0.20827807486057281,
0.13823473453521729,
0.054644469171762466,
0.10141918808221817,
0.03875322267413139,
-0.03293547034263611,
-0.18261252343654633,
0.05623164772987366,
-0.04131860285997391,
0.14998182654380798,
-0.010530774481594563,
0.2081417292356491,
0.12028644979000092,
-0.14819784462451935,
0.05402679368853569,
-0.03568876162171364,
-0.08135230839252472,
-0.11039847880601883,
-0.08190574496984482,
-0.09321577101945877,
-0.15103082358837128,
-0.01187313161790371,
-0.1237790584564209,
0.06044412776827812,
0.058542199432849884,
0.022617530077695847,
-0.0017334300791844726,
0.11372533440589905,
0.01824752241373062,
0.006011430639773607,
0.06168529391288757,
0.002437625080347061,
-0.03728220984339714,
-0.056241292506456375,
-0.08509591966867447,
0.011238430626690388,
0.00762531440705061,
0.05369826406240463,
0.007320051081478596,
0.00019484692893456668,
0.04189342260360718,
-0.04074636101722717,
-0.1120152398943901,
0.02208518795669079,
0.034745216369628906,
0.05975911393761635,
0.02635340765118599,
0.013027113862335682,
-0.01007112953811884,
-0.008962278254330158,
0.19139431416988373,
-0.07740618288516998,
-0.0755351334810257,
-0.11037018895149231,
0.23422060906887054,
0.02336173504590988,
-0.03994698077440262,
0.03261895850300789,
-0.06241334229707718,
-0.025312593206763268,
0.2001192271709442,
0.18697164952754974,
-0.02650107815861702,
-0.008990256115794182,
-0.019749628379940987,
-0.008679377846419811,
-0.021761402487754822,
0.10568960756063461,
0.13529957830905914,
0.02670273557305336,
-0.06448350846767426,
-0.03400636091828346,
-0.055261410772800446,
-0.004356290213763714,
-0.061842262744903564,
0.06965424120426178,
0.020535914227366447,
-0.005986371077597141,
-0.03086266852915287,
0.04892341047525406,
-0.06083298474550247,
-0.06801537424325943,
0.01611950248479843,
-0.20889312028884888,
-0.15602004528045654,
0.005090497899800539,
0.08451278507709503,
-0.013790136203169823,
0.05516907572746277,
0.004861532244831324,
-0.004743426106870174,
0.09591302275657654,
-0.01801958493888378,
-0.07502568513154984,
-0.06553226709365845,
0.10070470720529556,
-0.17842073738574982,
0.17933563888072968,
-0.03314128890633583,
0.0308191180229187,
0.14295899868011475,
0.05377867817878723,
-0.10166338831186295,
0.05602109804749489,
0.04442505165934563,
-0.054288219660520554,
0.009608466178178787,
0.118598572909832,
-0.02830931730568409,
0.0712922215461731,
0.050706423819065094,
-0.12243440002202988,
-0.01361719612032175,
-0.08728976547718048,
-0.03672812879085541,
-0.02005298249423504,
-0.04731740802526474,
-0.04576459899544716,
0.11494731903076172,
0.19486886262893677,
-0.03917165845632553,
0.004128564149141312,
-0.06520739197731018,
0.007334691006690264,
0.05928698182106018,
-0.010124802589416504,
-0.056509967893362045,
-0.2600800395011902,
0.001975024351850152,
0.084468774497509,
-0.0037308952305465937,
-0.2537882924079895,
-0.09033471345901489,
-0.00220554624684155,
-0.03677377104759216,
-0.11452711373567581,
0.08690669387578964,
0.08029662072658539,
0.042398400604724884,
-0.0647733211517334,
-0.0249649565666914,
-0.06532636284828186,
0.17396967113018036,
-0.14174601435661316,
-0.057263847440481186
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model_index": [{"name": "t5-small-finetuned-xsum", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "dataset": {"name": "squad", "type": "squad", "args": "plain_text"}}]}]}
|
text2text-generation
|
fadhilarkan/t5-small-finetuned-xsum
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-small-finetuned-xsum
This model is a fine-tuned version of t5-small on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
[
"# t5-small-finetuned-xsum\n\nThis model is a fine-tuned version of t5-small on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 10\n- eval_batch_size: 10\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.9.2\n- Pytorch 1.9.0+cu102\n- Datasets 1.11.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-small-finetuned-xsum\n\nThis model is a fine-tuned version of t5-small on the squad dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 10\n- eval_batch_size: 10\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.9.2\n- Pytorch 1.9.0+cu102\n- Datasets 1.11.0\n- Tokenizers 0.10.3"
] |
[
73,
33,
6,
12,
8,
3,
103,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# t5-small-finetuned-xsum\n\nThis model is a fine-tuned version of t5-small on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 10\n- eval_batch_size: 10\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1\n- mixed_precision_training: Native AMP### Framework versions\n\n- Transformers 4.9.2\n- Pytorch 1.9.0+cu102\n- Datasets 1.11.0\n- Tokenizers 0.10.3"
] |
[
-0.054408833384513855,
0.09293580800294876,
-0.003184547182172537,
0.05927075445652008,
0.14082135260105133,
0.03511885926127434,
0.12660479545593262,
0.13172131776809692,
-0.12751664221286774,
0.06132560968399048,
0.052797842770814896,
0.07831346243619919,
0.061221376061439514,
0.14354535937309265,
-0.039797600358724594,
-0.2632962465286255,
0.01622738316655159,
0.005455106031149626,
-0.037394747138023376,
0.09881701320409775,
0.09682492166757584,
-0.08822118490934372,
0.06289397925138474,
0.001968816854059696,
-0.16868095099925995,
0.040780145674943924,
-0.01343765389174223,
-0.05844641104340553,
0.08724380284547806,
0.02285115234553814,
0.09798495471477509,
0.015487734228372574,
0.10989701002836227,
-0.22650133073329926,
0.00538852671161294,
0.09899324178695679,
0.0195509921759367,
0.06229786202311516,
0.07993190735578537,
0.02302500419318676,
0.1665128469467163,
-0.14231759309768677,
0.0992773026227951,
0.024634553119540215,
-0.05780874565243721,
-0.12495608627796173,
-0.07004031538963318,
0.06586198508739471,
0.08471107482910156,
0.09830287098884583,
0.00008048873132793233,
0.1530475914478302,
-0.09019582718610764,
0.08332768827676773,
0.161393940448761,
-0.2696192264556885,
-0.06709378212690353,
0.04111025109887123,
0.048144299536943436,
0.08507824689149857,
-0.08388474583625793,
-0.01898481883108616,
0.023518096655607224,
0.04837554693222046,
0.11006282269954681,
-0.015779055655002594,
-0.08920016139745712,
-0.010704179294407368,
-0.14039264619350433,
-0.018686674535274506,
0.15898311138153076,
0.032105639576911926,
-0.021733200177550316,
-0.09978696703910828,
-0.07726144790649414,
-0.08001109212636948,
-0.013815989717841148,
-0.05751549452543259,
0.039328038692474365,
-0.024459440261125565,
-0.050168752670288086,
-0.051855046302080154,
-0.06975707411766052,
-0.032542865723371506,
-0.025597229599952698,
0.07753470540046692,
0.06354358047246933,
0.009296621195971966,
-0.05363338068127632,
0.08467688411474228,
0.019691282883286476,
-0.10409599542617798,
-0.0025050994008779526,
0.002615608973428607,
-0.08860253542661667,
-0.06865108758211136,
-0.04066512733697891,
-0.09390988200902939,
0.00612567039206624,
0.12572042644023895,
-0.04531696438789368,
0.08505470305681229,
-0.00217252504080534,
0.004582894034683704,
-0.021090608090162277,
0.11321444064378738,
-0.03150033578276634,
-0.04144786670804024,
0.015997745096683502,
0.0723705142736435,
0.026294132694602013,
-0.008943812921643257,
-0.0847381055355072,
-0.005791645962744951,
0.10513986647129059,
0.053791504353284836,
-0.04013079032301903,
0.03866736963391304,
-0.021557291969656944,
-0.043106816709041595,
-0.037648316472768784,
-0.13672365248203278,
0.04275874048471451,
-0.017256293445825577,
-0.06096750870347023,
0.03274178504943848,
0.013710915111005306,
-0.01080436073243618,
-0.07857509702444077,
0.09647541493177414,
-0.0766497254371643,
0.008222456090152264,
-0.08832937479019165,
-0.09039948880672455,
0.034143514931201935,
-0.08103741705417633,
-0.031113069504499435,
-0.08471933752298355,
-0.17176395654678345,
-0.033473946154117584,
0.04989024996757507,
-0.03839645907282829,
-0.056351352483034134,
-0.048796866089105606,
-0.05280119180679321,
0.019077712669968605,
-0.0072633749805390835,
0.12126221507787704,
-0.056024640798568726,
0.05820097401738167,
-0.02286006510257721,
0.023186462000012398,
0.013636315241456032,
0.04380436986684799,
-0.07557216286659241,
0.011016584932804108,
-0.12643353641033173,
0.07763512432575226,
-0.054497748613357544,
0.020909607410430908,
-0.11568160355091095,
-0.09907111525535583,
-0.01933484710752964,
-0.01770481839776039,
0.05349458381533623,
0.09799601137638092,
-0.18009629845619202,
-0.04159980267286301,
0.16734783351421356,
-0.08204978704452515,
-0.05111575126647949,
0.10895359516143799,
-0.04407109320163727,
0.014171634800732136,
0.06853097677230835,
0.1664058417081833,
0.10071524232625961,
-0.12948408722877502,
-0.011279023252427578,
-0.01033252477645874,
0.03917669132351875,
-0.004483398050069809,
0.04761935770511627,
-0.005876608658581972,
0.02090873382985592,
0.005294344387948513,
-0.016451120376586914,
0.011597788892686367,
-0.07081470638513565,
-0.08071661740541458,
-0.06339327991008759,
-0.06532035022974014,
0.008255111053586006,
0.03236439451575279,
0.047863755375146866,
-0.07273126393556595,
-0.11157400161027908,
0.14191517233848572,
0.1146913468837738,
-0.077333003282547,
0.0277810450643301,
-0.07892058044672012,
0.023847676813602448,
-0.027298739179968834,
-0.0032606557942926884,
-0.1975019872188568,
-0.11873292177915573,
0.04075118899345398,
-0.0766148567199707,
0.06113823503255844,
0.025387901812791824,
0.05121377483010292,
0.0565306581556797,
-0.048592887818813324,
-0.006527928169816732,
-0.0715043768286705,
-0.0003661605005618185,
-0.09477463364601135,
-0.18986178934574127,
-0.038810934871435165,
-0.019247211515903473,
0.13643845915794373,
-0.2161165028810501,
0.020987126976251602,
-0.021176232025027275,
0.13464286923408508,
0.021757032722234726,
-0.05149009823799133,
-0.007995445281267166,
0.0686957910656929,
-0.020358577370643616,
-0.09067634493112564,
0.051082976162433624,
0.005318902898579836,
-0.07024963200092316,
-0.05276202782988548,
-0.15204299986362457,
0.055558327585458755,
0.0897294133901596,
0.02427203767001629,
-0.08536715060472488,
-0.011942023411393166,
-0.05910918861627579,
-0.05364792421460152,
-0.0924973264336586,
0.008637464605271816,
0.18040356040000916,
-0.0012125219218432903,
0.12700943648815155,
-0.06510034203529358,
-0.0648767277598381,
0.003405394498258829,
0.02153739519417286,
-0.012571101076900959,
0.07479283213615417,
0.10985022783279419,
-0.11830335110425949,
0.09347397089004517,
0.08089425414800644,
-0.06938061118125916,
0.1525839865207672,
-0.04972269386053085,
-0.09012560546398163,
-0.016406070441007614,
0.009213176555931568,
-0.0027402639389038086,
0.08170675486326218,
-0.11675217002630234,
0.004406831227242947,
0.02365565486252308,
0.024561719968914986,
0.04875367134809494,
-0.1693030595779419,
0.012567861936986446,
0.020641576498746872,
-0.04665340483188629,
-0.0048867883160710335,
-0.019785309210419655,
0.040453050285577774,
0.09873823821544647,
0.01056482084095478,
-0.01110728457570076,
0.020376842468976974,
-0.00013568166468758136,
-0.09816684573888779,
0.17185883224010468,
-0.12315301597118378,
-0.16702356934547424,
-0.1015242412686348,
0.0456111878156662,
-0.06282638758420944,
-0.0397084578871727,
0.02727048471570015,
-0.08007394522428513,
-0.06204314902424812,
-0.0993959903717041,
-0.010441177524626255,
-0.05790982022881508,
0.0019865704234689474,
0.022190093994140625,
0.01861235499382019,
0.06625032424926758,
-0.1280815452337265,
0.0013193488121032715,
-0.009743111208081245,
-0.08791670948266983,
0.010890881530940533,
0.03928937017917633,
0.09280053526163101,
0.15150907635688782,
-0.026591377332806587,
0.024289444088935852,
-0.045544788241386414,
0.18385711312294006,
-0.05557812750339508,
0.018696708604693413,
0.11514807492494583,
0.01838129758834839,
0.057229604572057724,
0.11686751991510391,
0.029890280216932297,
-0.08052577078342438,
0.03002697415649891,
0.07246167957782745,
-0.01807699352502823,
-0.24269016087055206,
-0.04876088723540306,
-0.052324168384075165,
-0.06991779804229736,
0.1061420738697052,
0.05588681250810623,
0.0141123216599226,
0.0343550443649292,
-0.012991929426789284,
0.06906972080469131,
-0.0037202569656074047,
0.09154491871595383,
0.13053664565086365,
0.053386107087135315,
0.10617080330848694,
-0.044911161065101624,
-0.03007320687174797,
0.07767560333013535,
-0.0020148546900600195,
0.26499590277671814,
-0.006655554752796888,
0.0962899699807167,
0.03927505016326904,
0.12890130281448364,
-0.0010109018767252564,
0.031082231551408768,
0.02494020015001297,
0.01292029395699501,
0.010583534836769104,
-0.06312309205532074,
-0.01863587088882923,
0.02336869388818741,
-0.0009504597401246428,
0.01524141151458025,
-0.08216653019189835,
0.030936604365706444,
0.028816580772399902,
0.26584020256996155,
0.022342927753925323,
-0.283297598361969,
-0.06022413447499275,
0.004037834703922272,
-0.06369823217391968,
-0.04560182988643646,
0.01603379100561142,
0.12488521635532379,
-0.1131671741604805,
0.07780612260103226,
-0.07018548995256424,
0.09705285727977753,
-0.03810926899313927,
-0.004683316219598055,
0.060594070702791214,
0.1591857522726059,
0.001006947597488761,
0.07763206213712692,
-0.24802376329898834,
0.1877569854259491,
0.017128437757492065,
0.12113406509160995,
-0.0700150728225708,
0.0364522784948349,
0.023553382605314255,
0.05943552032113075,
0.06765947490930557,
0.0024482712615281343,
-0.09020144492387772,
-0.12184848636388779,
-0.08077375590801239,
0.046328477561473846,
0.11540614813566208,
0.013533983379602432,
0.08405394852161407,
-0.05464109033346176,
0.009190540760755539,
0.049391455948352814,
-0.060320354998111725,
-0.18313269317150116,
-0.1404452919960022,
0.0202852264046669,
0.03710699453949928,
-0.05975035950541496,
-0.06954508274793625,
-0.09812664240598679,
-0.027733532711863518,
0.2000924050807953,
0.0008634746773168445,
-0.041321754455566406,
-0.13700583577156067,
0.0678459033370018,
0.09540954977273941,
-0.06028197333216667,
0.017623500898480415,
0.014917824417352676,
0.11581987887620926,
0.02400582656264305,
-0.1260068565607071,
0.05450556054711342,
-0.0652279481291771,
-0.12769930064678192,
-0.052898108959198,
0.11444418132305145,
0.04085289686918259,
0.04874419793486595,
-0.002274201950058341,
0.012562118470668793,
0.015899894759058952,
-0.08495569229125977,
-0.00519543094560504,
0.10821755975484848,
0.08158323168754578,
0.05591903626918793,
-0.11196327954530716,
-0.02850564569234848,
-0.0368141233921051,
-0.027514735236763954,
0.1410665512084961,
0.15462252497673035,
-0.07407686114311218,
0.07867198437452316,
0.05570385232567787,
-0.09915929287672043,
-0.16795219480991364,
0.06554915755987167,
0.10021067410707474,
0.004287782125174999,
0.030778706073760986,
-0.20573756098747253,
0.10938186943531036,
0.12298784404993057,
0.006036858074367046,
0.05384708195924759,
-0.3693242371082306,
-0.13554556667804718,
0.06320154666900635,
0.11908480525016785,
0.03190777450799942,
-0.1490050107240677,
-0.026693470776081085,
-0.032867431640625,
-0.11721386015415192,
0.14265646040439606,
-0.11335322260856628,
0.11372581124305725,
-0.0036207870580255985,
0.08216576278209686,
0.016325203701853752,
-0.03940097987651825,
0.10613597929477692,
0.026084434241056442,
0.06807178258895874,
-0.06295700371265411,
0.04584389179944992,
0.08494367450475693,
-0.0629231259226799,
0.04879831522703171,
-0.05963288992643356,
0.04575212672352791,
-0.14116033911705017,
-0.02914293296635151,
-0.06045215204358101,
0.06344648450613022,
-0.03627674654126167,
-0.057787537574768066,
-0.05356088653206825,
0.03875379264354706,
0.08728817850351334,
-0.04236391559243202,
0.06287845224142075,
0.011712444014847279,
0.10693904012441635,
0.07389429211616516,
0.10658405721187592,
-0.03600846603512764,
-0.10522648692131042,
-0.016266219317913055,
-0.018539024516940117,
0.054123636335134506,
-0.09358148276805878,
0.016064316034317017,
0.13300339877605438,
0.02511206641793251,
0.1439160704612732,
0.04672958701848984,
-0.04066157341003418,
0.0030807191506028175,
0.04328307881951332,
-0.11831718683242798,
-0.17775607109069824,
-0.007747484836727381,
-0.03554658964276314,
-0.11280170828104019,
0.0070608737878501415,
0.09527312964200974,
-0.06157412752509117,
-0.008678583428263664,
-0.0029725844506174326,
0.021510424092411995,
-0.023307878524065018,
0.17961448431015015,
0.003362034447491169,
0.0428147092461586,
-0.07967240363359451,
0.1330028474330902,
0.08456707000732422,
-0.11482914537191391,
0.06010282039642334,
0.11361204087734222,
-0.08742581307888031,
-0.01923552341759205,
0.10122893005609512,
0.16523560881614685,
-0.03522760421037674,
-0.04618692025542259,
-0.08119747042655945,
-0.10965107381343842,
0.06878600269556046,
0.1336420625448227,
0.03140074014663696,
-0.010628539137542248,
-0.054232459515333176,
0.02781382016837597,
-0.14113883674144745,
0.07247684895992279,
0.05182233080267906,
0.07018763571977615,
-0.1275233030319214,
0.15565727651119232,
0.020667623728513718,
0.02432844787836075,
-0.018115466460585594,
0.011292076669633389,
-0.09679333120584488,
-0.02448859065771103,
-0.11753051728010178,
0.0017849005525931716,
-0.035582687705755234,
0.0034532479476183653,
-0.0022617410868406296,
-0.018019361421465874,
-0.05524709075689316,
0.04370565712451935,
-0.06527218967676163,
-0.061870280653238297,
0.0013052340364083648,
0.05778713524341583,
-0.12759675085544586,
0.008490418083965778,
-0.005539182107895613,
-0.09375342726707458,
0.07044536620378494,
0.05217829346656799,
0.0025555051397532225,
0.03546556085348129,
-0.10997757315635681,
-0.023871419951319695,
0.04179941862821579,
0.03677847981452942,
0.053275372833013535,
-0.0892496332526207,
0.00558438478037715,
0.006011407822370529,
0.03975839912891388,
0.022462721914052963,
0.07386798411607742,
-0.10960660129785538,
-0.013231380842626095,
-0.07395400106906891,
-0.0691993311047554,
-0.06634264439344406,
0.04445789009332657,
0.09726695716381073,
0.019367080181837082,
0.1807323843240738,
-0.09534440189599991,
0.0339805968105793,
-0.20022684335708618,
-0.027135955169796944,
0.011891706846654415,
-0.037244562059640884,
-0.03768785670399666,
-0.03393138572573662,
0.07166872918605804,
-0.05668752267956734,
0.10752028226852417,
0.007633577100932598,
0.0627993643283844,
0.05004848912358284,
-0.03819335624575615,
-0.058036934584379196,
0.0045424276031553745,
0.18930462002754211,
0.060153570026159286,
-0.027612730860710144,
0.06841377913951874,
-0.020154915750026703,
0.0679832249879837,
0.07064405083656311,
0.2279609888792038,
0.14967665076255798,
-0.04647846519947052,
0.07562541216611862,
0.04742151498794556,
-0.09696324914693832,
-0.15996316075325012,
0.12085606157779694,
-0.03722352162003517,
0.14812040328979492,
-0.0426582470536232,
0.1887710988521576,
0.10763397812843323,
-0.1688988208770752,
0.0524134635925293,
-0.04038289934396744,
-0.10617893189191818,
-0.12710319459438324,
-0.08509819954633713,
-0.08684708923101425,
-0.11116977781057358,
0.010991017334163189,
-0.11798088997602463,
0.05429135635495186,
0.05694718286395073,
0.03227247670292854,
0.003527548862621188,
0.12577016651630402,
-0.04164173826575279,
-0.008004773408174515,
0.06227594241499901,
0.014035837724804878,
-0.010587742552161217,
-0.06190194934606552,
-0.06494032591581345,
0.024495350196957588,
-0.0017040662933140993,
0.08012675493955612,
-0.03201846778392792,
0.010979841463267803,
0.039178453385829926,
-0.033678360283374786,
-0.05144317075610161,
0.03320903703570366,
0.008372576907277107,
0.03122584894299507,
0.0627211183309555,
0.055678755044937134,
-0.03199135139584541,
-0.03216111660003662,
0.24303694069385529,
-0.06433939188718796,
-0.11548881977796555,
-0.12943793833255768,
0.21893209218978882,
0.032169487327337265,
-0.030678872019052505,
0.0774495080113411,
-0.09238302707672119,
-0.019668610766530037,
0.17439767718315125,
0.15789131820201874,
-0.03851332142949104,
-0.02057046629488468,
-0.013039127923548222,
-0.019327549263834953,
-0.03627314791083336,
0.15030193328857422,
0.09523988515138626,
0.06928791105747223,
-0.037109941244125366,
-0.0029067164286971092,
-0.014203985221683979,
-0.017783069983124733,
-0.10393156856298447,
0.06549493968486786,
0.02867092937231064,
-0.010259856469929218,
-0.007463169749826193,
0.06626380980014801,
-0.010639275424182415,
-0.12808632850646973,
0.03590903803706169,
-0.14260469377040863,
-0.1643928438425064,
-0.02392677590250969,
0.09008858352899551,
-0.05002053827047348,
0.03566199168562889,
-0.013005344197154045,
-0.004745457787066698,
0.11843858659267426,
-0.012855789624154568,
-0.08285006135702133,
-0.09559961408376694,
0.0858035683631897,
-0.0922810360789299,
0.18404990434646606,
-0.007296561263501644,
0.06493060290813446,
0.11131167411804199,
0.04421495273709297,
-0.11067559570074081,
0.06552758812904358,
0.04636095464229584,
-0.061529092490673065,
0.0369427427649498,
0.14812596142292023,
-0.05533468723297119,
0.08014869689941406,
0.039424363523721695,
-0.0844176784157753,
-0.013977625407278538,
-0.06174495816230774,
-0.013716461136937141,
-0.060941874980926514,
0.000983378035016358,
-0.07460518181324005,
0.15280954539775848,
0.2083313763141632,
-0.012934401631355286,
0.015576372854411602,
-0.09601417183876038,
0.01904202625155449,
0.04752456396818161,
0.10039688646793365,
-0.030435362830758095,
-0.2018730789422989,
0.010076478123664856,
-0.015586926601827145,
0.03468161076307297,
-0.23390556871891022,
-0.09799160063266754,
0.019076379016041756,
-0.04654281958937645,
-0.10268419235944748,
0.12516453862190247,
0.08725190907716751,
0.03849439322948456,
-0.04386086389422417,
-0.13509298861026764,
-0.036781035363674164,
0.1498529464006424,
-0.1546640396118164,
-0.037755195051431656
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-summarization
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4740
- Rouge1: 28.3487
- Rouge2: 7.7836
- Rougel: 22.3307
- Rougelsum: 22.3357
- Gen Len: 18.8307
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 14
- eval_batch_size: 14
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.7042 | 1.0 | 14575 | 2.4740 | 28.3487 | 7.7836 | 22.3307 | 22.3357 | 18.8307 |
### Framework versions
- Transformers 4.6.1
- Pytorch 1.7.0
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{"metrics": ["rouge"]}
|
text2text-generation
|
fadhilarkan/test-summarization
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
test-summarization
==================
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4740
* Rouge1: 28.3487
* Rouge2: 7.7836
* Rougel: 22.3307
* Rougelsum: 22.3357
* Gen Len: 18.8307
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 14
* eval\_batch\_size: 14
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.6.1
* Pytorch 1.7.0
* Datasets 1.11.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 14\n* eval\\_batch\\_size: 14\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.7.0\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 14\n* eval\\_batch\\_size: 14\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.7.0\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
48,
113,
4,
30
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 14\n* eval\\_batch\\_size: 14\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.6.1\n* Pytorch 1.7.0\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
-0.07622705399990082,
-0.01142213586717844,
-0.0022985348477959633,
0.08033488690853119,
0.1965385228395462,
0.03363955765962601,
0.10650551319122314,
0.11699125915765762,
-0.10706164687871933,
0.01911693997681141,
0.10412339866161346,
0.16413187980651855,
0.022859999909996986,
0.1302761733531952,
-0.07542525231838226,
-0.2954055964946747,
-0.0024443943984806538,
0.037272464483976364,
-0.04796401038765907,
0.13536877930164337,
0.09001745283603668,
-0.14044944941997528,
0.0668080523610115,
-0.012469173409044743,
-0.20384341478347778,
0.020764777436852455,
0.023435933515429497,
-0.06518197059631348,
0.15849177539348602,
0.030342742800712585,
0.13512299954891205,
0.009112024679780006,
0.08194518834352493,
-0.19837826490402222,
0.012392962351441383,
0.06058337911963463,
0.01938812807202339,
0.07222641259431839,
0.07596807181835175,
-0.016884975135326385,
0.1340610831975937,
-0.08912529051303864,
0.05903103947639465,
0.019805800169706345,
-0.12812240421772003,
-0.21527443826198578,
-0.060323622077703476,
-0.006171379704028368,
0.07277822494506836,
0.10641327500343323,
-0.017631400376558304,
0.14513961970806122,
-0.10581781715154648,
0.10776741802692413,
0.2207024246454239,
-0.26975351572036743,
-0.07037477195262909,
-0.0027153680566698313,
0.01410528365522623,
0.0990128219127655,
-0.09782782196998596,
-0.005771642550826073,
0.046870794147253036,
0.06294664740562439,
0.12565405666828156,
-0.030613243579864502,
-0.10657265782356262,
0.009626025334000587,
-0.14641323685646057,
-0.027786867693066597,
0.10590488463640213,
0.020608287304639816,
-0.026743948459625244,
-0.026595981791615486,
-0.07575204968452454,
-0.1418531835079193,
-0.0512634702026844,
-0.0239725261926651,
0.035034626722335815,
-0.03955657780170441,
-0.07163646817207336,
0.0027177927549928427,
-0.09171892702579498,
-0.06056787073612213,
-0.07009520381689072,
0.17442691326141357,
0.04546252638101578,
-0.0036377343349158764,
-0.043016791343688965,
0.10159731656312943,
-0.02281378023326397,
-0.13086120784282684,
0.030467785894870758,
0.020032815635204315,
-0.02860405296087265,
-0.054762035608291626,
-0.07834013551473618,
-0.09653177857398987,
-0.001314141321927309,
0.08506632596254349,
-0.07255908101797104,
0.067982979118824,
-0.00530896196141839,
0.02288368158042431,
-0.08883698284626007,
0.18631796538829803,
-0.017449725419282913,
-0.024944305419921875,
0.006473642773926258,
0.03875944763422012,
-0.004098053090274334,
-0.020983871072530746,
-0.0927913710474968,
-0.0023285343777388334,
0.12734273076057434,
0.020183265209197998,
-0.07042756676673889,
0.06894858926534653,
-0.03698880597949028,
-0.02387794479727745,
-0.04878439009189606,
-0.10694622248411179,
0.03944703936576843,
-0.013091090135276318,
-0.06849362701177597,
0.022394638508558273,
0.016138831153512,
0.020201431587338448,
-0.05140872299671173,
0.14530447125434875,
-0.07378723472356796,
0.04089122265577316,
-0.10843413323163986,
-0.1315055936574936,
0.006268671713769436,
-0.03440164402127266,
0.011583814397454262,
-0.1106899306178093,
-0.15933364629745483,
-0.009854058735072613,
0.0477791354060173,
-0.03819406032562256,
-0.04943480342626572,
-0.052525702863931656,
-0.061358608305454254,
0.029487309977412224,
-0.028614915907382965,
0.17554320394992828,
-0.05980818346142769,
0.11539581418037415,
0.06530456244945526,
0.06724042445421219,
-0.027765946462750435,
0.057862285524606705,
-0.08377384394407272,
-0.00018620611808728427,
-0.20533955097198486,
0.06872620433568954,
-0.03515799716114998,
0.07220867276191711,
-0.07424706220626831,
-0.11406659334897995,
-0.0016100771026685834,
0.0029527076985687017,
0.1003255695104599,
0.10717269033193588,
-0.1497519314289093,
-0.08676657825708389,
0.17034409940242767,
-0.06985655426979065,
-0.1041594073176384,
0.12577828764915466,
-0.0554683655500412,
0.029415924102067947,
0.07967326045036316,
0.16387253999710083,
0.05058706924319267,
-0.07334142923355103,
0.0378539003431797,
-0.03672492504119873,
0.05742499604821205,
-0.027065470814704895,
0.050043072551488876,
0.019070105627179146,
-0.033995602279901505,
0.02515411376953125,
0.012900497764348984,
0.07115131616592407,
-0.11690899729728699,
-0.07542766630649567,
-0.0506284162402153,
-0.08380156010389328,
0.05323990806937218,
0.06408987939357758,
0.08456819504499435,
-0.1195240318775177,
-0.07564304769039154,
0.06856705993413925,
0.06278552860021591,
-0.058403193950653076,
0.03989938274025917,
-0.05119355022907257,
0.05171217769384384,
-0.034412529319524765,
-0.0013264225563034415,
-0.20534339547157288,
-0.029128406196832657,
0.007305965293198824,
0.05783931538462639,
0.03371451795101166,
0.0002971660578623414,
0.07576121389865875,
0.06493072956800461,
-0.06692884117364883,
-0.022486405447125435,
-0.02123257890343666,
-0.006050447933375835,
-0.14250889420509338,
-0.17870888113975525,
-0.012776749208569527,
-0.014110986143350601,
0.10111524164676666,
-0.20760612189769745,
0.02189621515572071,
-0.040264423936605453,
0.08254134654998779,
0.0006892484379932284,
0.005475405603647232,
-0.05736270546913147,
0.10000975430011749,
-0.03192029893398285,
-0.03614164516329765,
0.07468094676733017,
-0.003737169783562422,
-0.07819408923387527,
-0.033998195081949234,
-0.12550503015518188,
0.16980907320976257,
0.13476191461086273,
-0.17118093371391296,
-0.0865989401936531,
-0.003685899544507265,
-0.051812320947647095,
-0.03049958124756813,
-0.05464769899845123,
0.02754630148410797,
0.19069373607635498,
-0.004796660505235195,
0.1563212126493454,
-0.06500623375177383,
-0.03287499397993088,
0.019011860713362694,
-0.03264721855521202,
0.04277470335364342,
0.11632228642702103,
0.09418061375617981,
-0.09000809490680695,
0.11343733221292496,
0.14273904263973236,
-0.0931273102760315,
0.13493148982524872,
-0.026387151330709457,
-0.08775022625923157,
0.00400875648483634,
-0.02574433945119381,
0.00044724449981004,
0.05701391398906708,
-0.14148525893688202,
-0.0015565147623419762,
0.019195420667529106,
0.0411149226129055,
0.027747340500354767,
-0.220101460814476,
-0.028192397207021713,
0.040169671177864075,
-0.031631920486688614,
-0.030156360939145088,
-0.01721651665866375,
0.03802334517240524,
0.1277310848236084,
0.006366505287587643,
-0.055823005735874176,
0.01975773647427559,
-0.0029765935614705086,
-0.08774035423994064,
0.20981809496879578,
-0.08360535651445389,
-0.1716672033071518,
-0.09491836279630661,
-0.10752526670694351,
-0.04204469174146652,
0.009716936387121677,
0.06390196084976196,
-0.12024138867855072,
-0.03386816382408142,
-0.04073766618967056,
0.05798085778951645,
-0.019755426794290543,
0.044901877641677856,
-0.004596429876983166,
-0.0030881634447723627,
0.06070709601044655,
-0.09212320297956467,
-0.022009028121829033,
-0.04720684513449669,
-0.06293193995952606,
0.07378339767456055,
0.03675156086683273,
0.10527929663658142,
0.16713224351406097,
-0.03942111134529114,
0.01933159865438938,
-0.038478679955005646,
0.22381341457366943,
-0.07053828239440918,
-0.035635415464639664,
0.13702872395515442,
-0.01188076101243496,
0.0548047199845314,
0.1127154529094696,
0.053349848836660385,
-0.09226744621992111,
0.03568427264690399,
0.0372249074280262,
-0.03259739652276039,
-0.22215181589126587,
-0.04374456778168678,
-0.06832991540431976,
-0.03223299980163574,
0.08159220218658447,
0.011992846615612507,
0.04107856750488281,
0.05602666735649109,
0.033100105822086334,
0.07485803216695786,
-0.030981605872511864,
0.06181938573718071,
0.1520071178674698,
0.0455738790333271,
0.1375626176595688,
-0.043515413999557495,
-0.08519395440816879,
0.036166492849588394,
-0.043836865574121475,
0.22544099390506744,
0.007633793633431196,
0.06719490140676498,
0.03573828190565109,
0.1458507925271988,
0.015253094956278801,
0.09744764119386673,
0.01169939897954464,
-0.05303989350795746,
-0.008955593220889568,
-0.03567476198077202,
-0.050415027886629105,
0.016820844262838364,
-0.046191807836294174,
0.039317671209573746,
-0.1487734168767929,
-0.024522848427295685,
0.05356452241539955,
0.2421897053718567,
0.033834490925073624,
-0.31466636061668396,
-0.0798381045460701,
0.014705200679600239,
-0.06761828064918518,
-0.02971033565700054,
0.012148969806730747,
0.07599681615829468,
-0.11662198603153229,
0.05306660383939743,
-0.07370322197675705,
0.11167069524526596,
-0.021630948409438133,
0.057219889014959335,
0.033523108810186386,
0.11622603237628937,
-0.0038095240015536547,
0.07778014987707138,
-0.35215073823928833,
0.27793416380882263,
0.0029784077778458595,
0.07020854204893112,
-0.08163593709468842,
-0.004300720989704132,
0.038007739931344986,
0.05152996629476547,
0.014513515867292881,
-0.019319284707307816,
-0.06144573912024498,
-0.19371569156646729,
-0.02516116388142109,
0.04212469980120659,
0.12590304017066956,
-0.006649035029113293,
0.12125534564256668,
-0.03931180760264397,
0.009604604914784431,
0.07661991566419601,
-0.004024802707135677,
-0.09626637399196625,
-0.07857305556535721,
-0.01230902224779129,
0.0214743260294199,
-0.005662631243467331,
-0.06096465140581131,
-0.11528871953487396,
-0.1048002690076828,
0.16247698664665222,
0.04851407930254936,
-0.024930454790592194,
-0.127873957157135,
0.1037680059671402,
0.07386847585439682,
-0.08429507911205292,
0.03442765772342682,
0.013766742311418056,
0.0673576071858406,
0.026992684230208397,
-0.08071020990610123,
0.11858000606298447,
-0.0693817287683487,
-0.15119265019893646,
-0.05042121186852455,
0.1074615940451622,
0.014707245863974094,
0.07465773820877075,
-0.01915346458554268,
0.013742834329605103,
-0.025431672111153603,
-0.08468729257583618,
0.020553110167384148,
-0.022237185388803482,
0.06065027788281441,
0.054881222546100616,
-0.06806348264217377,
0.006135588511824608,
-0.0734572485089302,
-0.044526971876621246,
0.22133295238018036,
0.23250728845596313,
-0.07032027095556259,
0.0067435456439852715,
0.025330735370516777,
-0.06888070702552795,
-0.1826813817024231,
0.052189797163009644,
0.08057284355163574,
0.013026623986661434,
0.03169320896267891,
-0.17700529098510742,
0.09937503933906555,
0.08027275651693344,
0.008962764404714108,
0.09160816669464111,
-0.311799556016922,
-0.13447967171669006,
0.10982222855091095,
0.15643762052059174,
0.15302100777626038,
-0.14280952513217926,
-0.014586420729756355,
-0.045725736767053604,
-0.11150393635034561,
0.10522603243589401,
-0.07723639905452728,
0.12870080769062042,
-0.016414394602179527,
0.11028078943490982,
0.017289619892835617,
-0.04790332540869713,
0.09805940091609955,
-0.01219868566840887,
0.0849737748503685,
-0.07339048385620117,
0.008958983235061169,
0.009956993162631989,
-0.03695983067154884,
-0.0028383738826960325,
-0.053274255245923996,
0.02010188065469265,
-0.09804987907409668,
-0.031355760991573334,
-0.09007950872182846,
0.024277551099658012,
-0.022707831114530563,
-0.06387870013713837,
-0.01341215893626213,
0.008986962027847767,
0.05486312136054039,
-0.021258141845464706,
0.10690828412771225,
-0.023371435701847076,
0.17507053911685944,
0.09171205013990402,
0.10497529804706573,
-0.08502747863531113,
-0.015992814674973488,
-0.004184487275779247,
-0.015129825100302696,
0.03616400435566902,
-0.12624454498291016,
0.03246217593550682,
0.1496967226266861,
0.007463640999048948,
0.13719725608825684,
0.09794460982084274,
-0.01593519188463688,
0.01497920136898756,
0.07673818618059158,
-0.1612890511751175,
-0.09775993227958679,
-0.019202128052711487,
-0.04765188694000244,
-0.09684336930513382,
0.03192329406738281,
0.10205941647291183,
-0.07321374118328094,
-0.010970140807330608,
-0.019925326108932495,
-0.005248911678791046,
-0.07125696539878845,
0.21214567124843597,
0.04152362048625946,
0.05456077307462692,
-0.10002235323190689,
0.0672183558344841,
0.04962161183357239,
-0.09560878574848175,
0.015964724123477936,
0.13282907009124756,
-0.07359357178211212,
-0.036140430718660355,
0.10218370705842972,
0.1837107092142105,
-0.06782471388578415,
-0.03148099407553673,
-0.14033663272857666,
-0.14380770921707153,
0.08661539107561111,
0.1712396740913391,
0.0885109081864357,
-0.005892341025173664,
-0.0651942640542984,
0.02644418179988861,
-0.136588454246521,
0.07881241291761398,
0.06677448749542236,
0.07184918969869614,
-0.13431471586227417,
0.19946496188640594,
0.004601280204951763,
0.05744476616382599,
-0.036103177815675735,
0.011199869215488434,
-0.11063779890537262,
0.03432532772421837,
-0.1539095640182495,
-0.05303356796503067,
-0.0038144257850944996,
-0.007213899865746498,
-0.00589224835857749,
-0.07056467980146408,
-0.06215474009513855,
-0.0027287534903734922,
-0.12123221158981323,
-0.024839265272021294,
0.017076818272471428,
0.038416244089603424,
-0.10198605060577393,
-0.03762895241379738,
0.031010832637548447,
-0.06501749902963638,
0.05777624621987343,
0.06274048984050751,
-0.005038429517298937,
0.07851609587669373,
-0.14373183250427246,
-0.018455391749739647,
0.06758932769298553,
0.005100916605442762,
0.0745004191994667,
-0.08521932363510132,
0.00861462950706482,
0.007319040130823851,
0.09012611210346222,
0.03313278406858444,
0.07808298617601395,
-0.12958857417106628,
-0.00236681685782969,
-0.03691721707582474,
-0.10927868634462357,
-0.058631204068660736,
0.02884639985859394,
0.05131691321730614,
0.014931921847164631,
0.16934248805046082,
-0.10021188110113144,
0.05787574499845505,
-0.2231770008802414,
-0.004390193615108728,
-0.02259650267660618,
-0.11486634612083435,
-0.08653104305267334,
-0.0784895196557045,
0.08502070605754852,
-0.0483117550611496,
0.11385920643806458,
0.025585373863577843,
0.10070927441120148,
0.03654530271887779,
-0.040213361382484436,
0.0073452601209282875,
0.027332214638590813,
0.2265377789735794,
0.05217912420630455,
-0.04584485664963722,
0.057499319314956665,
0.06524182111024857,
0.10889563709497452,
0.13216528296470642,
0.23582984507083893,
0.14894354343414307,
0.0010986452689394355,
0.09563397616147995,
0.014282983727753162,
-0.05264264717698097,
-0.14559468626976013,
0.008548075333237648,
-0.0459243506193161,
0.10062472522258759,
-0.033471304923295975,
0.20439955592155457,
0.08116870373487473,
-0.1571674346923828,
0.055541180074214935,
-0.0703250989317894,
-0.0930141881108284,
-0.11602679640054703,
-0.006301515735685825,
-0.09299938380718231,
-0.167836531996727,
-0.000984772341325879,
-0.12249954044818878,
0.0494510792195797,
0.10557191073894501,
0.025793056935071945,
-0.026290182024240494,
0.16788159310817719,
0.05065853148698807,
0.006449512206017971,
0.08227348327636719,
-0.006437620148062706,
0.004776445683091879,
-0.07254461944103241,
-0.07262624800205231,
-0.015366028994321823,
-0.011422360315918922,
0.03873418644070625,
-0.03911478444933891,
-0.10545986145734787,
0.039427440613508224,
-0.03474430367350578,
-0.10977619141340256,
0.0242108516395092,
0.02622714824974537,
0.07148930430412292,
0.06526967138051987,
0.00631328159943223,
-0.002290709177032113,
-0.024512946605682373,
0.24746252596378326,
-0.09672103077173233,
-0.10931815952062607,
-0.09727595001459122,
0.31486502289772034,
0.046914242208004,
0.0013187606818974018,
0.022253455594182014,
-0.05856931582093239,
-0.015944862738251686,
0.2521749436855316,
0.17242781817913055,
-0.10713014751672745,
-0.014724954031407833,
0.0006994143477641046,
-0.007429551333189011,
-0.00924145057797432,
0.14625699818134308,
0.1492493450641632,
0.05960531160235405,
-0.1048072874546051,
-0.018134035170078278,
-0.051733117550611496,
-0.011898808181285858,
-0.04775966703891754,
0.08213670551776886,
0.061404842883348465,
0.004964177962392569,
-0.031920161098241806,
0.07478545606136322,
-0.07307495176792145,
-0.05563129484653473,
-0.002866410417482257,
-0.20547950267791748,
-0.15934237837791443,
-0.01819067820906639,
0.09367316216230392,
-0.0034120262134820223,
0.06585469841957092,
-0.025781819596886635,
-0.0016857279697433114,
0.05594227463006973,
-0.019241079688072205,
-0.052422311156988144,
-0.08674155920743942,
0.09961263835430145,
-0.11721254885196686,
0.14684031903743744,
-0.03966100141406059,
0.05272715166211128,
0.12941621243953705,
0.06668221950531006,
-0.057465046644210815,
0.0795002430677414,
0.03667247295379639,
-0.08962821215391159,
0.042123131453990936,
0.13594090938568115,
-0.04035354033112526,
0.031441666185855865,
0.04720192775130272,
-0.1507311910390854,
0.028466807678341866,
-0.09437944740056992,
-0.041201166808605194,
-0.018411245197057724,
-0.04913058131933212,
-0.05822810158133507,
0.1147039383649826,
0.23620183765888214,
-0.022104915231466293,
0.03154154494404793,
-0.09460675716400146,
0.0052604759112000465,
0.03462652489542961,
0.07028541713953018,
-0.08534751832485199,
-0.26302486658096313,
-0.003785444889217615,
0.09938611090183258,
-0.025446122512221336,
-0.2598761022090912,
-0.08854138106107712,
0.0005602310411632061,
-0.06081823632121086,
-0.11645472049713135,
0.11169775575399399,
0.0824870839715004,
0.036913491785526276,
-0.049773216247558594,
-0.14714659750461578,
-0.06396542489528656,
0.19185996055603027,
-0.1475033313035965,
-0.07630523294210434
] |
null | null |
transformers
|
# test DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
faketermz/DialoGPT
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# test DialoGPT Model
|
[
"# test DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# test DialoGPT Model"
] |
[
51,
7
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# test DialoGPT Model"
] |
[
-0.058454714715480804,
0.054849859327077866,
-0.004869355354458094,
0.019150514155626297,
0.09150604903697968,
-0.006544081959873438,
0.1494974046945572,
0.15603479743003845,
0.028035923838615417,
-0.035270605236291885,
0.1401039958000183,
0.1623115837574005,
-0.008783511817455292,
0.06414789706468582,
-0.09861502796411514,
-0.2093149870634079,
0.05586855486035347,
0.06806384027004242,
0.04782457277178764,
0.1618170291185379,
0.0970136821269989,
-0.04119293764233589,
0.06944122910499573,
0.011731921695172787,
-0.15551045536994934,
0.0191457811743021,
0.007759189233183861,
-0.11941414326429367,
0.12551811337471008,
0.022183502092957497,
0.038173697888851166,
0.048153962939977646,
-0.06395348161458969,
-0.13320717215538025,
0.02491115592420101,
-0.027845701202750206,
-0.001629888080060482,
0.02514367736876011,
0.021239858120679855,
-0.06791474670171738,
0.1254461407661438,
0.15228617191314697,
0.012522995471954346,
0.036126088351011276,
-0.11515181511640549,
-0.040248479694128036,
-0.017945963889360428,
0.01445676852017641,
0.06579438596963882,
0.13734322786331177,
-0.07059428095817566,
0.19655130803585052,
-0.1334463506937027,
0.12238873541355133,
0.0833498165011406,
-0.3017609119415283,
-0.018900733441114426,
0.11468781530857086,
0.09184467047452927,
0.032848112285137177,
-0.015478502959012985,
0.06723278015851974,
0.059066496789455414,
0.037155769765377045,
-0.09091409295797348,
-0.0871148556470871,
-0.11432796716690063,
0.01610618643462658,
-0.11762023717164993,
-0.04796699807047844,
0.24762365221977234,
-0.03425273671746254,
0.026874223724007607,
-0.08787276595830917,
-0.04574678838253021,
-0.04064542427659035,
0.00033346202690154314,
-0.059280186891555786,
-0.09031906723976135,
0.037614911794662476,
-0.031396109610795975,
-0.07800453156232834,
-0.11591324955224991,
-0.09253411740064621,
-0.1838322877883911,
0.13470417261123657,
0.051137860864400864,
0.024284031242132187,
-0.1901329904794693,
0.12057295441627502,
0.0032377031166106462,
-0.10034241527318954,
-0.045879196375608444,
-0.09226091206073761,
-0.021190974861383438,
-0.0143235232681036,
-0.06118772178888321,
-0.07306955009698868,
0.11778464168310165,
0.12681500613689423,
-0.02081223763525486,
0.032691191881895065,
-0.059091631323099136,
0.034338708966970444,
0.03227529674768448,
0.1347121298313141,
0.05753312632441521,
-0.02795943059027195,
0.007872167974710464,
-0.0538443885743618,
-0.032866086810827255,
-0.018135584890842438,
-0.1250055432319641,
-0.043608617037534714,
0.11646825075149536,
0.08025936037302017,
0.050520963966846466,
0.08109351992607117,
-0.020388437435030937,
-0.02221030369400978,
0.012409097515046597,
-0.059038612991571426,
-0.013676952570676804,
0.016980506479740143,
-0.024934304878115654,
0.06013813614845276,
0.010789730586111546,
0.03574361652135849,
-0.12216432392597198,
-0.03852637857198715,
-0.025252651423215866,
0.009040998294949532,
-0.029451966285705566,
-0.052693646401166916,
0.016880404204130173,
-0.011835316196084023,
-0.018818935379385948,
-0.1672831028699875,
-0.19920334219932556,
-0.037303078919649124,
-0.022787155583500862,
-0.04938225448131561,
-0.06718356907367706,
-0.10711118578910828,
-0.02007245644927025,
0.01590443029999733,
-0.05980628356337547,
-0.02528882399201393,
-0.047439511865377426,
0.09419794380664825,
0.06464263051748276,
0.07677150517702103,
-0.011239194311201572,
0.06594876199960709,
-0.11797385662794113,
-0.01836390234529972,
-0.06805621832609177,
0.16485495865345,
-0.04164442792534828,
0.046624522656202316,
-0.0583755187690258,
-0.047979120165109634,
-0.09155350178480148,
0.058172985911369324,
-0.011609026230871677,
0.2869161069393158,
-0.10176746547222137,
-0.08694947510957718,
0.2871631681919098,
-0.06729502230882645,
-0.1681573987007141,
0.17321836948394775,
-0.005662125535309315,
0.08434148132801056,
0.1458483189344406,
0.20242097973823547,
0.036679331213235855,
-0.06982307136058807,
0.06610346585512161,
0.10163009911775589,
-0.06774018704891205,
0.0159312691539526,
0.04796474426984787,
-0.07013561576604843,
-0.10035333782434464,
0.037662483751773834,
0.035901784896850586,
0.05465921014547348,
-0.05863593891263008,
-0.028357233852148056,
-0.04442752152681351,
-0.005883077159523964,
0.12295838445425034,
0.005269973073154688,
0.11714410036802292,
-0.0723094791173935,
-0.06714286655187607,
-0.00963416788727045,
0.03430144488811493,
-0.0692492127418518,
0.013825044967234135,
-0.11759655177593231,
0.10783547163009644,
-0.042049508541822433,
0.04190414398908615,
-0.14326126873493195,
-0.027180619537830353,
-0.0049763089045882225,
0.15543679893016815,
0.06109524518251419,
0.10191883891820908,
0.0830189436674118,
-0.07119312882423401,
0.016974104568362236,
0.008264495991170406,
0.15129679441452026,
-0.024509763345122337,
-0.08084318786859512,
-0.055974725633859634,
0.10929803550243378,
-0.04928668960928917,
0.11129365116357803,
-0.0846387967467308,
0.0031721419654786587,
-0.015374132432043552,
0.08368207514286041,
-0.043865710496902466,
0.029321201145648956,
0.02182772010564804,
-0.010339019820094109,
-0.04862494021654129,
-0.004948901943862438,
0.06311853229999542,
-0.00604849960654974,
-0.07639694958925247,
0.2026381939649582,
-0.12419036030769348,
0.0949496328830719,
0.15560908615589142,
-0.1867033839225769,
-0.0341830737888813,
-0.10101998597383499,
-0.039037298411130905,
0.0161844901740551,
0.012440241873264313,
-0.036104656755924225,
0.2559361159801483,
-0.026020856574177742,
0.15171363949775696,
-0.07120358198881149,
-0.05087471753358841,
-0.04232212156057358,
-0.07747453451156616,
0.039309725165367126,
0.11888479441404343,
0.040811702609062195,
-0.15411873161792755,
0.12326928228139877,
0.03497624024748802,
-0.03586103022098541,
0.15842053294181824,
0.0014126397436484694,
-0.019493548199534416,
0.04872272536158562,
0.08602794259786606,
-0.04667172208428383,
-0.06355796009302139,
-0.3210277259349823,
-0.017428047955036163,
0.07226964086294174,
0.06320029497146606,
0.10473031550645828,
-0.10398541390895844,
-0.014157657511532307,
0.006904085632413626,
-0.05514471232891083,
0.016831200569868088,
0.12987978756427765,
0.037081386893987656,
0.0995415598154068,
-0.02794845961034298,
-0.07398311793804169,
0.06615743786096573,
0.00842159241437912,
-0.13789595663547516,
0.21935713291168213,
-0.10228922963142395,
-0.351380318403244,
-0.07798030227422714,
-0.14230568706989288,
-0.043414708226919174,
0.04082268103957176,
0.1278688758611679,
-0.13504822552204132,
0.002140265889465809,
-0.0071036010049283504,
0.07623924314975739,
-0.03012276440858841,
0.018732087686657906,
-0.04211229830980301,
-0.011621891520917416,
-0.0988372340798378,
-0.09592793136835098,
-0.06463591754436493,
-0.013895131647586823,
-0.06176260858774185,
0.1348438560962677,
-0.15241123735904694,
0.01777040585875511,
0.2230299413204193,
0.025681564584374428,
0.06849886476993561,
-0.04621488228440285,
0.23949366807937622,
-0.1321290284395218,
-0.018847346305847168,
0.20788225531578064,
-0.022986773401498795,
0.011788264848291874,
0.09856043010950089,
-0.043529361486434937,
-0.06920145452022552,
0.05489673838019371,
-0.04377273470163345,
-0.046189114451408386,
-0.2655889093875885,
-0.11465185135602951,
-0.07716473191976547,
0.06016466021537781,
0.009002579376101494,
0.05059518665075302,
0.17120978236198425,
0.08232632279396057,
0.005490353796631098,
-0.04270629957318306,
0.04460737854242325,
0.06263939291238785,
0.2743052840232849,
-0.07302570343017578,
0.13908229768276215,
-0.028281500563025475,
-0.1613590121269226,
0.03890436515212059,
0.050126805901527405,
0.09708860516548157,
0.04968543350696564,
0.012783254496753216,
0.03824257850646973,
0.05118744075298309,
0.11116833984851837,
0.03133596479892731,
0.04323854669928551,
-0.05486233904957771,
0.011408190242946148,
-0.043146539479494095,
-0.039166029542684555,
0.022502293810248375,
0.10174766927957535,
-0.13526582717895508,
-0.03933142125606537,
-0.05440516769886017,
0.06854544579982758,
0.040510233491659164,
0.11623764038085938,
-0.22330570220947266,
-0.02811270020902157,
0.007951779291033745,
-0.06950613856315613,
-0.09898509830236435,
0.06624045222997665,
-0.0020685831550508738,
-0.16554704308509827,
0.0382755771279335,
-0.02306016907095909,
0.10852667689323425,
-0.03900473937392235,
0.055670611560344696,
-0.11482951045036316,
-0.08686720579862595,
0.001418977277353406,
0.1310579478740692,
-0.34720489382743835,
0.1921512633562088,
-0.018785808235406876,
-0.008973367512226105,
-0.1116703525185585,
-0.007289990317076445,
0.012535588815808296,
0.07615324854850769,
0.12236164510250092,
-0.0367874950170517,
0.06051143258810043,
0.0166466161608696,
-0.060440488159656525,
0.04908055439591408,
0.0767110213637352,
0.04110727459192276,
0.023286882787942886,
-0.03427311033010483,
0.0076326956041157246,
-0.031848110258579254,
-0.10525447875261307,
-0.054235316812992096,
-0.08960884809494019,
0.051539096981287,
0.044878821820020676,
0.077382393181324,
0.012124489061534405,
-0.045855388045310974,
-0.11207980662584305,
0.2706051170825958,
-0.009881890378892422,
-0.10997073352336884,
-0.07459985464811325,
-0.01366905402392149,
0.10455609858036041,
-0.06383665651082993,
0.008727332577109337,
-0.0545499250292778,
0.02226313389837742,
-0.035375434905290604,
-0.13758137822151184,
0.07034696638584137,
-0.0937955379486084,
-0.04983924701809883,
-0.005518049001693726,
0.2052101492881775,
-0.009376054629683495,
0.05064765363931656,
0.0899551510810852,
-0.004778609611093998,
-0.12866626679897308,
-0.08799124509096146,
-0.0236807893961668,
-0.014748654328286648,
-0.051659729331731796,
0.06924217939376831,
0.011515798978507519,
-0.049098994582891464,
-0.0815337672829628,
-0.013482983224093914,
0.3251459002494812,
0.1308719664812088,
-0.030886685475707054,
0.13827869296073914,
0.07455132901668549,
-0.06599841266870499,
-0.2753336727619171,
-0.05593071132898331,
-0.004109115339815617,
0.0009279991500079632,
-0.09591929614543915,
-0.11729458719491959,
0.07816115021705627,
-0.022252582013607025,
-0.029169345274567604,
0.12748727202415466,
-0.31080004572868347,
-0.09567046165466309,
0.22354020178318024,
-0.047274548560380936,
0.303570419549942,
-0.11134189367294312,
-0.09181136637926102,
-0.016782080754637718,
-0.1331019550561905,
0.09437451511621475,
0.01971776969730854,
0.11464765667915344,
-0.01670672371983528,
0.1475977599620819,
0.05319061875343323,
-0.02369678020477295,
0.1180834099650383,
-0.0021039156708866358,
-0.0693134143948555,
-0.07371712476015091,
0.010891949757933617,
-0.037655238062143326,
0.014403223991394043,
0.09428992122411728,
-0.05124399811029434,
0.07107353210449219,
-0.16588160395622253,
-0.0626865029335022,
-0.09268821775913239,
0.08915496617555618,
0.03317294642329216,
-0.032328199595212936,
-0.025900837033987045,
-0.06484688818454742,
-0.02357528917491436,
0.009307267144322395,
0.08925804495811462,
-0.13776692748069763,
0.1612735241651535,
0.08838483691215515,
0.1845770627260208,
-0.07790207117795944,
-0.051760513335466385,
-0.023004546761512756,
-0.056241706013679504,
0.057940445840358734,
-0.06865041702985764,
0.009621568024158478,
0.13467873632907867,
-0.0035723799373954535,
0.09898031502962112,
0.07685713469982147,
-0.02669421024620533,
0.018519213423132896,
0.08489738404750824,
-0.19064347445964813,
-0.03180111199617386,
-0.0804634690284729,
-0.049049001187086105,
0.015458212234079838,
0.08038663864135742,
0.18582649528980255,
-0.011249285191297531,
-0.03098529577255249,
-0.0036591109819710255,
-0.010518142022192478,
-0.0726601853966713,
0.1655663549900055,
0.0017973622307181358,
0.03877574950456619,
-0.13751128315925598,
0.04153814539313316,
-0.03182561323046684,
-0.07847652584314346,
0.054848380386829376,
0.1338367909193039,
-0.08696423470973969,
-0.13125291466712952,
-0.05923584848642349,
0.11829932779073715,
-0.12494949251413345,
-0.03240529075264931,
-0.07682308554649353,
-0.12645423412322998,
0.06299314647912979,
0.09946855902671814,
0.08371288329362869,
0.07774490118026733,
-0.04786419868469238,
-0.03923014923930168,
-0.03998357430100441,
0.037138137966394424,
0.06832768768072128,
-0.006618075538426638,
-0.05796827748417854,
0.08674520999193192,
-0.029653333127498627,
0.1310734897851944,
-0.10023680329322815,
-0.10233759135007858,
-0.14999645948410034,
0.07634498178958893,
-0.16427560150623322,
-0.06851678341627121,
-0.06469745188951492,
-0.04902215674519539,
0.024453742429614067,
-0.03767972066998482,
-0.02496998943388462,
-0.026698438450694084,
-0.1154048964381218,
0.03709735348820686,
-0.030145864933729172,
0.010503530502319336,
-0.08502773940563202,
0.044890936464071274,
0.06939425319433212,
-0.04093350097537041,
0.1404537409543991,
0.1418255865573883,
-0.1364632546901703,
0.08360781520605087,
-0.14520063996315002,
-0.05157753825187683,
0.1034243181347847,
0.014297890476882458,
0.040113795548677444,
-0.013348469510674477,
0.0452757403254509,
0.10868445038795471,
0.07968773692846298,
0.0977620854973793,
0.04256525635719299,
-0.07501073181629181,
0.0035135881043970585,
-0.07545894384384155,
-0.1014469638466835,
-0.04288777336478233,
-0.010369316674768925,
-0.002110555302351713,
0.07937587797641754,
0.09711065888404846,
-0.083875373005867,
0.08098902553319931,
-0.08568625897169113,
0.01201139111071825,
-0.0012806035811081529,
-0.09962452203035355,
-0.008271715603768826,
-0.05279671773314476,
0.07912836968898773,
0.015200342983007431,
0.2039761245250702,
0.03525412827730179,
0.018965288996696472,
0.00934840552508831,
0.04136067256331444,
0.1021413654088974,
-0.020224733278155327,
0.21893568336963654,
0.09200558066368103,
-0.034528639167547226,
-0.0716996043920517,
0.058325644582509995,
0.003977341111749411,
-0.05586417019367218,
0.11495020240545273,
0.037620075047016144,
-0.0855429396033287,
0.1061040461063385,
-0.0009836751269176602,
0.027369007468223572,
-0.06896647065877914,
-0.15417204797267914,
-0.0558401495218277,
0.07802252471446991,
-0.026615388691425323,
0.16059410572052002,
0.13386170566082,
-0.03831009939312935,
0.017519518733024597,
-0.01762382686138153,
-0.08813165873289108,
-0.1907293051481247,
-0.1360551118850708,
-0.10229163616895676,
-0.17812412977218628,
0.04984592646360397,
-0.12169785052537918,
0.028916416689753532,
-0.008355679921805859,
0.104253388941288,
-0.09299057722091675,
0.12396393716335297,
0.043293148279190063,
-0.12403465062379837,
0.09757477045059204,
-0.06543520838022232,
0.08988867700099945,
0.0012928369687870145,
0.006293184123933315,
-0.01032754871994257,
0.05515429750084877,
0.044296469539403915,
0.02063763700425625,
-0.0774684026837349,
0.01758519560098648,
-0.13003961741924286,
-0.06137846037745476,
-0.03617681935429573,
0.07973537594079971,
0.019738605245947838,
0.15622523427009583,
0.03249668329954147,
-0.020491018891334534,
0.014693236909806728,
0.23480357229709625,
-0.07108572125434875,
-0.16085906326770782,
-0.11766039580106735,
0.288775235414505,
-0.026304736733436584,
0.08767376840114594,
-0.01947002299129963,
-0.0008486983715556562,
-0.0674542710185051,
0.3482770025730133,
0.25160446763038635,
-0.06493043899536133,
-0.014332713559269905,
0.011789110489189625,
0.02393379434943199,
0.07832381129264832,
0.09435038268566132,
0.09390745311975479,
0.3169805109500885,
-0.07054763287305832,
-0.032593220472335815,
-0.020893925800919533,
-0.04717233404517174,
-0.0660860612988472,
0.006177202332764864,
0.056367553770542145,
-0.06511129438877106,
-0.006414288654923439,
0.14549164474010468,
-0.23514917492866516,
0.10448009520769119,
-0.15506908297538757,
-0.12318719178438187,
-0.10488470643758774,
0.007667371071875095,
0.07273745536804199,
0.0032971338368952274,
0.060189615935087204,
-0.013672963716089725,
-0.0445433109998703,
0.04276106879115105,
-0.027405912056565285,
-0.14222432672977448,
-0.026559237390756607,
0.07964788377285004,
-0.06021779403090477,
-0.04851333051919937,
-0.006879991851747036,
0.17818358540534973,
0.08485501259565353,
0.03263808414340019,
-0.02355680987238884,
0.0774456113576889,
0.016655102372169495,
-0.07189899682998657,
0.0863724797964096,
0.05652570724487305,
0.05991189926862717,
-0.0748266652226448,
0.08401535451412201,
-0.09818252176046371,
0.0405905544757843,
-0.05464518070220947,
0.006547361146658659,
-0.1310739368200302,
0.04552610591053963,
-0.076016865670681,
0.07189974933862686,
0.11426668614149094,
-0.02573787420988083,
0.03964782506227493,
-0.04094333201646805,
0.02463437244296074,
-0.025362994521856308,
-0.07840605080127716,
-0.07869792729616165,
-0.20892135798931122,
-0.08113861083984375,
0.07829083502292633,
-0.038717228919267654,
-0.22615940868854523,
0.014125785790383816,
-0.10439585149288177,
0.020266849547624588,
-0.05749313160777092,
0.10231843590736389,
0.08090648800134659,
0.05316178873181343,
0.010411521419882774,
-0.04713747650384903,
0.029959427192807198,
0.10475810617208481,
-0.1484200805425644,
-0.07842686772346497
] |
null | null | null |
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`sdk`: _string_
Can be either `gradio` or `streamlit`
`sdk_version` : _string_
Only applicable for `streamlit` SDK.
See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
`app_file`: _string_
Path to your main application file (which contains either `gradio` or `streamlit` Python code).
Path is relative to the root of the repository.
`pinned`: _boolean_
Whether the Space stays on top of your list.
|
{"title": "Test Space", "emoji": "\ud83d\udd25", "colorFrom": "indigo", "colorTo": "blue", "sdk": "gradio", "app_file": "app.py", "pinned": false}
| null |
omerXfaruq/test-space
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Configuration
'title': _string_
Display title for the Space
'emoji': _string_
Space emoji (emoji-only character allowed)
'colorFrom': _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
'colorTo': _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
'sdk': _string_
Can be either 'gradio' or 'streamlit'
'sdk_version' : _string_
Only applicable for 'streamlit' SDK.
See doc for more info on supported versions.
'app_file': _string_
Path to your main application file (which contains either 'gradio' or 'streamlit' Python code).
Path is relative to the root of the repository.
'pinned': _boolean_
Whether the Space stays on top of your list.
|
[
"# Configuration\n\n'title': _string_ \nDisplay title for the Space\n\n'emoji': _string_ \nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'sdk': _string_ \nCan be either 'gradio' or 'streamlit'\n\n'sdk_version' : _string_ \nOnly applicable for 'streamlit' SDK. \nSee doc for more info on supported versions.\n\n'app_file': _string_ \nPath to your main application file (which contains either 'gradio' or 'streamlit' Python code). \nPath is relative to the root of the repository.\n\n'pinned': _boolean_ \nWhether the Space stays on top of your list."
] |
[
"TAGS\n#region-us \n",
"# Configuration\n\n'title': _string_ \nDisplay title for the Space\n\n'emoji': _string_ \nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'sdk': _string_ \nCan be either 'gradio' or 'streamlit'\n\n'sdk_version' : _string_ \nOnly applicable for 'streamlit' SDK. \nSee doc for more info on supported versions.\n\n'app_file': _string_ \nPath to your main application file (which contains either 'gradio' or 'streamlit' Python code). \nPath is relative to the root of the repository.\n\n'pinned': _boolean_ \nWhether the Space stays on top of your list."
] |
[
6,
223
] |
[
"passage: TAGS\n#region-us \n# Configuration\n\n'title': _string_ \nDisplay title for the Space\n\n'emoji': _string_ \nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'sdk': _string_ \nCan be either 'gradio' or 'streamlit'\n\n'sdk_version' : _string_ \nOnly applicable for 'streamlit' SDK. \nSee doc for more info on supported versions.\n\n'app_file': _string_ \nPath to your main application file (which contains either 'gradio' or 'streamlit' Python code). \nPath is relative to the root of the repository.\n\n'pinned': _boolean_ \nWhether the Space stays on top of your list."
] |
[
0.01188071072101593,
0.07758358120918274,
-0.005383076146245003,
-0.017373213544487953,
0.08687979727983475,
-0.014924844726920128,
0.014434631913900375,
0.06603410094976425,
0.10808582603931427,
0.1384299099445343,
0.02763586863875389,
0.09634341299533844,
0.009154659695923328,
0.1522364765405655,
0.006923646666109562,
-0.22283697128295898,
0.042200393974781036,
-0.0709344744682312,
0.04902972653508186,
0.06943602859973907,
0.06273981928825378,
-0.059291329234838486,
0.07202128320932388,
-0.0033088354393839836,
-0.14888131618499756,
0.00013695177040062845,
-0.010616403073072433,
-0.049460891634225845,
0.014828769490122795,
-0.0402386374771595,
0.08266407251358032,
-0.04675670340657234,
-0.07371697574853897,
-0.1268932968378067,
0.033357325941324234,
0.12697471678256989,
0.033907450735569,
0.002259220229461789,
0.12230198830366135,
-0.12981177866458893,
0.21807576715946198,
-0.1327449381351471,
0.05513199418783188,
-0.014647294767200947,
-0.018211161717772484,
-0.15117362141609192,
-0.0412733368575573,
-0.05453411117196083,
0.14670124650001526,
0.009025073610246181,
-0.008476284332573414,
0.01575237512588501,
-0.12312111258506775,
0.08234678208827972,
0.06933248043060303,
-0.04727475345134735,
-0.002634770004078746,
0.1116911768913269,
0.08801890909671783,
0.037870731204748154,
-0.12108538299798965,
0.0075067500583827496,
-0.03785065934062004,
-0.010196342132985592,
-0.03730084374547005,
-0.05277198180556297,
-0.10130736231803894,
0.02550225891172886,
-0.08296948671340942,
-0.0009194708545692265,
0.25919461250305176,
0.012544205412268639,
-0.05758257955312729,
-0.11951438337564468,
-0.06565803289413452,
-0.045700762420892715,
0.02577689290046692,
0.060137517750263214,
0.053701069205999374,
0.06539168953895569,
0.10311403125524521,
-0.0007050674175843596,
-0.12313074618577957,
-0.010358340106904507,
-0.12153321504592896,
0.15558311343193054,
-0.027887029573321342,
0.020990798249840736,
-0.11456135660409927,
0.08364246785640717,
-0.08822081238031387,
-0.13817736506462097,
0.014043902978301048,
-0.10895320028066635,
0.00031807392952032387,
0.06523092091083527,
-0.0740472599864006,
-0.17337968945503235,
0.0711340457201004,
0.1878124326467514,
0.055560074746608734,
0.0983857661485672,
-0.0769592747092247,
0.04823235049843788,
0.10609938204288483,
0.191655695438385,
-0.07476762682199478,
0.00942059326916933,
0.02892918698489666,
-0.1705435961484909,
0.09933915734291077,
-0.08998927474021912,
-0.1121046394109726,
0.018870292231440544,
-0.001934586907736957,
-0.000014767882021260448,
0.10403572767972946,
0.0064650182612240314,
-0.08259890973567963,
-0.06819719076156616,
0.11214480549097061,
-0.08646366745233536,
0.08762615919113159,
0.04516882076859474,
-0.03648534417152405,
0.05987478047609329,
-0.027545860037207603,
0.033786140382289886,
0.03255436569452286,
0.18013893067836761,
-0.04335426539182663,
-0.03640882298350334,
-0.1593049317598343,
-0.10343391448259354,
0.04724676534533501,
-0.09820203483104706,
0.05610208958387375,
-0.06877691298723221,
-0.045289840549230576,
-0.044973164796829224,
0.014098557643592358,
-0.000728745711967349,
0.05700746551156044,
0.05061240494251251,
-0.11077108979225159,
0.12225869297981262,
0.0473838746547699,
-0.010428318753838539,
-0.045939430594444275,
0.04395761713385582,
-0.014665937051177025,
0.08389930427074432,
-0.07425318658351898,
0.006531501188874245,
-0.08674965053796768,
0.030134806409478188,
-0.31573250889778137,
0.0038507028948515654,
-0.01876232586801052,
0.10097015649080276,
-0.001635766588151455,
-0.00516595458611846,
-0.03464873880147934,
-0.03676575794816017,
-0.05587775260210037,
0.046409010887145996,
-0.25948604941368103,
0.011800145730376244,
0.1486954241991043,
-0.00558213796466589,
-0.010950867086648941,
0.039532337337732315,
0.015502169728279114,
-0.19489143788814545,
0.0033431637566536665,
0.382671982049942,
0.12433561682701111,
-0.15602239966392517,
-0.038460731506347656,
0.002253052545711398,
-0.12435132265090942,
0.05122614651918411,
0.10650063306093216,
-0.028285304084420204,
0.06810537725687027,
0.06492038071155548,
-0.13770891726016998,
0.03194277361035347,
0.08137232065200806,
0.07254006713628769,
-0.06185802444815636,
0.03309902548789978,
0.12447807937860489,
-0.001426891190931201,
-0.09820882230997086,
-0.12830325961112976,
-0.044034551829099655,
0.05778408423066139,
0.12614697217941284,
0.010718805715441704,
-0.005161978770047426,
-0.08368363976478577,
0.15632237493991852,
0.046334970742464066,
-0.01825164072215557,
-0.11742157489061356,
-0.10392706096172333,
0.039694882929325104,
0.15260495245456696,
-0.04313033074140549,
0.00972882192581892,
0.02338254079222679,
0.0057436628267169,
0.06214966997504234,
-0.06808450818061829,
0.005501836538314819,
-0.0404035858809948,
0.07184121012687683,
-0.06626638770103455,
0.0597655288875103,
-0.04996529221534729,
-0.07479199767112732,
-0.054193515330553055,
-0.01938489079475403,
0.154617577791214,
0.16577643156051636,
0.08953801542520523,
-0.08143158257007599,
0.07282175868749619,
-0.0971396267414093,
-0.07091189175844193,
-0.05644267797470093,
-0.0658361092209816,
-0.014500929042696953,
0.10139623284339905,
0.12220189720392227,
-0.18264023959636688,
0.05132247507572174,
0.12537862360477448,
0.0016902342904359102,
0.06873060762882233,
0.0471314862370491,
0.000008568487828597426,
0.07041674107313156,
-0.03394358977675438,
-0.026566192507743835,
0.039171043783426285,
0.06057918071746826,
-0.004764103796333075,
-0.045834168791770935,
-0.03129440173506737,
0.0007111160084605217,
-0.08782535046339035,
-0.046953946352005005,
0.01650119572877884,
0.09927795827388763,
0.021815184503793716,
0.06723066419363022,
0.07200349122285843,
0.11227423697710037,
0.23267285525798798,
-0.01663021929562092,
-0.04860781878232956,
-0.05390782281756401,
-0.0038662166334688663,
-0.072230763733387,
0.05298231542110443,
-0.044093791395425797,
-0.013703000731766224,
0.06197461113333702,
0.02880055084824562,
-0.0241240244358778,
-0.07767774909734726,
-0.048432767391204834,
0.015537015162408352,
0.01294061541557312,
0.058635298162698746,
0.13916529715061188,
0.03883228823542595,
0.013341385871171951,
-0.03627052530646324,
0.03799179568886757,
-0.08247525244951248,
-0.06778591871261597,
-0.009052245877683163,
0.0850275531411171,
-0.2374516725540161,
-0.27590590715408325,
-0.06182103976607323,
-0.1899542510509491,
-0.05442836135625839,
0.10173444449901581,
0.06149669736623764,
-0.0981135293841362,
-0.06572254002094269,
-0.010261597111821175,
-0.017206426709890366,
-0.10618901252746582,
-0.030549757182598114,
-0.19057513773441315,
-0.00282036024145782,
-0.0530216358602047,
-0.08368751406669617,
-0.04087941721081734,
0.07227448374032974,
0.07781261950731277,
0.13454799354076385,
0.11990272998809814,
0.13019338250160217,
0.14590215682983398,
-0.04064978286623955,
-0.017342550680041313,
0.030587121844291687,
0.1189412996172905,
-0.11379950493574142,
0.09215140342712402,
0.15846885740756989,
0.04160125181078911,
0.10976667702198029,
0.17799557745456696,
-0.01809956505894661,
-0.08431658893823624,
0.09001435339450836,
0.03243125602602959,
0.002199815586209297,
-0.14897596836090088,
-0.09807609766721725,
-0.09747834503650665,
-0.024533651769161224,
-0.01050117053091526,
0.08103906363248825,
-0.02973933517932892,
0.00024748386931605637,
0.007691757287830114,
-0.031353335827589035,
-0.11500386148691177,
0.10164745151996613,
0.10783034563064575,
-0.051677361130714417,
0.06240885704755783,
-0.03621061518788338,
0.013111197389662266,
0.12872914969921112,
-0.005574287846684456,
0.05351933091878891,
0.0009217691840603948,
0.02266230620443821,
0.07706180214881897,
0.12979085743427277,
0.05972360819578171,
-0.06920131295919418,
-0.012724172323942184,
-0.018883759155869484,
-0.02852329984307289,
-0.04018702358007431,
-0.051866352558135986,
0.016110291704535484,
0.07158230245113373,
-0.07070600986480713,
0.007464050315320492,
-0.09313590079545975,
0.04392194375395775,
-0.013408638536930084,
0.040764421224594116,
-0.09933379292488098,
0.11283421516418457,
0.11212150752544403,
0.06982560455799103,
-0.20660647749900818,
-0.0049742055125534534,
0.1760960966348648,
-0.06036512181162834,
0.026148896664381027,
0.044760819524526596,
0.07438094913959503,
-0.013310940004885197,
-0.014131118543446064,
-0.011971941217780113,
0.04165353998541832,
0.008321966044604778,
0.10816291719675064,
-0.06053003668785095,
-0.06731956452131271,
-0.009565652348101139,
-0.01802060380578041,
0.0145226139575243,
-0.031234830617904663,
0.023711146786808968,
0.15951348841190338,
-0.0065234447829425335,
0.05780748277902603,
-0.17307095229625702,
-0.08096909523010254,
-0.054502543061971664,
-0.021976597607135773,
0.16028445959091187,
-0.09405897557735443,
0.021642018109560013,
-0.017742451280355453,
-0.03450736030936241,
-0.029039451852440834,
-0.06952325999736786,
-0.0375576987862587,
-0.08077406883239746,
0.022223329171538353,
0.004659013357013464,
0.04072054848074913,
-0.07474403083324432,
0.03581817448139191,
0.04575775936245918,
0.07663757354021072,
0.008526667021214962,
-0.027415957301855087,
-0.09199786931276321,
-0.1843370795249939,
0.06291311979293823,
-0.049490317702293396,
0.04931570217013359,
-0.0418127179145813,
0.1938740313053131,
0.06365559250116348,
-0.05853547528386116,
0.05742616578936577,
-0.03859667852520943,
0.03838387504220009,
-0.13857907056808472,
0.07207682728767395,
-0.07659886032342911,
-0.016371112316846848,
-0.007355353329330683,
0.11035165935754776,
-0.1062881276011467,
-0.15562652051448822,
0.06501084566116333,
0.16950535774230957,
0.10116761177778244,
0.000806302996352315,
-0.022203318774700165,
0.0725877434015274,
0.05569668486714363,
0.0068956539034843445,
0.06090042367577553,
0.1454852670431137,
-0.12914572656154633,
0.1224699541926384,
-0.022552968934178352,
-0.016733380034565926,
-0.13407345116138458,
0.03897436335682869,
-0.02403831109404564,
0.05824385955929756,
0.03790315240621567,
-0.18016976118087769,
0.08470097184181213,
-0.03233107551932335,
0.01641049236059189,
0.23477694392204285,
-0.18917188048362732,
-0.06158251315355301,
0.05588087812066078,
0.024497007951140404,
-0.04277771711349487,
-0.11648520082235336,
-0.09813736379146576,
-0.029879916459321976,
-0.05052601546049118,
0.11488782614469528,
-0.04573405534029007,
0.04909282177686691,
-0.031638309359550476,
0.1112939864397049,
0.047230225056409836,
-0.045135047286748886,
0.1427699476480484,
-0.14035338163375854,
0.0977340042591095,
-0.12127474695444107,
0.019972048699855804,
0.09111752361059189,
-0.07159112393856049,
0.10494855046272278,
-0.06665443629026413,
0.06383790075778961,
-0.2465050369501114,
0.0022746575996279716,
-0.008453061804175377,
0.036550372838974,
0.0271987933665514,
-0.05680028721690178,
-0.12901988625526428,
-0.03715227171778679,
-0.027381405234336853,
-0.01813647337257862,
-0.11713598668575287,
-0.0031143249943852425,
-0.14133024215698242,
-0.057005152106285095,
-0.08749523013830185,
0.02312685362994671,
-0.21061889827251434,
-0.004185882862657309,
0.008126300759613514,
0.020437484607100487,
-0.17857614159584045,
-0.04297766089439392,
0.03774886205792427,
0.004375527147203684,
0.08832122385501862,
-0.023119816556572914,
-0.04940380901098251,
0.014956346713006496,
0.13044969737529755,
-0.12233772873878479,
-0.0006630075513385236,
-0.03437092527747154,
0.14777947962284088,
-0.011739841662347317,
-0.11739847809076309,
0.0032358316238969564,
0.07741342484951019,
-0.032717783004045486,
-0.003090545302256942,
0.04521242901682854,
0.08729007840156555,
-0.016245700418949127,
0.05914886295795441,
0.0005832412862218916,
-0.07385926693677902,
0.01753399521112442,
0.07166670262813568,
-0.03103218413889408,
0.0253831148147583,
0.04003673419356346,
-0.0640636757016182,
-0.036020029336214066,
0.11157411336898804,
0.08526992052793503,
0.14425128698349,
0.0024558203294873238,
0.07784372568130493,
-0.022310519590973854,
0.00008107958274194971,
0.007436560466885567,
0.06408929824829102,
0.039241448044776917,
-0.06688732653856277,
-0.048639725893735886,
0.0198516845703125,
0.10516323149204254,
-0.026003355160355568,
0.057434793561697006,
-0.12909218668937683,
-0.08399637043476105,
0.037286579608917236,
-0.0039027638267725706,
-0.01567487232387066,
-0.08358988910913467,
-0.04786192625761032,
-0.05242536962032318,
-0.04371176287531853,
0.0586145780980587,
0.1470838487148285,
-0.0020743575878441334,
0.005813091527670622,
-0.01973113976418972,
-0.04642482101917267,
-0.025618139654397964,
-0.055881984531879425,
-0.07332699000835419,
-0.048941005021333694,
0.06819722801446915,
-0.11012738198041916,
-0.07231386750936508,
0.16018956899642944,
-0.03682415932416916,
-0.03815343603491783,
0.024433957412838936,
0.01551902573555708,
-0.00039856525836512446,
-0.14088329672813416,
-0.10607647895812988,
0.14317864179611206,
0.029827237129211426,
0.012417588382959366,
-0.15009038150310516,
0.02611035853624344,
-0.01723400130867958,
0.014754951931536198,
-0.06131420657038689,
0.018221061676740646,
-0.1636582612991333,
-0.01831182837486267,
-0.043449562042951584,
-0.1960551142692566,
-0.07952366024255753,
-0.02105804532766342,
-0.006272517144680023,
0.11141568422317505,
0.16052664816379547,
0.051811136305332184,
0.006949711591005325,
0.005004440434277058,
-0.02623261883854866,
-0.006475407164543867,
-0.013523302972316742,
0.06349780410528183,
0.039204467087984085,
0.0024625249207019806,
-0.02758321352303028,
0.0603170283138752,
0.10930006951093674,
-0.12960131466388702,
-0.036593466997146606,
0.15204331278800964,
-0.012650004588067532,
0.031441085040569305,
0.1599513441324234,
-0.011068953201174736,
0.02534940093755722,
0.08766882121562958,
0.05402905493974686,
0.06659457087516785,
-0.011877855286002159,
0.022817092016339302,
0.12724968791007996,
0.02715393155813217,
-0.05997871980071068,
-0.08889736980199814,
-0.01388144213706255,
-0.2833007872104645,
-0.07705571502447128,
-0.03963233903050423,
0.08437919616699219,
-0.036246880888938904,
0.26679450273513794,
0.12403151392936707,
-0.1266176998615265,
0.0516984798014164,
0.0333801694214344,
-0.04046184569597244,
-0.0784478411078453,
-0.15067048370838165,
-0.02651614509522915,
-0.1089402586221695,
0.002701305551454425,
-0.10374482721090317,
0.08816846460103989,
-0.03821096569299698,
-0.0022927771788090467,
-0.025286901742219925,
0.08528466522693634,
-0.04238740727305412,
-0.14017419517040253,
-0.0010722818551585078,
-0.005889455787837505,
-0.05190213397145271,
0.08433298766613007,
0.07148399204015732,
-0.01335175707936287,
-0.02401786483824253,
0.07422652095556259,
0.024782950058579445,
-0.004932098090648651,
0.009790784679353237,
-0.14268329739570618,
-0.02815517596900463,
0.03501781448721886,
0.007378872949630022,
-0.05755603685975075,
0.06268104165792465,
0.07430897653102875,
-0.02182610146701336,
-0.0035758463200181723,
0.3859916031360626,
-0.002008322160691023,
-0.00420699967071414,
0.021913999691605568,
-0.17859221994876862,
0.0202576145529747,
0.050627049058675766,
-0.06285877525806427,
-0.1814487725496292,
-0.12856777012348175,
0.1440022885799408,
0.010789863765239716,
0.04751095920801163,
0.00413023354485631,
0.0036507844924926758,
0.01860237494111061,
0.024117661640048027,
0.12237895280122757,
0.05381025746464729,
0.20935729146003723,
0.02940075471997261,
0.037343814969062805,
-0.013649571686983109,
-0.039105575531721115,
-0.1428230255842209,
-0.08808896690607071,
-0.041783563792705536,
-0.10876533389091492,
-0.06712602078914642,
0.07452433556318283,
0.03308483213186264,
0.08906224370002747,
-0.017640581354498863,
0.012726355344057083,
-0.047113630920648575,
0.05965685844421387,
0.17941376566886902,
-0.03781856968998909,
0.06464013457298279,
0.0029464554972946644,
-0.07043617963790894,
0.08819912374019623,
0.0008902386180125177,
-0.12900254130363464,
-0.004896699916571379,
0.0014196228003129363,
-0.07022278755903244,
0.15389502048492432,
-0.02325236052274704,
0.005016704089939594,
0.06732019782066345,
0.04813087359070778,
-0.08010302484035492,
0.059342797845602036,
0.0013369007501751184,
-0.08320751786231995,
0.03120153211057186,
0.17014345526695251,
0.010565650649368763,
-0.07222873717546463,
0.12183177471160889,
-0.01722615398466587,
0.013417752459645271,
-0.08210001140832901,
0.16389773786067963,
-0.1405809074640274,
0.08207181096076965,
-0.16919106245040894,
0.029745303094387054,
0.022214224562048912,
0.012673028744757175,
-0.008893320336937904,
-0.06229591369628906,
-0.008654721081256866,
-0.01413356140255928,
-0.040051933377981186,
-0.010774712078273296,
0.05027569457888603,
-0.026332490146160126,
0.2655848264694214,
0.013165590353310108,
-0.08682727068662643,
-0.07146721333265305,
-0.027951331809163094,
0.04633788391947746,
-0.0429544635117054,
0.09822317212820053,
-0.024975966662168503,
0.008648942224681377,
-0.08020338416099548,
-0.11325552314519882,
0.0160344447940588,
0.07119356095790863,
-0.12669649720191956,
-0.05326187238097191
] |
null | null |
fastai
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using the 🤗Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join our fastai community on the Hugging Face Discord!
Greetings fellow fastlearner 🤝!
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
{"tags": ["fastai", "image-classification"]}
|
image-classification
|
fastai/fastbook_04_mnist_basics
|
[
"fastai",
"image-classification",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#fastai #image-classification #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and documentation here)!
2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).
3. Join our fastai community on the Hugging Face Discord!
Greetings fellow fastlearner !
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
[
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on the Hugging Face Discord!\n\nGreetings fellow fastlearner !\n\n\n---",
"# Model card",
"## Model description\nMore information needed",
"## Intended uses & limitations\nMore information needed",
"## Training and evaluation data\nMore information needed"
] |
[
"TAGS\n#fastai #image-classification #region-us \n",
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on the Hugging Face Discord!\n\nGreetings fellow fastlearner !\n\n\n---",
"# Model card",
"## Model description\nMore information needed",
"## Intended uses & limitations\nMore information needed",
"## Training and evaluation data\nMore information needed"
] |
[
14,
20,
66,
3,
6,
12,
8
] |
[
"passage: TAGS\n#fastai #image-classification #region-us \n# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on the Hugging Face Discord!\n\nGreetings fellow fastlearner !\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed"
] |
[
-0.043135710060596466,
-0.0480441153049469,
0.002067365450784564,
0.09406481683254242,
0.17428547143936157,
0.11595744639635086,
0.10578379780054092,
0.07675820589065552,
0.11263571679592133,
0.012933879159390926,
0.09528832882642746,
-0.10546129941940308,
0.10350865125656128,
0.2567436695098877,
0.07784169167280197,
-0.2770094573497772,
0.012813934125006199,
-0.007953129708766937,
0.0933113694190979,
0.06120896339416504,
0.12847793102264404,
-0.0340941958129406,
0.16186320781707764,
-0.0032289919909089804,
-0.22723549604415894,
-0.05442282557487488,
-0.035241130739450455,
-0.0008513794164173305,
0.11844152212142944,
-0.03443443030118942,
0.027914218604564667,
-0.004232730250805616,
0.00510071124881506,
-0.08619315922260284,
0.059358056634664536,
0.022516973316669464,
0.01743599958717823,
0.051905132830142975,
-0.026123041287064552,
0.08707024902105331,
0.07905260473489761,
-0.040730543434619904,
-0.11513078212738037,
0.08185576647520065,
-0.1443115621805191,
-0.16801327466964722,
-0.08478699624538422,
-0.11538249254226685,
0.0826377272605896,
-0.005096469074487686,
-0.021996311843395233,
0.09613007307052612,
-0.1569768339395523,
-0.028199873864650726,
0.1706697940826416,
-0.1165284812450409,
-0.06350965052843094,
-0.013901366852223873,
0.05250532180070877,
-0.08845742791891098,
-0.07898663729429245,
0.09973388910293579,
0.10610274970531464,
-0.034963712096214294,
0.012514031492173672,
0.016721894964575768,
0.03414324298501015,
-0.02308172732591629,
-0.0479145385324955,
0.0686134621500969,
-0.039595410227775574,
0.08148591965436935,
-0.1220196858048439,
-0.14820213615894318,
0.027280371636152267,
0.05988028645515442,
-0.061631590127944946,
-0.03823978081345558,
0.0826442688703537,
-0.01776871271431446,
-0.11668780446052551,
-0.09055625647306442,
-0.0562506765127182,
-0.11699071526527405,
0.009308401495218277,
0.07822392880916595,
-0.014650757424533367,
0.07005138695240021,
-0.08558835834264755,
0.04940911754965782,
-0.19470584392547607,
-0.04850504919886589,
-0.07143335789442062,
-0.10438532382249832,
0.016327984631061554,
-0.02445412054657936,
0.045131951570510864,
0.1857319325208664,
0.15608258545398712,
0.019407980144023895,
0.032645273953676224,
-0.01819371059536934,
0.00469429325312376,
0.054605912417173386,
0.01280822604894638,
0.06697534769773483,
-0.0206596702337265,
-0.19677616655826569,
-0.0049900272861123085,
-0.01557135209441185,
0.10349574685096741,
-0.07035461068153381,
-0.0629304051399231,
-0.010664394125342369,
-0.11958497017621994,
0.06761164963245392,
-0.0800957977771759,
0.014090508222579956,
-0.006535483989864588,
0.0015902144368737936,
0.2216138392686844,
0.05701671168208122,
0.0018828930333256721,
0.01112278550863266,
-0.14458723366260529,
-0.10899219661951065,
-0.054492413997650146,
0.043661460280418396,
0.037110041826963425,
0.0068321749567985535,
-0.06815827637910843,
0.05887800082564354,
-0.024025017395615578,
-0.004513882100582123,
0.014964091591536999,
-0.21053393185138702,
0.014274601824581623,
-0.09068627655506134,
-0.0645553469657898,
0.05323205143213272,
-0.015847288072109222,
-0.06705991923809052,
0.07854674011468887,
-0.03332813084125519,
0.03636208549141884,
-0.037716954946517944,
0.01206962764263153,
0.01988861709833145,
-0.07712192833423615,
0.013829362578690052,
0.2004818469285965,
0.13786356151103973,
-0.048133738338947296,
-0.018403515219688416,
-0.11051812767982483,
0.030601641163229942,
-0.15388061106204987,
0.05590495467185974,
-0.07877650856971741,
0.17316985130310059,
-0.016681216657161713,
-0.00954410433769226,
-0.018222911283373833,
0.070732980966568,
0.0695413202047348,
0.20989961922168732,
-0.20927298069000244,
-0.0864877700805664,
0.17135030031204224,
-0.1028808206319809,
-0.20001867413520813,
0.2073664516210556,
0.0037236532662063837,
0.08018296211957932,
-0.025776075199246407,
0.19611068069934845,
-0.03752565383911133,
-0.15284352004528046,
-0.021860264241695404,
-0.01987580768764019,
-0.23738797008991241,
-0.037454407662153244,
0.08996538817882538,
0.12026983499526978,
-0.06928714364767075,
0.019999906420707703,
-0.022698676213622093,
0.20344729721546173,
-0.08743656426668167,
-0.031762272119522095,
0.025296220555901527,
-0.10002098977565765,
0.016979429870843887,
0.03881438076496124,
0.02478625252842903,
-0.06543570756912231,
0.0015125152422115207,
-0.11599341779947281,
0.09862883388996124,
0.07253655046224594,
-0.03770461678504944,
-0.06350032985210419,
0.12406552582979202,
-0.04497717693448067,
-0.04611393064260483,
0.09792304784059525,
-0.06290939450263977,
0.05256460979580879,
0.07789719104766846,
0.0895533561706543,
-0.027086131274700165,
0.09694752097129822,
0.07247143983840942,
0.007704056333750486,
0.017469873651862144,
0.12334408611059189,
-0.037424635142087936,
-0.03010450303554535,
-0.0015248177805915475,
0.07065773755311966,
-0.013815510086715221,
0.37256643176078796,
-0.19437465071678162,
0.01264515146613121,
-0.07431638985872269,
0.07827471941709518,
0.06558379530906677,
0.008369416929781437,
0.09666746109724045,
-0.05257272347807884,
-0.016364678740501404,
-0.05276801064610481,
0.09637202322483063,
-0.06473593413829803,
-0.08030439168214798,
0.245479553937912,
-0.04435107111930847,
0.0015527111245319247,
0.1119016781449318,
-0.07846727222204208,
-0.0774184912443161,
-0.07033362239599228,
-0.028132472187280655,
0.03005247935652733,
-0.036516476422548294,
0.07750328630208969,
-0.05086011439561844,
-0.06716867536306381,
0.164797842502594,
-0.041989438235759735,
0.06883152574300766,
0.045540209859609604,
-0.0591207817196846,
-0.06222207471728325,
0.06130985543131828,
0.11461935937404633,
-0.10475140810012817,
0.032870352268218994,
0.11699222028255463,
-0.0046376315876841545,
0.1674608290195465,
0.0668523907661438,
-0.0463855154812336,
-0.07240559905767441,
-0.01209191232919693,
0.00755219766870141,
0.18750041723251343,
-0.1262729912996292,
-0.06423264741897583,
0.037490129470825195,
-0.04403343051671982,
0.0411548875272274,
-0.07618264853954315,
-0.061276182532310486,
0.0019428496016189456,
-0.07555006444454193,
0.03570283576846123,
0.10342660546302795,
-0.07675479352474213,
0.05655413493514061,
0.04517119377851486,
-0.08173984289169312,
0.018242493271827698,
0.012910636141896248,
-0.03104432485997677,
0.052935924381017685,
0.07264021039009094,
-0.2419428825378418,
-0.09361378103494644,
-0.1663937121629715,
-0.002626647474244237,
0.032305411994457245,
0.019495362415909767,
-0.1286236047744751,
0.04093075916171074,
-0.07128695398569107,
-0.05111696943640709,
0.07645750790834427,
-0.020088685676455498,
-0.09898541867733002,
-0.019337348639965057,
-0.004159604664891958,
-0.04456043615937233,
-0.03249547258019447,
-0.0828743726015091,
0.014113122597336769,
0.0625327080488205,
-0.0005850419402122498,
0.12264519929885864,
-0.0182399433106184,
-0.03249913081526756,
0.016461730003356934,
-0.03851296752691269,
0.15194565057754517,
-0.14628317952156067,
0.06290345638990402,
0.13647639751434326,
0.08691491931676865,
0.03498184308409691,
0.014357306994497776,
0.01888621784746647,
-0.09653738886117935,
0.03061218559741974,
0.02812359668314457,
-0.10880009084939957,
-0.02390168607234955,
-0.02810594066977501,
-0.017189426347613335,
0.24837341904640198,
-0.08590604364871979,
0.014641075395047665,
0.04842265695333481,
0.09561057388782501,
0.11257214844226837,
-0.04483693465590477,
-0.12010210007429123,
0.0437953844666481,
-0.2770012617111206,
-0.05331218242645264,
0.0034633467439562082,
-0.09107033908367157,
-0.08132261782884598,
0.16335411369800568,
-0.03697102516889572,
0.046548038721084595,
-0.011720911599695683,
0.05299021676182747,
0.00932406261563301,
0.11544059216976166,
0.08117494732141495,
-0.055646784603595734,
0.008533512242138386,
-0.10210198163986206,
-0.0706220269203186,
-0.044772714376449585,
-0.07318025827407837,
0.06035754084587097,
0.15553075075149536,
-0.03332701325416565,
-0.04884640499949455,
0.019017783924937248,
0.08505929261445999,
0.04301110655069351,
0.17213056981563568,
-0.18511416018009186,
-0.007468045689165592,
0.03231125324964523,
0.004559095483273268,
-0.04790284484624863,
-0.009308310225605965,
0.08271279186010361,
-0.051327720284461975,
0.03435641527175903,
-0.0033591578248888254,
0.06670107692480087,
0.00583041924983263,
0.030286476016044617,
-0.03503349795937538,
0.13878925144672394,
-0.01977924443781376,
-0.003170720301568508,
-0.07537979632616043,
0.14675933122634888,
0.014169539324939251,
-0.030842578038573265,
-0.049590103328228,
-0.0530262365937233,
0.20665694773197174,
0.05273042246699333,
0.12341324239969254,
0.01690969429910183,
-0.07317589968442917,
-0.12108731269836426,
-0.12186117470264435,
0.01795782707631588,
0.11305365711450577,
-0.03651094064116478,
-0.029258979484438896,
0.03206599876284599,
-0.04900307208299637,
-0.06283751875162125,
0.15639695525169373,
-0.11433583498001099,
-0.0006148363463580608,
-0.036594539880752563,
0.026475686579942703,
-0.05151570588350296,
0.05945201590657234,
0.03615500405430794,
-0.08295443654060364,
0.08569244295358658,
0.24987472593784332,
0.12029357254505157,
-0.08264528214931488,
-0.07273224741220474,
0.002617470920085907,
-0.02786053717136383,
-0.020367488265037537,
-0.021884938701987267,
0.04934200644493103,
0.009905995801091194,
0.0004229980695527047,
0.11496644467115402,
-0.06797561049461365,
0.016827818006277084,
-0.06810642033815384,
0.07184386253356934,
-0.04323017969727516,
-0.0028854708652943373,
-0.00307843298651278,
-0.03579486161470413,
-0.016158048063516617,
-0.0422385036945343,
0.19092562794685364,
-0.03435656428337097,
-0.08914748579263687,
0.10777997970581055,
0.006099659949541092,
-0.010074339807033539,
-0.09088420867919922,
-0.009726413525640965,
0.17160965502262115,
0.33205264806747437,
-0.05090351402759552,
0.08274491876363754,
0.12986552715301514,
0.03720282390713692,
-0.2415936142206192,
0.05336371064186096,
-0.10078507661819458,
0.049300696700811386,
0.028043881058692932,
-0.04503197222948074,
0.04855889454483986,
0.1074744164943695,
-0.03213361278176308,
0.18884874880313873,
0.014890321530401707,
-0.08196082711219788,
-0.030725494027137756,
0.05735310912132263,
0.29306864738464355,
-0.1206628829240799,
-0.012375160120427608,
-0.11899732053279877,
-0.1831255853176117,
0.09127216786146164,
-0.12136732786893845,
0.13883338868618011,
-0.06503088772296906,
-0.01128410454839468,
-0.00135329260956496,
-0.056972239166498184,
0.1969723105430603,
-0.15176847577095032,
0.04598135128617287,
-0.16029277443885803,
-0.13859347999095917,
-0.0493440143764019,
-0.09242217242717743,
0.13723771274089813,
-0.06290692836046219,
-0.041892312467098236,
-0.21003976464271545,
0.016344062983989716,
-0.022265831008553505,
0.1083710715174675,
0.03837580978870392,
-0.0648311972618103,
-0.11631380766630173,
0.12938262522220612,
-0.06921392679214478,
0.056820694357156754,
-0.047558363527059555,
-0.05949753150343895,
-0.007661539129912853,
-0.09887956827878952,
0.08431664854288101,
-0.06787542998790741,
0.16960689425468445,
-0.05376526713371277,
-0.03638920933008194,
0.06585593521595001,
-0.2144651561975479,
0.0166571494191885,
0.03131101280450821,
-0.012965804897248745,
0.11123693734407425,
-0.01633749157190323,
-0.047351494431495667,
0.12050335854291916,
0.15230409801006317,
-0.04691421613097191,
-0.2445915937423706,
-0.09298652410507202,
-0.04959504306316376,
0.05545267090201378,
0.059482406824827194,
0.044635310769081116,
-0.04266856238245964,
-0.009003440849483013,
-0.03170306235551834,
0.03940223157405853,
-0.09892602264881134,
-0.06588069349527359,
0.09053584933280945,
-0.00817983876913786,
-0.07454292476177216,
0.10300100594758987,
-0.022258969023823738,
-0.04005026817321777,
-0.018872611224651337,
0.16877278685569763,
-0.00872802920639515,
-0.09444648772478104,
-0.05856668949127197,
0.2785908281803131,
-0.03326436132192612,
-0.07237108051776886,
-0.07370047271251678,
-0.011851859278976917,
-0.06296929717063904,
0.041280463337898254,
0.044611480087041855,
-0.019301583990454674,
0.09459996968507767,
0.07632751762866974,
-0.13422319293022156,
-0.04825257509946823,
-0.08253509551286697,
0.023763936012983322,
-0.09391538053750992,
0.04638104513287544,
0.03239896893501282,
0.13846981525421143,
-0.08944819867610931,
-0.03877723962068558,
-0.11869994550943375,
-0.06565392762422562,
-0.14193984866142273,
-0.017984768375754356,
-0.030354734510183334,
-0.016195911914110184,
0.04378911852836609,
0.035043247044086456,
-0.05438777804374695,
-0.046839725226163864,
-0.054815664887428284,
0.03332525119185448,
0.07172195613384247,
0.03302118554711342,
-0.000739118258934468,
0.024337897077202797,
0.06358452141284943,
-0.008854986168444157,
0.1939183622598648,
0.061528053134679794,
0.039933547377586365,
-0.05893708020448685,
-0.2132119983434677,
-0.0598771795630455,
0.03549247607588768,
-0.11447872966527939,
0.10493447631597519,
-0.015037617646157742,
0.022291040048003197,
-0.0777871385216713,
0.02952207624912262,
0.030745264142751694,
0.13215720653533936,
0.010184871032834053,
0.1054762452840805,
0.015861721709370613,
-0.07867103070020676,
-0.053909119218587875,
0.025915324687957764,
0.10166049748659134,
0.05523233860731125,
-0.006063671782612801,
-0.007831367664039135,
0.03637465462088585,
-0.04801254719495773,
0.022043542936444283,
-0.0416007824242115,
-0.1349056214094162,
0.018430570140480995,
-0.04111394286155701,
0.007220780942589045,
-0.009335651993751526,
0.1632072478532791,
0.07893939316272736,
-0.03843383863568306,
-0.019855517894029617,
0.05185551568865776,
-0.0005758315091952682,
-0.029132919386029243,
-0.01664462871849537,
0.01242818683385849,
-0.011641372926533222,
-0.027803935110569,
0.12793557345867157,
0.04118882119655609,
0.0337052047252655,
0.03223983943462372,
0.08624710142612457,
-0.03031889535486698,
0.1351775974035263,
0.090060293674469,
0.0012925242772325873,
-0.08949068933725357,
-0.02499302476644516,
-0.09216870367527008,
0.04371993616223335,
-0.044952891767024994,
0.13794651627540588,
0.11835663765668869,
-0.07657405734062195,
-0.02159014157950878,
-0.06422453373670578,
-0.03567412495613098,
-0.05832476541399956,
0.06457220762968063,
-0.036254268139600754,
-0.11074639856815338,
0.0795147716999054,
0.0487079843878746,
-0.0749422088265419,
0.10327911376953125,
0.019404537975788116,
-0.0516987144947052,
0.11024338006973267,
-0.10676587373018265,
0.08653149753808975,
0.12639398872852325,
-0.048159126192331314,
-0.1274534910917282,
0.018654676154255867,
-0.08037962764501572,
-0.06715868413448334,
-0.010014939121901989,
0.0060997516848146915,
-0.07008650153875351,
-0.04766400158405304,
0.09101904928684235,
-0.023412268608808517,
-0.09781716018915176,
-0.0008946926682256162,
-0.007512894459068775,
0.0707123652100563,
-0.0013006697408854961,
0.0025908693205565214,
0.03761951997876167,
0.024346277117729187,
0.13499146699905396,
-0.010488980449736118,
0.09446719288825989,
-0.13800489902496338,
0.16271324455738068,
-0.17424793541431427,
-0.023526016622781754,
-0.19467030465602875,
-0.08556114882230759,
-0.03965567424893379,
0.21183790266513824,
0.23956528306007385,
-0.18419544398784637,
-0.040272392332553864,
-0.017754072323441505,
-0.00530411908403039,
-0.08042346686124802,
0.1485847383737564,
0.0328950472176075,
-0.0008285317453555763,
-0.050059664994478226,
-0.04119158536195755,
0.01906406879425049,
-0.05878768488764763,
-0.009829306975007057,
0.12060701102018356,
0.020988527685403824,
0.058587390929460526,
-0.10601888597011566,
0.04354076460003853,
-0.16034962236881256,
-0.09522249549627304,
0.031089019030332565,
-0.1770310252904892,
-0.08044994622468948,
-0.008362904191017151,
0.00033926876494660974,
0.10942710936069489,
0.04765947535634041,
-0.01021200604736805,
0.08884991705417633,
-0.09390634298324585,
0.0006667477427981794,
-0.1471072882413864,
0.01179987471550703,
0.003775576362386346,
-0.10497850924730301,
0.24912607669830322,
-0.02273484878242016,
-0.1089288592338562,
0.07065869867801666,
-0.0394342839717865,
-0.0996435284614563,
0.07207883149385452,
-0.05799354612827301,
-0.058163970708847046,
-0.05888238549232483,
0.2342519909143448,
-0.01402230840176344,
-0.1793975979089737,
0.04475797340273857,
-0.13360072672367096,
0.03672799840569496,
0.05115630850195885,
-0.01632162556052208,
-0.06009916588664055,
0.056012704968452454,
-0.05951359122991562,
0.09143692255020142,
0.12558197975158691,
0.028852302581071854,
-0.024508308619260788,
-0.056836724281311035,
0.07209930568933487,
0.056752145290374756,
-0.07044989615678787,
-0.08954179286956787,
-0.07587070018053055,
-0.00455087423324585,
0.0499885231256485,
-0.10768280178308487,
-0.1991163194179535,
-0.04256344586610794,
-0.12377270311117172,
-0.0023479368537664413,
0.04336123168468475,
0.08650597184896469,
0.2383410483598709,
0.06379329413175583,
0.015083998441696167,
-0.18092140555381775,
0.09031050652265549,
0.09377514570951462,
-0.01314084604382515,
-0.08979006111621857
] |
null | null |
fastai
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using the 🤗Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join our fastai community on the Hugging Face Discord!
Greetings fellow fastlearner 🤝!
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
{"tags": ["fastai"]}
| null |
fastai/fastbook_06_multicat_Biwi_Kinect_Head_Pose
|
[
"fastai",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and documentation here)!
2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).
3. Join our fastai community on the Hugging Face Discord!
Greetings fellow fastlearner !
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
[
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on the Hugging Face Discord!\n\nGreetings fellow fastlearner !\n\n\n---",
"# Model card",
"## Model description\nMore information needed",
"## Intended uses & limitations\nMore information needed",
"## Training and evaluation data\nMore information needed"
] |
[
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on the Hugging Face Discord!\n\nGreetings fellow fastlearner !\n\n\n---",
"# Model card",
"## Model description\nMore information needed",
"## Intended uses & limitations\nMore information needed",
"## Training and evaluation data\nMore information needed"
] |
[
9,
20,
66,
3,
6,
12,
8
] |
[
"passage: TAGS\n#fastai #region-us \n# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on the Hugging Face Discord!\n\nGreetings fellow fastlearner !\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed"
] |
[
-0.033277031034231186,
-0.08227241784334183,
0.0022239338140934706,
0.07713031768798828,
0.17038100957870483,
0.10486842691898346,
0.08670254796743393,
0.07142214477062225,
0.11567308753728867,
0.013446890749037266,
0.09491732716560364,
-0.10746689885854721,
0.09321203827857971,
0.2697221636772156,
0.08565160632133484,
-0.2643716335296631,
0.007085119374096394,
-0.007957587949931622,
0.09378449618816376,
0.0647154450416565,
0.12935099005699158,
-0.01542446669191122,
0.14802193641662598,
-0.006468500941991806,
-0.20873704552650452,
-0.048613112419843674,
-0.0367392972111702,
0.006289718206971884,
0.12599752843379974,
-0.05386224761605263,
0.030972018837928772,
-0.015907105058431625,
-0.0041992454789578915,
-0.0924329087138176,
0.05714719742536545,
0.027781732380390167,
0.033576205372810364,
0.04195935279130936,
-0.03856709972023964,
0.090358667075634,
0.08452040702104568,
-0.028579043224453926,
-0.11602935940027237,
0.09493833780288696,
-0.15224945545196533,
-0.17045728862285614,
-0.10770466178655624,
-0.15297317504882812,
0.06646230816841125,
0.01824142225086689,
-0.032926321029663086,
0.12947368621826172,
-0.17055726051330566,
-0.03268762677907944,
0.19679228961467743,
-0.1398683488368988,
-0.054200977087020874,
-0.022875504568219185,
0.08887587487697601,
-0.08454488962888718,
-0.06776228547096252,
0.08554226160049438,
0.10405685752630234,
-0.02733820304274559,
0.010947559960186481,
0.013491141609847546,
0.028794609010219574,
-0.00744158448651433,
-0.05253235623240471,
0.07537523657083511,
-0.030229059979319572,
0.061034366488456726,
-0.12459655851125717,
-0.13618654012680054,
0.020395886152982712,
0.07018813490867615,
-0.051939528435468674,
-0.06627117097377777,
0.08689701557159424,
0.00023925417917780578,
-0.12471684068441391,
-0.10193783044815063,
-0.044013865292072296,
-0.11784403026103973,
0.010395646095275879,
0.0941244587302208,
-0.017060521990060806,
0.05990614369511604,
-0.09526018053293228,
0.052144426852464676,
-0.19857820868492126,
-0.03546607121825218,
-0.0739876851439476,
-0.09697233885526657,
0.015451633371412754,
-0.0281793512403965,
0.03342891484498978,
0.19603703916072845,
0.15404142439365387,
0.040892187505960464,
0.04508811607956886,
-0.03108343854546547,
0.017177764326334,
0.04148007556796074,
-0.0008477934752590954,
0.0648224875330925,
-0.009331580251455307,
-0.22554752230644226,
-0.0034632382448762655,
-0.03554622828960419,
0.11059566587209702,
-0.07053891569375992,
-0.048477303236722946,
-0.017674023285508156,
-0.14979448914527893,
0.08524873852729797,
-0.0650213286280632,
0.0014887347351759672,
-0.004883226472884417,
0.017286252230405807,
0.19425265491008759,
0.046697601675987244,
0.00032520972308702767,
-0.0058996472507715225,
-0.1312907189130783,
-0.10475128889083862,
-0.0633954331278801,
0.05420992895960808,
0.03653633967041969,
0.002547129988670349,
-0.08216937631368637,
0.06124689429998398,
-0.02218456007540226,
0.0038849383126944304,
0.022792907431721687,
-0.22105424106121063,
0.018331365659832954,
-0.08542918413877487,
-0.07521633058786392,
0.05388824641704559,
-0.029907379299402237,
-0.06049371510744095,
0.068365678191185,
-0.03459030017256737,
0.0346822515130043,
-0.03688182309269905,
0.0016914508305490017,
0.017987843602895737,
-0.071634441614151,
0.004566612187772989,
0.22498995065689087,
0.12187080085277557,
-0.0373307466506958,
-0.01403727661818266,
-0.11625855416059494,
0.03486724942922592,
-0.12201837450265884,
0.045901890844106674,
-0.07823994755744934,
0.18674558401107788,
-0.014955941587686539,
0.01712312549352646,
-0.025887764990329742,
0.07750066369771957,
0.0900055542588234,
0.21741575002670288,
-0.20995363593101501,
-0.09242399781942368,
0.20153622329235077,
-0.09993880242109299,
-0.20968188345432281,
0.2143387496471405,
0.010933434590697289,
0.10466093569993973,
-0.02445356920361519,
0.2072000801563263,
-0.06392479687929153,
-0.17961379885673523,
-0.02127908729016781,
-0.011059229262173176,
-0.24161723256111145,
-0.051771532744169235,
0.10153976082801819,
0.08293270319700241,
-0.05680832639336586,
0.03393745422363281,
-0.0017613122472539544,
0.19719333946704865,
-0.08100304007530212,
-0.0327664315700531,
0.0037850369699299335,
-0.09438452124595642,
0.008816446177661419,
0.024635400623083115,
0.030300842598080635,
-0.07669425010681152,
0.0045998780988156796,
-0.10862505435943604,
0.10374045372009277,
0.08856850862503052,
-0.018831197172403336,
-0.05914812535047531,
0.11219041049480438,
-0.030924323946237564,
-0.03990488499403,
0.10462885349988937,
-0.07421869039535522,
0.054816972464323044,
0.07873906940221786,
0.0769772157073021,
-0.011359816417098045,
0.09274003654718399,
0.06584157794713974,
0.013920036144554615,
0.03992454335093498,
0.11420150846242905,
-0.03665994852781296,
-0.031658392399549484,
0.02031930349767208,
0.0778738409280777,
-0.022186169400811195,
0.3688422739505768,
-0.17356127500534058,
0.01821484975516796,
-0.10282918810844421,
0.08845876902341843,
0.06183716654777527,
-0.0008401357918046415,
0.0835038349032402,
-0.0367698073387146,
-0.0032264823094010353,
-0.055524107068777084,
0.08583492785692215,
-0.05812237039208412,
-0.08662345260381699,
0.2658982574939728,
-0.03440943360328674,
0.0036153150722384453,
0.09868631511926651,
-0.054184261709451675,
-0.062347497791051865,
-0.06147386133670807,
-0.026257529854774475,
0.0223053228110075,
-0.027185508981347084,
0.07022673636674881,
-0.06264147907495499,
-0.060661379247903824,
0.16288144886493683,
-0.03619345277547836,
0.06410842388868332,
0.027257069945335388,
-0.08201233297586441,
-0.04750926420092583,
0.06294645369052887,
0.09405282884836197,
-0.06485769897699356,
0.038453102111816406,
0.1124429702758789,
-0.0016101401997730136,
0.16678395867347717,
0.0592765286564827,
-0.056373707950115204,
-0.09419229626655579,
-0.005355950444936752,
0.007470285519957542,
0.1591932624578476,
-0.13831892609596252,
-0.054418422281742096,
0.029514815658330917,
-0.023972032591700554,
0.05227358266711235,
-0.05514098331332207,
-0.07087593525648117,
0.0013438323512673378,
-0.07329598814249039,
0.025018898770213127,
0.12582840025424957,
-0.09504543989896774,
0.03497644141316414,
0.04957837611436844,
-0.05680005997419357,
0.0234662014991045,
0.02370806410908699,
-0.029213670641183853,
0.04664962738752365,
0.07227440923452377,
-0.206625297665596,
-0.10228221863508224,
-0.18699663877487183,
0.016714517027139664,
0.024553729221224785,
0.027458691969513893,
-0.1259651482105255,
0.04575458914041519,
-0.06818679720163345,
-0.038054630160331726,
0.0717974603176117,
-0.024877948686480522,
-0.09307482093572617,
-0.022279124706983566,
-0.014280120842158794,
-0.05402163788676262,
-0.027896815910935402,
-0.08477012068033218,
-0.0009268703288398683,
0.03632563725113869,
-0.00031268977909348905,
0.13541896641254425,
-0.02366817370057106,
-0.009438908658921719,
0.005859281402081251,
-0.03213220462203026,
0.1568119376897812,
-0.135063037276268,
0.045071423053741455,
0.13406558334827423,
0.10207222402095795,
0.03798583522439003,
0.012921007350087166,
0.018819143995642662,
-0.08992904424667358,
0.02965068444609642,
0.031484205275774,
-0.09813158959150314,
-0.056053221225738525,
-0.024970686063170433,
-0.030953072011470795,
0.26418179273605347,
-0.12343134731054306,
0.0034723030403256416,
0.03854964300990105,
0.07769693434238434,
0.12518101930618286,
-0.05048064887523651,
-0.14797833561897278,
0.048560041934251785,
-0.2661287486553192,
-0.056680064648389816,
0.004655467811971903,
-0.09040501713752747,
-0.08040545135736465,
0.15619415044784546,
-0.027873646467924118,
0.03271074593067169,
-0.006139150820672512,
0.09560710936784744,
0.010217266157269478,
0.13245338201522827,
0.06668516248464584,
-0.05331530049443245,
0.02290397323668003,
-0.10145124793052673,
-0.07250506430864334,
-0.03798116743564606,
-0.07836087048053741,
0.059070851653814316,
0.15479473769664764,
-0.04539763554930687,
-0.03424506261944771,
0.01731664128601551,
0.09378930926322937,
0.04389014467597008,
0.15866267681121826,
-0.14041966199874878,
-0.004812900442630053,
0.04247099161148071,
-0.019690074026584625,
-0.031159594655036926,
-0.01611192710697651,
0.08120068907737732,
-0.05842692777514458,
0.00590216601267457,
0.006586797069758177,
0.06998071074485779,
0.04643845185637474,
0.03342098370194435,
-0.05887731909751892,
0.17546813189983368,
-0.021034201607108116,
-0.0014303643256425858,
-0.09125334024429321,
0.1439528912305832,
0.00908610224723816,
-0.017340699210762978,
-0.03325200453400612,
-0.04431763291358948,
0.19507499039173126,
0.028783265501260757,
0.11954433470964432,
0.023984292522072792,
-0.07356978952884674,
-0.09644994139671326,
-0.1253555417060852,
0.022706180810928345,
0.11147056519985199,
-0.026222921907901764,
-0.025333024561405182,
0.02208506129682064,
-0.0478445440530777,
-0.06646084040403366,
0.17884555459022522,
-0.10769326984882355,
-0.005300077609717846,
-0.03208669275045395,
0.01915164291858673,
-0.04101448506116867,
0.04576132446527481,
0.046079907566308975,
-0.04248332232236862,
0.09340482950210571,
0.2727794051170349,
0.1320023387670517,
-0.08205481618642807,
-0.10265505313873291,
0.021805085241794586,
-0.03450016677379608,
-0.013096245005726814,
-0.01371059101074934,
0.020579569041728973,
-0.0014869094593450427,
0.02443990297615528,
0.11256244778633118,
-0.07091707736253738,
0.024737318977713585,
-0.06810358166694641,
0.08501476794481277,
-0.050070133060216904,
-0.004111715592443943,
-0.012967436574399471,
-0.03546015918254852,
-0.02919752709567547,
-0.04813423380255699,
0.1696971207857132,
-0.038042016327381134,
-0.09443683922290802,
0.09035778790712357,
-0.0018192444695159793,
0.01589462161064148,
-0.08407724648714066,
-0.04203187674283981,
0.18087424337863922,
0.3359929919242859,
-0.04722631722688675,
0.09998135268688202,
0.15277111530303955,
0.03294616937637329,
-0.21692126989364624,
0.030374795198440552,
-0.10977433621883392,
0.051621805876493454,
0.03609371930360794,
-0.03846230357885361,
0.034110233187675476,
0.09874881058931351,
-0.028129929676651955,
0.17585526406764984,
0.01953643187880516,
-0.08693647384643555,
-0.033929161727428436,
0.036069389432668686,
0.3122052550315857,
-0.10846572369337082,
-0.020826220512390137,
-0.10692492872476578,
-0.2016768604516983,
0.06194566190242767,
-0.14693762362003326,
0.14843909442424774,
-0.06627882272005081,
0.0003900393203366548,
-0.0052173142321407795,
-0.06483335793018341,
0.1994384527206421,
-0.14798803627490997,
0.026006877422332764,
-0.15147830545902252,
-0.12328807264566422,
-0.041251420974731445,
-0.08403646945953369,
0.14865940809249878,
-0.06570124626159668,
-0.04344901069998741,
-0.19091598689556122,
0.007350549101829529,
-0.027974670752882957,
0.11063537746667862,
0.028815733268857002,
-0.0752638578414917,
-0.09923963248729706,
0.11788176000118256,
-0.06749717146158218,
0.03910348564386368,
-0.07890404015779495,
-0.06121758371591568,
0.010397613979876041,
-0.09941333532333374,
0.08287406712770462,
-0.0862501934170723,
0.1852070838212967,
-0.03763248398900032,
-0.04029596224427223,
0.0446147620677948,
-0.2003331333398819,
0.002552860416471958,
0.026034075766801834,
-0.021374043077230453,
0.1076977401971817,
-0.018087027594447136,
-0.07767273485660553,
0.1185331791639328,
0.1408364474773407,
-0.051157817244529724,
-0.23953555524349213,
-0.09481517970561981,
-0.06125766783952713,
0.03499162942171097,
0.06602034717798233,
0.04337223991751671,
-0.037582479417324066,
-0.018169071525335312,
-0.041513841599226,
0.033625271171331406,
-0.1189054325222969,
-0.06319139897823334,
0.07589460164308548,
-0.006073975935578346,
-0.0763356164097786,
0.09338429570198059,
-0.032820019870996475,
-0.03311539068818092,
-0.01368726510554552,
0.20198774337768555,
-0.008855608291924,
-0.09226503223180771,
-0.06877847015857697,
0.27900266647338867,
-0.03048190288245678,
-0.0664299875497818,
-0.08531653881072998,
-0.006670560222119093,
-0.06582149863243103,
0.04188055172562599,
0.041328541934490204,
-0.002010792028158903,
0.08545565605163574,
0.07762014120817184,
-0.13911466300487518,
-0.05826771259307861,
-0.05318617820739746,
0.011891456320881844,
-0.07135025411844254,
0.05160493031144142,
0.032012127339839935,
0.13050486147403717,
-0.08505744487047195,
-0.03419254347681999,
-0.12417411804199219,
-0.06809327751398087,
-0.14624148607254028,
-0.05798319727182388,
-0.03375891596078873,
-0.025243792682886124,
0.0439620241522789,
0.031299322843551636,
-0.05921275168657303,
-0.04215855151414871,
-0.04741876199841499,
0.030783573165535927,
0.057625751942396164,
0.023527050390839577,
-0.009588590823113918,
0.043739497661590576,
0.08983426541090012,
-0.006243378389626741,
0.1896410435438156,
0.08729267865419388,
0.055043112486600876,
-0.04603677615523338,
-0.19039814174175262,
-0.05211332067847252,
0.02223200537264347,
-0.12218629568815231,
0.11196397244930267,
-0.015412883833050728,
0.012895013205707073,
-0.07319827377796173,
0.04668041691184044,
0.02726844884455204,
0.12743760645389557,
0.002793054562062025,
0.1049814224243164,
0.043434470891952515,
-0.10533586889505386,
-0.061283957213163376,
-0.0021762026008218527,
0.10733196139335632,
0.06612566858530045,
-0.01128743588924408,
-0.0033717022743076086,
0.04405450448393822,
-0.035242099314928055,
0.019733525812625885,
-0.04306551441550255,
-0.13787303864955902,
0.034886833280324936,
-0.04694775119423866,
-0.0011652560206130147,
0.00710663478821516,
0.17317064106464386,
0.054182033985853195,
-0.0254618301987648,
-0.024032916873693466,
0.028941364958882332,
0.025683606043457985,
-0.045264825224876404,
-0.01727895624935627,
0.025978242978453636,
-0.021640481427311897,
-0.043705880641937256,
0.13222450017929077,
0.028626786544919014,
0.014563014730811119,
0.019564509391784668,
0.06472164392471313,
-0.015074329450726509,
0.13567432761192322,
0.08222419023513794,
-0.008827874436974525,
-0.09915708005428314,
-0.07344666868448257,
-0.08103683590888977,
0.011100872419774532,
-0.05304981768131256,
0.15240591764450073,
0.11740470677614212,
-0.07795556634664536,
-0.029058072715997696,
-0.06257851421833038,
-0.041331928223371506,
-0.07843977957963943,
0.1036653220653534,
-0.02496233582496643,
-0.10892316699028015,
0.0774388313293457,
0.048255205154418945,
-0.06232855096459389,
0.09361261129379272,
0.008523445576429367,
-0.04945077374577522,
0.12077987939119339,
-0.08857360482215881,
0.0779586061835289,
0.09074028581380844,
-0.056155528873205185,
-0.11285826563835144,
0.02101917378604412,
-0.0790560320019722,
-0.10934588313102722,
-0.01704513281583786,
-0.006802526768296957,
-0.0675000324845314,
-0.06630554795265198,
0.09924396872520447,
-0.033378325402736664,
-0.0808417946100235,
-0.009302894584834576,
0.009268181398510933,
0.07236810028553009,
0.0006998069584369659,
0.010767156258225441,
0.02342168800532818,
0.02797984518110752,
0.15616022050380707,
-0.011072474531829357,
0.07178742438554764,
-0.1418774425983429,
0.16658848524093628,
-0.15504102408885956,
-0.020312270149588585,
-0.2072305679321289,
-0.07219657301902771,
-0.04165418818593025,
0.21532052755355835,
0.23293757438659668,
-0.17166124284267426,
-0.038346052169799805,
-0.012377340346574783,
-0.007216373458504677,
-0.08423903584480286,
0.1459619104862213,
0.03773199021816254,
0.02572815492749214,
-0.05505906045436859,
-0.028217488899827003,
0.03330636024475098,
-0.06944887340068817,
-0.027143921703100204,
0.13746559619903564,
0.007842977531254292,
0.05865885317325592,
-0.08870512992143631,
0.033735159784555435,
-0.1761062890291214,
-0.07380182296037674,
-0.005655340384691954,
-0.17696590721607208,
-0.06730128079652786,
-0.010545771569013596,
0.002838961547240615,
0.10502849519252777,
0.06459736824035645,
0.003863506717607379,
0.09335818141698837,
-0.06752859055995941,
0.006134026683866978,
-0.15302301943302155,
0.011434430256485939,
0.02834193967282772,
-0.08365264534950256,
0.22153930366039276,
-0.025956200435757637,
-0.12750713527202606,
0.073382169008255,
-0.0354037769138813,
-0.11012329161167145,
0.08625926822423935,
-0.050227366387844086,
-0.06749723851680756,
-0.07164901494979858,
0.20211242139339447,
-0.014962103217840195,
-0.20338715612888336,
0.031386494636535645,
-0.14145377278327942,
0.04773411899805069,
0.0645521879196167,
-0.000413411675253883,
-0.05528945103287697,
0.03904610127210617,
-0.05273297429084778,
0.09188544005155563,
0.12330617010593414,
0.02328065037727356,
-0.010847174562513828,
-0.05439227819442749,
0.08116582781076431,
0.06407524645328522,
-0.07506254315376282,
-0.09338168799877167,
-0.09387233853340149,
-0.0019494221778586507,
0.11523110419511795,
-0.12432260066270828,
-0.1959688514471054,
-0.0497063584625721,
-0.11102040857076645,
-0.0017136825481429696,
0.043178826570510864,
0.06693094968795776,
0.24413280189037323,
0.0637032687664032,
0.024202952161431313,
-0.20832905173301697,
0.09214244782924652,
0.09660524874925613,
-0.01729140244424343,
-0.09274700284004211
] |
null | null |
fastai
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using the 🤗Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join our fastai community on the Hugging Face Discord!
Greetings fellow fastlearner 🤝!
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
{"tags": ["fastai"]}
| null |
fastai/fastbook_06_multicat_PASCAL
|
[
"fastai",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#fastai #region-us
|
# Amazing!
Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (template below and documentation here)!
2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).
3. Join our fastai community on the Hugging Face Discord!
Greetings fellow fastlearner !
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
[
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on the Hugging Face Discord!\n\nGreetings fellow fastlearner !\n\n\n---",
"# Model card",
"## Model description\nMore information needed",
"## Intended uses & limitations\nMore information needed",
"## Training and evaluation data\nMore information needed"
] |
[
"TAGS\n#fastai #region-us \n",
"# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!",
"# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on the Hugging Face Discord!\n\nGreetings fellow fastlearner !\n\n\n---",
"# Model card",
"## Model description\nMore information needed",
"## Intended uses & limitations\nMore information needed",
"## Training and evaluation data\nMore information needed"
] |
[
9,
20,
66,
3,
6,
12,
8
] |
[
"passage: TAGS\n#fastai #region-us \n# Amazing!\n\nCongratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (template below and documentation here)!\n\n2. Create a demo in Gradio or Streamlit using the Spaces (documentation here).\n\n3. Join our fastai community on the Hugging Face Discord!\n\nGreetings fellow fastlearner !\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed"
] |
[
-0.033277031034231186,
-0.08227241784334183,
0.0022239338140934706,
0.07713031768798828,
0.17038100957870483,
0.10486842691898346,
0.08670254796743393,
0.07142214477062225,
0.11567308753728867,
0.013446890749037266,
0.09491732716560364,
-0.10746689885854721,
0.09321203827857971,
0.2697221636772156,
0.08565160632133484,
-0.2643716335296631,
0.007085119374096394,
-0.007957587949931622,
0.09378449618816376,
0.0647154450416565,
0.12935099005699158,
-0.01542446669191122,
0.14802193641662598,
-0.006468500941991806,
-0.20873704552650452,
-0.048613112419843674,
-0.0367392972111702,
0.006289718206971884,
0.12599752843379974,
-0.05386224761605263,
0.030972018837928772,
-0.015907105058431625,
-0.0041992454789578915,
-0.0924329087138176,
0.05714719742536545,
0.027781732380390167,
0.033576205372810364,
0.04195935279130936,
-0.03856709972023964,
0.090358667075634,
0.08452040702104568,
-0.028579043224453926,
-0.11602935940027237,
0.09493833780288696,
-0.15224945545196533,
-0.17045728862285614,
-0.10770466178655624,
-0.15297317504882812,
0.06646230816841125,
0.01824142225086689,
-0.032926321029663086,
0.12947368621826172,
-0.17055726051330566,
-0.03268762677907944,
0.19679228961467743,
-0.1398683488368988,
-0.054200977087020874,
-0.022875504568219185,
0.08887587487697601,
-0.08454488962888718,
-0.06776228547096252,
0.08554226160049438,
0.10405685752630234,
-0.02733820304274559,
0.010947559960186481,
0.013491141609847546,
0.028794609010219574,
-0.00744158448651433,
-0.05253235623240471,
0.07537523657083511,
-0.030229059979319572,
0.061034366488456726,
-0.12459655851125717,
-0.13618654012680054,
0.020395886152982712,
0.07018813490867615,
-0.051939528435468674,
-0.06627117097377777,
0.08689701557159424,
0.00023925417917780578,
-0.12471684068441391,
-0.10193783044815063,
-0.044013865292072296,
-0.11784403026103973,
0.010395646095275879,
0.0941244587302208,
-0.017060521990060806,
0.05990614369511604,
-0.09526018053293228,
0.052144426852464676,
-0.19857820868492126,
-0.03546607121825218,
-0.0739876851439476,
-0.09697233885526657,
0.015451633371412754,
-0.0281793512403965,
0.03342891484498978,
0.19603703916072845,
0.15404142439365387,
0.040892187505960464,
0.04508811607956886,
-0.03108343854546547,
0.017177764326334,
0.04148007556796074,
-0.0008477934752590954,
0.0648224875330925,
-0.009331580251455307,
-0.22554752230644226,
-0.0034632382448762655,
-0.03554622828960419,
0.11059566587209702,
-0.07053891569375992,
-0.048477303236722946,
-0.017674023285508156,
-0.14979448914527893,
0.08524873852729797,
-0.0650213286280632,
0.0014887347351759672,
-0.004883226472884417,
0.017286252230405807,
0.19425265491008759,
0.046697601675987244,
0.00032520972308702767,
-0.0058996472507715225,
-0.1312907189130783,
-0.10475128889083862,
-0.0633954331278801,
0.05420992895960808,
0.03653633967041969,
0.002547129988670349,
-0.08216937631368637,
0.06124689429998398,
-0.02218456007540226,
0.0038849383126944304,
0.022792907431721687,
-0.22105424106121063,
0.018331365659832954,
-0.08542918413877487,
-0.07521633058786392,
0.05388824641704559,
-0.029907379299402237,
-0.06049371510744095,
0.068365678191185,
-0.03459030017256737,
0.0346822515130043,
-0.03688182309269905,
0.0016914508305490017,
0.017987843602895737,
-0.071634441614151,
0.004566612187772989,
0.22498995065689087,
0.12187080085277557,
-0.0373307466506958,
-0.01403727661818266,
-0.11625855416059494,
0.03486724942922592,
-0.12201837450265884,
0.045901890844106674,
-0.07823994755744934,
0.18674558401107788,
-0.014955941587686539,
0.01712312549352646,
-0.025887764990329742,
0.07750066369771957,
0.0900055542588234,
0.21741575002670288,
-0.20995363593101501,
-0.09242399781942368,
0.20153622329235077,
-0.09993880242109299,
-0.20968188345432281,
0.2143387496471405,
0.010933434590697289,
0.10466093569993973,
-0.02445356920361519,
0.2072000801563263,
-0.06392479687929153,
-0.17961379885673523,
-0.02127908729016781,
-0.011059229262173176,
-0.24161723256111145,
-0.051771532744169235,
0.10153976082801819,
0.08293270319700241,
-0.05680832639336586,
0.03393745422363281,
-0.0017613122472539544,
0.19719333946704865,
-0.08100304007530212,
-0.0327664315700531,
0.0037850369699299335,
-0.09438452124595642,
0.008816446177661419,
0.024635400623083115,
0.030300842598080635,
-0.07669425010681152,
0.0045998780988156796,
-0.10862505435943604,
0.10374045372009277,
0.08856850862503052,
-0.018831197172403336,
-0.05914812535047531,
0.11219041049480438,
-0.030924323946237564,
-0.03990488499403,
0.10462885349988937,
-0.07421869039535522,
0.054816972464323044,
0.07873906940221786,
0.0769772157073021,
-0.011359816417098045,
0.09274003654718399,
0.06584157794713974,
0.013920036144554615,
0.03992454335093498,
0.11420150846242905,
-0.03665994852781296,
-0.031658392399549484,
0.02031930349767208,
0.0778738409280777,
-0.022186169400811195,
0.3688422739505768,
-0.17356127500534058,
0.01821484975516796,
-0.10282918810844421,
0.08845876902341843,
0.06183716654777527,
-0.0008401357918046415,
0.0835038349032402,
-0.0367698073387146,
-0.0032264823094010353,
-0.055524107068777084,
0.08583492785692215,
-0.05812237039208412,
-0.08662345260381699,
0.2658982574939728,
-0.03440943360328674,
0.0036153150722384453,
0.09868631511926651,
-0.054184261709451675,
-0.062347497791051865,
-0.06147386133670807,
-0.026257529854774475,
0.0223053228110075,
-0.027185508981347084,
0.07022673636674881,
-0.06264147907495499,
-0.060661379247903824,
0.16288144886493683,
-0.03619345277547836,
0.06410842388868332,
0.027257069945335388,
-0.08201233297586441,
-0.04750926420092583,
0.06294645369052887,
0.09405282884836197,
-0.06485769897699356,
0.038453102111816406,
0.1124429702758789,
-0.0016101401997730136,
0.16678395867347717,
0.0592765286564827,
-0.056373707950115204,
-0.09419229626655579,
-0.005355950444936752,
0.007470285519957542,
0.1591932624578476,
-0.13831892609596252,
-0.054418422281742096,
0.029514815658330917,
-0.023972032591700554,
0.05227358266711235,
-0.05514098331332207,
-0.07087593525648117,
0.0013438323512673378,
-0.07329598814249039,
0.025018898770213127,
0.12582840025424957,
-0.09504543989896774,
0.03497644141316414,
0.04957837611436844,
-0.05680005997419357,
0.0234662014991045,
0.02370806410908699,
-0.029213670641183853,
0.04664962738752365,
0.07227440923452377,
-0.206625297665596,
-0.10228221863508224,
-0.18699663877487183,
0.016714517027139664,
0.024553729221224785,
0.027458691969513893,
-0.1259651482105255,
0.04575458914041519,
-0.06818679720163345,
-0.038054630160331726,
0.0717974603176117,
-0.024877948686480522,
-0.09307482093572617,
-0.022279124706983566,
-0.014280120842158794,
-0.05402163788676262,
-0.027896815910935402,
-0.08477012068033218,
-0.0009268703288398683,
0.03632563725113869,
-0.00031268977909348905,
0.13541896641254425,
-0.02366817370057106,
-0.009438908658921719,
0.005859281402081251,
-0.03213220462203026,
0.1568119376897812,
-0.135063037276268,
0.045071423053741455,
0.13406558334827423,
0.10207222402095795,
0.03798583522439003,
0.012921007350087166,
0.018819143995642662,
-0.08992904424667358,
0.02965068444609642,
0.031484205275774,
-0.09813158959150314,
-0.056053221225738525,
-0.024970686063170433,
-0.030953072011470795,
0.26418179273605347,
-0.12343134731054306,
0.0034723030403256416,
0.03854964300990105,
0.07769693434238434,
0.12518101930618286,
-0.05048064887523651,
-0.14797833561897278,
0.048560041934251785,
-0.2661287486553192,
-0.056680064648389816,
0.004655467811971903,
-0.09040501713752747,
-0.08040545135736465,
0.15619415044784546,
-0.027873646467924118,
0.03271074593067169,
-0.006139150820672512,
0.09560710936784744,
0.010217266157269478,
0.13245338201522827,
0.06668516248464584,
-0.05331530049443245,
0.02290397323668003,
-0.10145124793052673,
-0.07250506430864334,
-0.03798116743564606,
-0.07836087048053741,
0.059070851653814316,
0.15479473769664764,
-0.04539763554930687,
-0.03424506261944771,
0.01731664128601551,
0.09378930926322937,
0.04389014467597008,
0.15866267681121826,
-0.14041966199874878,
-0.004812900442630053,
0.04247099161148071,
-0.019690074026584625,
-0.031159594655036926,
-0.01611192710697651,
0.08120068907737732,
-0.05842692777514458,
0.00590216601267457,
0.006586797069758177,
0.06998071074485779,
0.04643845185637474,
0.03342098370194435,
-0.05887731909751892,
0.17546813189983368,
-0.021034201607108116,
-0.0014303643256425858,
-0.09125334024429321,
0.1439528912305832,
0.00908610224723816,
-0.017340699210762978,
-0.03325200453400612,
-0.04431763291358948,
0.19507499039173126,
0.028783265501260757,
0.11954433470964432,
0.023984292522072792,
-0.07356978952884674,
-0.09644994139671326,
-0.1253555417060852,
0.022706180810928345,
0.11147056519985199,
-0.026222921907901764,
-0.025333024561405182,
0.02208506129682064,
-0.0478445440530777,
-0.06646084040403366,
0.17884555459022522,
-0.10769326984882355,
-0.005300077609717846,
-0.03208669275045395,
0.01915164291858673,
-0.04101448506116867,
0.04576132446527481,
0.046079907566308975,
-0.04248332232236862,
0.09340482950210571,
0.2727794051170349,
0.1320023387670517,
-0.08205481618642807,
-0.10265505313873291,
0.021805085241794586,
-0.03450016677379608,
-0.013096245005726814,
-0.01371059101074934,
0.020579569041728973,
-0.0014869094593450427,
0.02443990297615528,
0.11256244778633118,
-0.07091707736253738,
0.024737318977713585,
-0.06810358166694641,
0.08501476794481277,
-0.050070133060216904,
-0.004111715592443943,
-0.012967436574399471,
-0.03546015918254852,
-0.02919752709567547,
-0.04813423380255699,
0.1696971207857132,
-0.038042016327381134,
-0.09443683922290802,
0.09035778790712357,
-0.0018192444695159793,
0.01589462161064148,
-0.08407724648714066,
-0.04203187674283981,
0.18087424337863922,
0.3359929919242859,
-0.04722631722688675,
0.09998135268688202,
0.15277111530303955,
0.03294616937637329,
-0.21692126989364624,
0.030374795198440552,
-0.10977433621883392,
0.051621805876493454,
0.03609371930360794,
-0.03846230357885361,
0.034110233187675476,
0.09874881058931351,
-0.028129929676651955,
0.17585526406764984,
0.01953643187880516,
-0.08693647384643555,
-0.033929161727428436,
0.036069389432668686,
0.3122052550315857,
-0.10846572369337082,
-0.020826220512390137,
-0.10692492872476578,
-0.2016768604516983,
0.06194566190242767,
-0.14693762362003326,
0.14843909442424774,
-0.06627882272005081,
0.0003900393203366548,
-0.0052173142321407795,
-0.06483335793018341,
0.1994384527206421,
-0.14798803627490997,
0.026006877422332764,
-0.15147830545902252,
-0.12328807264566422,
-0.041251420974731445,
-0.08403646945953369,
0.14865940809249878,
-0.06570124626159668,
-0.04344901069998741,
-0.19091598689556122,
0.007350549101829529,
-0.027974670752882957,
0.11063537746667862,
0.028815733268857002,
-0.0752638578414917,
-0.09923963248729706,
0.11788176000118256,
-0.06749717146158218,
0.03910348564386368,
-0.07890404015779495,
-0.06121758371591568,
0.010397613979876041,
-0.09941333532333374,
0.08287406712770462,
-0.0862501934170723,
0.1852070838212967,
-0.03763248398900032,
-0.04029596224427223,
0.0446147620677948,
-0.2003331333398819,
0.002552860416471958,
0.026034075766801834,
-0.021374043077230453,
0.1076977401971817,
-0.018087027594447136,
-0.07767273485660553,
0.1185331791639328,
0.1408364474773407,
-0.051157817244529724,
-0.23953555524349213,
-0.09481517970561981,
-0.06125766783952713,
0.03499162942171097,
0.06602034717798233,
0.04337223991751671,
-0.037582479417324066,
-0.018169071525335312,
-0.041513841599226,
0.033625271171331406,
-0.1189054325222969,
-0.06319139897823334,
0.07589460164308548,
-0.006073975935578346,
-0.0763356164097786,
0.09338429570198059,
-0.032820019870996475,
-0.03311539068818092,
-0.01368726510554552,
0.20198774337768555,
-0.008855608291924,
-0.09226503223180771,
-0.06877847015857697,
0.27900266647338867,
-0.03048190288245678,
-0.0664299875497818,
-0.08531653881072998,
-0.006670560222119093,
-0.06582149863243103,
0.04188055172562599,
0.041328541934490204,
-0.002010792028158903,
0.08545565605163574,
0.07762014120817184,
-0.13911466300487518,
-0.05826771259307861,
-0.05318617820739746,
0.011891456320881844,
-0.07135025411844254,
0.05160493031144142,
0.032012127339839935,
0.13050486147403717,
-0.08505744487047195,
-0.03419254347681999,
-0.12417411804199219,
-0.06809327751398087,
-0.14624148607254028,
-0.05798319727182388,
-0.03375891596078873,
-0.025243792682886124,
0.0439620241522789,
0.031299322843551636,
-0.05921275168657303,
-0.04215855151414871,
-0.04741876199841499,
0.030783573165535927,
0.057625751942396164,
0.023527050390839577,
-0.009588590823113918,
0.043739497661590576,
0.08983426541090012,
-0.006243378389626741,
0.1896410435438156,
0.08729267865419388,
0.055043112486600876,
-0.04603677615523338,
-0.19039814174175262,
-0.05211332067847252,
0.02223200537264347,
-0.12218629568815231,
0.11196397244930267,
-0.015412883833050728,
0.012895013205707073,
-0.07319827377796173,
0.04668041691184044,
0.02726844884455204,
0.12743760645389557,
0.002793054562062025,
0.1049814224243164,
0.043434470891952515,
-0.10533586889505386,
-0.061283957213163376,
-0.0021762026008218527,
0.10733196139335632,
0.06612566858530045,
-0.01128743588924408,
-0.0033717022743076086,
0.04405450448393822,
-0.035242099314928055,
0.019733525812625885,
-0.04306551441550255,
-0.13787303864955902,
0.034886833280324936,
-0.04694775119423866,
-0.0011652560206130147,
0.00710663478821516,
0.17317064106464386,
0.054182033985853195,
-0.0254618301987648,
-0.024032916873693466,
0.028941364958882332,
0.025683606043457985,
-0.045264825224876404,
-0.01727895624935627,
0.025978242978453636,
-0.021640481427311897,
-0.043705880641937256,
0.13222450017929077,
0.028626786544919014,
0.014563014730811119,
0.019564509391784668,
0.06472164392471313,
-0.015074329450726509,
0.13567432761192322,
0.08222419023513794,
-0.008827874436974525,
-0.09915708005428314,
-0.07344666868448257,
-0.08103683590888977,
0.011100872419774532,
-0.05304981768131256,
0.15240591764450073,
0.11740470677614212,
-0.07795556634664536,
-0.029058072715997696,
-0.06257851421833038,
-0.041331928223371506,
-0.07843977957963943,
0.1036653220653534,
-0.02496233582496643,
-0.10892316699028015,
0.0774388313293457,
0.048255205154418945,
-0.06232855096459389,
0.09361261129379272,
0.008523445576429367,
-0.04945077374577522,
0.12077987939119339,
-0.08857360482215881,
0.0779586061835289,
0.09074028581380844,
-0.056155528873205185,
-0.11285826563835144,
0.02101917378604412,
-0.0790560320019722,
-0.10934588313102722,
-0.01704513281583786,
-0.006802526768296957,
-0.0675000324845314,
-0.06630554795265198,
0.09924396872520447,
-0.033378325402736664,
-0.0808417946100235,
-0.009302894584834576,
0.009268181398510933,
0.07236810028553009,
0.0006998069584369659,
0.010767156258225441,
0.02342168800532818,
0.02797984518110752,
0.15616022050380707,
-0.011072474531829357,
0.07178742438554764,
-0.1418774425983429,
0.16658848524093628,
-0.15504102408885956,
-0.020312270149588585,
-0.2072305679321289,
-0.07219657301902771,
-0.04165418818593025,
0.21532052755355835,
0.23293757438659668,
-0.17166124284267426,
-0.038346052169799805,
-0.012377340346574783,
-0.007216373458504677,
-0.08423903584480286,
0.1459619104862213,
0.03773199021816254,
0.02572815492749214,
-0.05505906045436859,
-0.028217488899827003,
0.03330636024475098,
-0.06944887340068817,
-0.027143921703100204,
0.13746559619903564,
0.007842977531254292,
0.05865885317325592,
-0.08870512992143631,
0.033735159784555435,
-0.1761062890291214,
-0.07380182296037674,
-0.005655340384691954,
-0.17696590721607208,
-0.06730128079652786,
-0.010545771569013596,
0.002838961547240615,
0.10502849519252777,
0.06459736824035645,
0.003863506717607379,
0.09335818141698837,
-0.06752859055995941,
0.006134026683866978,
-0.15302301943302155,
0.011434430256485939,
0.02834193967282772,
-0.08365264534950256,
0.22153930366039276,
-0.025956200435757637,
-0.12750713527202606,
0.073382169008255,
-0.0354037769138813,
-0.11012329161167145,
0.08625926822423935,
-0.050227366387844086,
-0.06749723851680756,
-0.07164901494979858,
0.20211242139339447,
-0.014962103217840195,
-0.20338715612888336,
0.031386494636535645,
-0.14145377278327942,
0.04773411899805069,
0.0645521879196167,
-0.000413411675253883,
-0.05528945103287697,
0.03904610127210617,
-0.05273297429084778,
0.09188544005155563,
0.12330617010593414,
0.02328065037727356,
-0.010847174562513828,
-0.05439227819442749,
0.08116582781076431,
0.06407524645328522,
-0.07506254315376282,
-0.09338168799877167,
-0.09387233853340149,
-0.0019494221778586507,
0.11523110419511795,
-0.12432260066270828,
-0.1959688514471054,
-0.0497063584625721,
-0.11102040857076645,
-0.0017136825481429696,
0.043178826570510864,
0.06693094968795776,
0.24413280189037323,
0.0637032687664032,
0.024202952161431313,
-0.20832905173301697,
0.09214244782924652,
0.09660524874925613,
-0.01729140244424343,
-0.09274700284004211
] |
null | null |
transformers
|
# Hermione Granger DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
fatemaMeem98/DialoGPT-medium-HermioneGrangerBot
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Hermione Granger DialoGPT Model
|
[
"# Hermione Granger DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Hermione Granger DialoGPT Model"
] |
[
55,
11
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# Hermione Granger DialoGPT Model"
] |
[
-0.02176320180296898,
0.11018767952919006,
-0.005054037552326918,
0.0373448021709919,
0.1193646714091301,
-0.007625901605933905,
0.11977442353963852,
0.14867106080055237,
-0.01303309015929699,
0.005280165001749992,
0.11973198503255844,
0.15366265177726746,
-0.021985279396176338,
0.07234776020050049,
-0.09334313869476318,
-0.3039616048336029,
0.0340433195233345,
0.04297415167093277,
-0.01715810038149357,
0.10388048738241196,
0.07873019576072693,
-0.0583416186273098,
0.07935819774866104,
0.013310728594660759,
-0.1553538739681244,
0.0015540755121037364,
-0.01697980985045433,
-0.11496368050575256,
0.13891029357910156,
0.06797932833433151,
0.02359657920897007,
0.025824682787060738,
-0.06890413910150528,
-0.11815343797206879,
0.03428035229444504,
-0.028739044442772865,
-0.03150780126452446,
0.08140774816274643,
-0.02205853909254074,
-0.05015261098742485,
0.171212300658226,
0.10412183403968811,
0.009731319732964039,
0.020647333934903145,
-0.15818874537944794,
-0.038738787174224854,
-0.003888678504154086,
0.021844156086444855,
-0.015759015455842018,
0.10359831899404526,
-0.039322368800640106,
0.13414441049098969,
-0.09782405197620392,
0.07330995053052902,
0.16735069453716278,
-0.3449242115020752,
-0.032594144344329834,
0.1651172637939453,
0.06374356150627136,
0.09270313382148743,
-0.0804150402545929,
0.08407220244407654,
-0.0023040755186229944,
-0.011113351210951805,
-0.04549252986907959,
-0.05580807849764824,
-0.03959028422832489,
0.039537765085697174,
-0.12081736326217651,
-0.006864072289317846,
0.25180673599243164,
-0.03598058968782425,
0.07880152016878128,
-0.06730984896421432,
-0.10457953810691833,
0.03880073502659798,
-0.030488552525639534,
-0.06037358567118645,
-0.06516869366168976,
0.0631810650229454,
0.004384312313050032,
-0.11075548082590103,
-0.1194012239575386,
0.015528004616498947,
-0.18402443826198578,
0.16434064507484436,
0.019778737798333168,
0.04190465062856674,
-0.20075452327728271,
0.10298323631286621,
-0.03188815340399742,
-0.08719020336866379,
0.0021101145539432764,
-0.1099766343832016,
0.03531818464398384,
0.0011764183873310685,
-0.04671180620789528,
-0.013502324931323528,
0.0675564557313919,
0.12005205452442169,
-0.0022278104443103075,
0.009099236689507961,
0.019991619512438774,
0.05468429625034332,
0.07360181212425232,
0.04598672688007355,
-0.029593678191304207,
-0.0697617307305336,
0.02062837965786457,
-0.05675239861011505,
0.0011325274826958776,
-0.057149987667798996,
-0.17913351953029633,
-0.07087689638137817,
0.0418388806283474,
0.029474763199687004,
0.053421199321746826,
0.10149910300970078,
0.018270744010806084,
-0.02364993467926979,
0.0529787503182888,
-0.01119180116802454,
0.018480053171515465,
-0.005013451911509037,
0.0003676831256598234,
0.12756673991680145,
0.020073367282748222,
0.026197602972388268,
-0.08163327723741531,
-0.022000186145305634,
-0.10125560313463211,
-0.0014072228223085403,
-0.009201863780617714,
-0.05320967361330986,
0.004558885935693979,
-0.07961224764585495,
0.02040284126996994,
-0.15175262093544006,
-0.10360835492610931,
0.011217698454856873,
-0.016043081879615784,
-0.08196461200714111,
-0.11215972155332565,
-0.07764466106891632,
-0.04391133785247803,
0.09519574046134949,
-0.04032493382692337,
0.015258326195180416,
-0.04637962207198143,
0.06806749850511551,
-0.026611775159835815,
0.1137499064207077,
-0.09497660398483276,
0.06742928177118301,
-0.06817058473825455,
-0.04836635664105415,
-0.06246574968099594,
0.0769350603222847,
-0.0012918474385514855,
0.04960952326655388,
-0.025098731741309166,
-0.02772124484181404,
-0.07215047627687454,
0.04915856570005417,
-0.021687641739845276,
0.21373094618320465,
-0.06599678099155426,
-0.09615623950958252,
0.19147884845733643,
-0.059035349637269974,
-0.10648071765899658,
0.1156638115644455,
0.0012087960494682193,
0.11180848628282547,
0.08898715674877167,
0.22245906293392181,
0.018097111955285072,
-0.010034957900643349,
0.09271634370088577,
0.11118058115243912,
-0.06606340408325195,
0.029269030317664146,
0.002515672706067562,
-0.037940703332424164,
-0.04725223779678345,
0.034429773688316345,
0.06267742812633514,
0.04933853819966316,
-0.049925755709409714,
-0.03478606045246124,
-0.024816757068037987,
-0.004948107525706291,
0.09240497648715973,
-0.010546332225203514,
0.1397162228822708,
-0.04593110457062721,
-0.07889995723962784,
-0.0015206868993118405,
0.011997118592262268,
-0.045662906020879745,
0.05236125364899635,
-0.04622167348861694,
0.10682430863380432,
0.02836369350552559,
0.041013821959495544,
-0.1290683150291443,
-0.023246141150593758,
-0.05678971856832504,
0.13535365462303162,
0.07300795614719391,
0.16530677676200867,
0.07995697110891342,
-0.03861608728766441,
-0.01935683749616146,
0.05006122961640358,
0.11734072864055634,
0.014254250563681126,
-0.09262315928936005,
-0.11259988695383072,
0.07443682849407196,
-0.05726279690861702,
0.10878611356019974,
-0.06782474368810654,
0.02092120051383972,
0.03617982193827629,
0.08220122009515762,
-0.01659507118165493,
0.012008578516542912,
0.013949275016784668,
-0.02303669974207878,
-0.06304425746202469,
0.015429720282554626,
0.07961539924144745,
-0.015061063691973686,
-0.038314204663038254,
0.19797064363956451,
-0.18796338140964508,
0.14710748195648193,
0.1886191964149475,
-0.20373640954494476,
0.018744541332125664,
-0.10168299078941345,
-0.03888465464115143,
0.019037559628486633,
0.06593792885541916,
-0.02241481840610504,
0.15063460171222687,
-0.05496622622013092,
0.14072079956531525,
-0.033040013164281845,
-0.03311986103653908,
-0.04005669057369232,
-0.06469786167144775,
-0.018641633912920952,
0.07335854321718216,
0.0849100649356842,
-0.15416832268238068,
0.158392071723938,
0.1194940134882927,
-0.0039190445095300674,
0.17647546529769897,
0.08372396230697632,
-0.006791363004595041,
0.04541080817580223,
-0.057675428688526154,
-0.07241598516702652,
-0.06460670381784439,
-0.32928401231765747,
-0.06129048764705658,
0.08568010479211807,
0.04170471057295799,
0.10063932836055756,
-0.08957906812429428,
-0.038708463311195374,
0.01031416840851307,
0.01762991212308407,
0.05160904303193092,
0.15561163425445557,
0.027650263160467148,
0.12619858980178833,
-0.007110105827450752,
-0.08391513675451279,
0.05365980044007301,
0.01942981593310833,
-0.06413751095533371,
0.15770834684371948,
-0.11558429151773453,
-0.3282853364944458,
-0.0800154060125351,
-0.17571298778057098,
0.006526568438857794,
0.03816822171211243,
0.10791157186031342,
-0.08377990871667862,
-0.03500084951519966,
-0.0552683062851429,
0.10140372812747955,
-0.14384056627750397,
0.01791631244122982,
-0.08582711219787598,
0.020809177309274673,
-0.12276913970708847,
-0.11288928985595703,
-0.03458597883582115,
-0.04475787654519081,
-0.07084190100431442,
0.11757132411003113,
-0.07803711295127869,
0.03907286003232002,
0.2082720398902893,
0.024527419358491898,
0.04658426716923714,
-0.03411382436752319,
0.23183274269104004,
-0.09633401781320572,
0.03778643533587456,
0.1555168181657791,
-0.0032803404610604048,
0.06687338650226593,
0.14100609719753265,
0.007507664151489735,
-0.045603204518556595,
0.0031775867100805044,
-0.028335146605968475,
-0.05794340372085571,
-0.1882660835981369,
-0.11879432201385498,
-0.1278962641954422,
0.08594857156276703,
0.029445894062519073,
0.045086927711963654,
0.15804412961006165,
0.06163150072097778,
-0.021531913429498672,
-0.05620572715997696,
0.044727761298418045,
0.08034844696521759,
0.2808246612548828,
-0.05541328713297844,
0.16047826409339905,
-0.004063347354531288,
-0.13271844387054443,
0.05922712758183479,
0.06141003966331482,
0.11326765269041061,
0.034580543637275696,
0.02971055917441845,
0.023800870403647423,
0.05949154496192932,
0.15074551105499268,
0.014209654182195663,
0.05035773292183876,
-0.04480163753032684,
-0.05492410808801651,
-0.035394854843616486,
0.027346601709723473,
0.07062681019306183,
0.09029579907655716,
-0.16924066841602325,
-0.04502274468541145,
-0.025550981983542442,
0.04634886234998703,
-0.05557933449745178,
0.1334024965763092,
-0.1206451803445816,
-0.004367700777947903,
0.0828346535563469,
-0.058193862438201904,
-0.11257067322731018,
0.0920831710100174,
0.042432595044374466,
-0.147615447640419,
-0.022845806553959846,
0.023234514519572258,
0.08564361929893494,
-0.052348148077726364,
0.048111896961927414,
-0.17916437983512878,
-0.09870415925979614,
-0.008205913007259369,
0.10979806631803513,
-0.2588026821613312,
0.21268384158611298,
-0.02333230711519718,
-0.0788184404373169,
-0.06821895390748978,
-0.012916642241179943,
0.03608470410108566,
0.09700436145067215,
0.06502404063940048,
-0.007843447849154472,
0.03212320804595947,
0.051019199192523956,
-0.013462270610034466,
0.009308313950896263,
0.06956849247217178,
0.005440644919872284,
0.003432624973356724,
-0.043800704181194305,
0.006860850844532251,
-0.007478795014321804,
-0.0021639640908688307,
0.023082084953784943,
-0.21275779604911804,
0.09327047318220139,
0.04175376519560814,
0.013968801125884056,
0.022218961268663406,
-0.06782126426696777,
-0.13439233601093292,
0.20911972224712372,
0.06134737655520439,
-0.0745071992278099,
-0.07170283794403076,
0.01279792282730341,
0.08317147940397263,
-0.0485730841755867,
0.08077600598335266,
-0.048766884952783585,
0.022422779351472855,
-0.07634583115577698,
-0.17170047760009766,
0.10820300132036209,
-0.06490223109722137,
-0.04565301164984703,
-0.015056698583066463,
0.18467485904693604,
-0.029505249112844467,
0.03514225408434868,
0.04085763171315193,
0.05807911232113838,
-0.19584672152996063,
-0.0704452395439148,
0.03890901803970337,
0.032526060938835144,
-0.006544297095388174,
0.02882685326039791,
0.011870748363435268,
-0.03893151134252548,
-0.005562616977840662,
-0.00709218205884099,
0.3337843120098114,
0.16079747676849365,
-0.07260335236787796,
0.14921073615550995,
0.06121806427836418,
-0.05253702029585838,
-0.28394898772239685,
-0.07498307526111603,
-0.0926932841539383,
-0.04094644635915756,
-0.041868310421705246,
-0.15460778772830963,
0.08027387410402298,
0.0013507995754480362,
-0.024741092696785927,
0.0922510102391243,
-0.27880674600601196,
-0.09154526889324188,
0.1799633502960205,
-0.023502418771386147,
0.3792060911655426,
-0.10834842920303345,
-0.045512158423662186,
-0.005222570616751909,
-0.14863379299640656,
0.09752704203128815,
-0.042766768485307693,
0.12269600480794907,
-0.030733633786439896,
0.15591423213481903,
0.05811811238527298,
-0.010392077267169952,
0.11934775114059448,
0.00809104461222887,
-0.054565995931625366,
-0.10946767032146454,
-0.10059165209531784,
0.031312379986047745,
0.012965895235538483,
0.04378420114517212,
-0.0705731213092804,
0.01821490004658699,
-0.10541988909244537,
-0.02146177738904953,
-0.09539011865854263,
0.04048499837517738,
0.003576508956030011,
-0.05160416290163994,
-0.06405327469110489,
-0.03736233338713646,
-0.013081133365631104,
0.027354281395673752,
0.17493653297424316,
-0.10412289947271347,
0.13889352977275848,
0.1294422447681427,
0.10240098088979721,
-0.15811674296855927,
-0.02484055608510971,
-0.010674809105694294,
-0.06761088967323303,
0.05626612901687622,
-0.15259288251399994,
0.00043690865277312696,
0.0813787430524826,
-0.009918092750012875,
0.06799642741680145,
0.08887877315282822,
-0.0016860670875757933,
-0.00046005845069885254,
0.1083025187253952,
-0.26205018162727356,
-0.12632250785827637,
-0.07594732195138931,
-0.0031289267353713512,
0.059659507125616074,
0.07327943295240402,
0.212363138794899,
-0.008562629111111164,
-0.0468740239739418,
0.007790885865688324,
0.02779027260839939,
-0.035772040486335754,
0.08493129163980484,
0.01554280985146761,
0.027984684333205223,
-0.12812912464141846,
0.03788479045033455,
0.029461124911904335,
-0.08264247328042984,
0.04434088245034218,
0.17183105647563934,
-0.10329902172088623,
-0.14730769395828247,
-0.1049872413277626,
0.04523848742246628,
-0.10066767781972885,
0.04981543496251106,
-0.04765656962990761,
-0.11198912560939789,
0.05852996185421944,
0.10240017622709274,
0.06842027604579926,
0.06503603607416153,
-0.09896855801343918,
-0.02636048011481762,
-0.009599673561751842,
0.02466828189790249,
0.024309072643518448,
0.009334743022918701,
-0.0835161805152893,
0.11937350034713745,
-0.05545638129115105,
0.08492109179496765,
-0.08577711880207062,
-0.10997374355792999,
-0.14281699061393738,
0.02301807701587677,
-0.061106838285923004,
-0.09034862369298935,
-0.09056343883275986,
-0.053772348910570145,
-0.001838662545196712,
-0.04237997531890869,
-0.016680460423231125,
-0.048420414328575134,
-0.10740002989768982,
0.012079259380698204,
-0.029796484857797623,
0.01936544105410576,
-0.07139783352613449,
0.025998782366514206,
0.05739085376262665,
-0.0367710180580616,
0.14572389423847198,
0.12880241870880127,
-0.09670878201723099,
0.05816326290369034,
-0.0881827101111412,
-0.09268063306808472,
0.10257182270288467,
0.009137177839875221,
0.057515595108270645,
0.018638476729393005,
-0.013678316958248615,
0.024657895788550377,
0.09065356105566025,
0.056823745369911194,
0.009649408049881458,
-0.06973505020141602,
0.06484463810920715,
-0.058498281985521317,
-0.1624908149242401,
-0.037128597497940063,
-0.04940899461507797,
0.040638260543346405,
0.01049814186990261,
0.09135951101779938,
-0.031690988689661026,
0.03657076880335808,
-0.058991678059101105,
0.03356853872537613,
-0.0007833791896700859,
-0.14778609573841095,
0.018830537796020508,
-0.09733298420906067,
0.03866663947701454,
-0.00830809585750103,
0.21660712361335754,
0.07453800737857819,
-0.1050255075097084,
0.028300965204834938,
0.03427984192967415,
0.07459685206413269,
0.015469666570425034,
0.21119216084480286,
0.11940260231494904,
-0.06569549441337585,
-0.06732417643070221,
0.09215132892131805,
0.028126182034611702,
0.111788809299469,
0.10081927478313446,
-0.029301779344677925,
-0.013398846611380577,
0.08000016957521439,
0.004982748068869114,
0.01743696630001068,
-0.12556593120098114,
-0.10725020617246628,
0.02248106710612774,
0.047661617398262024,
-0.10284651070833206,
0.07581257820129395,
0.1939937323331833,
-0.01993485540151596,
0.0230526365339756,
-0.043188195675611496,
-0.07869219034910202,
-0.17953316867351532,
-0.12886421382427216,
-0.09400825202465057,
-0.16206854581832886,
-0.013842744752764702,
-0.13247567415237427,
0.04189075902104378,
0.02824842371046543,
0.05820824205875397,
-0.07690457254648209,
0.054813794791698456,
0.08028905093669891,
-0.09853153675794601,
0.051896754652261734,
-0.028468403965234756,
0.08568539470434189,
-0.04340104013681412,
0.01701432280242443,
-0.08969230204820633,
0.0216998141258955,
-0.01282224990427494,
0.04430133104324341,
-0.07129383832216263,
-0.00035723872133530676,
-0.13614825904369354,
-0.09450037777423859,
-0.061984628438949585,
0.06794846057891846,
-0.006390053778886795,
0.15021120011806488,
0.018879404291510582,
-0.046730268746614456,
0.007253321353346109,
0.26995569467544556,
-0.08833786845207214,
-0.06831140071153641,
-0.02899865433573723,
0.22865407168865204,
0.03408302739262581,
0.09929480403661728,
-0.021708739921450615,
-0.01969718746840954,
-0.08318984508514404,
0.27305030822753906,
0.342896431684494,
-0.09612375497817993,
0.03446989879012108,
0.051942598074674606,
0.025622274726629257,
0.11252501606941223,
0.06023108586668968,
0.1391560435295105,
0.26581940054893494,
-0.08080489188432693,
-0.03955189511179924,
-0.02369869500398636,
-0.022035695612430573,
-0.08948156982660294,
0.041424475610256195,
0.06366599351167679,
-0.056362900882959366,
-0.03591519221663475,
0.07581007480621338,
-0.20852579176425934,
0.10436835885047913,
-0.12374208867549896,
-0.20433560013771057,
-0.07212875038385391,
0.021920939907431602,
0.08469881862401962,
-0.006348176393657923,
0.11035401374101639,
-0.01603548787534237,
-0.0582515113055706,
0.07195238769054413,
0.031107118353247643,
-0.21734271943569183,
-0.0194381233304739,
0.1114632710814476,
-0.1116097941994667,
-0.048222772777080536,
-0.04805976152420044,
0.039351001381874084,
0.09073475748300552,
0.06024652346968651,
-0.042361147701740265,
0.02444344013929367,
0.010154351592063904,
-0.06793054938316345,
0.027862876653671265,
0.06520398706197739,
0.02844790369272232,
-0.05735868215560913,
0.121042400598526,
-0.11622749269008636,
0.03351334482431412,
-0.011426523327827454,
-0.007875227369368076,
-0.008187727071344852,
0.016137108206748962,
-0.07149848341941833,
0.07089248299598694,
0.0829535499215126,
-0.018427545204758644,
-0.02030465006828308,
-0.027407871559262276,
-0.04911865293979645,
-0.02417164295911789,
-0.07857339829206467,
-0.1104612648487091,
-0.16131237149238586,
-0.10277637094259262,
0.025490814819931984,
0.019005747511982918,
-0.1766766756772995,
-0.005922373849898577,
-0.11971552670001984,
0.05286078900098801,
-0.11595727503299713,
0.11524387449026108,
0.12266995757818222,
-0.032419316470623016,
-0.0026043266989290714,
0.009077084250748158,
0.040101271122694016,
0.10280716419219971,
-0.16080360114574432,
-0.06691881269216537
] |
null | null |
transformers
|
# FERNET-C5
FERNET-C5 (**F**lexible **E**mbedding **R**epresentation **NET**work) is a monolingual Czech BERT-base model pre-trained from 93GB of Czech Colossal Clean Crawled Corpus (C5). See our paper for details.
## Paper
https://link.springer.com/chapter/10.1007/978-3-030-89579-2_3
The preprint of our paper is available at https://arxiv.org/abs/2107.10042.
## Citation
If you find this model useful, please cite our paper:
```
@inproceedings{FERNETC5,
title = {Comparison of Czech Transformers on Text Classification Tasks},
author = {Lehe{\v{c}}ka, Jan and {\v{S}}vec, Jan},
year = 2021,
booktitle = {Statistical Language and Speech Processing},
publisher = {Springer International Publishing},
address = {Cham},
pages = {27--37},
doi = {10.1007/978-3-030-89579-2_3},
isbn = {978-3-030-89579-2},
editor = {Espinosa-Anke, Luis and Mart{\'i}n-Vide, Carlos and Spasi{\'{c}}, Irena}
}
```
|
{"language": "cs", "license": "cc-by-nc-sa-4.0", "tags": ["Czech", "KKY", "FAV"]}
|
fill-mask
|
fav-kky/FERNET-C5
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"bert",
"fill-mask",
"Czech",
"KKY",
"FAV",
"cs",
"arxiv:2107.10042",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2107.10042"
] |
[
"cs"
] |
TAGS
#transformers #pytorch #tf #safetensors #bert #fill-mask #Czech #KKY #FAV #cs #arxiv-2107.10042 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# FERNET-C5
FERNET-C5 (Flexible Embedding Representation NETwork) is a monolingual Czech BERT-base model pre-trained from 93GB of Czech Colossal Clean Crawled Corpus (C5). See our paper for details.
## Paper
URL
The preprint of our paper is available at URL
If you find this model useful, please cite our paper:
|
[
"# FERNET-C5\nFERNET-C5 (Flexible Embedding Representation NETwork) is a monolingual Czech BERT-base model pre-trained from 93GB of Czech Colossal Clean Crawled Corpus (C5). See our paper for details.",
"## Paper\nURL\n\nThe preprint of our paper is available at URL\n\nIf you find this model useful, please cite our paper:"
] |
[
"TAGS\n#transformers #pytorch #tf #safetensors #bert #fill-mask #Czech #KKY #FAV #cs #arxiv-2107.10042 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# FERNET-C5\nFERNET-C5 (Flexible Embedding Representation NETwork) is a monolingual Czech BERT-base model pre-trained from 93GB of Czech Colossal Clean Crawled Corpus (C5). See our paper for details.",
"## Paper\nURL\n\nThe preprint of our paper is available at URL\n\nIf you find this model useful, please cite our paper:"
] |
[
77,
62,
25
] |
[
"passage: TAGS\n#transformers #pytorch #tf #safetensors #bert #fill-mask #Czech #KKY #FAV #cs #arxiv-2107.10042 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# FERNET-C5\nFERNET-C5 (Flexible Embedding Representation NETwork) is a monolingual Czech BERT-base model pre-trained from 93GB of Czech Colossal Clean Crawled Corpus (C5). See our paper for details.## Paper\nURL\n\nThe preprint of our paper is available at URL\n\nIf you find this model useful, please cite our paper:"
] |
[
-0.09297949075698853,
0.004257638938724995,
-0.0012516416609287262,
0.024785419926047325,
0.0432237945497036,
-0.008725176565349102,
0.0898021012544632,
0.043437860906124115,
0.015404030680656433,
0.02876175381243229,
0.1414552628993988,
0.0356619618833065,
-0.018511349335312843,
0.1508406102657318,
0.022256778553128242,
-0.23423875868320465,
0.11833710223436356,
0.06282416731119156,
-0.00023959796817507595,
0.06183476373553276,
0.11786531656980515,
-0.09407006949186325,
0.01314450055360794,
0.03702195733785629,
-0.0769842341542244,
0.011366371065378189,
-0.057153694331645966,
-0.04566260799765587,
0.1260666847229004,
0.010179020464420319,
0.12452651560306549,
0.08992597460746765,
0.05476045981049538,
0.0035827727988362312,
0.030591124668717384,
-0.03822154924273491,
-0.10210159420967102,
0.07952374219894409,
0.04142599552869797,
0.019788434728980064,
0.2983482778072357,
-0.10257896035909653,
-0.09200341254472733,
-0.027763154357671738,
-0.03505387529730797,
-0.2131177932024002,
-0.0059395343996584415,
0.10420607030391693,
0.031632501631975174,
0.07489706575870514,
-0.023480497300624847,
0.1645619422197342,
0.004212225787341595,
0.10282549262046814,
0.10822403430938721,
-0.3359823226928711,
-0.08334513753652573,
0.13360285758972168,
0.04888089373707771,
0.009257965721189976,
0.00020261929603293538,
0.05981706455349922,
0.04105393588542938,
-0.04380134865641594,
0.06254485994577408,
-0.07514414936304092,
-0.1463187336921692,
-0.036094631999731064,
-0.166656956076622,
-0.0036838946398347616,
0.1452353596687317,
-0.034210942685604095,
-0.039352111518383026,
0.020265333354473114,
-0.05121761932969093,
0.11902044713497162,
-0.04287658631801605,
-0.004359366837888956,
-0.00775314774364233,
-0.030314115807414055,
0.01749197207391262,
-0.08695667237043381,
-0.1318616420030594,
-0.02646629698574543,
-0.06982825696468353,
0.18697042763233185,
-0.004608791787177324,
0.024211490526795387,
0.020061979070305824,
0.10574860870838165,
-0.13506971299648285,
-0.10382220149040222,
0.02344115450978279,
-0.035998228937387466,
0.042572133243083954,
-0.014834878034889698,
-0.06165701895952225,
-0.06235753744840622,
0.07221873104572296,
0.011143251322209835,
0.08743074536323547,
-0.0564698651432991,
0.05719061195850372,
0.05950574949383736,
-0.032773859798908234,
0.022711411118507385,
-0.16948765516281128,
0.03423076495528221,
0.023353779688477516,
-0.0786701962351799,
0.0767999142408371,
-0.023507975041866302,
-0.19371718168258667,
-0.12019377201795578,
0.06280258297920227,
0.021075356751680374,
-0.05010640248656273,
0.08743766695261002,
0.043657030910253525,
0.017668599262833595,
0.08337391167879105,
-0.01645219512283802,
-0.04594574123620987,
-0.0015677481424063444,
0.009890339337289333,
0.048820991069078445,
0.024711953476071358,
-0.004208419006317854,
-0.06520918011665344,
0.0927618145942688,
-0.0827653631567955,
0.0053432900458574295,
-0.053337592631578445,
-0.19357357919216156,
0.03075215592980385,
-0.174957275390625,
0.05111856386065483,
-0.17675036191940308,
-0.06033160537481308,
0.036060865968465805,
0.06674223393201828,
-0.022805100306868553,
0.0371880829334259,
-0.007121260277926922,
-0.0700945183634758,
-0.03227180242538452,
0.021466173231601715,
0.0530436635017395,
-0.044771458953619,
0.026144521310925484,
-0.07675832509994507,
0.07283544540405273,
-0.21184799075126648,
-0.031984228640794754,
-0.08917754143476486,
-0.029028410091996193,
-0.10703196376562119,
-0.12615390121936798,
-0.10390926152467728,
0.031491197645664215,
-0.03902149200439453,
-0.07830323278903961,
-0.022847313433885574,
-0.019964346662163734,
0.06168048456311226,
0.09396461397409439,
-0.09863940626382828,
0.0035931456368416548,
0.20922411978244781,
-0.16115324199199677,
-0.08163385093212128,
0.10860565304756165,
0.013674256391823292,
0.08216843008995056,
0.011383443139493465,
0.07624713331460953,
0.16842618584632874,
-0.1897016018629074,
-0.027055446058511734,
0.11587274074554443,
-0.012281890027225018,
-0.141577810049057,
0.07144070416688919,
0.02590591087937355,
-0.05439842864871025,
0.024316709488630295,
-0.17550137639045715,
-0.003694144543260336,
-0.060562942177057266,
-0.017662426456809044,
-0.014685450121760368,
-0.0665859803557396,
0.07776682823896408,
0.0069975582882761955,
0.08418487012386322,
-0.05574316158890724,
-0.017763681709766388,
-0.01191924698650837,
0.06923745572566986,
-0.0526166595518589,
0.03575004264712334,
-0.029953572899103165,
0.1894495189189911,
-0.100202776491642,
0.009758290834724903,
-0.1289583295583725,
-0.031149299815297127,
0.015630003064870834,
-0.0181104838848114,
-0.014098750427365303,
0.174566388130188,
0.040905412286520004,
0.06334351748228073,
-0.027752405032515526,
0.031071096658706665,
0.0004974813782609999,
0.02710547298192978,
-0.039999689906835556,
-0.12829463183879852,
-0.013582311570644379,
-0.07267866283655167,
0.16081121563911438,
-0.05688612535595894,
-0.001866274164058268,
0.08268827944993973,
0.09049063175916672,
-0.030825698748230934,
0.06974902749061584,
-0.0025922239292412996,
0.04698854684829712,
-0.006498412229120731,
0.022967394441366196,
0.09083244204521179,
0.08323505520820618,
-0.05742032825946808,
0.04406299069523811,
-0.051638584583997726,
0.13869188725948334,
0.12382005900144577,
-0.004967973101884127,
-0.03232427313923836,
-0.10663578659296036,
-0.022898124530911446,
0.04720097780227661,
-0.005795647390186787,
-0.04703553020954132,
0.06896836310625076,
-0.03943122178316116,
0.0634014904499054,
-0.06132444739341736,
0.02354900725185871,
0.06730211526155472,
-0.05404256284236908,
-0.038200750946998596,
0.1213400810956955,
0.10927507281303406,
-0.14830008149147034,
0.07500825822353363,
0.13267944753170013,
-0.1056898906826973,
0.14156684279441833,
0.0029724028427153826,
-0.10511817038059235,
-0.013482356444001198,
0.015779457986354828,
0.027959182858467102,
0.20310385525226593,
-0.16038671135902405,
-0.01489213202148676,
0.06383128464221954,
-0.08145289123058319,
0.05363650619983673,
-0.07668289542198181,
0.006917586550116539,
-0.01507826428860426,
-0.058356329798698425,
-0.0355752557516098,
0.08282710611820221,
-0.10713320225477219,
0.0940602645277977,
-0.0030521161388605833,
-0.1237010806798935,
0.0240503940731287,
0.01897110790014267,
-0.07355503737926483,
0.16131040453910828,
-0.07898732274770737,
-0.17541059851646423,
-0.13148613274097443,
0.032209113240242004,
0.016731131821870804,
0.034384146332740784,
0.053098227828741074,
0.025396263226866722,
-0.11223240941762924,
-0.03318442404270172,
-0.05360323563218117,
0.045366182923316956,
0.010121893137693405,
-0.08463041484355927,
0.024058474227786064,
-0.028263315558433533,
-0.0807524248957634,
0.003201457904651761,
-0.048503972589969635,
0.014985723420977592,
0.08721227198839188,
-0.07543957978487015,
0.17614276707172394,
0.021851809695363045,
-0.03983322158455849,
0.042356885969638824,
-0.010950046591460705,
0.07886163890361786,
-0.040560681372880936,
0.05503108724951744,
0.1250748336315155,
-0.03361379727721214,
-0.011744819581508636,
0.09959089756011963,
0.035920754075050354,
0.03475847840309143,
0.032888010144233704,
-0.11638528853654861,
-0.08667182922363281,
-0.0847947895526886,
-0.16519232094287872,
-0.06357655674219131,
0.001228352659381926,
0.006646042224019766,
0.0016038899775594473,
-0.06872257590293884,
0.10306595265865326,
-0.024836840108036995,
-0.09323020279407501,
-0.018242746591567993,
0.06278813630342484,
0.10823036730289459,
-0.010967064648866653,
0.07724767178297043,
-0.07331093400716782,
-0.09003626555204391,
0.0578920878469944,
-0.06589001417160034,
0.17519064247608185,
-0.027015889063477516,
0.05196543037891388,
0.036389101296663284,
0.14034630358219147,
0.09636866301298141,
0.22664421796798706,
-0.024234086275100708,
-0.03007826767861843,
-0.024216843768954277,
-0.0850071907043457,
0.06277310848236084,
-0.0030985726043581963,
-0.08163551241159439,
-0.0522623248398304,
-0.0791688859462738,
-0.07078245282173157,
0.053681857883930206,
0.01452572364360094,
0.1263931542634964,
-0.1672316938638687,
-0.05738648772239685,
-0.04372572898864746,
0.10132353007793427,
-0.07619007676839828,
-0.06019606813788414,
0.10320048034191132,
-0.06506514549255371,
0.0012942326720803976,
-0.032707225531339645,
0.043492335826158524,
0.034090690314769745,
0.05503698065876961,
-0.08955854922533035,
0.011660265736281872,
-0.03418611362576485,
0.11735964566469193,
-0.15860775113105774,
0.31648194789886475,
0.028160173445940018,
0.03163409233093262,
-0.0990922600030899,
-0.026954410597682,
0.05141618847846985,
0.13016341626644135,
0.17531588673591614,
0.0004657946992665529,
0.07971734553575516,
-0.01982894539833069,
-0.13956445455551147,
0.07833360135555267,
0.037962231785058975,
-0.041544824838638306,
0.0751064270734787,
-0.004847935400903225,
-0.056847114115953445,
0.0027924664318561554,
0.12314900010824203,
-0.07319805771112442,
-0.12698249518871307,
0.08572398871183395,
0.019781213253736496,
-0.06539128720760345,
-0.03427441418170929,
-0.12687037885189056,
-0.10860048234462738,
0.17581583559513092,
-0.04595959931612015,
-0.07446149736642838,
-0.10993107408285141,
0.04156595095992088,
0.10672439634799957,
-0.0824187770485878,
0.10843662172555923,
-0.04463193938136101,
0.11168089509010315,
-0.08550882339477539,
-0.05668332055211067,
0.04630974307656288,
-0.11775645613670349,
0.003535000141710043,
0.015974698588252068,
0.026486940681934357,
0.032534558326005936,
0.03831285610795021,
0.05110156536102295,
0.03593268245458603,
-0.041870083659887314,
-0.088866226375103,
-0.0542207695543766,
-0.018240030854940414,
0.04409037157893181,
-0.02928132563829422,
-0.14929789304733276,
-0.12162289768457413,
0.02375544235110283,
-0.010493610985577106,
0.1263028234243393,
0.11221747100353241,
-0.09698101133108139,
0.03626243770122528,
0.1928398758172989,
-0.00393947958946228,
-0.35047727823257446,
-0.05750153958797455,
-0.039964668452739716,
0.04391886293888092,
-0.095095694065094,
-0.057583149522542953,
0.08567164093255997,
0.06029706820845604,
-0.05504960939288139,
0.030937649309635162,
-0.08680669218301773,
-0.08031424880027771,
0.2562100291252136,
0.10452160239219666,
0.23108720779418945,
-0.05418086051940918,
-0.022292857989668846,
-0.004277058877050877,
-0.09456498920917511,
0.07055268436670303,
-0.2242392748594284,
0.034695811569690704,
0.0007059104973450303,
-0.11312936246395111,
0.02326452173292637,
-0.07062537968158722,
0.10554901510477066,
-0.030466940253973007,
-0.024636853486299515,
-0.07923402637243271,
-0.02063804492354393,
0.0752553716301918,
-0.0027848680038005114,
0.10872606188058853,
-0.0246430654078722,
0.05177225172519684,
0.017231842502951622,
-0.05079678073525429,
-0.016451414674520493,
0.1435128003358841,
-0.03154265135526657,
-0.07691814750432968,
-0.047249823808670044,
0.0733017697930336,
-0.07742266356945038,
-0.009276381693780422,
0.06030299514532089,
-0.04954669252038002,
0.06227840855717659,
0.1703084409236908,
0.13886211812496185,
-0.08324955403804779,
0.17755348980426788,
0.06514322012662888,
-0.07730526477098465,
0.0587143748998642,
0.104469433426857,
-0.04739272594451904,
0.12985727190971375,
0.006358228623867035,
-0.007244567386806011,
0.036312829703092575,
-0.04504450783133507,
-0.04313448816537857,
0.10656742751598358,
-0.2405560463666916,
0.024028323590755463,
-0.04221112281084061,
-0.014044282026588917,
-0.038625337183475494,
0.06438953429460526,
0.13923004269599915,
-0.09746266901493073,
0.003761525033041835,
-0.008240234106779099,
-0.04844409227371216,
0.014667565934360027,
0.0828334391117096,
0.10213418304920197,
0.027845194563269615,
-0.06449699401855469,
-0.03575139865279198,
0.001485113170929253,
-0.14031371474266052,
-0.06347424536943436,
-0.039529699832201004,
-0.1892550140619278,
-0.1280740648508072,
0.06870879977941513,
0.10083472728729248,
-0.16530463099479675,
-0.10153992474079132,
-0.08150606602430344,
-0.11874837428331375,
-0.008094551041722298,
0.2433602660894394,
0.13766518235206604,
-0.014572088606655598,
-0.0008626904455013573,
-0.022169673815369606,
-0.08720733970403671,
0.12371087819337845,
0.03923529386520386,
0.02497481368482113,
-0.05118914693593979,
0.12340661138296127,
-0.05652439966797829,
0.029164709150791168,
-0.07360517978668213,
0.055481307208538055,
-0.0735720694065094,
-0.05191661790013313,
-0.08676522970199585,
0.013395018875598907,
-0.07508300989866257,
-0.053801119327545166,
-0.029714612290263176,
-0.04119919240474701,
0.0038281965535134077,
0.013422592543065548,
-0.05848386138677597,
0.06179099157452583,
-0.016009612008929253,
-0.000009789974683371838,
-0.039268676191568375,
-0.043567903339862823,
0.020489444956183434,
-0.03150695189833641,
0.09154342114925385,
0.07380212098360062,
0.016973411664366722,
0.1131397932767868,
-0.16713830828666687,
0.0331660658121109,
0.03001219592988491,
0.058872342109680176,
0.06615723669528961,
-0.027854079380631447,
0.023686902597546577,
0.041095390915870667,
-0.004558265209197998,
0.036246489733457565,
0.0807202160358429,
-0.0140481973066926,
0.06479424238204956,
-0.034356489777565,
-0.028143547475337982,
0.0054171462543308735,
0.07040859758853912,
0.07414355874061584,
0.06737376749515533,
0.087910957634449,
-0.0983557179570198,
0.012721012346446514,
0.02618611603975296,
0.05678865313529968,
-0.04385583475232124,
-0.1049085408449173,
-0.06172594800591469,
-0.05321061238646507,
0.018725302070379257,
-0.03472663834691048,
0.13016723096370697,
0.030762458220124245,
0.06066365912556648,
0.00032005648245103657,
-0.01962786167860031,
0.10945979505777359,
-0.007455358747392893,
0.20236022770404816,
0.056234993040561676,
0.03389611840248108,
-0.14040902256965637,
0.09525123238563538,
-0.006998009979724884,
-0.014085622504353523,
0.1645764410495758,
0.052316997200250626,
0.005643326323479414,
0.11488943547010422,
0.062402669340372086,
-0.010549888014793396,
0.031960874795913696,
-0.08451148867607117,
0.0065034837462008,
0.045523181557655334,
-0.026581857353448868,
0.09930125623941422,
0.17746764421463013,
-0.08641377836465836,
-0.034894902259111404,
0.07201468199491501,
-0.06090693548321724,
-0.08934443444013596,
-0.12977144122123718,
-0.07640858739614487,
-0.039796892553567886,
-0.02914697863161564,
-0.09654867649078369,
-0.07412008196115494,
-0.04504837095737457,
0.04449593275785446,
-0.017110560089349747,
0.18309062719345093,
-0.029802924022078514,
-0.03819437325000763,
0.11296723783016205,
0.016485240310430527,
0.01420524250715971,
-0.06971511244773865,
-0.039160747081041336,
-0.07944938540458679,
0.047799430787563324,
-0.03632931783795357,
-0.011975635774433613,
0.056349433958530426,
-0.034931596368551254,
0.007112017832696438,
-0.03372132033109665,
-0.057229768484830856,
0.011827816255390644,
0.0638778880238533,
0.12288262695074081,
0.033346425741910934,
-0.04981992766261101,
-0.010727323591709137,
0.09783036261796951,
0.00017655866395216435,
-0.13300397992134094,
-0.04525964334607124,
0.11233698576688766,
-0.017172718420624733,
0.054594479501247406,
0.014499249868094921,
-0.022123953327536583,
0.029539184644818306,
0.2560761272907257,
0.2538183033466339,
0.008131268434226513,
0.07663793116807938,
0.02641684003174305,
0.006864939350634813,
0.09183871001005173,
0.059694573283195496,
0.01390112191438675,
0.3136559724807739,
-0.07889344543218613,
-0.0846438854932785,
-0.10148826986551285,
0.07725095748901367,
-0.13298241794109344,
-0.03076382353901863,
0.06908385455608368,
-0.1107637956738472,
-0.024170147255063057,
0.09086352586746216,
-0.08220332860946655,
-0.04171373322606087,
-0.06003902852535248,
-0.03518196567893028,
-0.07268636673688889,
-0.019286014139652252,
0.0022306833416223526,
0.034741129726171494,
0.09706654399633408,
-0.1292487531900406,
0.028240248560905457,
0.0299411378800869,
0.037210673093795776,
-0.060538940131664276,
-0.04863806813955307,
0.060700200498104095,
-0.041419174522161484,
0.13243983685970306,
-0.00903032161295414,
0.04392882436513901,
0.10124915093183517,
-0.01644940860569477,
-0.0891394391655922,
0.09992829710245132,
0.024693511426448822,
-0.09197373688220978,
0.11466841399669647,
-0.0625622346997261,
-0.061596062034368515,
-0.042792629450559616,
0.03586198017001152,
-0.11449669301509857,
0.09376762062311172,
0.09112659096717834,
-0.009413527324795723,
-0.035994697362184525,
0.14805416762828827,
-0.1171930730342865,
0.09277531504631042,
0.13172514736652374,
-0.0338057242333889,
-0.06065002828836441,
-0.0045995647087693214,
0.040960028767585754,
0.04118439555168152,
-0.027472112327814102,
0.053544387221336365,
-0.08240505307912827,
0.009391705505549908,
-0.16211514174938202,
-0.051949892193078995,
-0.21752788126468658,
-0.02566843293607235,
-0.11709277331829071,
0.015852484852075577,
-0.07838119566440582,
-0.04544109106063843,
0.08412306755781174,
-0.0022574039176106453,
-0.004408661741763353,
0.15823929011821747,
-0.02624065987765789,
0.006110480520874262,
-0.11274342238903046,
-0.10807853192090988
] |
null | null |
transformers
|
# FERNET-CC_sk
FERNET-CC_sk is a monolingual Slovak BERT-base model pre-trained from 29GB of filtered Slovak Common Crawl dataset.
It is a Slovak version of our Czech [FERNET-C5](https://huggingface.co/fav-kky/FERNET-C5) model.
Preprint of our paper is available at https://arxiv.org/abs/2107.10042.
|
{"language": "sk", "license": "cc-by-nc-sa-4.0", "tags": ["Slovak", "KKY", "FAV"]}
|
fill-mask
|
fav-kky/FERNET-CC_sk
|
[
"transformers",
"pytorch",
"tf",
"bert",
"fill-mask",
"Slovak",
"KKY",
"FAV",
"sk",
"arxiv:2107.10042",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2107.10042"
] |
[
"sk"
] |
TAGS
#transformers #pytorch #tf #bert #fill-mask #Slovak #KKY #FAV #sk #arxiv-2107.10042 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# FERNET-CC_sk
FERNET-CC_sk is a monolingual Slovak BERT-base model pre-trained from 29GB of filtered Slovak Common Crawl dataset.
It is a Slovak version of our Czech FERNET-C5 model.
Preprint of our paper is available at URL
|
[
"# FERNET-CC_sk\nFERNET-CC_sk is a monolingual Slovak BERT-base model pre-trained from 29GB of filtered Slovak Common Crawl dataset.\n\nIt is a Slovak version of our Czech FERNET-C5 model.\n\nPreprint of our paper is available at URL"
] |
[
"TAGS\n#transformers #pytorch #tf #bert #fill-mask #Slovak #KKY #FAV #sk #arxiv-2107.10042 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# FERNET-CC_sk\nFERNET-CC_sk is a monolingual Slovak BERT-base model pre-trained from 29GB of filtered Slovak Common Crawl dataset.\n\nIt is a Slovak version of our Czech FERNET-C5 model.\n\nPreprint of our paper is available at URL"
] |
[
72,
65
] |
[
"passage: TAGS\n#transformers #pytorch #tf #bert #fill-mask #Slovak #KKY #FAV #sk #arxiv-2107.10042 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# FERNET-CC_sk\nFERNET-CC_sk is a monolingual Slovak BERT-base model pre-trained from 29GB of filtered Slovak Common Crawl dataset.\n\nIt is a Slovak version of our Czech FERNET-C5 model.\n\nPreprint of our paper is available at URL"
] |
[
-0.10966793447732925,
-0.06461170315742493,
-0.0014212687965482473,
0.02242891862988472,
0.06720659136772156,
-0.015158298425376415,
0.07360371202230453,
0.02421675994992256,
0.0900321826338768,
0.06558707356452942,
0.14608551561832428,
0.09873066842556,
-0.016996582970023155,
0.17764897644519806,
0.036943335086107254,
-0.168272003531456,
0.11668208241462708,
-0.005058970768004656,
-0.1378786414861679,
0.06421852111816406,
0.11279023438692093,
-0.04958845674991608,
0.043233733624219894,
0.04751031473278999,
-0.12328268587589264,
-0.004129147622734308,
0.02089385874569416,
-0.056155022233724594,
0.10191676020622253,
0.02472653239965439,
0.09595481306314468,
0.08171091228723526,
0.05790814012289047,
0.02473538927733898,
0.02088199555873871,
-0.04653390124440193,
-0.058848705142736435,
0.04009293392300606,
-0.016522753983736038,
0.06663883477449417,
0.17788678407669067,
-0.021304965019226074,
-0.055081967264413834,
-0.04304616153240204,
-0.009152012877166271,
-0.17452432215213776,
-0.039323292672634125,
0.10819181799888611,
0.045875418931245804,
0.0740685686469078,
0.026506245136260986,
0.1239393800497055,
-0.045157961547374725,
0.08334611356258392,
0.020472651347517967,
-0.3295094668865204,
-0.06978465616703033,
0.10567633807659149,
-0.06806385517120361,
0.02098357304930687,
-0.026069246232509613,
0.10365776717662811,
0.030021971091628075,
-0.041374240070581436,
0.04363533854484558,
-0.0897446796298027,
-0.1712544560432434,
-0.022256137803196907,
-0.13261869549751282,
0.0032925442792475224,
0.0920640230178833,
-0.03924059867858887,
-0.013235916383564472,
0.10634719580411911,
-0.022221727296710014,
0.055997028946876526,
-0.04740948602557182,
0.0679674744606018,
-0.06498263776302338,
0.0066214860416948795,
-0.024559635668992996,
-0.0689919963479042,
-0.1276252716779709,
-0.07574458420276642,
-0.1364196091890335,
0.27017730474472046,
0.009405922144651413,
0.0720800906419754,
0.02599654532968998,
0.01370072178542614,
-0.10809732973575592,
-0.096279077231884,
0.03956279903650284,
-0.012870699167251587,
0.006812979932874441,
0.021717526018619537,
-0.0448441281914711,
0.0048558698035776615,
0.10565289109945297,
0.122347891330719,
0.11425202339887619,
-0.02592439576983452,
0.05714491754770279,
0.08450399339199066,
-0.05683037266135216,
0.025730308145284653,
-0.1428389847278595,
0.04408889636397362,
0.033672090619802475,
-0.12973752617835999,
0.06112358346581459,
-0.09484808146953583,
-0.17740705609321594,
-0.08494856208562851,
-0.00982029177248478,
0.004235970787703991,
-0.01022038422524929,
0.0859912782907486,
0.07718242704868317,
0.023153576999902725,
0.09878012537956238,
0.000046394394303206354,
-0.02724541909992695,
-0.023106172680854797,
0.04037749022245407,
0.09415090829133987,
-0.04639389365911484,
0.02476325072348118,
-0.005961735267192125,
0.11051753908395767,
-0.07887327671051025,
-0.009890842251479626,
-0.011297941207885742,
-0.17437373101711273,
0.05290516838431358,
-0.1504204422235489,
0.0070637003518640995,
-0.22344769537448883,
-0.09850577265024185,
-0.008958637714385986,
0.0075027476996183395,
-0.03723642975091934,
-0.031118782237172127,
0.03637910261750221,
-0.04340774565935135,
0.08415348082780838,
0.004457412287592888,
0.038490019738674164,
-0.050612520426511765,
0.049535736441612244,
-0.13634523749351501,
0.0932031124830246,
-0.19912421703338623,
-0.034670133143663406,
-0.07434574514627457,
-0.059598881751298904,
-0.19368666410446167,
-0.0672515258193016,
-0.2204584926366806,
0.07092072814702988,
-0.06790751963853836,
-0.04332853481173515,
-0.09797417372465134,
-0.025473253801465034,
0.08489672839641571,
0.08658011257648468,
-0.1385328471660614,
-0.022036956623196602,
0.05068091303110123,
-0.1938319057226181,
-0.04481463134288788,
0.13512200117111206,
0.04930219054222107,
0.02546483650803566,
0.0011510476469993591,
0.1923992782831192,
0.23280447721481323,
-0.13701321184635162,
-0.009868374094367027,
0.13302238285541534,
-0.024186627939343452,
-0.21845106780529022,
0.10295701026916504,
-0.010888997465372086,
-0.06867529451847076,
0.027262123301625252,
-0.17749015986919403,
0.1064298003911972,
-0.05106641724705696,
-0.015300248749554157,
0.013233283534646034,
-0.07301818579435349,
0.07543879002332687,
-0.01300742570310831,
0.12661492824554443,
-0.053485121577978134,
0.02391836605966091,
-0.08952394127845764,
0.07330317050218582,
-0.02229446917772293,
0.04862998425960541,
-0.05063191056251526,
0.21786880493164062,
-0.054678987711668015,
0.025740601122379303,
-0.08785770833492279,
-0.0505044087767601,
0.008469748310744762,
0.015090069733560085,
-0.00024682795628905296,
0.0700550377368927,
0.031058887019753456,
-0.03733817860484123,
-0.00322915380820632,
0.06670223921537399,
0.01062371302396059,
0.029954291880130768,
-0.0683022290468216,
-0.16905203461647034,
0.01945723593235016,
-0.018700262531638145,
0.17508335411548615,
-0.014280295930802822,
-0.020509181544184685,
0.17347538471221924,
0.11381889134645462,
-0.09782581776380539,
0.05964963138103485,
-0.037954457104206085,
0.07215555012226105,
0.012096434831619263,
0.030422376468777657,
0.06480714678764343,
0.12512317299842834,
-0.036273498088121414,
0.1693427413702011,
0.06162858381867409,
0.11389875411987305,
0.1286463886499405,
-0.12314295768737793,
-0.036059990525245667,
-0.051052965223789215,
0.0017126086167991161,
0.017214186489582062,
0.006847335956990719,
-0.06554321944713593,
0.04040636122226715,
-0.015186102129518986,
0.056735921651124954,
-0.012791622430086136,
0.02804228663444519,
0.0795063003897667,
-0.062294088304042816,
0.011834942735731602,
0.0855693444609642,
0.11612092703580856,
-0.0963502898812294,
0.10497494786977768,
0.10420693457126617,
-0.08537519723176956,
0.16946859657764435,
-0.0298780407756567,
-0.07533201575279236,
-0.030844377353787422,
-0.03629462793469429,
0.012609949335455894,
0.1538418084383011,
-0.1021975576877594,
-0.05681426078081131,
0.08006428927183151,
-0.08075185120105743,
0.04286455363035202,
-0.07570163160562515,
-0.0000909761365619488,
-0.027111588045954704,
-0.03778079152107239,
-0.07534980028867722,
0.10836944729089737,
-0.12838414311408997,
0.06656070798635483,
0.011749600991606712,
-0.13457216322422028,
0.028986595571041107,
0.024544907733798027,
-0.061934396624565125,
0.11021450161933899,
-0.13222536444664001,
-0.13112755119800568,
-0.24062466621398926,
0.10740872472524643,
-0.04813557118177414,
-0.0036762298550456762,
0.038208503276109695,
-0.002800954505801201,
-0.08531797677278519,
0.01719016581773758,
-0.0386253260076046,
0.011690724641084671,
0.05536133795976639,
-0.0017928770976141095,
-0.038936201483011246,
-0.09051906317472458,
-0.05944271758198738,
-0.017604373395442963,
-0.06296653300523758,
0.007344198413193226,
0.10850824415683746,
-0.0414484366774559,
0.2093229442834854,
0.07704820483922958,
-0.012072490528225899,
0.04178784787654877,
-0.026074284687638283,
0.11205057799816132,
-0.0738433301448822,
0.07660456746816635,
0.12004788964986801,
0.013056748546659946,
0.016224199905991554,
0.07714906334877014,
0.05289926752448082,
0.0851331576704979,
0.018288463354110718,
-0.04465785622596741,
-0.14886632561683655,
-0.10358496755361557,
-0.14502528309822083,
-0.0718517005443573,
-0.014753302559256554,
-0.007599650416523218,
0.02608487568795681,
0.060439541935920715,
0.1222735345363617,
-0.022239970043301582,
-0.03414391353726387,
-0.042868874967098236,
0.032093122601509094,
0.26252371072769165,
0.024864932522177696,
0.13218878209590912,
-0.08310800045728683,
-0.01361616887152195,
0.08986945450305939,
-0.05740205571055412,
0.15647456049919128,
-0.007905468344688416,
0.07240674644708633,
0.026769038289785385,
0.18348857760429382,
0.12350830435752869,
0.1806875765323639,
-0.019714349880814552,
-0.016728850081562996,
0.008948682807385921,
-0.0670943558216095,
0.030169354751706123,
-0.0007176338112913072,
-0.0805492028594017,
-0.14055635035037994,
-0.004022642038762569,
0.00662012305110693,
0.06071029230952263,
0.15368486940860748,
0.03768531233072281,
-0.05968005955219269,
-0.08569391816854477,
-0.06440798193216324,
0.12052637338638306,
-0.011277438141405582,
-0.012530127540230751,
0.1284165233373642,
-0.06526868790388107,
0.0293407142162323,
0.02385878376662731,
0.04318000748753548,
0.03650851920247078,
0.04904985800385475,
-0.020783059298992157,
-0.03778037801384926,
-0.032175950706005096,
0.0792597085237503,
-0.10999967157840729,
0.2828034460544586,
0.027594594284892082,
-0.0049644955433905125,
-0.07385352998971939,
-0.038775164633989334,
0.0693252831697464,
0.07591258734464645,
0.11073754727840424,
0.06393475085496902,
-0.08510741591453552,
-0.19478023052215576,
-0.12196031212806702,
0.016533222049474716,
0.008297055028378963,
-0.06664514541625977,
0.021687474101781845,
-0.011701864190399647,
-0.027028001844882965,
-0.016665617004036903,
0.14417529106140137,
-0.02514231763780117,
-0.06140529736876488,
0.09293290227651596,
0.1573074758052826,
-0.12836235761642456,
-0.014894031919538975,
-0.13016097247600555,
-0.18152980506420135,
0.033761005848646164,
-0.12556655704975128,
-0.09632337093353271,
-0.13889503479003906,
0.019789624959230423,
0.09120029956102371,
-0.13732506334781647,
0.09914585202932358,
-0.013026798143982887,
0.06936803460121155,
-0.03804784640669823,
-0.08158189803361893,
0.044831838458776474,
-0.104708231985569,
-0.029741719365119934,
0.05752572789788246,
0.05712618678808212,
-0.026711435988545418,
0.048031970858573914,
0.025753166526556015,
0.054992035031318665,
-0.0046479483135044575,
-0.155964657664299,
-0.018180089071393013,
-0.09805875271558762,
-0.08093991130590439,
-0.0030211913399398327,
-0.0937245637178421,
0.03315136954188347,
0.0012303489493206143,
-0.06408282369375229,
0.16663828492164612,
0.0740325003862381,
-0.1329660564661026,
0.041032373905181885,
0.22514653205871582,
-0.003000996308401227,
-0.4138437509536743,
-0.05949897691607475,
-0.00144622300285846,
0.008286839351058006,
-0.14942021667957306,
-0.08087647706270218,
0.13580304384231567,
0.13963928818702698,
-0.05106927827000618,
0.004184183664619923,
-0.0890440121293068,
-0.10678771138191223,
0.21034465730190277,
-0.009571908041834831,
0.3521692156791687,
-0.06555232405662537,
-0.05835024639964104,
-0.007273232564330101,
-0.10498590022325516,
0.007357134949415922,
-0.2197253257036209,
-0.0017540932167321444,
0.015285275876522064,
-0.02029833197593689,
0.005702848080545664,
-0.038353726267814636,
0.10840236395597458,
0.08869108557701111,
0.00741093885153532,
-0.06429234147071838,
-0.032888174057006836,
0.12260488420724869,
0.0180767010897398,
0.0536111555993557,
0.030680997297167778,
0.031203223392367363,
-0.0324794203042984,
-0.007807007059454918,
-0.07693026959896088,
0.1028394103050232,
0.009612835012376308,
-0.05562504753470421,
-0.03405695781111717,
0.03877439349889755,
-0.06368041783571243,
-0.014396773651242256,
0.07026880234479904,
-0.03444896265864372,
0.02261650562286377,
0.1353626400232315,
0.0014966193120926619,
-0.15618613362312317,
0.038055215030908585,
0.044940028339624405,
-0.10407383739948273,
0.07619152963161469,
0.08986961096525192,
-0.002901821630075574,
0.13465142250061035,
0.05211159959435463,
-0.04788605868816376,
0.03671154007315636,
-0.010642433539032936,
-0.02007167972624302,
0.09855066239833832,
-0.1631297767162323,
0.07148130238056183,
-0.013870293274521828,
-0.036039285361766815,
0.042875371873378754,
0.03391602262854576,
0.1768745332956314,
-0.061287157237529755,
0.03561650589108467,
-0.01582816056907177,
-0.07303637266159058,
0.00762591976672411,
0.16519659757614136,
0.13970555365085602,
0.009559296071529388,
-0.11647151410579681,
-0.046363379806280136,
0.01582820899784565,
0.004423255100846291,
-0.026982005685567856,
-0.014682436361908913,
-0.19673854112625122,
-0.12621960043907166,
-0.000005410283847595565,
0.07185231894254684,
-0.23292627930641174,
-0.0790250152349472,
-0.05438855662941933,
-0.1308702528476715,
-0.0016128247370943427,
0.23021651804447174,
0.11568979918956757,
0.004977790173143148,
-0.017281509935855865,
-0.021037425845861435,
-0.04400145262479782,
0.07445388287305832,
0.0031008534133434296,
0.030730348080396652,
-0.08717097342014313,
0.15919247269630432,
-0.07090912759304047,
0.036138735711574554,
-0.07586333900690079,
0.02339881658554077,
-0.07121748477220535,
-0.027086574584245682,
0.008337501436471939,
-0.0019282076973468065,
-0.0597432516515255,
-0.04544452577829361,
0.0053343153558671474,
-0.10190101712942123,
-0.050819020718336105,
0.01683702878654003,
-0.0607595294713974,
0.03357382491230965,
-0.007018791977316141,
0.024549489840865135,
-0.04196260869503021,
-0.07805996388196945,
0.027887070551514626,
0.001667393371462822,
0.06625159084796906,
0.1502724587917328,
0.06764309108257294,
0.11668548732995987,
-0.08588704466819763,
0.025586454197764397,
0.048283226788043976,
0.07558972388505936,
0.05752935633063316,
0.05864102020859718,
0.017415553331375122,
0.03205632045865059,
0.0323026217520237,
0.06416112929582596,
0.03695300966501236,
-0.018812479451298714,
0.038553062826395035,
-0.04576556384563446,
-0.05814359709620476,
-0.011223692446947098,
0.03771456703543663,
0.17753536999225616,
0.13999195396900177,
0.09462463110685349,
-0.09145570546388626,
-0.008743916638195515,
0.03495163843035698,
0.05561656877398491,
-0.041326262056827545,
-0.07701896876096725,
-0.054892342537641525,
-0.07656959444284439,
0.015393855981528759,
-0.011065348982810974,
0.06418304890394211,
0.044489797204732895,
0.005761478096246719,
0.0231846421957016,
0.030727718025445938,
0.06065322831273079,
-0.012544401921331882,
0.13098689913749695,
0.1256350874900818,
0.029359567910432816,
-0.11032866686582565,
0.07556021213531494,
0.017672181129455566,
0.051668617874383926,
0.12088631093502045,
-0.031157618388533592,
0.15838101506233215,
0.07827206701040268,
0.12352271378040314,
-0.02072523720562458,
0.028719371184706688,
-0.051242049783468246,
-0.10676984488964081,
0.024409973993897438,
-0.029157401993870735,
0.12248987704515457,
0.15515650808811188,
-0.12449206411838531,
-0.045708462595939636,
0.0725431814789772,
-0.05347476154565811,
-0.10327129811048508,
-0.16167834401130676,
-0.066722072660923,
-0.11742237210273743,
-0.008970980532467365,
-0.06440073251724243,
-0.052638839930295944,
0.014390161260962486,
0.05970768257975578,
0.024439843371510506,
0.10007110983133316,
-0.12089377641677856,
-0.009985636919736862,
0.12968142330646515,
-0.007845580577850342,
-0.016352461650967598,
-0.10272636264562607,
-0.05625654384493828,
-0.0385432131588459,
0.046945396810770035,
-0.03339572623372078,
-0.01793419010937214,
0.07168610394001007,
-0.028549352660775185,
-0.008207034319639206,
-0.053044967353343964,
-0.05538441613316536,
-0.04756386950612068,
0.08846829831600189,
0.11097823828458786,
0.05242333561182022,
-0.01689838245511055,
-0.02133309654891491,
0.04255509376525879,
-0.004373069852590561,
-0.12036953866481781,
-0.07755938917398453,
-0.043626729398965836,
-0.005117786582559347,
0.08033456653356552,
-0.01704702526330948,
-0.026716100051999092,
-0.033863380551338196,
0.24232813715934753,
0.19237451255321503,
0.032023582607507706,
0.053464170545339584,
-0.006674738600850105,
-0.00020708434749394655,
0.056494638323783875,
0.09301214665174484,
0.04505540058016777,
0.22947297990322113,
-0.03307630866765976,
-0.1199663057923317,
-0.10840269923210144,
0.022744683548808098,
-0.17107994854450226,
-0.03387192636728287,
0.008991030976176262,
-0.09587456285953522,
-0.07000742852687836,
0.09587972611188889,
-0.04990154132246971,
-0.030026588588953018,
-0.010382574051618576,
-0.041231099516153336,
-0.04034155234694481,
-0.007955302484333515,
-0.052001018077135086,
0.05265853554010391,
0.06505871564149857,
-0.10297798365354538,
0.057449281215667725,
-0.046853844076395035,
0.03876984864473343,
-0.07166828960180283,
-0.11282934248447418,
0.09913754463195801,
0.03334580361843109,
0.10400513559579849,
-0.049866627901792526,
0.10879942774772644,
0.10792278498411179,
0.033609308302402496,
-0.08113639056682587,
0.05117662996053696,
-0.005489461123943329,
-0.14549006521701813,
0.0018395996885374188,
-0.07583126425743103,
-0.06857860833406448,
-0.05032913759350777,
0.05041604861617088,
-0.08943572640419006,
0.047904253005981445,
0.04634934291243553,
0.05095604434609413,
-0.044499319046735764,
0.0904405489563942,
-0.12494437396526337,
0.10004638135433197,
0.06586699932813644,
-0.02501738630235195,
-0.08472727239131927,
0.005922682583332062,
-0.06684137880802155,
0.011660287156701088,
-0.04496799781918526,
-0.007582847028970718,
-0.0938887894153595,
-0.008828393183648586,
-0.2047417312860489,
-0.012897510081529617,
-0.05014793574810028,
0.0020057917572557926,
-0.14223648607730865,
0.0361451730132103,
-0.007850930094718933,
0.00011523860302986577,
0.005425949580967426,
-0.03787955641746521,
0.00592358922585845,
-0.00018008676124736667,
-0.000040504524804418907,
0.026279324665665627,
-0.09579148143529892,
-0.10067440569400787
] |
null | null |
transformers
|
# FERNET-News
FERNET-News is a monolingual Czech RoBERTa-base model pre-trained from 20.5GB of thoroughly cleaned Czech news corpus.
Preprint of our paper is available at https://arxiv.org/abs/2107.10042.
|
{"language": "cs", "license": "cc-by-nc-sa-4.0", "tags": ["Czech", "KKY", "FAV"]}
|
fill-mask
|
fav-kky/FERNET-News
|
[
"transformers",
"pytorch",
"tf",
"roberta",
"fill-mask",
"Czech",
"KKY",
"FAV",
"cs",
"arxiv:2107.10042",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2107.10042"
] |
[
"cs"
] |
TAGS
#transformers #pytorch #tf #roberta #fill-mask #Czech #KKY #FAV #cs #arxiv-2107.10042 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# FERNET-News
FERNET-News is a monolingual Czech RoBERTa-base model pre-trained from 20.5GB of thoroughly cleaned Czech news corpus.
Preprint of our paper is available at URL
|
[
"# FERNET-News\nFERNET-News is a monolingual Czech RoBERTa-base model pre-trained from 20.5GB of thoroughly cleaned Czech news corpus.\n\nPreprint of our paper is available at URL"
] |
[
"TAGS\n#transformers #pytorch #tf #roberta #fill-mask #Czech #KKY #FAV #cs #arxiv-2107.10042 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# FERNET-News\nFERNET-News is a monolingual Czech RoBERTa-base model pre-trained from 20.5GB of thoroughly cleaned Czech news corpus.\n\nPreprint of our paper is available at URL"
] |
[
73,
47
] |
[
"passage: TAGS\n#transformers #pytorch #tf #roberta #fill-mask #Czech #KKY #FAV #cs #arxiv-2107.10042 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# FERNET-News\nFERNET-News is a monolingual Czech RoBERTa-base model pre-trained from 20.5GB of thoroughly cleaned Czech news corpus.\n\nPreprint of our paper is available at URL"
] |
[
-0.04676123335957527,
-0.04692944884300232,
-0.002103062579408288,
0.006601882632821798,
0.07909203320741653,
0.06429008394479752,
0.08581627160310745,
0.06737980991601944,
-0.0007335738046094775,
0.03511781245470047,
0.1556273251771927,
0.06661994010210037,
-0.06584497541189194,
0.06499826163053513,
0.043653931468725204,
-0.18272681534290314,
0.1513802409172058,
0.001791047747246921,
0.027994727715849876,
0.05391189828515053,
0.0977783203125,
0.01525199692696333,
-0.009056403301656246,
0.011607377789914608,
-0.12562960386276245,
0.02515607513487339,
0.036948882043361664,
-0.05515093728899956,
0.11356962472200394,
0.03305402770638466,
0.006381257902830839,
0.033142633736133575,
0.004880737513303757,
-0.011138093657791615,
0.03019782155752182,
-0.027068210765719414,
-0.15281015634536743,
0.05135716497898102,
0.03404546156525612,
-0.061770446598529816,
0.3376857340335846,
-0.18781787157058716,
-0.10974694043397903,
-0.020544810220599174,
-0.10235460847616196,
-0.2689017653465271,
-0.01800346188247204,
0.061431434005498886,
0.05647777020931244,
0.19637151062488556,
-0.03165683522820473,
0.15321144461631775,
-0.03555900603532791,
0.09803204238414764,
0.023728685453534126,
-0.32302647829055786,
-0.06983909755945206,
0.02405128814280033,
-0.05419658496975899,
0.05564424768090248,
-0.011493072845041752,
0.12255891412496567,
0.059945277869701385,
-0.0458860881626606,
-0.10781948268413544,
-0.07439427822828293,
-0.06233293563127518,
-0.05044852942228317,
-0.11675943434238434,
-0.018071487545967102,
0.010413609445095062,
-0.025925960391759872,
0.02629755809903145,
0.07656436413526535,
-0.024007366970181465,
0.08140157163143158,
-0.03681395575404167,
0.11153343319892883,
-0.11711359024047852,
-0.011583490297198296,
-0.01887371949851513,
-0.07056214660406113,
-0.11855169385671616,
-0.011443219147622585,
-0.13193826377391815,
0.28082945942878723,
-0.01080265361815691,
0.08762215822935104,
0.015890805050730705,
0.05097290128469467,
-0.03664474934339523,
-0.11564358323812485,
0.036097511649131775,
-0.012614191509783268,
0.05691692978143692,
0.04262719675898552,
-0.07541929185390472,
-0.0325440838932991,
0.04110559821128845,
-0.006846773903816938,
-0.02436264045536518,
-0.04741033539175987,
0.04050932079553604,
0.08488839119672775,
-0.012768466025590897,
-0.03617516905069351,
-0.1220802292227745,
0.010958218015730381,
-0.03067067824304104,
-0.14972761273384094,
0.10069900751113892,
-0.0016721164574846625,
-0.17168457806110382,
-0.09023910015821457,
-0.08497922867536545,
0.0141089903190732,
0.00836915522813797,
0.10967439413070679,
0.07832230627536774,
0.06165461614727974,
0.005008074454963207,
0.0037011527456343174,
-0.064695805311203,
-0.034404460340738297,
0.04522371292114258,
0.13025839626789093,
-0.05945524573326111,
-0.009244542568922043,
-0.06286102533340454,
0.14020712673664093,
-0.09538089483976364,
0.003677239641547203,
-0.01762007363140583,
-0.13095815479755402,
0.026121973991394043,
-0.14944061636924744,
0.04639733210206032,
-0.15451762080192566,
0.025894081220030785,
-0.019290558993816376,
0.010891709476709366,
-0.06330131739377975,
-0.026829365640878677,
-0.037078484892845154,
-0.033067211508750916,
0.08345330506563187,
0.026576519012451172,
0.04458384960889816,
-0.07645109295845032,
0.10403725504875183,
-0.10473493486642838,
0.0921855941414833,
-0.19998228549957275,
-0.027877632528543472,
-0.10208205878734589,
-0.09043944627046585,
-0.009907753206789494,
-0.022460628300905228,
-0.1503627896308899,
0.09045145660638809,
-0.03667346388101578,
-0.01595494896173477,
-0.03259577974677086,
0.02863294444978237,
0.027199624106287956,
0.11709638684988022,
-0.04588063433766365,
-0.04760723188519478,
0.1438629925251007,
-0.1139838844537735,
0.06985398381948471,
0.09681377559900284,
0.02920311689376831,
0.07906991988420486,
0.08040045201778412,
0.11607779562473297,
0.1611776351928711,
-0.11456185579299927,
-0.02864087000489235,
0.06570615619421005,
-0.016700947657227516,
-0.045820724219083786,
0.040422819554805756,
0.02817610464990139,
-0.07897010445594788,
0.0407693050801754,
-0.16143448650836945,
0.08329092711210251,
0.003207162255421281,
-0.0027310156729072332,
0.04000317305326462,
-0.0018542550969868898,
0.03288964927196503,
-0.036053672432899475,
0.08883830159902573,
-0.09661629796028137,
-0.054050810635089874,
-0.09810792654752731,
0.08685189485549927,
0.04023607075214386,
0.05592859163880348,
-0.057443633675575256,
0.12685608863830566,
-0.04112859070301056,
0.07959884405136108,
-0.06701287627220154,
0.0824110135436058,
-0.05517154186964035,
0.05166415497660637,
0.025599414482712746,
0.13372863829135895,
0.00534306513145566,
-0.08108200877904892,
-0.07111624628305435,
0.03011321648955345,
-0.031689658761024475,
-0.01652764528989792,
0.010750435292720795,
-0.1623689979314804,
0.1269797682762146,
-0.039992593228816986,
0.18249250948429108,
-0.03454958274960518,
-0.04209411144256592,
0.11235305666923523,
0.030780848115682602,
-0.07747451961040497,
0.07402635365724564,
-0.04541001841425896,
0.06362695246934891,
-0.025298379361629486,
0.05775267630815506,
0.08514128625392914,
0.09642394632101059,
-0.04168335720896721,
0.1546841263771057,
0.007191434968262911,
0.04174458608031273,
0.1247790977358818,
-0.0925956442952156,
-0.04123780131340027,
0.05713195353746414,
-0.012146000750362873,
0.03653964400291443,
0.005491348914802074,
-0.03712470084428787,
-0.00008043838897719979,
-0.07310676574707031,
0.03584084287285805,
0.005770524498075247,
0.02165176346898079,
0.07409358769655228,
-0.039319101721048355,
-0.07395648956298828,
0.14678192138671875,
0.053639255464076996,
-0.2196015864610672,
0.0915723368525505,
0.16768543422222137,
-0.08522343635559082,
0.259059876203537,
0.03308338671922684,
-0.07306315004825592,
-0.021233689039945602,
-0.07183032482862473,
0.022430419921875,
0.12158744782209396,
-0.09042644500732422,
0.041880346834659576,
0.034642186015844345,
-0.05947285518050194,
0.056297168135643005,
0.00014954757352825254,
-0.07205840945243835,
-0.05868414789438248,
-0.05133311077952385,
-0.10914188623428345,
0.10111773759126663,
-0.10493098199367523,
0.07508539408445358,
0.024665430188179016,
-0.05465901270508766,
0.06515881419181824,
0.013680278323590755,
-0.05132683739066124,
0.06903799623250961,
-0.1056554839015007,
-0.19784635305404663,
-0.16764892637729645,
-0.01665404997766018,
0.026268355548381805,
-0.014499482698738575,
0.06357081234455109,
-0.05731773003935814,
-0.05982865020632744,
0.02528562769293785,
-0.028174689039587975,
0.026888485997915268,
0.02050125226378441,
0.014635743573307991,
0.04452690854668617,
-0.031380634754896164,
-0.03828974813222885,
-0.006978205405175686,
-0.08175560086965561,
-0.04589490219950676,
0.08596830815076828,
-0.07817024737596512,
0.1879645735025406,
-0.030231289565563202,
0.04301916062831879,
0.05610156059265137,
-0.01913941465318203,
0.11940773576498032,
-0.15285393595695496,
0.030411221086978912,
0.0760253518819809,
-0.01963135413825512,
-0.004053943324834108,
0.11720406264066696,
0.023823801428079605,
0.054259613156318665,
0.052076563239097595,
-0.022827651351690292,
-0.06209755316376686,
-0.11579279601573944,
-0.1330081671476364,
-0.06791909784078598,
0.03713930398225784,
-0.030200693756341934,
0.012035765685141087,
-0.002481148112565279,
0.044781871140003204,
-0.11318016797304153,
-0.11017517000436783,
-0.022199109196662903,
0.05814342200756073,
0.2541978061199188,
-0.003288282547146082,
0.06923338770866394,
-0.09606925398111343,
-0.05910271033644676,
0.08898421376943588,
-0.11861459910869598,
0.09332305938005447,
0.02360742911696434,
0.08964578807353973,
-0.01029050163924694,
0.0859069973230362,
0.10419803857803345,
0.13223694264888763,
0.023826660588383675,
-0.052590612322092056,
0.00726068252697587,
-0.03554892912507057,
0.10141395777463913,
-0.009111881256103516,
-0.11393319070339203,
-0.10251603275537491,
0.011834921315312386,
-0.09273716807365417,
0.07200156152248383,
0.08773770928382874,
0.12948133051395416,
-0.01262179110199213,
-0.0660514235496521,
-0.03953060135245323,
0.08310093730688095,
-0.10728700459003448,
-0.05989863723516464,
0.039374470710754395,
-0.03363588824868202,
0.06607996672391891,
0.019605286419391632,
0.02909722365438938,
0.0446523055434227,
0.08354542404413223,
-0.03632105886936188,
-0.16688786447048187,
-0.047065120190382004,
0.14748062193393707,
-0.2216949611902237,
0.32020339369773865,
0.03313635289669037,
0.03158935159444809,
-0.059455323964357376,
-0.09251147508621216,
0.02145352028310299,
0.09540381282567978,
0.09427792578935623,
0.046495743095874786,
-0.06052621081471443,
-0.043655749410390854,
-0.1505236029624939,
0.0417824313044548,
0.01499284990131855,
-0.10151727497577667,
0.06808263808488846,
-0.019043248146772385,
-0.0394657701253891,
-0.0024078204296529293,
0.08314662426710129,
0.0009063986944966018,
-0.14626279473304749,
0.10019055008888245,
0.09475332498550415,
-0.060096271336078644,
-0.0008845138945616782,
-0.13940200209617615,
-0.055733371526002884,
0.08329162001609802,
-0.09799141436815262,
-0.06295232474803925,
-0.1375764161348343,
0.07966102659702301,
-0.008799447678029537,
-0.11220132559537888,
0.02956055849790573,
0.008464566431939602,
0.0738302543759346,
-0.04920751228928566,
-0.07490710914134979,
0.020833590999245644,
-0.06744155287742615,
-0.04434938728809357,
0.036253754049539566,
0.044267069548368454,
0.0676390528678894,
0.037569914013147354,
0.015213485807180405,
0.08659762144088745,
-0.0022145507391542196,
-0.08883834630250931,
0.017129406332969666,
-0.10167226195335388,
-0.10463183373212814,
-0.028451846912503242,
-0.1255573034286499,
-0.11731528490781784,
-0.07793242484331131,
-0.11075782775878906,
0.12928961217403412,
0.20065435767173767,
-0.10397133976221085,
0.023422207683324814,
0.15955984592437744,
0.012491391971707344,
-0.3645211458206177,
-0.075505331158638,
-0.004696184769272804,
0.01935439370572567,
-0.05245239660143852,
-0.09200479090213776,
0.025286300107836723,
0.1032107025384903,
-0.056839097291231155,
0.09442351013422012,
-0.09611250460147858,
-0.03388979285955429,
0.1681625247001648,
-0.09232453256845474,
0.4816630780696869,
-0.045471709221601486,
-0.02959965541958809,
-0.03272305801510811,
0.020680420100688934,
0.10411514341831207,
-0.19144180417060852,
0.06144874542951584,
-0.023383697494864464,
-0.007427414413541555,
0.02492665871977806,
-0.003653181018307805,
0.0879010483622551,
-0.00831257551908493,
-0.04153876379132271,
-0.13676701486110687,
-0.018267717212438583,
0.09835642576217651,
0.035581715404987335,
-0.03560962527990341,
-0.08057326823472977,
0.0033072354272007942,
-0.04913407191634178,
-0.03358149155974388,
-0.08149798959493637,
0.09400038421154022,
-0.030084775760769844,
-0.02732507325708866,
-0.02470795437693596,
0.06028125435113907,
-0.09938625246286392,
-0.04410659894347191,
0.17056718468666077,
-0.08593644946813583,
0.0772550031542778,
-0.03912518918514252,
-0.005288207903504372,
0.0012138740858063102,
0.1383567452430725,
-0.0007721240981481969,
-0.09506135433912277,
0.0764031931757927,
0.08746732771396637,
-0.06614375114440918,
0.08172496408224106,
0.01690652035176754,
-0.06227460131049156,
0.02304110676050186,
-0.09446123987436295,
0.013142235577106476,
0.10034230351448059,
-0.26496389508247375,
0.08101802319288254,
-0.0654970332980156,
-0.03627840057015419,
0.06863008439540863,
-0.04646052047610283,
0.18736295402050018,
-0.01457817293703556,
0.005829531233757734,
0.04182589054107666,
-0.06075819209218025,
0.03464069589972496,
0.0837518572807312,
0.06969655305147171,
-0.018329031765460968,
-0.09284061938524246,
-0.011901871301233768,
0.037634413689374924,
-0.1327676624059677,
-0.014088790863752365,
0.11150240898132324,
-0.16611020267009735,
-0.1355118304491043,
-0.016000080853700638,
0.06734225898981094,
-0.25116899609565735,
-0.06750673800706863,
-0.08892656117677689,
-0.09355836361646652,
0.054993148893117905,
0.21831321716308594,
0.11576122790575027,
0.010893070138990879,
0.005503517575562,
-0.023871585726737976,
-0.03300892934203148,
0.08558091521263123,
0.0020822661463171244,
-0.011124800890684128,
0.013577556237578392,
0.018796406686306,
-0.07350457459688187,
0.1027490645647049,
-0.08196820318698883,
0.037163566797971725,
-0.06415415555238724,
-0.0029358721803873777,
-0.046975765377283096,
0.00006047540227882564,
0.014582091011106968,
-0.07312946021556854,
-0.052933864295482635,
-0.08567553758621216,
-0.026648536324501038,
-0.04608519747853279,
-0.05429091677069664,
0.02249395102262497,
-0.008228529244661331,
-0.007014753296971321,
-0.008417696692049503,
-0.02655291184782982,
-0.0013763863826170564,
-0.00947472732514143,
0.08300625532865524,
0.1622706949710846,
0.038838353008031845,
0.1542319506406784,
-0.11739730834960938,
0.032200321555137634,
0.018237994983792305,
-0.01479143276810646,
0.033463262021541595,
0.1091144010424614,
-0.004254940897226334,
0.03193860501050949,
0.012655275873839855,
0.08736768364906311,
-0.029537569731473923,
-0.06095584109425545,
0.1197485625743866,
0.016881411895155907,
-0.08899011462926865,
0.024705950170755386,
0.009578349068760872,
0.09359622746706009,
0.0706225261092186,
0.2094084471464157,
-0.129205122590065,
0.022297268733382225,
0.09229348599910736,
0.07710513472557068,
-0.04711000621318817,
-0.12208305299282074,
-0.10252299904823303,
-0.03623950853943825,
0.021579114720225334,
-0.015082512982189655,
0.0569022074341774,
0.003999575041234493,
-0.0008288747048936784,
0.05031643062829971,
-0.022891541942954063,
-0.009025531820952892,
-0.058863963931798935,
0.1414547711610794,
0.1318659633398056,
0.04162180423736572,
-0.20828592777252197,
-0.005323770921677351,
-0.04662159085273743,
0.024032985791563988,
0.07848897576332092,
0.021431980654597282,
0.23577232658863068,
0.08530370891094208,
0.07548261433839798,
-0.029716933146119118,
0.11969847977161407,
-0.07923014461994171,
-0.08541236072778702,
0.012210467830300331,
0.012781520374119282,
0.11152845621109009,
0.21570543944835663,
-0.04424373805522919,
-0.06433746218681335,
0.024979794397950172,
-0.006294045597314835,
-0.06715545803308487,
-0.11296823620796204,
-0.061514049768447876,
-0.09811457991600037,
-0.01376098021864891,
-0.0233566053211689,
0.03505457937717438,
-0.03870616853237152,
0.07155535370111465,
-0.04703390225768089,
0.11313984543085098,
0.010917354375123978,
-0.11634623259305954,
0.18129539489746094,
-0.004032752010971308,
0.03218149021267891,
-0.09680747985839844,
-0.00806108396500349,
-0.043223753571510315,
0.07427778095006943,
0.006523486692458391,
0.02281554602086544,
0.044846002012491226,
-0.032564517110586166,
-0.0924278125166893,
-0.07612840086221695,
-0.03154929727315903,
0.07093069702386856,
0.10392629355192184,
0.1593555063009262,
0.0590226985514164,
-0.006698562763631344,
-0.012443494983017445,
0.11367915570735931,
0.03425677493214607,
-0.1168002337217331,
-0.003096719039604068,
0.019303513690829277,
0.004221753217279911,
0.10860451310873032,
-0.06937330961227417,
-0.025468694046139717,
0.004114754032343626,
0.24146820604801178,
0.2416193038225174,
0.044527243822813034,
0.06570116430521011,
-0.010837332345545292,
0.035691045224666595,
0.1485733538866043,
0.03387191519141197,
0.03502083197236061,
0.237283393740654,
-0.06555986404418945,
-0.1230083554983139,
-0.0892086997628212,
0.07664346694946289,
-0.16706249117851257,
-0.042062170803546906,
0.026878008618950844,
-0.0894429087638855,
-0.048067547380924225,
0.12727276980876923,
-0.09130658954381943,
0.01254566852003336,
-0.08698827028274536,
-0.037055451422929764,
-0.05654047429561615,
0.001833309535868466,
0.047594863921403885,
0.0438544787466526,
0.09906043112277985,
-0.060605861246585846,
0.031994085758924484,
-0.04311102256178856,
0.007280601654201746,
-0.022635528817772865,
-0.1741112470626831,
0.06876567006111145,
-0.008109920658171177,
0.05998886749148369,
-0.04733169078826904,
-0.03560246154665947,
0.09134329855442047,
-0.010173051618039608,
-0.06392446160316467,
0.10703673958778381,
-0.02340850792825222,
-0.07609539479017258,
0.07450087368488312,
-0.027075476944446564,
-0.032244808971881866,
-0.11158142238855362,
0.12719152867794037,
-0.06325743347406387,
0.09475774317979813,
-0.03965136781334877,
-0.02791409194469452,
-0.026336094364523888,
0.1773710995912552,
-0.065594382584095,
0.08485035598278046,
0.21761323511600494,
-0.030368873849511147,
-0.05101488530635834,
0.006681215018033981,
-0.03940756246447563,
0.07433660328388214,
-0.06143667548894882,
0.023674387484788895,
-0.05269123241305351,
-0.012028830125927925,
-0.14141274988651276,
-0.018265238031744957,
-0.14495235681533813,
0.030853938311338425,
-0.14559772610664368,
0.024506952613592148,
-0.051032520830631256,
-0.03537402302026749,
0.017883557826280594,
-0.051665619015693665,
0.024337979033589363,
-0.027656886726617813,
0.06585003435611725,
-0.02539905719459057,
-0.09668795764446259,
-0.09397675842046738
] |
null | null |
transformers
|
# FERNET-News_sk
FERNET-News_sk is a monolingual Slovak RoBERTa-base model pre-trained from 4.5GB of thoroughly cleaned Slovak news corpus.
It is a Slovak version of our Czech [FERNET-News](https://huggingface.co/fav-kky/FERNET-News) model.
Preprint of our paper is available at https://arxiv.org/abs/2107.10042.
|
{"language": "sk", "license": "cc-by-nc-sa-4.0", "tags": ["Slovak", "KKY", "FAV"]}
|
fill-mask
|
fav-kky/FERNET-News_sk
|
[
"transformers",
"pytorch",
"tf",
"roberta",
"fill-mask",
"Slovak",
"KKY",
"FAV",
"sk",
"arxiv:2107.10042",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2107.10042"
] |
[
"sk"
] |
TAGS
#transformers #pytorch #tf #roberta #fill-mask #Slovak #KKY #FAV #sk #arxiv-2107.10042 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# FERNET-News_sk
FERNET-News_sk is a monolingual Slovak RoBERTa-base model pre-trained from 4.5GB of thoroughly cleaned Slovak news corpus.
It is a Slovak version of our Czech FERNET-News model.
Preprint of our paper is available at URL
|
[
"# FERNET-News_sk\nFERNET-News_sk is a monolingual Slovak RoBERTa-base model pre-trained from 4.5GB of thoroughly cleaned Slovak news corpus.\n\nIt is a Slovak version of our Czech FERNET-News model.\n\nPreprint of our paper is available at URL"
] |
[
"TAGS\n#transformers #pytorch #tf #roberta #fill-mask #Slovak #KKY #FAV #sk #arxiv-2107.10042 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# FERNET-News_sk\nFERNET-News_sk is a monolingual Slovak RoBERTa-base model pre-trained from 4.5GB of thoroughly cleaned Slovak news corpus.\n\nIt is a Slovak version of our Czech FERNET-News model.\n\nPreprint of our paper is available at URL"
] |
[
73,
64
] |
[
"passage: TAGS\n#transformers #pytorch #tf #roberta #fill-mask #Slovak #KKY #FAV #sk #arxiv-2107.10042 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# FERNET-News_sk\nFERNET-News_sk is a monolingual Slovak RoBERTa-base model pre-trained from 4.5GB of thoroughly cleaned Slovak news corpus.\n\nIt is a Slovak version of our Czech FERNET-News model.\n\nPreprint of our paper is available at URL"
] |
[
-0.04115327447652817,
-0.09924004971981049,
-0.002679569413885474,
-0.035357024520635605,
0.08180879801511765,
0.019352277740836143,
0.1110849529504776,
0.04556041210889816,
0.031057029962539673,
0.046218711882829666,
0.14437071979045868,
0.09541811048984528,
-0.06721934676170349,
0.11627719551324844,
0.04702911525964737,
-0.1839950531721115,
0.1545095145702362,
-0.024520544335246086,
-0.04174519330263138,
0.056872524321079254,
0.10947230458259583,
-0.006155848503112793,
-0.008394208736717701,
0.020137052983045578,
-0.07986607402563095,
0.024911874905228615,
0.0436597503721714,
-0.06016959995031357,
0.10481305420398712,
0.023362619802355766,
-0.008719694800674915,
0.0638333186507225,
-0.006001335568726063,
-0.002927835565060377,
0.037201713770627975,
-0.024641525000333786,
-0.08517706394195557,
0.033548563718795776,
0.0063965278677642345,
0.004951507318764925,
0.2390575259923935,
-0.1421862542629242,
-0.1004844382405281,
-0.041275788098573685,
-0.0633063092827797,
-0.27569860219955444,
-0.029897423461079597,
0.09677866846323013,
0.05407091975212097,
0.1515556126832962,
-0.017522061243653297,
0.13453352451324463,
-0.03620576858520508,
0.08159909397363663,
-0.0016566921258345246,
-0.28316637873649597,
-0.07105185091495514,
0.031969454139471054,
-0.08459168672561646,
0.04258616268634796,
0.009246855042874813,
0.1549450010061264,
0.034388281404972076,
-0.02124825306236744,
-0.07296161353588104,
-0.07334514707326889,
-0.08490338176488876,
-0.04737645015120506,
-0.11677505075931549,
-0.013618214055895805,
0.003986491356045008,
-0.022603852674365044,
0.021692203357815742,
0.07168829441070557,
-0.0026370170526206493,
0.11790195852518082,
-0.06284870207309723,
0.1088515892624855,
-0.123292475938797,
0.006259462796151638,
-0.03609486296772957,
-0.08974489569664001,
-0.11797147244215012,
-0.04074275866150856,
-0.12511290609836578,
0.3450908362865448,
0.020238196477293968,
0.09612864255905151,
0.006389460992068052,
0.03836184740066528,
-0.08970007300376892,
-0.11873520910739899,
0.04979752004146576,
-0.028279708698391914,
0.07153891026973724,
0.05536128208041191,
-0.03835614398121834,
-0.06730610132217407,
0.07311117649078369,
0.05076238512992859,
-0.007767296861857176,
-0.05824423208832741,
0.05940254032611847,
0.09008610993623734,
-0.0356278270483017,
-0.0538456104695797,
-0.13582809269428253,
0.02219734713435173,
0.00501263327896595,
-0.136647030711174,
0.09251827001571655,
-0.04811180755496025,
-0.1448860913515091,
-0.07271972298622131,
-0.079095259308815,
0.0004322355380281806,
0.05826108157634735,
0.1349382996559143,
0.1402548998594284,
0.03645132854580879,
0.026893189176917076,
0.005807072855532169,
-0.06797060370445251,
-0.07044142484664917,
0.05739063397049904,
0.16240525245666504,
-0.08411386609077454,
0.01938469707965851,
-0.048205871134996414,
0.14398905634880066,
-0.07458675652742386,
-0.039578020572662354,
-0.01757027767598629,
-0.13479779660701752,
0.02980792336165905,
-0.13469359278678894,
0.01594104804098606,
-0.20811215043067932,
-0.08572755008935928,
-0.0453583262860775,
-0.03252768516540527,
-0.06870681047439575,
-0.044967301189899445,
0.010510263033211231,
-0.051031485199928284,
0.093789242208004,
0.008297454565763474,
0.07586538791656494,
-0.06168103218078613,
0.09983488917350769,
-0.13118281960487366,
0.07588628679513931,
-0.201711967587471,
-0.023737479001283646,
-0.09716776758432388,
-0.07349393516778946,
-0.11451012641191483,
-0.022090516984462738,
-0.1941191703081131,
0.11643039435148239,
-0.056204866617918015,
-0.007444590330123901,
-0.13345517218112946,
0.020464086905121803,
0.06013207510113716,
0.10141773521900177,
-0.07779446244239807,
-0.03997588902711868,
0.05576208978891373,
-0.16864988207817078,
0.06250599026679993,
0.10861995071172714,
0.03912152722477913,
0.10714498162269592,
0.07041628658771515,
0.16768555343151093,
0.2250022143125534,
-0.09176334738731384,
-0.012388275004923344,
0.06268709897994995,
-0.042481567710638046,
-0.10940594226121902,
0.06696881353855133,
-0.005684204399585724,
-0.06742323935031891,
0.05979654937982559,
-0.18884141743183136,
0.11860719323158264,
-0.02187548577785492,
0.019007142633199692,
0.02897721529006958,
-0.015946608036756516,
0.070262610912323,
-0.046306658536195755,
0.11401315778493881,
-0.07774054259061813,
-0.028635291382670403,
-0.08712984621524811,
0.0825217068195343,
0.02530299499630928,
0.06081821769475937,
-0.06905118376016617,
0.20253679156303406,
-0.011045370250940323,
0.09005215764045715,
-0.060608480125665665,
0.04387309402227402,
-0.03734366595745087,
0.02222197689116001,
0.023613302037119865,
0.07642354816198349,
0.007918325252830982,
-0.09696310013532639,
-0.036744922399520874,
0.044482216238975525,
-0.01484362781047821,
0.007553852163255215,
0.001136244973167777,
-0.1491185575723648,
0.10006488859653473,
-0.019935278221964836,
0.1461673527956009,
-0.0171598419547081,
-0.024316364899277687,
0.08917953073978424,
0.06551210582256317,
-0.09565282613039017,
0.08429113030433655,
-0.05210814252495766,
0.07636052370071411,
0.009956294670701027,
0.04618962109088898,
0.0879160687327385,
0.11991254985332489,
0.011258101090788841,
0.18561764061450958,
0.05974969640374184,
0.056119270622730255,
0.10466033220291138,
-0.16408436000347137,
-0.022327614948153496,
0.04617854580283165,
-0.012102649547159672,
0.03488384932279587,
-0.01944723166525364,
-0.07674098014831543,
-0.013475308194756508,
-0.027957964688539505,
0.026712562888860703,
0.003561737248674035,
0.024322709068655968,
0.07217098772525787,
-0.054190680384635925,
-0.01637876406311989,
0.09778136759996414,
0.07285928726196289,
-0.1477726399898529,
0.07360593974590302,
0.12252742797136307,
-0.07528945058584213,
0.2446572631597519,
-0.007229695096611977,
-0.06614425778388977,
-0.007561496924608946,
-0.0846715196967125,
-0.006709611974656582,
0.07401304692029953,
-0.10098806023597717,
0.01918274536728859,
0.06281709671020508,
-0.07063423097133636,
0.07391573488712311,
0.014387354254722595,
-0.06550199538469315,
-0.05815014988183975,
-0.04571899399161339,
-0.10847945511341095,
0.1586596518754959,
-0.1039929911494255,
0.05788521096110344,
0.023094123229384422,
-0.04003853350877762,
0.04169060289859772,
0.022848820313811302,
-0.05819080024957657,
0.05477242171764374,
-0.08529560267925262,
-0.16103911399841309,
-0.2654481530189514,
0.09066827595233917,
0.005110458470880985,
-0.02990036830306053,
0.06244010105729103,
-0.044446129351854324,
-0.06248444318771362,
0.007067135535180569,
-0.003410464385524392,
0.051194556057453156,
0.040430258959531784,
0.03066626749932766,
0.022346289828419685,
-0.0656975582242012,
-0.03691490367054939,
-0.02179291844367981,
-0.10312709212303162,
-0.014143387787044048,
0.08144474774599075,
-0.05390129238367081,
0.19512341916561127,
0.012311013415455818,
0.03916133567690849,
0.08026789873838425,
-0.02284531481564045,
0.13036811351776123,
-0.13702286779880524,
0.09076336771249771,
0.18072761595249176,
0.018521541729569435,
0.01412298996001482,
0.1321650892496109,
0.03318537399172783,
0.1138940081000328,
0.048790667206048965,
0.0039004995487630367,
-0.09759809821844101,
-0.10948453098535538,
-0.12718705832958221,
-0.048288360238075256,
-0.002443359699100256,
-0.07998786866664886,
0.026006197556853294,
0.05738815665245056,
0.07916358858346939,
-0.0891302153468132,
-0.11391034722328186,
0.002177204005420208,
0.05054709315299988,
0.3381120264530182,
0.004927197005599737,
0.10367972403764725,
-0.0941840261220932,
-0.04411543533205986,
0.09607616811990738,
-0.13288216292858124,
0.12949296832084656,
0.010837666690349579,
0.037153929471969604,
0.048359472304582596,
0.06667573750019073,
0.14332695305347443,
0.1270531564950943,
0.007886801846325397,
-0.04472861438989639,
0.007297334726899862,
-0.055808763951063156,
0.10726428776979446,
-0.024136703461408615,
-0.15831798315048218,
-0.13671563565731049,
0.04233653098344803,
-0.03570336475968361,
0.060649000108242035,
0.15414465963840485,
0.06636644154787064,
0.006139417178928852,
-0.07032918930053711,
-0.054667744785547256,
0.08223677426576614,
-0.04515041410923004,
-0.05687309056520462,
0.09101957082748413,
-0.04547084495425224,
0.05438355356454849,
0.05535135790705681,
0.036616791039705276,
0.051978569477796555,
0.09222587198019028,
0.0024270748253911734,
-0.13826033473014832,
-0.05579087510704994,
0.1278226524591446,
-0.1221320629119873,
0.30782896280288696,
0.013215133920311928,
0.041506607085466385,
-0.0230806153267622,
-0.08088900148868561,
0.04099797084927559,
0.09902822971343994,
0.06773260980844498,
0.05586300790309906,
-0.08226316422224045,
-0.12880542874336243,
-0.14402757585048676,
0.01750854402780533,
-0.01456827949732542,
-0.08610579371452332,
0.06277941167354584,
-0.038153063505887985,
-0.03367110714316368,
-0.007829468697309494,
0.1348947286605835,
-0.036312226206064224,
-0.07109212875366211,
0.09673987329006195,
0.14213095605373383,
-0.08678998798131943,
-0.04056822881102562,
-0.14443762600421906,
-0.12024292349815369,
-0.0380423329770565,
-0.07257726788520813,
-0.1158197671175003,
-0.14081765711307526,
0.0747450664639473,
0.019167480990290642,
-0.13499104976654053,
0.019420292228460312,
0.0283860694617033,
0.0879954919219017,
-0.02649872563779354,
-0.09450513124465942,
-0.017081834375858307,
-0.06552677601575851,
-0.06941045075654984,
0.07087887823581696,
0.059998925775289536,
0.04174656793475151,
0.007302547805011272,
0.027834879234433174,
0.06513460725545883,
0.013170426711440086,
-0.1459273248910904,
0.015613197349011898,
-0.0996173694729805,
-0.1376464068889618,
-0.031249623745679855,
-0.11443372070789337,
-0.04762716218829155,
-0.05872161686420441,
-0.11155277490615845,
0.1303585171699524,
0.15143916010856628,
-0.11058147996664047,
-0.0070963590405881405,
0.21285435557365417,
0.005263032857328653,
-0.3967377543449402,
-0.09052208065986633,
0.018403323367238045,
-0.017953822389245033,
-0.09172139316797256,
-0.04568309336900711,
0.0894927829504013,
0.12566472589969635,
-0.052763231098651886,
0.02200058475136757,
-0.13358727097511292,
-0.07397190481424332,
0.19300466775894165,
-0.10861971229314804,
0.48433414101600647,
-0.06046682596206665,
-0.06842741370201111,
-0.0060136448591947556,
-0.022714363411068916,
0.04102744534611702,
-0.28308847546577454,
0.013374864123761654,
-0.007405982818454504,
0.00422575743868947,
0.011174316518008709,
-0.012101777829229832,
0.07347890734672546,
0.07609432935714722,
-0.006557515822350979,
-0.1155010536313057,
-0.028393102809786797,
0.11618991941213608,
0.035092439502477646,
0.011714673601090908,
0.03402050957083702,
0.007917487993836403,
-0.07342630624771118,
-0.03438235819339752,
-0.1120760589838028,
0.07769616693258286,
-0.012392861768603325,
-0.02567395009100437,
-0.0157617274671793,
0.06049524247646332,
-0.08881448209285736,
-0.023863188922405243,
0.12653754651546478,
-0.09651145339012146,
0.08474323153495789,
0.046288520097732544,
-0.04788286238908768,
-0.1123749315738678,
0.06357882171869278,
0.015331795439124107,
-0.11245305091142654,
0.04448675364255905,
0.1257297396659851,
-0.049257129430770874,
0.07133664935827255,
0.02517257072031498,
-0.07392573356628418,
0.029075797647237778,
-0.06633997708559036,
0.004536673426628113,
0.08626821637153625,
-0.20682108402252197,
0.061287082731723785,
-0.05542668327689171,
-0.025211375206708908,
0.06669237464666367,
-0.019357062876224518,
0.19843249022960663,
-0.013209770433604717,
0.03510028496384621,
0.025656485930085182,
-0.07316672056913376,
0.03183566406369209,
0.12795767188072205,
0.12289856374263763,
-0.006739538162946701,
-0.08859901875257492,
-0.03995237126946449,
0.030367590487003326,
0.006657998543232679,
-0.03350461646914482,
0.08184032887220383,
-0.1916203498840332,
-0.13696295022964478,
-0.046046867966651917,
0.050250064581632614,
-0.22705575823783875,
-0.09318526089191437,
-0.07397343218326569,
-0.12919992208480835,
0.04600757732987404,
0.2027549296617508,
0.09846387803554535,
-0.009357933886349201,
-0.012741552665829659,
-0.026261573657393456,
-0.0004501879739109427,
0.08496169745922089,
0.029938582330942154,
-0.007257400546222925,
-0.017283828929066658,
0.07788442075252533,
-0.07415954768657684,
0.07742498815059662,
-0.10038871318101883,
0.00824691355228424,
-0.04470270499587059,
0.005506610032171011,
-0.012451710179448128,
-0.00651875976473093,
-0.02029063180088997,
-0.07435578107833862,
-0.03647845238447189,
-0.12166602164506912,
-0.04241563007235527,
-0.03297269344329834,
-0.043904077261686325,
0.022340573370456696,
-0.008518248796463013,
0.011334882117807865,
-0.02333482727408409,
-0.0397837795317173,
0.009530551731586456,
0.003056124085560441,
0.06684235483407974,
0.13701072335243225,
0.04219977557659149,
0.13659480214118958,
-0.06882500648498535,
0.015564563684165478,
0.017828714102506638,
0.004292414523661137,
0.010346789844334126,
0.12037504464387894,
0.00966219324618578,
0.0263996422290802,
0.01795533113181591,
0.08623111993074417,
-0.06050950661301613,
-0.04887489229440689,
0.14170938730239868,
0.010675729252398014,
-0.06126505881547928,
0.014181197620928288,
0.014119815081357956,
0.12117645889520645,
0.1280631422996521,
0.17894840240478516,
-0.1323312222957611,
-0.009251156821846962,
0.054700180888175964,
0.09413010627031326,
-0.03568265587091446,
-0.09057113528251648,
-0.02482934668660164,
-0.0502597838640213,
0.01639738120138645,
-0.005130440462380648,
0.04839635640382767,
0.050124641507864,
-0.033479928970336914,
0.046418964862823486,
0.011845691129565239,
-0.003517423290759325,
-0.047064319252967834,
0.09635630995035172,
0.15719254314899445,
0.05338099226355553,
-0.2030171900987625,
-0.014347769320011139,
-0.014042139053344727,
-0.004546777345240116,
0.11626297980546951,
0.017389610409736633,
0.2573956549167633,
0.07913821190595627,
0.12829028069972992,
-0.00622142618522048,
0.11334925889968872,
-0.04306710138916969,
-0.09544595330953598,
0.011806913651525974,
-0.026994220912456512,
0.1229303628206253,
0.216302752494812,
-0.07769376039505005,
-0.05452960357069969,
0.07801003754138947,
0.003165832255035639,
-0.08358677476644516,
-0.1781972199678421,
-0.06285926699638367,
-0.14212653040885925,
-0.012455376796424389,
-0.02872510254383087,
-0.008406409062445164,
0.003409882076084614,
0.07110771536827087,
-0.03858978673815727,
0.06599926203489304,
-0.027701156213879585,
-0.08291849493980408,
0.19324451684951782,
-0.03742900490760803,
0.027604293078184128,
-0.13747183978557587,
-0.012105769477784634,
-0.02203022874891758,
0.1196136474609375,
-0.015826037153601646,
0.02234233357012272,
0.06081661581993103,
-0.03851265832781792,
-0.0876629501581192,
-0.06704447418451309,
-0.046608373522758484,
0.031211860477924347,
0.10704129934310913,
0.10001417994499207,
0.05795099958777428,
-0.019450660794973373,
-0.01144937239587307,
0.10065538436174393,
0.04823818430304527,
-0.18207910656929016,
-0.016516758129000664,
-0.11091326922178268,
0.034509893506765366,
0.11232683807611465,
-0.058896470814943314,
-0.043830592185258865,
-0.038999248296022415,
0.26256847381591797,
0.23562097549438477,
0.0825122594833374,
0.06235319375991821,
-0.0037787100300192833,
0.020056581124663353,
0.12019411474466324,
0.08374497294425964,
0.029048260301351547,
0.234523743391037,
-0.038224924355745316,
-0.09437405318021774,
-0.07953005284070969,
0.047726068645715714,
-0.1950177401304245,
-0.018750226125121117,
0.02167559787631035,
-0.0779714286327362,
-0.056542541831731796,
0.151081383228302,
-0.11752788722515106,
-0.002890173811465502,
-0.11832116544246674,
-0.013625500723719597,
-0.03722669556736946,
-0.02469702996313572,
-0.004208508413285017,
0.028217362239956856,
0.08807846158742905,
-0.0725020095705986,
0.05537009984254837,
-0.061882384121418,
0.01065497100353241,
0.02569119818508625,
-0.18173278868198395,
0.09407608956098557,
0.041714366525411606,
0.05838513374328613,
-0.07104525715112686,
-0.018238220363855362,
0.10345859080553055,
-0.02472371980547905,
-0.0630434975028038,
0.10716415196657181,
-0.038457196205854416,
-0.08451780676841736,
0.04443567618727684,
-0.07860612869262695,
-0.025767654180526733,
-0.10480766743421555,
0.10917096585035324,
-0.09373831003904343,
0.07053078711032867,
-0.046904366463422775,
-0.033154021948575974,
-0.05456214398145676,
0.14231321215629578,
-0.09253056347370148,
0.07685169577598572,
0.13616852462291718,
-0.036254215985536575,
-0.05192708969116211,
0.0073446789756417274,
-0.05646314099431038,
0.04087398201227188,
-0.058428797870874405,
0.028180047869682312,
-0.06222217530012131,
-0.005853742826730013,
-0.19578228890895844,
0.004620547406375408,
-0.13587437570095062,
0.047259747982025146,
-0.14324800670146942,
0.039787471294403076,
0.013910090550780296,
-0.03670960292220116,
0.021512718871235847,
-0.053233660757541656,
0.018769513815641403,
-0.08107412606477737,
0.04080751910805702,
0.0036045117303729057,
-0.10252979397773743,
-0.07153387367725372
] |
null | null |
transformers
|
## Proc-RoBERTa
Proc-RoBERTa is a pre-trained language model for procedural text. It was built by fine-tuning the RoBERTa-based model on a procedural corpus (PubMed articles/chemical patents/cooking recipes), which contains 1.05B tokens. More details can be found in the following [paper](https://arxiv.org/abs/2109.04711):
```
@inproceedings{bai-etal-2021-pre,
title = "Pre-train or Annotate? Domain Adaptation with a Constrained Budget",
author = "Bai, Fan and
Ritter, Alan and
Xu, Wei",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
}
```
## Usage
```
from transformers import *
tokenizer = AutoTokenizer.from_pretrained("fbaigt/proc_roberta")
model = AutoModelForTokenClassification.from_pretrained("fbaigt/proc_roberta")
```
More usage details can be found [here](https://github.com/bflashcp3f/ProcBERT).
|
{"language": ["en"], "datasets": ["pubmed", "chemical patent", "cooking recipe"]}
|
feature-extraction
|
fbaigt/proc_roberta
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"en",
"arxiv:2109.04711",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2109.04711"
] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #feature-extraction #en #arxiv-2109.04711 #endpoints_compatible #region-us
|
## Proc-RoBERTa
Proc-RoBERTa is a pre-trained language model for procedural text. It was built by fine-tuning the RoBERTa-based model on a procedural corpus (PubMed articles/chemical patents/cooking recipes), which contains 1.05B tokens. More details can be found in the following paper:
## Usage
More usage details can be found here.
|
[
"## Proc-RoBERTa\nProc-RoBERTa is a pre-trained language model for procedural text. It was built by fine-tuning the RoBERTa-based model on a procedural corpus (PubMed articles/chemical patents/cooking recipes), which contains 1.05B tokens. More details can be found in the following paper:",
"## Usage\n\n\nMore usage details can be found here."
] |
[
"TAGS\n#transformers #pytorch #roberta #feature-extraction #en #arxiv-2109.04711 #endpoints_compatible #region-us \n",
"## Proc-RoBERTa\nProc-RoBERTa is a pre-trained language model for procedural text. It was built by fine-tuning the RoBERTa-based model on a procedural corpus (PubMed articles/chemical patents/cooking recipes), which contains 1.05B tokens. More details can be found in the following paper:",
"## Usage\n\n\nMore usage details can be found here."
] |
[
41,
79,
11
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #feature-extraction #en #arxiv-2109.04711 #endpoints_compatible #region-us \n## Proc-RoBERTa\nProc-RoBERTa is a pre-trained language model for procedural text. It was built by fine-tuning the RoBERTa-based model on a procedural corpus (PubMed articles/chemical patents/cooking recipes), which contains 1.05B tokens. More details can be found in the following paper:## Usage\n\n\nMore usage details can be found here."
] |
[
0.04305489361286163,
-0.051469553261995316,
-0.0014334459556266665,
-0.011421282775700092,
0.06703007966279984,
-0.046355824917554855,
0.019881488755345345,
0.05875914916396141,
-0.09452122449874878,
0.04523053392767906,
0.17688363790512085,
0.1660536229610443,
-0.05219467356801033,
0.12561030685901642,
0.015987694263458252,
-0.27327123284339905,
0.032913122326135635,
0.05117703601717949,
0.12283935397863388,
0.12904852628707886,
0.12393215298652649,
-0.04895453527569771,
0.0942172110080719,
0.037544433027505875,
-0.04918897897005081,
0.032899998128414154,
0.07878776639699936,
-0.13875913619995117,
0.08163146674633026,
0.03137236088514328,
0.04999538138508797,
0.02524905651807785,
-0.014454403892159462,
-0.16866962611675262,
0.029779834672808647,
-0.015642421320080757,
-0.004490834195166826,
0.040477853268384933,
0.0023171110078692436,
-0.07893554121255875,
0.22045768797397614,
0.036477863788604736,
0.03716106340289116,
0.008774161338806152,
-0.17604181170463562,
-0.06319739669561386,
-0.009322628378868103,
0.03508667275309563,
-0.029468322172760963,
0.08380597084760666,
0.0005446654977276921,
0.12412714958190918,
-0.09108634293079376,
0.06500472873449326,
0.16473476588726044,
-0.1867627501487732,
-0.09580282866954803,
-0.06949632614850998,
0.14609669148921967,
0.08111534267663956,
0.11313872039318085,
0.009421431459486485,
0.0634232759475708,
0.016113489866256714,
-0.06509687751531601,
-0.13903820514678955,
-0.13648007810115814,
-0.050816237926483154,
-0.11063539981842041,
-0.05068676173686981,
0.23943576216697693,
-0.0921272486448288,
-0.0787845253944397,
0.04738542437553406,
-0.013683773577213287,
-0.05712904781103134,
-0.020151279866695404,
-0.03821577504277229,
-0.03293471783399582,
-0.019960451871156693,
-0.04795296862721443,
-0.030880939215421677,
-0.05678354203701019,
-0.07425080239772797,
-0.07742996513843536,
0.2713688910007477,
0.01994211971759796,
0.037918571382761,
-0.1741185486316681,
0.008120100945234299,
-0.11330252885818481,
-0.04887127876281738,
-0.02192554622888565,
-0.06337036937475204,
0.13788537681102753,
-0.012823499739170074,
-0.0452459454536438,
-0.0044354465790092945,
0.04037768393754959,
0.31128016114234924,
0.09851094335317612,
0.03931267559528351,
-0.002723779296502471,
0.04513610526919365,
0.05578694865107536,
0.11182712018489838,
0.02288365736603737,
-0.016261771321296692,
0.012017808854579926,
-0.0842909887433052,
-0.02352149598300457,
-0.022336499765515327,
-0.0944470763206482,
-0.05729874223470688,
0.0571066290140152,
0.013289110735058784,
0.03389288857579231,
0.022550225257873535,
-0.030022649094462395,
0.023873597383499146,
0.03927084803581238,
-0.06786144524812698,
-0.06987026333808899,
-0.060276832431554794,
0.034254129976034164,
0.08803462237119675,
-0.005534240044653416,
-0.00845049973577261,
-0.0595698282122612,
0.04104653373360634,
-0.08835787326097488,
-0.08785146474838257,
-0.058558832854032516,
-0.027656950056552887,
-0.04292474314570427,
-0.12406127899885178,
0.056594256311655045,
-0.17475438117980957,
-0.0061616795137524605,
-0.07152106612920761,
0.05656619742512703,
-0.07339387387037277,
-0.006194619461894035,
-0.004776806104928255,
0.09585241228342056,
-0.030579952523112297,
-0.06101207062602043,
0.06401747465133667,
-0.049968037754297256,
0.09458452463150024,
0.037134379148483276,
0.07982441782951355,
-0.13461458683013916,
0.038491543382406235,
-0.12107275426387787,
0.027037912979722023,
-0.21615013480186462,
-0.03512772172689438,
-0.0044332947582006454,
0.04145833104848862,
-0.10999660193920135,
-0.08470037579536438,
-0.10529232770204544,
0.05695277824997902,
0.0012465047184377909,
0.023668134585022926,
-0.019084466621279716,
-0.07343034446239471,
0.2212473303079605,
-0.11872006952762604,
-0.022212287411093712,
0.0701896995306015,
-0.012673639692366123,
0.21379458904266357,
0.08267989754676819,
0.18198099732398987,
0.09406685084104538,
-0.027154870331287384,
0.12882563471794128,
0.053014982491731644,
0.02527480013668537,
-0.10459421575069427,
0.03624577447772026,
-0.04993139207363129,
-0.2010975331068039,
0.04405060410499573,
-0.09350893646478653,
0.006831641774624586,
-0.045924440026283264,
-0.009421763941645622,
0.0485265851020813,
-0.018632346764206886,
-0.06978470087051392,
-0.03370872884988785,
0.03816172480583191,
-0.0408736951649189,
-0.011530379764735699,
-0.09665632992982864,
0.011508626863360405,
-0.014789309352636337,
0.056826427578926086,
-0.05963882803916931,
0.1748456060886383,
0.04857507720589638,
-0.03300335630774498,
-0.24658113718032837,
0.16414254903793335,
-0.019285568967461586,
0.11663364619016647,
0.08655495196580887,
0.00950372964143753,
0.024612603709101677,
-0.038776054978370667,
0.0756998360157013,
0.0688081830739975,
0.1121135875582695,
0.003994845785200596,
-0.014125334098935127,
-0.10035669803619385,
0.04677700251340866,
-0.07508647441864014,
-0.04903562366962433,
0.0794345885515213,
-0.056519366800785065,
0.01973927579820156,
-0.019473956897854805,
0.014697408303618431,
-0.007203545421361923,
-0.03134722635149956,
0.10768689215183258,
-0.018085341900587082,
0.08970264345407486,
0.059707850217819214,
-0.02643500454723835,
-0.004806273151189089,
0.06781147420406342,
-0.023787913843989372,
0.13178861141204834,
0.1139066219329834,
-0.08672112226486206,
-0.08916221559047699,
-0.05100693181157112,
-0.02821779064834118,
0.05621747300028801,
0.0048468224704265594,
0.011821328662335873,
0.23615621030330658,
-0.033920127898454666,
0.06787694990634918,
-0.059003859758377075,
0.044679734855890274,
0.0674893856048584,
-0.12124249339103699,
-0.01545675564557314,
0.10694358497858047,
0.08678999543190002,
-0.1265580654144287,
0.02617402747273445,
0.0766308456659317,
0.025471042841672897,
0.10554869472980499,
0.002442713128402829,
-0.04090164974331856,
-0.016040243208408356,
0.028228923678398132,
0.03177427500486374,
0.039052098989486694,
-0.20668427646160126,
-0.0696653202176094,
0.027171891182661057,
-0.027678435668349266,
0.043547485023736954,
-0.10753825306892395,
-0.006121697835624218,
0.024738499894738197,
0.014693559147417545,
-0.04878043010830879,
0.0034372126683592796,
-0.054549410939216614,
0.08636008203029633,
0.09153617918491364,
-0.0967441275715828,
-0.0210498608648777,
0.010751981288194656,
-0.03931793197989464,
0.22448065876960754,
-0.05626947805285454,
-0.006801928859204054,
-0.173897847533226,
-0.2433440238237381,
0.1066371351480484,
0.04026585444808006,
0.04409552365541458,
-0.09508088231086731,
-0.05520102381706238,
0.02132340706884861,
0.051306791603565216,
-0.0997297465801239,
0.0008865692652761936,
-0.0648844763636589,
0.04559842869639397,
-0.031055597588419914,
-0.0751713290810585,
-0.054017774760723114,
-0.06274232268333435,
-0.010836761444807053,
0.08774266391992569,
-0.12188105285167694,
0.16170795261859894,
0.07599709928035736,
0.016879407688975334,
0.037919558584690094,
-0.02874745987355709,
0.21191826462745667,
-0.0344427265226841,
-0.09622772783041,
0.16273415088653564,
-0.08791972696781158,
0.02404329553246498,
0.03709566965699196,
0.0011138067347928882,
-0.07190649211406708,
0.015536121092736721,
-0.10358518362045288,
-0.09988625347614288,
-0.17100447416305542,
-0.06752784550189972,
-0.03739137575030327,
0.02145223505795002,
0.11826453357934952,
0.005322616547346115,
-0.01529629249125719,
0.08967739343643188,
0.08521763235330582,
0.07731016725301743,
0.012397203594446182,
0.03210728242993355,
0.139551043510437,
-0.060356587171554565,
0.07837431132793427,
-0.027697943150997162,
-0.09679629653692245,
0.060230135917663574,
-0.020034044981002808,
0.2030094861984253,
0.13683845102787018,
0.0962567925453186,
0.06968161463737488,
0.055389512330293655,
0.10410653799772263,
0.12543350458145142,
-0.03710611164569855,
0.05416237935423851,
-0.08671918511390686,
-0.04894381761550903,
-0.13797049224376678,
0.022797422483563423,
-0.119822658598423,
-0.1351151019334793,
-0.04457494243979454,
-0.04479183256626129,
-0.017640411853790283,
0.09696362167596817,
-0.003712915349751711,
-0.17614099383354187,
0.015599052421748638,
0.04554116725921631,
-0.02428111620247364,
-0.059559233486652374,
0.1266227662563324,
-0.06722036749124527,
-0.07973045110702515,
0.0022902311757206917,
-0.012686196714639664,
0.09392445534467697,
-0.10243351012468338,
0.07363107055425644,
-0.05748661607503891,
-0.0269018467515707,
0.011344080790877342,
0.09185367822647095,
-0.25119414925575256,
0.2959195375442505,
-0.009908013045787811,
-0.027018139138817787,
-0.08703630417585373,
-0.020485082641243935,
-0.08513431251049042,
0.03430021181702614,
0.17620262503623962,
-0.008533271960914135,
-0.22135968506336212,
-0.15349769592285156,
-0.10142145305871964,
0.06146537512540817,
0.13138434290885925,
0.013145753182470798,
0.07334033399820328,
-0.06925272196531296,
0.03113791160285473,
-0.037098366767168045,
-0.1057654321193695,
-0.0312021654099226,
-0.14112626016139984,
0.005344408564269543,
-0.09098824858665466,
0.010158848017454147,
-0.021534185856580734,
0.02018008939921856,
0.16362012922763824,
0.01580166257917881,
-0.10006563365459442,
-0.022526944056153297,
-0.0720105916261673,
-0.04614066705107689,
0.07165616750717163,
-0.10070909559726715,
0.02166719362139702,
-0.045899875462055206,
-0.07242098450660706,
-0.047263722866773605,
-0.09600556641817093,
0.06640032678842545,
-0.04863503575325012,
-0.016192253679037094,
-0.030760543420910835,
-0.003526251297444105,
0.05809706449508667,
0.035145584493875504,
0.032436273992061615,
0.052711647003889084,
-0.11950060725212097,
-0.08235691487789154,
-0.06893670558929443,
-0.06057122349739075,
0.026901060715317726,
-0.00972156785428524,
-0.11199824512004852,
-0.002278213854879141,
-0.07573218643665314,
0.008637318387627602,
0.13519003987312317,
0.05984489992260933,
-0.004099952057003975,
0.06714864820241928,
0.20380249619483948,
-0.0496348962187767,
-0.25819942355155945,
-0.09745592623949051,
-0.04763667657971382,
0.004123905207961798,
-0.046429798007011414,
-0.13638530671596527,
0.10671108961105347,
0.10317176580429077,
0.0013830194948241115,
-0.025267343968153,
-0.1854933500289917,
-0.09811612963676453,
0.19688524305820465,
0.08684983104467392,
0.4358683228492737,
-0.09190911054611206,
-0.062421418726444244,
-0.007207163609564304,
-0.12422628700733185,
0.042686283588409424,
-0.09933855384588242,
0.04467049986124039,
-0.03838792443275452,
0.001436964375898242,
-0.005752003286033869,
-0.035921625792980194,
0.049312859773635864,
0.0032667587511241436,
-0.01094694435596466,
-0.04483197629451752,
-0.13919231295585632,
0.033819954842329025,
0.06284487992525101,
-0.02898082695901394,
0.056425340473651886,
0.025308268144726753,
-0.10919907689094543,
-0.08647535741329193,
-0.07030128687620163,
0.0182621031999588,
-0.004633414559066296,
-0.06478988379240036,
0.0032418023329228163,
0.02589908242225647,
-0.1040172427892685,
-0.024171214550733566,
0.15174663066864014,
-0.09806162118911743,
0.1773187220096588,
0.11785843968391418,
0.15649007260799408,
-0.02461579442024231,
-0.029298406094312668,
0.07595754414796829,
-0.06743587553501129,
0.07818430662155151,
-0.002245227573439479,
-0.06538980454206467,
0.03541412204504013,
0.0066159931011497974,
0.016979476436972618,
0.04278706759214401,
-0.04883068799972534,
0.028807248920202255,
0.11803334951400757,
-0.14236366748809814,
-0.04011492431163788,
0.01588389091193676,
-0.09269966185092926,
-0.0029883626848459244,
-0.0731816217303276,
0.11601551622152328,
-0.039618439972400665,
-0.0587463453412056,
-0.026825066655874252,
-0.015948904678225517,
-0.10001438856124878,
0.12980401515960693,
0.11808304488658905,
0.019398195669054985,
-0.06614918261766434,
-0.04848039150238037,
0.08923181146383286,
-0.07738015055656433,
0.0025216317735612392,
-0.006616737227886915,
-0.07231511175632477,
-0.06338320672512054,
-0.024087676778435707,
0.07231508195400238,
-0.1737181842327118,
-0.03470435366034508,
-0.08591734617948532,
-0.1112116351723671,
0.07731480896472931,
0.14181913435459137,
0.07229351997375488,
-0.010991341434419155,
-0.05414271727204323,
-0.0034181480295956135,
0.028422951698303223,
-0.03743986040353775,
-0.021627327427268028,
-0.008677151054143906,
0.035797636955976486,
0.06703142076730728,
-0.02516520768404007,
0.07393643260002136,
-0.049901071935892105,
-0.042115118354558945,
-0.2025356888771057,
0.06497523188591003,
-0.09971863031387329,
0.020120343193411827,
-0.1218104362487793,
-0.033428553491830826,
-0.02773062139749527,
0.016399899497628212,
-0.037047356367111206,
-0.0343644917011261,
-0.07252147793769836,
0.05110007897019386,
-0.03833629935979843,
0.007533240132033825,
-0.04112815856933594,
0.034119732677936554,
0.10866878926753998,
-0.00540805421769619,
-0.010577571578323841,
0.058340854942798615,
-0.04149342328310013,
0.049522846937179565,
0.007544285152107477,
-0.0161384716629982,
0.05102789029479027,
0.011309409514069557,
0.027561331167817116,
-0.03817408159375191,
0.030922051519155502,
0.01360220555216074,
0.05328936502337456,
0.03112073428928852,
0.04506989195942879,
-0.11709509789943695,
0.14921757578849792,
0.05842689424753189,
-0.14358380436897278,
-0.03313281759619713,
-0.0011883318657055497,
0.07160922139883041,
0.04323901981115341,
0.05090571939945221,
-0.05597595125436783,
0.029700538143515587,
0.01253619883209467,
0.04482903704047203,
-0.009857986122369766,
-0.15411090850830078,
-0.11932280659675598,
-0.1378404051065445,
-0.011300364509224892,
0.03438461199402809,
0.25478997826576233,
0.07496427744626999,
0.06403906643390656,
0.051753539592027664,
-0.016498969867825508,
0.020815221592783928,
-0.029156215488910675,
0.09296350181102753,
0.12709133327007294,
-0.004777317401021719,
-0.08922310173511505,
0.038233350962400436,
-0.014894727617502213,
0.048777807503938675,
0.2105644941329956,
0.14213059842586517,
0.13168452680110931,
0.0521431602537632,
0.016929998993873596,
0.055849846452474594,
-0.01149806845933199,
-0.11505865305662155,
0.057027511298656464,
0.03264159336686134,
0.00377687462605536,
0.001572958193719387,
0.14575506746768951,
-0.07262071967124939,
0.010466262698173523,
0.04732491448521614,
-0.023010455071926117,
-0.2234104722738266,
-0.10273641347885132,
-0.05130559578537941,
-0.03383610397577286,
-0.02211686410009861,
-0.09637150168418884,
-0.007153159473091364,
-0.05817652866244316,
-0.037691324949264526,
0.027573302388191223,
0.07247618585824966,
-0.017479997128248215,
-0.09370444715023041,
0.07617084681987762,
-0.009349547326564789,
0.05250170826911926,
-0.1119663268327713,
-0.018852822482585907,
-0.09270919859409332,
-0.0678229108452797,
0.013472769409418106,
0.0316397063434124,
-0.06803024560213089,
-0.014353347942233086,
-0.07990007847547531,
-0.039209943264722824,
-0.06304706633090973,
0.07527775317430496,
0.050964295864105225,
0.10810758173465729,
-0.0012487180065363646,
-0.08979305624961853,
0.03830068185925484,
0.17434102296829224,
-0.0209348127245903,
-0.09879828244447708,
-0.03928016871213913,
0.338951051235199,
0.05859776958823204,
0.057958509773015976,
-0.019911479204893112,
0.036988675594329834,
0.011235012672841549,
0.3990013301372528,
0.2359294891357422,
0.038604315370321274,
0.02738865278661251,
0.03165023773908615,
0.0500120148062706,
0.13715095818042755,
0.16218264400959015,
0.051565974950790405,
0.33051615953445435,
-0.0493471622467041,
-0.08696514368057251,
-0.007300468627363443,
0.034608371555805206,
-0.09501203894615173,
-0.0450509674847126,
0.13371577858924866,
-0.09072522073984146,
-0.06407136470079422,
0.11708080768585205,
-0.17897747457027435,
0.016246967017650604,
0.009967967867851257,
-0.12578372657299042,
-0.11607962846755981,
-0.0762157067656517,
0.028574543073773384,
0.03346642851829529,
0.14865005016326904,
-0.03359357267618179,
-0.07965031266212463,
-0.003949650563299656,
0.07494308054447174,
-0.20558404922485352,
-0.05649467185139656,
0.14774926006793976,
0.22205302119255066,
-0.0042329574935138226,
-0.016370458528399467,
0.01798820309340954,
0.06849724054336548,
0.022486841306090355,
-0.03235947713255882,
0.07652828842401505,
0.003501680213958025,
0.00775346253067255,
0.026280030608177185,
-0.061408303678035736,
-0.03546629101037979,
-0.034565601497888565,
0.029597043991088867,
-0.12224295735359192,
0.07620464265346527,
0.09274788200855255,
0.022137043997645378,
-0.0677671954035759,
0.0018124481430277228,
-0.1184288039803505,
0.1234482154250145,
0.16824008524417877,
0.0011574475793167949,
-0.012531650252640247,
-0.029519781470298767,
0.04507782682776451,
0.008414112031459808,
0.026008279994130135,
-0.11392094939947128,
-0.07306090742349625,
-0.16529487073421478,
-0.01755824126303196,
-0.05922446399927139,
-0.28419554233551025,
0.022003507241606712,
-0.03897242620587349,
0.04744414612650871,
-0.09072238206863403,
0.06023724749684334,
0.028636479750275612,
0.036296240985393524,
0.010462554171681404,
0.06753872334957123,
0.027294844388961792,
0.0647580549120903,
-0.1401074081659317,
-0.040518805384635925
] |
null | null |
transformers
|
## ProcBERT
ProcBERT is a pre-trained language model specifically for procedural text. It was pre-trained on a large-scale procedural corpus (PubMed articles/chemical patents/cooking recipes) containing over 12B tokens and shows great performance on downstream tasks. More details can be found in the following [paper](https://arxiv.org/abs/2109.04711):
```
@inproceedings{bai-etal-2021-pre,
title = "Pre-train or Annotate? Domain Adaptation with a Constrained Budget",
author = "Bai, Fan and
Ritter, Alan and
Xu, Wei",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
}
```
## Usage
```
from transformers import *
tokenizer = AutoTokenizer.from_pretrained("fbaigt/procbert")
model = AutoModelForTokenClassification.from_pretrained("fbaigt/procbert")
```
More usage details can be found [here](https://github.com/bflashcp3f/ProcBERT).
|
{"language": ["en"], "datasets": ["pubmed", "chemical patent", "cooking recipe"]}
|
feature-extraction
|
fbaigt/procbert
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"en",
"arxiv:2109.04711",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2109.04711"
] |
[
"en"
] |
TAGS
#transformers #pytorch #bert #feature-extraction #en #arxiv-2109.04711 #endpoints_compatible #region-us
|
## ProcBERT
ProcBERT is a pre-trained language model specifically for procedural text. It was pre-trained on a large-scale procedural corpus (PubMed articles/chemical patents/cooking recipes) containing over 12B tokens and shows great performance on downstream tasks. More details can be found in the following paper:
## Usage
More usage details can be found here.
|
[
"## ProcBERT\nProcBERT is a pre-trained language model specifically for procedural text. It was pre-trained on a large-scale procedural corpus (PubMed articles/chemical patents/cooking recipes) containing over 12B tokens and shows great performance on downstream tasks. More details can be found in the following paper:",
"## Usage\n\n\nMore usage details can be found here."
] |
[
"TAGS\n#transformers #pytorch #bert #feature-extraction #en #arxiv-2109.04711 #endpoints_compatible #region-us \n",
"## ProcBERT\nProcBERT is a pre-trained language model specifically for procedural text. It was pre-trained on a large-scale procedural corpus (PubMed articles/chemical patents/cooking recipes) containing over 12B tokens and shows great performance on downstream tasks. More details can be found in the following paper:",
"## Usage\n\n\nMore usage details can be found here."
] |
[
40,
79,
11
] |
[
"passage: TAGS\n#transformers #pytorch #bert #feature-extraction #en #arxiv-2109.04711 #endpoints_compatible #region-us \n## ProcBERT\nProcBERT is a pre-trained language model specifically for procedural text. It was pre-trained on a large-scale procedural corpus (PubMed articles/chemical patents/cooking recipes) containing over 12B tokens and shows great performance on downstream tasks. More details can be found in the following paper:## Usage\n\n\nMore usage details can be found here."
] |
[
-0.030100692063570023,
-0.02689710631966591,
-0.00011925531725864857,
-0.013133513741195202,
0.05601958930492401,
-0.042996492236852646,
0.014973573386669159,
0.045144423842430115,
-0.138955757021904,
0.04533974826335907,
0.1698436737060547,
0.048245180398225784,
-0.05645215883851051,
0.1770380735397339,
0.031627751886844635,
-0.24980677664279938,
0.044734977185726166,
0.08360649645328522,
0.12273579090833664,
0.10551842302083969,
0.10405132174491882,
-0.10769829899072647,
0.06747759133577347,
0.01464148610830307,
-0.057174522429704666,
0.022521471604704857,
0.013851694762706757,
-0.0961323231458664,
0.11364410072565079,
0.011944140307605267,
0.08382543921470642,
0.054860010743141174,
0.016029998660087585,
-0.15887661278247833,
0.019796283915638924,
0.0025517879985272884,
0.01899854466319084,
0.045645784586668015,
0.05855147913098335,
0.03658099099993706,
0.10945446789264679,
0.02070845104753971,
0.0149241853505373,
0.006048198789358139,
-0.12129364162683487,
-0.00753180542960763,
0.019314736127853394,
0.058324363082647324,
-0.03173326328396797,
0.10144933313131332,
0.009730502963066101,
0.10268426686525345,
-0.11056879162788391,
0.060389406979084015,
0.11125580221414566,
-0.23197300732135773,
-0.10126195102930069,
-0.021162906661629677,
0.20990993082523346,
0.053752537816762924,
0.034727152436971664,
-0.043032094836235046,
0.04737623780965805,
0.021921157836914062,
-0.046279337257146835,
-0.09654642641544342,
-0.048980411142110825,
-0.05233680456876755,
-0.1338823288679123,
-0.003394866595044732,
0.20231597125530243,
-0.10137365758419037,
-0.06320349872112274,
-0.053053583949804306,
-0.05787346512079239,
-0.040943510830402374,
-0.018987879157066345,
-0.05610726773738861,
0.0010010574478656054,
-0.007572080474346876,
-0.006570133380591869,
-0.04005309194326401,
-0.07962536066770554,
-0.09308330714702606,
-0.05845143273472786,
0.15810176730155945,
0.023892026394605637,
0.0012732624309137464,
-0.12090366333723068,
0.06534051150083542,
-0.13067211210727692,
-0.020528122782707214,
-0.008674900978803635,
-0.08898934721946716,
0.11516235023736954,
-0.026293905451893806,
-0.03584318608045578,
0.028414567932486534,
0.011513601988554,
0.19201257824897766,
0.0715145617723465,
0.06495766341686249,
0.0008041249820962548,
0.040915604680776596,
0.04471500217914581,
0.10807029902935028,
0.04420769587159157,
-0.03742683306336403,
-0.026180250570178032,
-0.010264333337545395,
-0.07710197567939758,
-0.0012013799278065562,
-0.06975448876619339,
-0.05841594934463501,
0.08392181247472763,
-0.04014597833156586,
-0.03526626527309418,
0.0038704629987478256,
-0.05133316293358803,
0.014698936603963375,
0.16852207481861115,
-0.05656374990940094,
-0.0480036698281765,
-0.041930265724658966,
0.009301261976361275,
0.09741049259901047,
-0.01860744133591652,
0.006354788318276405,
-0.09404503554105759,
0.06897736340761185,
-0.0936090424656868,
-0.038287028670310974,
-0.06578607112169266,
-0.041235730051994324,
-0.051949162036180496,
-0.14950329065322876,
0.0073081124573946,
-0.12053454667329788,
-0.012279150076210499,
-0.030104637145996094,
0.06323731690645218,
-0.040801696479320526,
-0.009729265235364437,
-0.03875809535384178,
0.09729050099849701,
-0.04513794556260109,
-0.0693465992808342,
0.0915818065404892,
-0.06099247932434082,
0.09746121615171432,
0.04357539862394333,
0.0548250786960125,
-0.13630835711956024,
0.03637820482254028,
-0.14747312664985657,
0.0305311381816864,
-0.17977768182754517,
-0.016082677990198135,
-0.019310392439365387,
-0.004501532297581434,
-0.12687097489833832,
-0.15647107362747192,
-0.12543514370918274,
0.03643018752336502,
0.005614433437585831,
0.03882012143731117,
-0.08887005597352982,
-0.039320703595876694,
0.2358631044626236,
-0.0927102118730545,
-0.006114592310041189,
0.07579539716243744,
-0.0265701524913311,
0.1840546429157257,
0.0742543637752533,
0.1948794573545456,
0.04428078606724739,
-0.08240752667188644,
0.11251312494277954,
0.11327718198299408,
0.06666406244039536,
-0.07409247010946274,
0.01948649249970913,
-0.04447383061051369,
-0.20650345087051392,
0.036402348428964615,
-0.14585630595684052,
-0.02285047248005867,
-0.02327483892440796,
0.003154529957100749,
0.052815720438957214,
-0.010977694764733315,
0.007841715589165688,
-0.003073472063988447,
0.08621875196695328,
-0.006264173425734043,
-0.019496049731969833,
-0.003034841502085328,
-0.02132975496351719,
-0.03249697387218475,
0.0471794493496418,
-0.03333376348018646,
0.06880703568458557,
0.07375704497098923,
-0.035207878798246384,
-0.24451522529125214,
0.1321500837802887,
0.029304908588528633,
0.10174078494310379,
0.05330156534910202,
0.09602460265159607,
0.06417303532361984,
0.06339822709560394,
0.0648399367928505,
0.012053236365318298,
0.07236974686384201,
-0.0030232390854507685,
-0.09438090771436691,
-0.03730815649032593,
0.024711385369300842,
-0.0750836730003357,
-0.011582962237298489,
-0.04718324914574623,
-0.028248975053429604,
-0.034686941653490067,
-0.05977988615632057,
-0.006954898126423359,
-0.03369436413049698,
-0.043801449239254,
0.1005547046661377,
-0.038589172065258026,
0.09492624551057816,
0.1010332852602005,
-0.02003004029393196,
-0.024363191798329353,
0.03407119959592819,
-0.038568057119846344,
0.17639867961406708,
0.10433422774076462,
-0.09557580947875977,
-0.05618615448474884,
-0.16860616207122803,
-0.04704183340072632,
0.009833087213337421,
-0.01995678059756756,
0.014110984280705452,
0.268552303314209,
-0.02983180433511734,
0.1089814305305481,
-0.07271067053079605,
0.03723399341106415,
0.04377815127372742,
-0.03612034395337105,
0.019947601482272148,
0.1105533316731453,
0.11421768367290497,
-0.04697847738862038,
0.002137945732101798,
0.13437071442604065,
0.05576635152101517,
0.05775536969304085,
0.019326848909258842,
-0.04339361563324928,
0.049074653536081314,
0.046086013317108154,
-0.01855357363820076,
0.07532500475645065,
-0.28511860966682434,
-0.04293151572346687,
0.008617976680397987,
0.03129178658127785,
0.06558547914028168,
-0.1637556105852127,
-0.0018265440594404936,
0.02501518651843071,
-0.007882294245064259,
-0.10539916157722473,
-0.024220025166869164,
-0.051430054008960724,
0.08641579002141953,
0.07455404102802277,
-0.09505125135183334,
0.002880099695175886,
0.021465064957737923,
-0.04798845946788788,
0.21461044251918793,
-0.07830378413200378,
-0.062363024801015854,
-0.10903140157461166,
-0.17129500210285187,
0.08926864713430405,
0.02682301588356495,
0.03032592497766018,
-0.0561169795691967,
-0.05612322688102722,
-0.03114146925508976,
-0.019231325015425682,
-0.1141643077135086,
-0.01156251784414053,
-0.043122898787260056,
-0.003874639282003045,
-0.0015211908612400293,
-0.09964262694120407,
-0.04023795202374458,
-0.03745070844888687,
-0.0453466959297657,
0.056207653135061264,
-0.10080050677061081,
0.1078701764345169,
0.07151642441749573,
0.018124932423233986,
0.02098669484257698,
-0.03185742348432541,
0.19880379736423492,
0.015685588121414185,
-0.09562906622886658,
0.12409837543964386,
-0.0778314545750618,
0.034275081008672714,
0.012654473073780537,
0.02834668941795826,
-0.07830295711755753,
0.02251812256872654,
-0.07880539447069168,
-0.051350370049476624,
-0.12377051264047623,
-0.09576676040887833,
-0.027789803221821785,
0.04896644130349159,
0.13871046900749207,
0.03508793190121651,
-0.06662526726722717,
0.03299001604318619,
0.10749088227748871,
0.11671089380979538,
0.04780306667089462,
0.01880800351500511,
0.10647950321435928,
-0.07394930720329285,
0.07964179664850235,
0.010491429828107357,
-0.07885117083787918,
0.03883799910545349,
-0.02313985861837864,
0.2124517560005188,
0.09054625779390335,
0.10290631651878357,
0.027307314798235893,
0.08009505271911621,
0.07746490836143494,
0.17190738022327423,
-0.06353925913572311,
0.029220566153526306,
-0.061923131346702576,
-0.038026776164770126,
-0.12342498451471329,
0.01378070842474699,
-0.05383952334523201,
-0.12872181832790375,
-0.08396600931882858,
-0.06718415766954422,
-0.0028401550371199846,
0.0781024917960167,
-0.04972420260310173,
-0.2015315592288971,
0.019605567678809166,
0.01755189336836338,
-0.0780516192317009,
-0.1063048467040062,
0.13501699268817902,
0.0775882750749588,
-0.09577286243438721,
-0.021316692233085632,
-0.010525851510465145,
0.10919465869665146,
-0.04572240635752678,
0.08933400362730026,
-0.013468733988702297,
-0.06495903432369232,
0.03452284261584282,
0.11333540081977844,
-0.2664707601070404,
0.24484442174434662,
-0.028411714360117912,
-0.012379059568047523,
-0.0926058441400528,
-0.03742840886116028,
-0.08625537157058716,
0.07610432058572769,
0.1720718890428543,
0.005811033770442009,
-0.04401044547557831,
-0.1805529147386551,
-0.18185627460479736,
0.0652303546667099,
0.13237245380878448,
-0.00492097856476903,
0.015907811000943184,
-0.0360846109688282,
0.0019888088572770357,
-0.04350212216377258,
-0.12944357097148895,
-0.10309351235628128,
-0.12519146502017975,
0.003275464754551649,
-0.12884441018104553,
0.09785450994968414,
-0.03961709141731262,
-0.02480234019458294,
0.10070638358592987,
0.06565668433904648,
-0.18898369371891022,
-0.005608600098639727,
-0.04384835064411163,
-0.06119528040289879,
0.030973320826888084,
-0.036822136491537094,
0.029253462329506874,
-0.040272049605846405,
-0.062105920165777206,
-0.04539712518453598,
-0.07591213285923004,
0.08371423184871674,
-0.08054212480783463,
-0.07334787398576736,
-0.037616051733493805,
0.029300598427653313,
0.11255750805139542,
0.06289716064929962,
0.03913108631968498,
0.016203945502638817,
-0.12747403979301453,
-0.09498948603868484,
-0.12573671340942383,
-0.02270825020968914,
0.08725908398628235,
-0.06430722773075104,
-0.11649496108293533,
0.005065689329057932,
-0.020500874146819115,
0.06592360138893127,
0.18862837553024292,
0.058440595865249634,
-0.00018289376748725772,
0.08700673282146454,
0.1764398217201233,
-0.0472019799053669,
-0.2933414578437805,
0.0009260347578674555,
0.015166275203227997,
-0.0017143349396064878,
-0.10545828193426132,
-0.17810624837875366,
0.16421407461166382,
0.14058785140514374,
0.019695350900292397,
0.03241657838225365,
-0.24578145146369934,
-0.10812880098819733,
0.12156280130147934,
0.06154509633779526,
0.3311101794242859,
-0.09675250947475433,
-0.02475658431649208,
0.022816786542534828,
-0.06117403879761696,
0.07098060846328735,
-0.017214221879839897,
0.028535014018416405,
-0.01601368933916092,
-0.05916406959295273,
-0.011912158690392971,
-0.06539306044578552,
0.052514176815748215,
0.05549090728163719,
-0.029940076172351837,
-0.013094558380544186,
-0.06442808359861374,
-0.0254148468375206,
0.05582347512245178,
0.0015608611283823848,
0.05671986564993858,
0.003868795232847333,
-0.1645040512084961,
-0.07631907612085342,
-0.057476677000522614,
0.0143059641122818,
-0.0061690364964306355,
-0.06132742390036583,
0.04510951414704323,
0.04054069146513939,
-0.0737924724817276,
0.016315016895532608,
0.1246664971113205,
-0.06769519299268723,
0.12341448664665222,
0.08272348344326019,
0.2157658040523529,
-0.09436832368373871,
-0.09174694120883942,
0.08318164199590683,
-0.0222904235124588,
0.0914800614118576,
-0.000043691696191672236,
-0.03400637209415436,
0.12136273086071014,
0.011796117760241032,
0.03449494019150734,
0.05850842222571373,
0.010669269599020481,
0.011544277891516685,
0.06472212076187134,
-0.21476876735687256,
-0.04778954014182091,
0.014653385616838932,
0.038159389048814774,
-0.02580980770289898,
-0.030619127675890923,
0.04870400205254555,
-0.07733499258756638,
-0.03770442306995392,
-0.020963730290532112,
0.003199314698576927,
-0.08989311009645462,
0.17481228709220886,
0.049001339823007584,
0.0619603767991066,
-0.0683538094162941,
-0.04389475658535957,
0.06138499081134796,
-0.17407453060150146,
-0.036468636244535446,
0.021391144022345543,
-0.09302587807178497,
-0.05189857259392738,
0.00046297069638967514,
0.03557121008634567,
0.019689032807946205,
-0.05978522077202797,
-0.0468001551926136,
-0.08340448141098022,
0.0915658175945282,
0.11040796339511871,
0.08065734058618546,
-0.022863784804940224,
-0.083602674305439,
0.021646857261657715,
0.0106267798691988,
-0.055229321122169495,
0.004754918627440929,
0.03773337975144386,
0.05785903334617615,
0.09375821799039841,
0.009530824609100819,
0.088860422372818,
-0.05102662742137909,
-0.05627276375889778,
-0.1624736487865448,
0.04616610333323479,
-0.08849360793828964,
0.040250927209854126,
-0.1196906641125679,
-0.030448172241449356,
-0.04213496297597885,
0.04051700606942177,
0.037808094173669815,
-0.056237559765577316,
-0.07040207833051682,
0.06429632753133774,
-0.025295652449131012,
0.00532825430855155,
-0.057735152542591095,
0.06402502208948135,
0.07172314822673798,
-0.02705320343375206,
0.02205607108771801,
0.04078199341893196,
-0.04193979874253273,
0.014177006669342518,
0.06752994656562805,
-0.06150148808956146,
0.0662703886628151,
0.05581377446651459,
0.01874164491891861,
0.021537821739912033,
0.04836614802479744,
0.04090796783566475,
0.05971751734614372,
0.025600826367735863,
0.1193196177482605,
-0.11773525178432465,
0.13225173950195312,
0.01651913858950138,
-0.11026191711425781,
-0.0373593233525753,
-0.03856134042143822,
0.0461016446352005,
0.09133680164813995,
0.07709188759326935,
-0.07413583248853683,
0.02834385447204113,
0.03381992504000664,
0.014876729808747768,
0.011555362492799759,
-0.171340212225914,
-0.09753306210041046,
-0.1208147406578064,
0.016388315707445145,
0.020612269639968872,
0.2682233452796936,
0.04267478734254837,
0.09734725952148438,
0.03632040694355965,
-0.0390591099858284,
-0.03258620575070381,
-0.04751207306981087,
0.1097707450389862,
0.10859419405460358,
0.019080525264143944,
-0.04192321375012398,
0.07802597433328629,
0.012647421099245548,
0.09012124687433243,
0.25521132349967957,
0.16960619390010834,
-0.012922020629048347,
0.0535895861685276,
-0.01682867668569088,
-0.011370904743671417,
-0.19675514101982117,
-0.06520839780569077,
0.037160106003284454,
0.10114485025405884,
-0.09116757661104202,
0.13751816749572754,
0.006788213737308979,
-0.09489491581916809,
-0.026194851845502853,
0.026261011138558388,
-0.08602464199066162,
-0.1582675576210022,
-0.02451200969517231,
-0.018848218023777008,
-0.0025514885783195496,
-0.04342115670442581,
-0.09757965803146362,
-0.013969965279102325,
0.0217902772128582,
0.011899665929377079,
0.03700917586684227,
0.16072264313697815,
-0.03108425810933113,
-0.07723496109247208,
0.06297716498374939,
0.0023663160391151905,
0.08547627180814743,
-0.1458359807729721,
-0.03919604420661926,
-0.10114031285047531,
-0.06631648540496826,
0.05334388464689255,
0.011383576318621635,
-0.055247750133275986,
-0.03951437771320343,
-0.04621869698166847,
-0.02005735971033573,
-0.09910060465335846,
0.06601394712924957,
0.02870764769613743,
0.15928512811660767,
0.01279109250754118,
-0.08434141427278519,
0.02642221376299858,
0.14253412187099457,
-0.040988098829984665,
-0.1033773347735405,
-0.03153175488114357,
0.36867210268974304,
0.06233115866780281,
0.03996438905596733,
0.015225528739392757,
0.06190608814358711,
0.004075785167515278,
0.38980865478515625,
0.21165262162685394,
-0.036224331706762314,
0.017504321411252022,
0.05356622859835625,
0.044477593153715134,
0.14621160924434662,
0.19241484999656677,
0.07793992012739182,
0.3125382363796234,
-0.06538978964090347,
-0.12229211628437042,
-0.022748274728655815,
0.02059762179851532,
-0.13672830164432526,
-0.08098369836807251,
0.1377461850643158,
-0.047364745289087296,
-0.0859455019235611,
0.08042628318071365,
-0.14935234189033508,
0.005747613497078419,
-0.0164765827357769,
-0.08631410449743271,
-0.10893511772155762,
-0.05196046829223633,
0.07045166939496994,
-0.022948915138840675,
0.09548113495111465,
-0.020199868828058243,
-0.010038231499493122,
0.048349395394325256,
0.045128028839826584,
-0.14197520911693573,
-0.0035399857442826033,
0.13293126225471497,
0.20204207301139832,
0.057339031249284744,
0.003247681772336364,
0.09052707254886627,
0.006967560388147831,
0.0651509165763855,
-0.03500023111701012,
0.07017271965742111,
0.00913206022232771,
-0.04069095104932785,
-0.04157843068242073,
-0.08679560571908951,
-0.028907153755426407,
0.0373409241437912,
0.013226393610239029,
-0.20048926770687103,
0.08714411407709122,
0.06015058979392052,
-0.024216391146183014,
-0.09127522259950638,
0.036642469465732574,
-0.07175355404615402,
0.11603404581546783,
0.16571368277072906,
0.025554753839969635,
-0.06205185130238533,
-0.049133725464344025,
0.04812873154878616,
-0.04678615182638168,
0.038416411727666855,
-0.130287304520607,
-0.14503498375415802,
-0.15615953505039215,
-0.1349307745695114,
-0.031088242307305336,
-0.21710273623466492,
0.0005010706372559071,
-0.04697177931666374,
0.09262512624263763,
-0.07513873279094696,
0.03626308962702751,
0.01627606898546219,
0.009846185334026814,
-0.0033122883178293705,
0.0581948421895504,
0.015208912082016468,
0.05755581706762314,
-0.10364402830600739,
-0.05869695544242859
] |
null | null |
transformers
|
This model is the fine-tuned model of "akdeniz27/bert-base-hungarian-cased-ner" using WikiANN-hu dataset.
|
{}
|
token-classification
|
fdominik98/bert-base-hu-cased-ner
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
|
This model is the fine-tuned model of "akdeniz27/bert-base-hungarian-cased-ner" using WikiANN-hu dataset.
|
[] |
[
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
37
] |
[
"passage: TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
-0.04952388256788254,
0.052763525396585464,
-0.008742042817175388,
0.033980391919612885,
0.16650345921516418,
0.031232766807079315,
0.056794650852680206,
0.08634597808122635,
0.05724777653813362,
-0.022096728906035423,
0.12041265517473221,
0.25661665201187134,
-0.04172574356198311,
0.09726614505052567,
-0.09425565600395203,
-0.2964369058609009,
0.072568878531456,
0.08518822491168976,
-0.03685073181986809,
0.10874255001544952,
0.0826217457652092,
-0.09953062981367111,
0.07087896764278412,
-0.021146323531866074,
-0.14565661549568176,
0.03782055899500847,
0.04566175118088722,
-0.12290510535240173,
0.10927622765302658,
0.024675650522112846,
0.195825457572937,
0.0250800009816885,
-0.04914003983139992,
-0.12460087239742279,
0.02292012982070446,
0.012434997595846653,
-0.0567244216799736,
0.05464257672429085,
0.07972842454910278,
-0.09245497733354568,
-0.02643316052854061,
0.0736164003610611,
0.040207646787166595,
0.040536463260650635,
-0.12785013020038605,
-0.130097895860672,
-0.01677129417657852,
0.04410306364297867,
0.06830945611000061,
0.03920266404747963,
0.028024563565850258,
0.19659002125263214,
-0.1535772681236267,
0.1040927842259407,
0.1168038472533226,
-0.2951667606830597,
-0.0032763986382633448,
0.12271767109632492,
0.02307300455868244,
0.002292903373017907,
-0.046535883098840714,
0.026178892701864243,
0.025552578270435333,
0.011873415671288967,
0.027952805161476135,
-0.08463964611291885,
-0.08450421690940857,
0.03911300376057625,
-0.10113436728715897,
-0.025793785229325294,
0.17653688788414001,
-0.04118471220135689,
0.051768165081739426,
0.011256481520831585,
-0.10429224371910095,
-0.06710886210203171,
-0.016446024179458618,
-0.016140753403306007,
-0.029147058725357056,
0.05014093220233917,
0.020645350217819214,
0.04166753962635994,
-0.09745467454195023,
0.026441968977451324,
-0.21823887526988983,
0.2394854873418808,
0.026930933818221092,
0.06543339043855667,
-0.1759624481201172,
0.06742089986801147,
0.002468109829351306,
-0.080185666680336,
0.03979361802339554,
-0.10338287800550461,
0.000461930176243186,
-0.05314353480935097,
-0.03661246970295906,
0.03140731528401375,
0.06317161023616791,
0.17395952343940735,
0.0793117806315422,
0.05008377507328987,
-0.005898697301745415,
0.07871981710195541,
0.034834783524274826,
0.12483206391334534,
0.004537689033895731,
-0.03222474083304405,
0.04727894067764282,
-0.12738829851150513,
-0.022797152400016785,
-0.057654689997434616,
-0.13722378015518188,
-0.035683806985616684,
0.08572584390640259,
0.08669279515743256,
-0.002899006474763155,
0.07131802290678024,
-0.07259770482778549,
-0.04170111194252968,
0.0977427288889885,
-0.06708423793315887,
0.03773060068488121,
0.011165250092744827,
0.01788998953998089,
0.10969475656747818,
-0.03837720677256584,
0.006092004477977753,
-0.038544073700904846,
0.16035914421081543,
-0.06788671761751175,
-0.0086200051009655,
-0.03894633799791336,
-0.07913154363632202,
0.03580477461218834,
-0.1462087333202362,
0.05359502136707306,
-0.16970540583133698,
-0.07705289125442505,
0.03400159999728203,
0.03982843458652496,
0.006068000569939613,
-0.023860258981585503,
0.004609986208379269,
0.0009630053536966443,
0.010919814929366112,
-0.06532157212495804,
-0.059639010578393936,
-0.06180674210190773,
0.0789342075586319,
-0.026356957852840424,
0.05199717730283737,
-0.09772148728370667,
0.06380254030227661,
-0.10777808725833893,
0.014992175623774529,
-0.1314254105091095,
-0.01425449550151825,
-0.07122194766998291,
0.1727602779865265,
-0.012890664860606194,
-0.0652521625161171,
-0.042813826352357864,
0.026928367093205452,
-0.05078446492552757,
0.1189238503575325,
-0.05899157002568245,
-0.11599802225828171,
0.16220137476921082,
-0.10287307947874069,
-0.12483754754066467,
0.07581168413162231,
-0.01511769462376833,
-0.007789896801114082,
0.06788471341133118,
0.1409188061952591,
0.11342629045248032,
-0.021826764568686485,
0.08137305825948715,
0.10294424742460251,
-0.1294003278017044,
-0.13587328791618347,
0.006361662410199642,
0.00798719096928835,
-0.14556561410427094,
0.052592433989048004,
0.05236833915114403,
0.0726967453956604,
-0.07074553519487381,
-0.03274666517972946,
-0.01327072735875845,
-0.016134168952703476,
0.11864199489355087,
0.06209159642457962,
0.11604267358779907,
-0.0657677873969078,
0.00820392556488514,
0.047656312584877014,
0.004204337950795889,
0.036543652415275574,
0.015800083056092262,
-0.08706195652484894,
0.12248360365629196,
-0.04112236201763153,
-0.0011841603554785252,
-0.1986646205186844,
-0.09740031510591507,
0.023004116490483284,
0.07795482873916626,
-0.03679869696497917,
0.13610167801380157,
0.0701465755701065,
-0.04764446243643761,
0.00010037058382295072,
-0.03370752930641174,
0.17777207493782043,
0.04204830154776573,
-0.07011453807353973,
-0.07916951924562454,
0.018525518476963043,
-0.07664067298173904,
-0.030626622959971428,
-0.05736034736037254,
0.004993060603737831,
0.09117806702852249,
0.15110987424850464,
0.013702728785574436,
0.07227712869644165,
-0.04311654344201088,
0.06486255675554276,
-0.07453466951847076,
0.009454507380723953,
0.10986973345279694,
-0.00844109058380127,
-0.05139068886637688,
0.12689460813999176,
-0.1288842260837555,
0.3503526449203491,
0.18760092556476593,
-0.3039218783378601,
-0.0014054732164368033,
-0.04166017845273018,
-0.015616725198924541,
0.008022678084671497,
0.052066002041101456,
0.02243027463555336,
0.055117130279541016,
0.004745377227663994,
0.16756683588027954,
-0.015916254371404648,
-0.05048782005906105,
0.01112330798059702,
-0.05658484622836113,
-0.04292769730091095,
0.0737248957157135,
0.08670081943273544,
-0.20279163122177124,
0.1850307285785675,
0.22442755103111267,
0.0013078266056254506,
0.10093411803245544,
-0.009863444603979588,
0.027319302782416344,
0.03913215547800064,
-0.0403471440076828,
-0.01890004798769951,
-0.015322047285735607,
-0.1852157860994339,
-0.04855918884277344,
0.07947539538145065,
0.031089356169104576,
0.044881563633680344,
-0.12700966000556946,
-0.023778779432177544,
0.019753707572817802,
0.05750936269760132,
-0.006435907445847988,
0.08618827164173126,
0.05749829113483429,
0.08228709548711777,
-0.00042137576383538544,
-0.12340108305215836,
0.11843181401491165,
0.008938729763031006,
-0.06630217283964157,
0.1701919436454773,
-0.13096192479133606,
-0.2919446527957916,
-0.12918199598789215,
-0.21609659492969513,
-0.02221912518143654,
0.04468577727675438,
0.06791006773710251,
-0.09210261702537537,
-0.05621439218521118,
0.07646377384662628,
-0.0043442221358418465,
-0.08893678337335587,
0.06794130802154541,
-0.08020073175430298,
0.05017327144742012,
-0.04635123163461685,
-0.06104189157485962,
-0.06701690703630447,
-0.041732318699359894,
-0.02662169374525547,
0.13805724680423737,
-0.09828965365886688,
0.06120699644088745,
0.17539545893669128,
-0.011075721122324467,
0.06305833160877228,
-0.023022029548883438,
0.17187125980854034,
-0.051036398857831955,
-0.01183983776718378,
0.15369866788387299,
-0.07339499890804291,
0.08695117384195328,
0.16216324269771576,
0.039562564343214035,
-0.05847073718905449,
0.00318810623139143,
-0.03215182200074196,
-0.10039623826742172,
-0.18391156196594238,
-0.12352752685546875,
-0.11139705777168274,
0.03804181143641472,
0.07061707973480225,
0.06962733715772629,
0.135431706905365,
0.09561170637607574,
0.051199477165937424,
0.010959632694721222,
-0.048153605312108994,
0.07466201484203339,
0.2208920270204544,
0.001999937929213047,
0.1435864269733429,
-0.04169393703341484,
-0.1389981210231781,
0.07379768788814545,
0.05560476705431938,
0.13692645728588104,
0.10865066200494766,
-0.01025706622749567,
0.01681477762758732,
0.15626244246959686,
0.18229423463344574,
0.1308237463235855,
0.006019692402333021,
-0.03366916626691818,
-0.00786581914871931,
0.01499095093458891,
-0.05352693051099777,
0.018093694001436234,
0.1126210168004036,
-0.1184353232383728,
-0.05580713599920273,
-0.1544867902994156,
0.06733953207731247,
0.09452182799577713,
0.05705242604017258,
-0.21316885948181152,
0.015583771280944347,
0.07424211502075195,
-0.02289263904094696,
-0.07244562357664108,
0.07719103991985321,
-0.06336886435747147,
-0.14039036631584167,
0.06610569357872009,
-0.05594165250658989,
0.1137172058224678,
-0.07018838077783585,
0.07125633209943771,
0.00436688307672739,
-0.09134659916162491,
0.03966715931892395,
0.09198823571205139,
-0.24242180585861206,
0.2353951781988144,
-0.005842206999659538,
-0.08202855288982391,
-0.07314476370811462,
-0.01116443332284689,
0.040539294481277466,
0.20659157633781433,
0.06959166377782822,
0.015105132944881916,
-0.09668217599391937,
-0.2116534262895584,
-0.01098657213151455,
0.0037440438754856586,
0.10247340053319931,
-0.042362287640571594,
-0.020731588825583458,
-0.04073396697640419,
-0.02784070186316967,
-0.013651788234710693,
-0.025264961645007133,
0.03604467213153839,
-0.12370047718286514,
0.0628918707370758,
0.03108403831720352,
0.0375843271613121,
0.011039070785045624,
-0.053596705198287964,
-0.131315678358078,
0.20352721214294434,
-0.08504047989845276,
-0.057783741503953934,
-0.11823975294828415,
-0.09986942261457443,
0.06580016016960144,
-0.09040012210607529,
0.08111396431922913,
-0.08633793145418167,
0.013077793642878532,
-0.03235287219285965,
-0.1910691112279892,
0.14946654438972473,
-0.11215386539697647,
-0.021799318492412567,
-0.08084782212972641,
0.13647201657295227,
-0.07384097576141357,
0.013447601348161697,
0.013732876628637314,
0.02437661960721016,
-0.08041444420814514,
-0.0828900933265686,
0.006355836056172848,
-0.014282151125371456,
0.031239764764904976,
0.025724230334162712,
-0.06753705441951752,
-0.0018155953148379922,
-0.011079292744398117,
0.043730828911066055,
0.24128669500350952,
0.1798989176750183,
-0.08305246382951736,
0.11879931390285492,
0.15382987260818481,
-0.04894930124282837,
-0.31972965598106384,
-0.07097899168729782,
-0.11553299427032471,
-0.045486610382795334,
-0.038512568920850754,
-0.1360434889793396,
0.1564210206270218,
0.02604006603360176,
-0.04126296564936638,
0.08387403935194016,
-0.14068999886512756,
-0.07977245002985,
0.22926518321037292,
0.0029623862355947495,
0.40268903970718384,
-0.08702389895915985,
-0.08436572551727295,
-0.01582670770585537,
-0.15964074432849884,
0.11801808327436447,
0.04110552370548248,
0.06407788395881653,
-0.02680877409875393,
0.053290240466594696,
0.039342157542705536,
-0.06043728440999985,
0.09375226497650146,
0.031009389087557793,
0.044987753033638,
-0.10373856127262115,
-0.13265928626060486,
0.028911620378494263,
-0.030960315838456154,
-0.014786438085138798,
0.057378023862838745,
0.022116998210549355,
-0.12861059606075287,
-0.025071382522583008,
-0.07482665032148361,
0.08682981133460999,
0.03502606973052025,
-0.06619370728731155,
-0.003656937973573804,
-0.011468438431620598,
-0.010950524359941483,
-0.007217009086161852,
0.25750675797462463,
0.003593818750232458,
0.14098960161209106,
0.10273806750774384,
0.09543769061565399,
-0.17969036102294922,
-0.03609991818666458,
-0.07452508062124252,
-0.06570184230804443,
0.096438467502594,
-0.030126970261335373,
0.07519517093896866,
0.1516796350479126,
-0.04122238606214523,
0.04297369718551636,
0.12120365351438522,
0.047616977244615555,
-0.0461055189371109,
0.14043885469436646,
-0.20969203114509583,
0.03770104795694351,
-0.02787908911705017,
-0.02063949778676033,
0.07864256948232651,
0.10774786025285721,
0.10418630391359329,
0.04115518182516098,
-0.035168662667274475,
0.01394918467849493,
-0.02965446747839451,
-0.03510928899049759,
0.07017704844474792,
0.06717298924922943,
0.043598420917987823,
-0.13562940061092377,
0.03572770580649376,
0.03545781224966049,
-0.15994900465011597,
-0.04561840742826462,
0.0798286497592926,
-0.15686658024787903,
-0.11122603714466095,
-0.02263965830206871,
0.11455298960208893,
-0.14521896839141846,
-0.039632488042116165,
-0.04641811549663544,
-0.13418996334075928,
0.06972779333591461,
0.18159188330173492,
0.13103143870830536,
0.10970540344715118,
-0.05561865121126175,
-0.021705783903598785,
-0.011595118790864944,
-0.01822039857506752,
0.006359242834150791,
0.06845489144325256,
-0.17037172615528107,
0.017899589613080025,
-0.01394583098590374,
0.14702916145324707,
-0.09660974144935608,
-0.07415182143449783,
-0.16891857981681824,
0.04697556421160698,
-0.09303826838731766,
-0.07159677147865295,
-0.08290879428386688,
-0.02118426188826561,
0.028802918270230293,
-0.08412807434797287,
-0.03680410608649254,
-0.03769955411553383,
-0.12591150403022766,
0.058045439422130585,
0.016383599489927292,
0.029256748035550117,
-0.04278605803847313,
-0.049387238919734955,
0.10788699239492416,
-0.040735047310590744,
0.09248634427785873,
0.11333432793617249,
-0.06952280551195145,
0.08524111658334732,
-0.09002139419317245,
-0.12434586137533188,
0.12248294800519943,
0.02376852184534073,
0.11538374423980713,
0.037905752658843994,
0.03187960386276245,
0.07364030182361603,
0.015629447996616364,
0.05559782683849335,
0.05336270108819008,
-0.12224778532981873,
0.02708340249955654,
-0.01786322332918644,
-0.1931128054857254,
-0.02867043949663639,
-0.07598401606082916,
0.12200799584388733,
-0.0023715540301054716,
0.15108288824558258,
-0.006962954066693783,
0.08709387481212616,
-0.04373457655310631,
-0.00553933484479785,
-0.020426053553819656,
-0.20569974184036255,
-0.038188423961400986,
-0.05518931895494461,
0.0054757255129516125,
-0.003793900366872549,
0.25238698720932007,
0.03206604719161987,
0.021465452387928963,
0.04169522970914841,
0.05841078236699104,
0.0034062466584146023,
0.03988324850797653,
0.17441175878047943,
0.1048174500465393,
-0.044596027582883835,
-0.06612113863229752,
0.07449153810739517,
0.014104411005973816,
-0.03425929695367813,
0.10280604660511017,
0.0579635351896286,
-0.022101202979683876,
0.061401285231113434,
0.006350876297801733,
0.03077852725982666,
-0.17194421589374542,
-0.18425245583057404,
-0.0518796369433403,
0.07249171286821365,
0.02366054244339466,
0.06857233494520187,
0.10899512469768524,
-0.026017313823103905,
0.0467560812830925,
-0.04243495315313339,
-0.03868629038333893,
-0.19200804829597473,
-0.11754908412694931,
-0.09499595314264297,
-0.10949105769395828,
0.012144351378083229,
-0.045467864722013474,
-0.025607097893953323,
0.112579844892025,
0.05665137246251106,
-0.02637707255780697,
0.07898925989866257,
0.0068581425584852695,
-0.015829376876354218,
0.035544686019420624,
-0.015088078565895557,
-0.002572772093117237,
-0.008363397791981697,
-0.025032468140125275,
-0.1646868884563446,
-0.01614730805158615,
-0.059904489666223526,
-0.0038963949773460627,
-0.0638962835073471,
0.002605070825666189,
-0.10870174318552017,
-0.11156944185495377,
-0.027449829503893852,
0.031112194061279297,
-0.07530830055475235,
0.08167865127325058,
-0.012178707867860794,
0.031354498118162155,
0.02408476360142231,
0.15556955337524414,
-0.0745464414358139,
-0.05411846935749054,
-0.04471001774072647,
0.26918578147888184,
0.0574658028781414,
0.1188357025384903,
0.007326812483370304,
0.018914537504315376,
-0.0766475573182106,
0.29574328660964966,
0.26567307114601135,
-0.03664971888065338,
0.05142061784863472,
0.04157736524939537,
0.01670730672776699,
0.09608681499958038,
0.1372172236442566,
0.07940847426652908,
0.23908233642578125,
-0.07535005360841751,
-0.04486403986811638,
-0.029895322397351265,
-0.017877299338579178,
-0.10601367056369781,
0.0677277147769928,
0.0556669756770134,
-0.0367586687207222,
-0.08847290277481079,
0.07175103574991226,
-0.16698697209358215,
0.1506146639585495,
0.055220942944288254,
-0.18291416764259338,
-0.07399025559425354,
-0.022136300802230835,
0.1443227231502533,
-0.019863387569785118,
0.0790862888097763,
-0.031677428632974625,
-0.10550876706838608,
0.039427150040864944,
0.01414680015295744,
-0.21330086886882782,
-0.05566805601119995,
0.0937623530626297,
0.0036555929109454155,
0.05017957463860512,
-0.023827895522117615,
0.03583429008722305,
0.08428710699081421,
0.0726693794131279,
-0.04607251659035683,
0.006363187450915575,
0.011929735541343689,
-0.08450563997030258,
-0.03499215841293335,
0.00016984343528747559,
0.013966171070933342,
-0.05488259717822075,
0.03193806856870651,
-0.18189109861850739,
0.04003556817770004,
-0.09101450443267822,
-0.036184389144182205,
-0.019026435911655426,
0.023357758298516273,
-0.029626764357089996,
0.05516811087727547,
0.07363101094961166,
0.009205193258821964,
-0.03664170578122139,
-0.06109684333205223,
-0.025447756052017212,
0.03078463301062584,
-0.11446559429168701,
-0.14089645445346832,
-0.08753776550292969,
-0.06155245006084442,
0.09708955883979797,
-0.01227374467998743,
-0.0782943144440651,
-0.04041222110390663,
-0.07965502887964249,
0.03774513676762581,
-0.14707180857658386,
0.06991016864776611,
0.03579777479171753,
0.04206673055887222,
-0.01093299314379692,
-0.03975704312324524,
0.019534343853592873,
0.054816145449876785,
-0.12402302771806717,
-0.09320621192455292
] |
null | null |
transformers
|
Magyar nyelvű token classification feladatra felkészített BERT modell.
|
{}
|
token-classification
|
fdominik98/ner-hu-model-2021
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
|
Magyar nyelvű token classification feladatra felkészített BERT modell.
|
[] |
[
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
37
] |
[
"passage: TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
-0.04952388256788254,
0.052763525396585464,
-0.008742042817175388,
0.033980391919612885,
0.16650345921516418,
0.031232766807079315,
0.056794650852680206,
0.08634597808122635,
0.05724777653813362,
-0.022096728906035423,
0.12041265517473221,
0.25661665201187134,
-0.04172574356198311,
0.09726614505052567,
-0.09425565600395203,
-0.2964369058609009,
0.072568878531456,
0.08518822491168976,
-0.03685073181986809,
0.10874255001544952,
0.0826217457652092,
-0.09953062981367111,
0.07087896764278412,
-0.021146323531866074,
-0.14565661549568176,
0.03782055899500847,
0.04566175118088722,
-0.12290510535240173,
0.10927622765302658,
0.024675650522112846,
0.195825457572937,
0.0250800009816885,
-0.04914003983139992,
-0.12460087239742279,
0.02292012982070446,
0.012434997595846653,
-0.0567244216799736,
0.05464257672429085,
0.07972842454910278,
-0.09245497733354568,
-0.02643316052854061,
0.0736164003610611,
0.040207646787166595,
0.040536463260650635,
-0.12785013020038605,
-0.130097895860672,
-0.01677129417657852,
0.04410306364297867,
0.06830945611000061,
0.03920266404747963,
0.028024563565850258,
0.19659002125263214,
-0.1535772681236267,
0.1040927842259407,
0.1168038472533226,
-0.2951667606830597,
-0.0032763986382633448,
0.12271767109632492,
0.02307300455868244,
0.002292903373017907,
-0.046535883098840714,
0.026178892701864243,
0.025552578270435333,
0.011873415671288967,
0.027952805161476135,
-0.08463964611291885,
-0.08450421690940857,
0.03911300376057625,
-0.10113436728715897,
-0.025793785229325294,
0.17653688788414001,
-0.04118471220135689,
0.051768165081739426,
0.011256481520831585,
-0.10429224371910095,
-0.06710886210203171,
-0.016446024179458618,
-0.016140753403306007,
-0.029147058725357056,
0.05014093220233917,
0.020645350217819214,
0.04166753962635994,
-0.09745467454195023,
0.026441968977451324,
-0.21823887526988983,
0.2394854873418808,
0.026930933818221092,
0.06543339043855667,
-0.1759624481201172,
0.06742089986801147,
0.002468109829351306,
-0.080185666680336,
0.03979361802339554,
-0.10338287800550461,
0.000461930176243186,
-0.05314353480935097,
-0.03661246970295906,
0.03140731528401375,
0.06317161023616791,
0.17395952343940735,
0.0793117806315422,
0.05008377507328987,
-0.005898697301745415,
0.07871981710195541,
0.034834783524274826,
0.12483206391334534,
0.004537689033895731,
-0.03222474083304405,
0.04727894067764282,
-0.12738829851150513,
-0.022797152400016785,
-0.057654689997434616,
-0.13722378015518188,
-0.035683806985616684,
0.08572584390640259,
0.08669279515743256,
-0.002899006474763155,
0.07131802290678024,
-0.07259770482778549,
-0.04170111194252968,
0.0977427288889885,
-0.06708423793315887,
0.03773060068488121,
0.011165250092744827,
0.01788998953998089,
0.10969475656747818,
-0.03837720677256584,
0.006092004477977753,
-0.038544073700904846,
0.16035914421081543,
-0.06788671761751175,
-0.0086200051009655,
-0.03894633799791336,
-0.07913154363632202,
0.03580477461218834,
-0.1462087333202362,
0.05359502136707306,
-0.16970540583133698,
-0.07705289125442505,
0.03400159999728203,
0.03982843458652496,
0.006068000569939613,
-0.023860258981585503,
0.004609986208379269,
0.0009630053536966443,
0.010919814929366112,
-0.06532157212495804,
-0.059639010578393936,
-0.06180674210190773,
0.0789342075586319,
-0.026356957852840424,
0.05199717730283737,
-0.09772148728370667,
0.06380254030227661,
-0.10777808725833893,
0.014992175623774529,
-0.1314254105091095,
-0.01425449550151825,
-0.07122194766998291,
0.1727602779865265,
-0.012890664860606194,
-0.0652521625161171,
-0.042813826352357864,
0.026928367093205452,
-0.05078446492552757,
0.1189238503575325,
-0.05899157002568245,
-0.11599802225828171,
0.16220137476921082,
-0.10287307947874069,
-0.12483754754066467,
0.07581168413162231,
-0.01511769462376833,
-0.007789896801114082,
0.06788471341133118,
0.1409188061952591,
0.11342629045248032,
-0.021826764568686485,
0.08137305825948715,
0.10294424742460251,
-0.1294003278017044,
-0.13587328791618347,
0.006361662410199642,
0.00798719096928835,
-0.14556561410427094,
0.052592433989048004,
0.05236833915114403,
0.0726967453956604,
-0.07074553519487381,
-0.03274666517972946,
-0.01327072735875845,
-0.016134168952703476,
0.11864199489355087,
0.06209159642457962,
0.11604267358779907,
-0.0657677873969078,
0.00820392556488514,
0.047656312584877014,
0.004204337950795889,
0.036543652415275574,
0.015800083056092262,
-0.08706195652484894,
0.12248360365629196,
-0.04112236201763153,
-0.0011841603554785252,
-0.1986646205186844,
-0.09740031510591507,
0.023004116490483284,
0.07795482873916626,
-0.03679869696497917,
0.13610167801380157,
0.0701465755701065,
-0.04764446243643761,
0.00010037058382295072,
-0.03370752930641174,
0.17777207493782043,
0.04204830154776573,
-0.07011453807353973,
-0.07916951924562454,
0.018525518476963043,
-0.07664067298173904,
-0.030626622959971428,
-0.05736034736037254,
0.004993060603737831,
0.09117806702852249,
0.15110987424850464,
0.013702728785574436,
0.07227712869644165,
-0.04311654344201088,
0.06486255675554276,
-0.07453466951847076,
0.009454507380723953,
0.10986973345279694,
-0.00844109058380127,
-0.05139068886637688,
0.12689460813999176,
-0.1288842260837555,
0.3503526449203491,
0.18760092556476593,
-0.3039218783378601,
-0.0014054732164368033,
-0.04166017845273018,
-0.015616725198924541,
0.008022678084671497,
0.052066002041101456,
0.02243027463555336,
0.055117130279541016,
0.004745377227663994,
0.16756683588027954,
-0.015916254371404648,
-0.05048782005906105,
0.01112330798059702,
-0.05658484622836113,
-0.04292769730091095,
0.0737248957157135,
0.08670081943273544,
-0.20279163122177124,
0.1850307285785675,
0.22442755103111267,
0.0013078266056254506,
0.10093411803245544,
-0.009863444603979588,
0.027319302782416344,
0.03913215547800064,
-0.0403471440076828,
-0.01890004798769951,
-0.015322047285735607,
-0.1852157860994339,
-0.04855918884277344,
0.07947539538145065,
0.031089356169104576,
0.044881563633680344,
-0.12700966000556946,
-0.023778779432177544,
0.019753707572817802,
0.05750936269760132,
-0.006435907445847988,
0.08618827164173126,
0.05749829113483429,
0.08228709548711777,
-0.00042137576383538544,
-0.12340108305215836,
0.11843181401491165,
0.008938729763031006,
-0.06630217283964157,
0.1701919436454773,
-0.13096192479133606,
-0.2919446527957916,
-0.12918199598789215,
-0.21609659492969513,
-0.02221912518143654,
0.04468577727675438,
0.06791006773710251,
-0.09210261702537537,
-0.05621439218521118,
0.07646377384662628,
-0.0043442221358418465,
-0.08893678337335587,
0.06794130802154541,
-0.08020073175430298,
0.05017327144742012,
-0.04635123163461685,
-0.06104189157485962,
-0.06701690703630447,
-0.041732318699359894,
-0.02662169374525547,
0.13805724680423737,
-0.09828965365886688,
0.06120699644088745,
0.17539545893669128,
-0.011075721122324467,
0.06305833160877228,
-0.023022029548883438,
0.17187125980854034,
-0.051036398857831955,
-0.01183983776718378,
0.15369866788387299,
-0.07339499890804291,
0.08695117384195328,
0.16216324269771576,
0.039562564343214035,
-0.05847073718905449,
0.00318810623139143,
-0.03215182200074196,
-0.10039623826742172,
-0.18391156196594238,
-0.12352752685546875,
-0.11139705777168274,
0.03804181143641472,
0.07061707973480225,
0.06962733715772629,
0.135431706905365,
0.09561170637607574,
0.051199477165937424,
0.010959632694721222,
-0.048153605312108994,
0.07466201484203339,
0.2208920270204544,
0.001999937929213047,
0.1435864269733429,
-0.04169393703341484,
-0.1389981210231781,
0.07379768788814545,
0.05560476705431938,
0.13692645728588104,
0.10865066200494766,
-0.01025706622749567,
0.01681477762758732,
0.15626244246959686,
0.18229423463344574,
0.1308237463235855,
0.006019692402333021,
-0.03366916626691818,
-0.00786581914871931,
0.01499095093458891,
-0.05352693051099777,
0.018093694001436234,
0.1126210168004036,
-0.1184353232383728,
-0.05580713599920273,
-0.1544867902994156,
0.06733953207731247,
0.09452182799577713,
0.05705242604017258,
-0.21316885948181152,
0.015583771280944347,
0.07424211502075195,
-0.02289263904094696,
-0.07244562357664108,
0.07719103991985321,
-0.06336886435747147,
-0.14039036631584167,
0.06610569357872009,
-0.05594165250658989,
0.1137172058224678,
-0.07018838077783585,
0.07125633209943771,
0.00436688307672739,
-0.09134659916162491,
0.03966715931892395,
0.09198823571205139,
-0.24242180585861206,
0.2353951781988144,
-0.005842206999659538,
-0.08202855288982391,
-0.07314476370811462,
-0.01116443332284689,
0.040539294481277466,
0.20659157633781433,
0.06959166377782822,
0.015105132944881916,
-0.09668217599391937,
-0.2116534262895584,
-0.01098657213151455,
0.0037440438754856586,
0.10247340053319931,
-0.042362287640571594,
-0.020731588825583458,
-0.04073396697640419,
-0.02784070186316967,
-0.013651788234710693,
-0.025264961645007133,
0.03604467213153839,
-0.12370047718286514,
0.0628918707370758,
0.03108403831720352,
0.0375843271613121,
0.011039070785045624,
-0.053596705198287964,
-0.131315678358078,
0.20352721214294434,
-0.08504047989845276,
-0.057783741503953934,
-0.11823975294828415,
-0.09986942261457443,
0.06580016016960144,
-0.09040012210607529,
0.08111396431922913,
-0.08633793145418167,
0.013077793642878532,
-0.03235287219285965,
-0.1910691112279892,
0.14946654438972473,
-0.11215386539697647,
-0.021799318492412567,
-0.08084782212972641,
0.13647201657295227,
-0.07384097576141357,
0.013447601348161697,
0.013732876628637314,
0.02437661960721016,
-0.08041444420814514,
-0.0828900933265686,
0.006355836056172848,
-0.014282151125371456,
0.031239764764904976,
0.025724230334162712,
-0.06753705441951752,
-0.0018155953148379922,
-0.011079292744398117,
0.043730828911066055,
0.24128669500350952,
0.1798989176750183,
-0.08305246382951736,
0.11879931390285492,
0.15382987260818481,
-0.04894930124282837,
-0.31972965598106384,
-0.07097899168729782,
-0.11553299427032471,
-0.045486610382795334,
-0.038512568920850754,
-0.1360434889793396,
0.1564210206270218,
0.02604006603360176,
-0.04126296564936638,
0.08387403935194016,
-0.14068999886512756,
-0.07977245002985,
0.22926518321037292,
0.0029623862355947495,
0.40268903970718384,
-0.08702389895915985,
-0.08436572551727295,
-0.01582670770585537,
-0.15964074432849884,
0.11801808327436447,
0.04110552370548248,
0.06407788395881653,
-0.02680877409875393,
0.053290240466594696,
0.039342157542705536,
-0.06043728440999985,
0.09375226497650146,
0.031009389087557793,
0.044987753033638,
-0.10373856127262115,
-0.13265928626060486,
0.028911620378494263,
-0.030960315838456154,
-0.014786438085138798,
0.057378023862838745,
0.022116998210549355,
-0.12861059606075287,
-0.025071382522583008,
-0.07482665032148361,
0.08682981133460999,
0.03502606973052025,
-0.06619370728731155,
-0.003656937973573804,
-0.011468438431620598,
-0.010950524359941483,
-0.007217009086161852,
0.25750675797462463,
0.003593818750232458,
0.14098960161209106,
0.10273806750774384,
0.09543769061565399,
-0.17969036102294922,
-0.03609991818666458,
-0.07452508062124252,
-0.06570184230804443,
0.096438467502594,
-0.030126970261335373,
0.07519517093896866,
0.1516796350479126,
-0.04122238606214523,
0.04297369718551636,
0.12120365351438522,
0.047616977244615555,
-0.0461055189371109,
0.14043885469436646,
-0.20969203114509583,
0.03770104795694351,
-0.02787908911705017,
-0.02063949778676033,
0.07864256948232651,
0.10774786025285721,
0.10418630391359329,
0.04115518182516098,
-0.035168662667274475,
0.01394918467849493,
-0.02965446747839451,
-0.03510928899049759,
0.07017704844474792,
0.06717298924922943,
0.043598420917987823,
-0.13562940061092377,
0.03572770580649376,
0.03545781224966049,
-0.15994900465011597,
-0.04561840742826462,
0.0798286497592926,
-0.15686658024787903,
-0.11122603714466095,
-0.02263965830206871,
0.11455298960208893,
-0.14521896839141846,
-0.039632488042116165,
-0.04641811549663544,
-0.13418996334075928,
0.06972779333591461,
0.18159188330173492,
0.13103143870830536,
0.10970540344715118,
-0.05561865121126175,
-0.021705783903598785,
-0.011595118790864944,
-0.01822039857506752,
0.006359242834150791,
0.06845489144325256,
-0.17037172615528107,
0.017899589613080025,
-0.01394583098590374,
0.14702916145324707,
-0.09660974144935608,
-0.07415182143449783,
-0.16891857981681824,
0.04697556421160698,
-0.09303826838731766,
-0.07159677147865295,
-0.08290879428386688,
-0.02118426188826561,
0.028802918270230293,
-0.08412807434797287,
-0.03680410608649254,
-0.03769955411553383,
-0.12591150403022766,
0.058045439422130585,
0.016383599489927292,
0.029256748035550117,
-0.04278605803847313,
-0.049387238919734955,
0.10788699239492416,
-0.040735047310590744,
0.09248634427785873,
0.11333432793617249,
-0.06952280551195145,
0.08524111658334732,
-0.09002139419317245,
-0.12434586137533188,
0.12248294800519943,
0.02376852184534073,
0.11538374423980713,
0.037905752658843994,
0.03187960386276245,
0.07364030182361603,
0.015629447996616364,
0.05559782683849335,
0.05336270108819008,
-0.12224778532981873,
0.02708340249955654,
-0.01786322332918644,
-0.1931128054857254,
-0.02867043949663639,
-0.07598401606082916,
0.12200799584388733,
-0.0023715540301054716,
0.15108288824558258,
-0.006962954066693783,
0.08709387481212616,
-0.04373457655310631,
-0.00553933484479785,
-0.020426053553819656,
-0.20569974184036255,
-0.038188423961400986,
-0.05518931895494461,
0.0054757255129516125,
-0.003793900366872549,
0.25238698720932007,
0.03206604719161987,
0.021465452387928963,
0.04169522970914841,
0.05841078236699104,
0.0034062466584146023,
0.03988324850797653,
0.17441175878047943,
0.1048174500465393,
-0.044596027582883835,
-0.06612113863229752,
0.07449153810739517,
0.014104411005973816,
-0.03425929695367813,
0.10280604660511017,
0.0579635351896286,
-0.022101202979683876,
0.061401285231113434,
0.006350876297801733,
0.03077852725982666,
-0.17194421589374542,
-0.18425245583057404,
-0.0518796369433403,
0.07249171286821365,
0.02366054244339466,
0.06857233494520187,
0.10899512469768524,
-0.026017313823103905,
0.0467560812830925,
-0.04243495315313339,
-0.03868629038333893,
-0.19200804829597473,
-0.11754908412694931,
-0.09499595314264297,
-0.10949105769395828,
0.012144351378083229,
-0.045467864722013474,
-0.025607097893953323,
0.112579844892025,
0.05665137246251106,
-0.02637707255780697,
0.07898925989866257,
0.0068581425584852695,
-0.015829376876354218,
0.035544686019420624,
-0.015088078565895557,
-0.002572772093117237,
-0.008363397791981697,
-0.025032468140125275,
-0.1646868884563446,
-0.01614730805158615,
-0.059904489666223526,
-0.0038963949773460627,
-0.0638962835073471,
0.002605070825666189,
-0.10870174318552017,
-0.11156944185495377,
-0.027449829503893852,
0.031112194061279297,
-0.07530830055475235,
0.08167865127325058,
-0.012178707867860794,
0.031354498118162155,
0.02408476360142231,
0.15556955337524414,
-0.0745464414358139,
-0.05411846935749054,
-0.04471001774072647,
0.26918578147888184,
0.0574658028781414,
0.1188357025384903,
0.007326812483370304,
0.018914537504315376,
-0.0766475573182106,
0.29574328660964966,
0.26567307114601135,
-0.03664971888065338,
0.05142061784863472,
0.04157736524939537,
0.01670730672776699,
0.09608681499958038,
0.1372172236442566,
0.07940847426652908,
0.23908233642578125,
-0.07535005360841751,
-0.04486403986811638,
-0.029895322397351265,
-0.017877299338579178,
-0.10601367056369781,
0.0677277147769928,
0.0556669756770134,
-0.0367586687207222,
-0.08847290277481079,
0.07175103574991226,
-0.16698697209358215,
0.1506146639585495,
0.055220942944288254,
-0.18291416764259338,
-0.07399025559425354,
-0.022136300802230835,
0.1443227231502533,
-0.019863387569785118,
0.0790862888097763,
-0.031677428632974625,
-0.10550876706838608,
0.039427150040864944,
0.01414680015295744,
-0.21330086886882782,
-0.05566805601119995,
0.0937623530626297,
0.0036555929109454155,
0.05017957463860512,
-0.023827895522117615,
0.03583429008722305,
0.08428710699081421,
0.0726693794131279,
-0.04607251659035683,
0.006363187450915575,
0.011929735541343689,
-0.08450563997030258,
-0.03499215841293335,
0.00016984343528747559,
0.013966171070933342,
-0.05488259717822075,
0.03193806856870651,
-0.18189109861850739,
0.04003556817770004,
-0.09101450443267822,
-0.036184389144182205,
-0.019026435911655426,
0.023357758298516273,
-0.029626764357089996,
0.05516811087727547,
0.07363101094961166,
0.009205193258821964,
-0.03664170578122139,
-0.06109684333205223,
-0.025447756052017212,
0.03078463301062584,
-0.11446559429168701,
-0.14089645445346832,
-0.08753776550292969,
-0.06155245006084442,
0.09708955883979797,
-0.01227374467998743,
-0.0782943144440651,
-0.04041222110390663,
-0.07965502887964249,
0.03774513676762581,
-0.14707180857658386,
0.06991016864776611,
0.03579777479171753,
0.04206673055887222,
-0.01093299314379692,
-0.03975704312324524,
0.019534343853592873,
0.054816145449876785,
-0.12402302771806717,
-0.09320621192455292
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7480
- Matthews Correlation: 0.5370
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5292 | 1.0 | 535 | 0.5110 | 0.4239 |
| 0.3508 | 2.0 | 1070 | 0.4897 | 0.4993 |
| 0.2346 | 3.0 | 1605 | 0.6275 | 0.5029 |
| 0.1806 | 4.0 | 2140 | 0.7480 | 0.5370 |
| 0.1291 | 5.0 | 2675 | 0.8841 | 0.5200 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.5370037450559281, "name": "Matthews Correlation"}]}]}]}
|
text-classification
|
federicopascual/distilbert-base-uncased-finetuned-cola
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7480
* Matthews Correlation: 0.5370
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
67,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
-0.10177629441022873,
0.09868992865085602,
-0.002423677360638976,
0.12112317979335785,
0.1650812178850174,
0.03426579013466835,
0.1299346536397934,
0.12770213186740875,
-0.08564593642950058,
0.021304525434970856,
0.1207699179649353,
0.16118186712265015,
0.023844653740525246,
0.10977903753519058,
-0.04856811463832855,
-0.26443108916282654,
-0.014250527136027813,
0.05101621896028519,
-0.05502324551343918,
0.13528308272361755,
0.09085173904895782,
-0.12123988568782806,
0.09099695831537247,
0.010560519993305206,
-0.19217939674854279,
0.0012736907228827477,
-0.00007869464025134221,
-0.05158104747533798,
0.1480625867843628,
0.026134053245186806,
0.1222379058599472,
0.004350635223090649,
0.08203282207250595,
-0.20211967825889587,
0.0109197236597538,
0.047976233065128326,
0.0034287353046238422,
0.09363500773906708,
0.04515067860484123,
0.002628615591675043,
0.12063173204660416,
-0.0803351029753685,
0.05412057787179947,
0.025187760591506958,
-0.11920642107725143,
-0.2130812108516693,
-0.07939155399799347,
0.036630984395742416,
0.07490450143814087,
0.10550613701343536,
-0.007987946271896362,
0.12026778608560562,
-0.08140391111373901,
0.09315772354602814,
0.22560088336467743,
-0.2846624255180359,
-0.06620592623949051,
0.04440826550126076,
0.014406891539692879,
0.04722030460834503,
-0.10258147865533829,
-0.03414628654718399,
0.04851415753364563,
0.0509343147277832,
0.1279968023300171,
-0.027470313012599945,
-0.11697384715080261,
0.006325297988951206,
-0.14067994058132172,
-0.031778451055288315,
0.16862210631370544,
0.04232211038470268,
-0.027326999232172966,
-0.05612191930413246,
-0.05705082044005394,
-0.1505020707845688,
-0.035502392798662186,
-0.01559263002127409,
0.049344535917043686,
-0.023357374593615532,
-0.04166480526328087,
-0.009466851130127907,
-0.10793615877628326,
-0.06508545577526093,
-0.07387420535087585,
0.11076346784830093,
0.03758743405342102,
0.006860504392534494,
-0.029825663194060326,
0.11246810108423233,
-0.007811444811522961,
-0.12158264964818954,
0.025255942717194557,
0.022893251851201057,
0.01350346952676773,
-0.03933148831129074,
-0.05274882912635803,
-0.06423640251159668,
0.01213061437010765,
0.12725643813610077,
-0.05570182576775551,
0.04239019751548767,
0.05044251307845116,
0.04930534213781357,
-0.09428779035806656,
0.19049058854579926,
-0.034276507794857025,
-0.025200827047228813,
0.0005440381937660277,
0.05053231865167618,
0.017217280343174934,
-0.011495225131511688,
-0.12165343761444092,
0.004494988825172186,
0.08820939809083939,
0.007856707088649273,
-0.06190384179353714,
0.07444976270198822,
-0.059118129312992096,
-0.024367066100239754,
0.0022545091342180967,
-0.09038243442773819,
0.021844087168574333,
0.0009826826862990856,
-0.07183235138654709,
-0.020876631140708923,
0.036078646779060364,
0.015619035810232162,
-0.01955125480890274,
0.1076565608382225,
-0.08774691820144653,
0.02745640277862549,
-0.09359776973724365,
-0.10990453511476517,
0.016444405540823936,
-0.10769655555486679,
0.022347012534737587,
-0.09183300286531448,
-0.179530531167984,
-0.017432404682040215,
0.05996386706829071,
-0.024156158789992332,
-0.057796917855739594,
-0.058523666113615036,
-0.06686114519834518,
0.011313402093946934,
-0.00775144575163722,
0.11726417392492294,
-0.06450776755809784,
0.09395397454500198,
0.025765664875507355,
0.06269765645265579,
-0.042549166828393936,
0.05981731042265892,
-0.10193105041980743,
0.013144214637577534,
-0.15104839205741882,
0.0400441437959671,
-0.05145822837948799,
0.06935621798038483,
-0.08194040507078171,
-0.10615845024585724,
0.002663051476702094,
-0.0028380511794239283,
0.06268789619207382,
0.09636878967285156,
-0.18458586931228638,
-0.08252619951963425,
0.1638277769088745,
-0.07281412184238434,
-0.12196049094200134,
0.12136763334274292,
-0.0580064132809639,
0.05861146003007889,
0.05951378867030144,
0.17990033328533173,
0.08656419813632965,
-0.07854954153299332,
0.0018196254968643188,
0.023756947368383408,
0.05026758089661598,
-0.06338667124509811,
0.06808595359325409,
0.0018259093631058931,
0.01989728771150112,
0.03576963394880295,
-0.027762150391936302,
0.0641099065542221,
-0.08784174174070358,
-0.09886786341667175,
-0.0402458980679512,
-0.08253508061170578,
0.0465523786842823,
0.07975026965141296,
0.06739030033349991,
-0.09414571523666382,
-0.07797343283891678,
0.05024334043264389,
0.08206507563591003,
-0.058022212237119675,
0.024602338671684265,
-0.049497149884700775,
0.07355623692274094,
-0.023844702169299126,
-0.021642537787556648,
-0.17968407273292542,
-0.032223403453826904,
0.007671972271054983,
0.0017039760714396834,
0.018056152388453484,
0.03337876871228218,
0.06358686834573746,
0.06016344204545021,
-0.05022745952010155,
-0.019244832918047905,
-0.0352572537958622,
-0.0006887496565468609,
-0.12579292058944702,
-0.19718441367149353,
-0.028450479730963707,
-0.02205497771501541,
0.16075244545936584,
-0.2082725465297699,
0.05154874175786972,
-0.014233555644750595,
0.06959009915590286,
0.012252251617610455,
-0.0065546054393053055,
-0.037181925028562546,
0.07644595205783844,
-0.04241294413805008,
-0.05049571767449379,
0.08215155452489853,
0.01450799684971571,
-0.09128501266241074,
-0.050215501338243484,
-0.09774215519428253,
0.1582167148590088,
0.12925271689891815,
-0.11003289371728897,
-0.07731720805168152,
-0.023380616679787636,
-0.0669984295964241,
-0.034903690218925476,
-0.04613172262907028,
0.026549331843852997,
0.1879379004240036,
-0.0049881828017532825,
0.14970116317272186,
-0.06918737292289734,
-0.043393924832344055,
0.018244462087750435,
-0.03694281727075577,
0.01636327989399433,
0.13443101942539215,
0.13418081402778625,
-0.06011265516281128,
0.15530750155448914,
0.14804664254188538,
-0.08511312305927277,
0.1510668396949768,
-0.04195278137922287,
-0.06577235460281372,
-0.01610550656914711,
-0.029684927314519882,
-0.011206655763089657,
0.10058020800352097,
-0.15690045058727264,
-0.002004367997869849,
0.030701281502842903,
0.015433641150593758,
0.02562039904296398,
-0.22722068428993225,
-0.04094555974006653,
0.03781639412045479,
-0.044886574149131775,
-0.006275756284594536,
-0.00595852779224515,
0.005161743611097336,
0.10115070641040802,
-0.0004888595431111753,
-0.08683908730745316,
0.03661135584115982,
0.0027518956921994686,
-0.08374463021755219,
0.2156449258327484,
-0.081173375248909,
-0.17226016521453857,
-0.13096117973327637,
-0.07049524784088135,
-0.047677770256996155,
-0.0015197350876405835,
0.06859971582889557,
-0.09709104150533676,
-0.02635144256055355,
-0.07209304720163345,
0.025860309600830078,
0.007600440643727779,
0.022702498361468315,
0.0029937936924397945,
0.007451718207448721,
0.06447025388479233,
-0.11222215741872787,
-0.015035846270620823,
-0.058496929705142975,
-0.04542740061879158,
0.044325705617666245,
0.02816983126103878,
0.11031338572502136,
0.15270480513572693,
-0.013083916157484055,
0.012781093828380108,
-0.03162777051329613,
0.23638051748275757,
-0.06001908704638481,
-0.0208893995732069,
0.1460668295621872,
-0.007206457667052746,
0.051826294511556625,
0.11371733248233795,
0.07455668598413467,
-0.07760798186063766,
0.003885192796587944,
0.037943821400403976,
-0.034547463059425354,
-0.23284892737865448,
-0.053616248071193695,
-0.05520634725689888,
0.011296672746539116,
0.089586041867733,
0.02373400144279003,
0.030841536819934845,
0.07044725120067596,
0.04116436094045639,
0.07381468266248703,
-0.037414710968732834,
0.05087532848119736,
0.1295747458934784,
0.030546288937330246,
0.12477082759141922,
-0.04690399020910263,
-0.06424792855978012,
0.040794432163238525,
-0.010863419622182846,
0.22334444522857666,
0.009917938150465488,
0.13131633400917053,
0.06607729941606522,
0.1649666577577591,
-0.009735438972711563,
0.07535355538129807,
-0.010470135137438774,
-0.03799179568886757,
-0.0162015613168478,
-0.03991588577628136,
-0.04029099643230438,
0.023612579330801964,
-0.06360199302434921,
0.06474608182907104,
-0.12359699606895447,
0.014717328362166882,
0.05894068628549576,
0.24849559366703033,
0.03377196192741394,
-0.3200716972351074,
-0.09709673374891281,
0.0007384793716482818,
-0.029719963669776917,
-0.019953938201069832,
0.026196908205747604,
0.09424502402544022,
-0.09753169119358063,
0.029457727447152138,
-0.07466296851634979,
0.0962631106376648,
-0.055720455944538116,
0.05116957798600197,
0.08169417083263397,
0.09054762125015259,
0.011883794330060482,
0.09314082562923431,
-0.2884000837802887,
0.27650073170661926,
0.0002981654542963952,
0.056524623185396194,
-0.07594560086727142,
0.008374286815524101,
0.041159119457006454,
0.06563553959131241,
0.07949315011501312,
-0.01224528532475233,
-0.01759219914674759,
-0.18658626079559326,
-0.06754712015390396,
0.027284175157546997,
0.06876907497644424,
-0.04146112501621246,
0.08209217339754105,
-0.031855158507823944,
0.008920346386730671,
0.07337794452905655,
0.0023068361915647984,
-0.053541041910648346,
-0.10786380618810654,
-0.005141935311257839,
0.022722337394952774,
-0.06008746474981308,
-0.06107710674405098,
-0.12087935954332352,
-0.12990501523017883,
0.155729740858078,
-0.035367779433727264,
-0.038886189460754395,
-0.106304831802845,
0.08325839787721634,
0.05899275466799736,
-0.08922765403985977,
0.0430733785033226,
0.0019881264306604862,
0.07539381086826324,
0.02154691517353058,
-0.07059939950704575,
0.10247620195150375,
-0.07363677769899368,
-0.1557348668575287,
-0.0653294250369072,
0.10667534172534943,
0.033229321241378784,
0.06670800596475601,
-0.014250626787543297,
0.00431025680154562,
-0.046546995639801025,
-0.08820211887359619,
0.020896978676319122,
0.004755881614983082,
0.07743469625711441,
0.018997633829712868,
-0.07543632388114929,
0.01201551128178835,
-0.06481624394655228,
-0.034086693078279495,
0.20510898530483246,
0.2221778780221939,
-0.09982394427061081,
0.024086005985736847,
0.025836989283561707,
-0.0738971009850502,
-0.19793148338794708,
0.03522804379463196,
0.05483577400445938,
0.008683490566909313,
0.04294142872095108,
-0.18465115129947662,
0.13146370649337769,
0.10747389495372772,
-0.011773521080613136,
0.10583452880382538,
-0.324044793844223,
-0.12059681862592697,
0.13578835129737854,
0.13629050552845,
0.09854068607091904,
-0.1321391612291336,
-0.02271113730967045,
-0.018754245713353157,
-0.1379910707473755,
0.11446153372526169,
-0.09142790734767914,
0.120912104845047,
-0.037892017513513565,
0.07596635818481445,
0.0028597572818398476,
-0.0584384985268116,
0.12073198705911636,
0.023140931501984596,
0.0945015400648117,
-0.058765899389982224,
-0.033337973058223724,
0.03047853522002697,
-0.042802438139915466,
0.03446131944656372,
-0.09962396323680878,
0.029662422835826874,
-0.10281215608119965,
-0.0251001063734293,
-0.06927776336669922,
0.04619433730840683,
-0.04536258056759834,
-0.06819522380828857,
-0.037817176431417465,
0.025476092472672462,
0.04637615382671356,
-0.007411271333694458,
0.12242395430803299,
0.02384062111377716,
0.1488822102546692,
0.09686450660228729,
0.07455138862133026,
-0.06877368688583374,
-0.08208677172660828,
-0.026989970356225967,
-0.01078053005039692,
0.050466980785131454,
-0.1369129866361618,
0.019369233399629593,
0.15191002190113068,
0.020388750359416008,
0.15307851135730743,
0.08298779278993607,
-0.021846970543265343,
-0.00145810900721699,
0.059030331671237946,
-0.16558411717414856,
-0.09374777227640152,
-0.017512774094939232,
-0.06781873852014542,
-0.12064754962921143,
0.04518076777458191,
0.09283262491226196,
-0.06830855458974838,
-0.006686370354145765,
-0.004924751818180084,
0.013755558989942074,
-0.05032142624258995,
0.18429741263389587,
0.06282955408096313,
0.047867823392152786,
-0.096428282558918,
0.07266043871641159,
0.0449543371796608,
-0.07330744713544846,
0.0033405697904527187,
0.07132815569639206,
-0.08534601330757141,
-0.05450327321887016,
0.06432835012674332,
0.1912047564983368,
-0.043927162885665894,
-0.04855562746524811,
-0.1453658491373062,
-0.12287921458482742,
0.07764768600463867,
0.1408335417509079,
0.11843205243349075,
0.01058033388108015,
-0.06616745889186859,
0.0029106447473168373,
-0.10731884837150574,
0.10160065442323685,
0.045956265181303024,
0.06211207062005997,
-0.14301945269107819,
0.14211498200893402,
0.020740197971463203,
0.04820193350315094,
-0.01806846633553505,
0.023208048194646835,
-0.10020429641008377,
0.007697694003582001,
-0.09298595041036606,
-0.019537312909960747,
-0.029065001755952835,
0.011588165536522865,
-0.005960927344858646,
-0.04729988053441048,
-0.0542338490486145,
0.010628738440573215,
-0.10766087472438812,
-0.023693302646279335,
0.030114110559225082,
0.07296796143054962,
-0.10916557163000107,
-0.035426318645477295,
0.030875829979777336,
-0.06108306720852852,
0.07371184974908829,
0.04329424351453781,
0.015606247819960117,
0.050780802965164185,
-0.13902859389781952,
0.02026754431426525,
0.07313340902328491,
0.029131997376680374,
0.06079116463661194,
-0.09932733327150345,
-0.007924248464405537,
-0.00831547100096941,
0.039642333984375,
0.02172110602259636,
0.07442112267017365,
-0.1410936713218689,
0.0035281842574477196,
-0.02308511547744274,
-0.08286073803901672,
-0.06700614094734192,
0.028149420395493507,
0.08893175423145294,
0.018416542559862137,
0.19928090274333954,
-0.07619873434305191,
0.049508191645145416,
-0.21921874582767487,
0.007203821558505297,
-0.006482485681772232,
-0.11031077802181244,
-0.10131534934043884,
-0.07205703109502792,
0.05513612926006317,
-0.060717787593603134,
0.1499030590057373,
0.04670583829283714,
0.0190992783755064,
0.02442006766796112,
-0.011104658246040344,
0.0123064573854208,
0.009836219251155853,
0.18994872272014618,
0.030648769810795784,
-0.03457606956362724,
0.05985496938228607,
0.044767893850803375,
0.10333859920501709,
0.11458932608366013,
0.2000289261341095,
0.14501407742500305,
-0.009634922258555889,
0.09320678561925888,
0.043747033923864365,
-0.055918898433446884,
-0.15551309287548065,
0.05201669782400131,
-0.0348605252802372,
0.10937416553497314,
-0.02125314436852932,
0.22091102600097656,
0.06478510051965714,
-0.1696740686893463,
0.051610227674245834,
-0.05148177221417427,
-0.08721215277910233,
-0.11527423560619354,
-0.049710892140865326,
-0.07701697945594788,
-0.13180910050868988,
-0.003880183445289731,
-0.11571096628904343,
-0.0028426540084183216,
0.12518630921840668,
0.003630818100646138,
-0.027395818382501602,
0.15849998593330383,
0.014401617459952831,
0.022212907671928406,
0.06015627831220627,
0.008367235772311687,
-0.03863980621099472,
-0.14036662876605988,
-0.059315942227840424,
-0.012330079451203346,
-0.008793477900326252,
0.03116128407418728,
-0.06153199076652527,
-0.04473326727747917,
0.03117159940302372,
-0.02042582258582115,
-0.09601709246635437,
0.006006556563079357,
0.011131567880511284,
0.0533316507935524,
0.044897403568029404,
0.009516828693449497,
0.018703876063227654,
-0.0037758296821266413,
0.20052506029605865,
-0.0717770978808403,
-0.06498154252767563,
-0.10246375948190689,
0.23358504474163055,
0.0361536405980587,
-0.018478771671652794,
0.03453867882490158,
-0.06657195836305618,
0.004409546032547951,
0.24914872646331787,
0.21641024947166443,
-0.07975707203149796,
-0.0060849878937006,
0.016893045976758003,
-0.007865218445658684,
-0.021979933604598045,
0.09790797531604767,
0.14306801557540894,
0.04558771848678589,
-0.09230106323957443,
-0.04356169328093529,
-0.058740004897117615,
-0.0174243226647377,
-0.03374728187918663,
0.0693250447511673,
0.051049165427684784,
0.009482786059379578,
-0.03563119098544121,
0.0567263662815094,
-0.06666052341461182,
-0.09042596817016602,
0.05730389058589935,
-0.21876642107963562,
-0.16741296648979187,
-0.01652800291776657,
0.1028960645198822,
0.0017809192650020123,
0.061948828399181366,
-0.029001614078879356,
-0.003035155590623617,
0.09021490812301636,
-0.019437670707702637,
-0.09757450222969055,
-0.07405272871255875,
0.0848613753914833,
-0.11156534403562546,
0.21836979687213898,
-0.047714509069919586,
0.054364051669836044,
0.1254379153251648,
0.06760457158088684,
-0.0644994005560875,
0.06536801904439926,
0.04187343269586563,
-0.04156811535358429,
0.023007867857813835,
0.06869390606880188,
-0.03251289203763008,
0.06446241587400436,
0.047979000955820084,
-0.13706746697425842,
0.02370663918554783,
-0.04812363535165787,
-0.06919568032026291,
-0.043872371315956116,
-0.020693952217698097,
-0.06029629707336426,
0.12902742624282837,
0.2190595120191574,
-0.024821428582072258,
-0.00955967791378498,
-0.07230399549007416,
0.00883461069315672,
0.05615578591823578,
0.021983714774250984,
-0.057219840586185455,
-0.21056394279003143,
0.016822319477796555,
0.04565083608031273,
-0.01846429333090782,
-0.25154390931129456,
-0.10084811598062515,
0.004124476574361324,
-0.07295944541692734,
-0.0947342962026596,
0.07150569558143616,
0.08810579776763916,
0.054769519716501236,
-0.05578319728374481,
-0.04721960425376892,
-0.07467550039291382,
0.14914295077323914,
-0.1454761028289795,
-0.09100616723299026
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetune-sentiment-analysis-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4558
- Accuracy: 0.8867
- F1: 0.8944
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetune-sentiment-analysis-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text"}, "metrics": [{"type": "accuracy", "value": 0.8866666666666667, "name": "Accuracy"}, {"type": "f1", "value": 0.8944099378881988, "name": "F1"}]}]}]}
|
text-classification
|
federicopascual/finetune-sentiment-analysis-model-3000-samples
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetune-sentiment-analysis-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4558
- Accuracy: 0.8867
- F1: 0.8944
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
[
"# finetune-sentiment-analysis-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4558\n- Accuracy: 0.8867\n- F1: 0.8944",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetune-sentiment-analysis-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4558\n- Accuracy: 0.8867\n- F1: 0.8944",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] |
[
67,
74,
6,
12,
8,
3,
90,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n# finetune-sentiment-analysis-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.4558\n- Accuracy: 0.8867\n- F1: 0.8944## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2### Training results### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] |
[
-0.11693186312913895,
0.1743505597114563,
-0.0029459395445883274,
0.08483696728944778,
0.14086918532848358,
0.02594788931310177,
0.07675006240606308,
0.1549052596092224,
-0.09307685494422913,
0.0902821496129036,
0.09251099079847336,
0.08320192992687225,
0.06356693804264069,
0.14335528016090393,
-0.0405014306306839,
-0.24688905477523804,
0.02951681986451149,
0.009553381241858006,
-0.06848286092281342,
0.10145802795886993,
0.12894685566425323,
-0.07945546507835388,
0.08893837779760361,
0.04858020693063736,
-0.17052818834781647,
0.0005353077431209385,
-0.010265622287988663,
-0.07312151789665222,
0.07504205405712128,
0.03033798187971115,
0.03981767222285271,
0.0006854942766949534,
0.07510975003242493,
-0.18381717801094055,
-0.009026524610817432,
0.056862130761146545,
0.03811394050717354,
0.10687889903783798,
0.054662175476551056,
0.031014664098620415,
0.08488840609788895,
-0.12308838218450546,
0.08089625090360641,
0.04706713184714317,
-0.08328141272068024,
-0.1846577227115631,
-0.09262741357088089,
0.11303100734949112,
0.07141049206256866,
0.09725520014762878,
0.005963603965938091,
0.15440189838409424,
-0.008545973338186741,
0.07838141173124313,
0.17202666401863098,
-0.2724810540676117,
-0.05736343562602997,
0.02821962721645832,
0.05097336322069168,
0.05596733093261719,
-0.09812057763338089,
0.00022159921354614198,
0.04543594270944595,
0.026919523254036903,
0.0995737761259079,
-0.0138024240732193,
0.00003635527900769375,
-0.045290831476449966,
-0.11712472140789032,
-0.08172494173049927,
0.23985370993614197,
0.07559571415185928,
-0.07748455554246902,
-0.13422845304012299,
-0.04830676689743996,
-0.10806533694267273,
-0.03667426109313965,
-0.05879145860671997,
0.03495616838335991,
-0.027679506689310074,
-0.031152712181210518,
-0.06230615824460983,
-0.07089255750179291,
-0.04019639268517494,
0.02568667195737362,
0.12048723548650742,
0.03913827985525131,
0.028930969536304474,
-0.007457258645445108,
0.0833292007446289,
-0.046942926943302155,
-0.14603520929813385,
-0.05894717946648598,
-0.012298008427023888,
-0.038900572806596756,
-0.05290880426764488,
-0.04596143588423729,
0.005540316924452782,
0.004373727831989527,
0.1648007333278656,
-0.061501167714595795,
0.06793875247240067,
0.03016859106719494,
-0.00515426229685545,
-0.02004786767065525,
0.17848116159439087,
-0.039723094552755356,
-0.03124893642961979,
0.01902529038488865,
0.10120408236980438,
0.01441743690520525,
-0.007276132237166166,
-0.08067408949136734,
-0.026747463271021843,
0.12519985437393188,
0.04859991371631622,
-0.017662324011325836,
0.041033051908016205,
-0.05894409492611885,
-0.05352982133626938,
0.0765845999121666,
-0.12345373630523682,
0.029643336310982704,
-0.03148181363940239,
-0.09461639076471329,
-0.03363701328635216,
0.03864353522658348,
-0.0050774067640304565,
-0.052173785865306854,
0.013452725484967232,
-0.09410576522350311,
-0.019244657829403877,
-0.052311353385448456,
-0.04010176658630371,
0.0031431633979082108,
-0.05205269530415535,
0.014353131875395775,
-0.08060071617364883,
-0.1833120882511139,
-0.03643481805920601,
0.031232984736561775,
-0.06781276315450668,
-0.0988980233669281,
0.005611964967101812,
-0.06742052733898163,
0.030674170702695847,
-0.006295301951467991,
0.07501477003097534,
-0.02267877385020256,
0.0640648603439331,
0.05920276418328285,
0.02068333886563778,
0.03114299848675728,
0.05514371022582054,
-0.09083177149295807,
0.04696086794137955,
-0.1139337494969368,
0.10590807348489761,
-0.08486074209213257,
0.03373036906123161,
-0.1335071474313736,
-0.10327525436878204,
0.003784714499488473,
-0.033775486052036285,
0.06996386498212814,
0.13521263003349304,
-0.09667083621025085,
-0.02656753547489643,
0.11063877493143082,
-0.0715336799621582,
-0.1219930350780487,
0.08127797394990921,
-0.011654082685709,
0.043204668909311295,
0.04571196436882019,
0.14153945446014404,
0.12724357843399048,
-0.08981406688690186,
-0.04432263970375061,
0.0420248918235302,
0.05364459007978439,
0.00820975098758936,
0.08909651637077332,
-0.01296304352581501,
0.020992089062929153,
0.032260384410619736,
-0.06213735044002533,
-0.018136847764253616,
-0.07364166527986526,
-0.08374867588281631,
-0.07690610736608505,
-0.06727714836597443,
0.0297671090811491,
0.027876557782292366,
0.04144586622714996,
-0.05451866239309311,
-0.11123678833246231,
0.0820891410112381,
0.14454537630081177,
-0.03527887538075447,
0.01181985903531313,
-0.0812390074133873,
0.07411151379346848,
-0.05899018794298172,
-0.01516754925251007,
-0.22239503264427185,
-0.084479920566082,
0.06422543525695801,
-0.09040019661188126,
0.026976699009537697,
-0.013270065188407898,
0.04798846319317818,
0.057665880769491196,
-0.024289870634675026,
-0.041742730885744095,
-0.08572521060705185,
-0.016916994005441666,
-0.10067460685968399,
-0.14225661754608154,
-0.0634663924574852,
-0.017245745286345482,
0.18795034289360046,
-0.21156306564807892,
0.018171437084674835,
0.01925976574420929,
0.13006176054477692,
0.008491694927215576,
-0.06509442627429962,
0.01656590774655342,
-0.007246545050293207,
-0.01583508402109146,
-0.10931560397148132,
0.03624901920557022,
0.031286269426345825,
-0.09691426157951355,
-0.04251840338110924,
-0.13427002727985382,
0.0952012911438942,
0.07981943339109421,
0.07127940654754639,
-0.07628273963928223,
-0.009097259491682053,
-0.05781342461705208,
-0.04510314390063286,
-0.03853613883256912,
-0.020749531686306,
0.16654996573925018,
0.005981022957712412,
0.1385786086320877,
-0.07514172792434692,
-0.0566411055624485,
0.030749035999178886,
-0.016176242381334305,
-0.04801680147647858,
0.056138746440410614,
-0.01897283084690571,
-0.1586460918188095,
0.10260327160358429,
0.0921371653676033,
-0.012544587254524231,
0.11051441729068756,
-0.06606943160295486,
-0.08120696246623993,
-0.042405132204294205,
0.01191057451069355,
0.00482184300199151,
0.08395311236381531,
-0.10600756853818893,
0.007611661683768034,
0.06396172940731049,
0.02136659063398838,
0.02449881285429001,
-0.13482561707496643,
0.025758661329746246,
0.046542052179574966,
-0.03754616528749466,
0.027086498215794563,
0.007516544312238693,
0.0006286604329943657,
0.07987207174301147,
0.04039282351732254,
-0.010051471181213856,
0.04192960634827614,
-0.007263206411153078,
-0.07527251541614532,
0.17237290740013123,
-0.11542124301195145,
-0.20112116634845734,
-0.16663013398647308,
0.04805079847574234,
-0.09458975493907928,
-0.014883022755384445,
0.018229885026812553,
-0.04094113036990166,
-0.06495922803878784,
-0.07311005890369415,
-0.03668319061398506,
-0.06564714759588242,
-0.004441381432116032,
0.09591013193130493,
-0.008798717521131039,
0.13096560537815094,
-0.12849827110767365,
-0.007924867793917656,
0.0077962069772183895,
-0.06087401509284973,
-0.02612927369773388,
0.027455510571599007,
0.10333361476659775,
0.0768071636557579,
-0.013233156874775887,
0.028702277690172195,
-0.014027290977537632,
0.2846577763557434,
-0.07976256310939789,
-0.013272040523588657,
0.17705115675926208,
0.023331979289650917,
0.07965469360351562,
0.10117363929748535,
0.02745506539940834,
-0.08554452657699585,
0.013571162708103657,
0.02718173898756504,
-0.0038768399972468615,
-0.2177646905183792,
-0.0343562550842762,
-0.029483618214726448,
-0.06483105570077896,
0.11737702041864395,
0.05625692009925842,
0.08279699087142944,
0.07926681637763977,
-0.03422349691390991,
0.07750348746776581,
-0.02037043683230877,
0.11063101887702942,
0.1449788361787796,
0.0529661625623703,
0.10035362094640732,
-0.025186480954289436,
-0.0045619229786098,
0.07580941915512085,
-0.018898017704486847,
0.22788791358470917,
-0.008201289921998978,
0.19116631150245667,
0.01647976227104664,
0.15743322670459747,
-0.01708589866757393,
0.042509835213422775,
0.009843703359365463,
0.01559433713555336,
0.007982397451996803,
-0.06361474096775055,
-0.06116607040166855,
0.020800266414880753,
-0.02058698982000351,
0.07698504626750946,
-0.10356634110212326,
0.05150679871439934,
0.008536139503121376,
0.24940887093544006,
0.03444472700357437,
-0.32769203186035156,
-0.11060340702533722,
0.0070545910857617855,
-0.029415447264909744,
-0.11618559807538986,
-0.013810931704938412,
0.06097650155425072,
-0.1551380753517151,
0.07116539776325226,
-0.06531103700399399,
0.09594400972127914,
-0.044868353754282,
0.0049496544525027275,
0.06109970435500145,
0.10372714698314667,
0.01338812243193388,
0.10088042169809341,
-0.20986071228981018,
0.17814379930496216,
0.02531396970152855,
0.08171334117650986,
-0.06877528131008148,
0.06621040403842926,
0.021357279270887375,
0.10560325533151627,
0.12009092420339584,
0.0015845198649913073,
-0.02888171374797821,
-0.17746105790138245,
-0.0973772332072258,
-0.0014074150240048766,
0.09945474565029144,
-0.07329367846250534,
0.07533162087202072,
-0.06435681134462357,
0.0062919496558606625,
0.025888705626130104,
-0.037388790398836136,
-0.14916202425956726,
-0.1360853612422943,
0.04744316264986992,
0.02496424876153469,
0.015718650072813034,
-0.07927320152521133,
-0.08905495703220367,
-0.010188179090619087,
0.18559809029102325,
0.004278680309653282,
-0.07423865795135498,
-0.1677369326353073,
0.09052968770265579,
0.13608454167842865,
-0.08431088179349899,
0.03840417414903641,
-0.024625182151794434,
0.16281846165657043,
0.0529702864587307,
-0.08609391003847122,
0.042282961308956146,
-0.06745308637619019,
-0.17339834570884705,
-0.016052531078457832,
0.13938626646995544,
-0.008103430271148682,
0.040132105350494385,
0.011383323930203915,
0.033908337354660034,
-0.0030796274077147245,
-0.09019219130277634,
-0.003465265966951847,
0.043906278908252716,
0.0931791216135025,
0.04152877256274223,
-0.03765425458550453,
0.044658735394477844,
-0.055809929966926575,
-0.0011061903787776828,
0.11318108439445496,
0.2104150503873825,
-0.08514449745416641,
0.03734118863940239,
0.03385625779628754,
-0.07218178361654282,
-0.15751121938228607,
0.01930394023656845,
0.12923160195350647,
0.02151905931532383,
0.058400824666023254,
-0.16328208148479462,
0.08918382972478867,
0.090486541390419,
-0.04188362509012222,
0.04132487624883652,
-0.26659369468688965,
-0.12806189060211182,
0.08011121302843094,
0.10002930462360382,
0.018605973571538925,
-0.14434342086315155,
-0.07314810156822205,
-0.028622714802622795,
-0.11291033774614334,
0.08465342968702316,
-0.013389135710895061,
0.10361257195472717,
-0.026933901011943817,
0.06063903495669365,
0.04097505658864975,
-0.019019829109311104,
0.1724454164505005,
0.03508656471967697,
0.064765565097332,
-0.06284759938716888,
0.04522707685828209,
0.09565431624650955,
-0.07967691123485565,
0.11292140185832977,
-0.028264032676815987,
0.09337270259857178,
-0.17774786055088043,
-0.009313518181443214,
-0.056456368416547775,
0.06724807620048523,
-0.06121336668729782,
-0.04132796451449394,
-0.039862800389528275,
0.03792929649353027,
0.04626769945025444,
-0.033103980123996735,
0.11261386424303055,
0.05224515497684479,
0.05031029134988785,
0.1738239973783493,
0.07225411385297775,
0.009255041368305683,
-0.16607800126075745,
-0.019966183230280876,
-0.009696616791188717,
0.05454588308930397,
-0.12620696425437927,
0.02819741517305374,
0.09620686620473862,
0.029487749561667442,
0.13535445928573608,
0.013693664222955704,
-0.06616803258657455,
-0.018001655116677284,
0.03414982184767723,
-0.09908761084079742,
-0.15825724601745605,
-0.0657438114285469,
-0.013993361033499241,
-0.1595481038093567,
0.011343659833073616,
0.10967747122049332,
-0.058804191648960114,
-0.016980955377221107,
-0.014605318196117878,
0.008254197426140308,
-0.0043394966050982475,
0.17134645581245422,
0.044913239777088165,
0.07392308861017227,
-0.08786589652299881,
0.12006749212741852,
0.08127067238092422,
-0.04770645126700401,
0.04881051927804947,
0.033309098333120346,
-0.09665707498788834,
-0.023931942880153656,
0.04839671775698662,
0.10718637704849243,
-0.036028455942869186,
-0.05212327837944031,
-0.0690477192401886,
-0.07142319530248642,
0.04851553589105606,
0.03026670403778553,
0.07693034410476685,
0.0013139719376340508,
-0.03550922870635986,
-0.00791282020509243,
-0.1328054964542389,
0.11137072741985321,
0.048746008425951004,
0.07047614455223083,
-0.19301410019397736,
0.04325035214424133,
0.007272620219737291,
0.0632917657494545,
-0.015852995216846466,
-0.013245939277112484,
-0.06644180417060852,
-0.04998549818992615,
-0.09136462956666946,
0.013134903274476528,
-0.04353722557425499,
0.005512678995728493,
-0.023743199184536934,
-0.05730156600475311,
-0.03644675388932228,
0.0644986480474472,
-0.05647548660635948,
-0.09370006620883942,
0.01475257147103548,
0.06610152125358582,
-0.12065406143665314,
-0.007616377901285887,
0.020349908620119095,
-0.10363812744617462,
0.09463419020175934,
0.06653086096048355,
0.03904424235224724,
0.008632473647594452,
0.028655311092734337,
0.018485352396965027,
0.03781549260020256,
0.031944677233695984,
0.05155019089579582,
-0.11179322749376297,
0.005048817954957485,
-0.021924013271927834,
0.014731870964169502,
-0.00010501075303182006,
0.05754359811544418,
-0.14213304221630096,
-0.06737642735242844,
-0.058122508227825165,
-0.011842777021229267,
-0.07073574513196945,
0.06075487658381462,
0.10930342972278595,
0.021082386374473572,
0.18127669394016266,
-0.04938395321369171,
0.016890691593289375,
-0.21883538365364075,
-0.014894346706569195,
-0.013687524013221264,
-0.05808287113904953,
-0.06893318891525269,
-0.04656689614057541,
0.06177064776420593,
-0.048953037708997726,
0.09280496835708618,
-0.012839173898100853,
0.10841382294893265,
0.042254865169525146,
0.011908610351383686,
0.017527379095554352,
-0.0118234409019351,
0.1638081818819046,
0.08048319071531296,
-0.006821601651608944,
0.12333844602108002,
-0.0018560132011771202,
0.06663920730352402,
0.02926694229245186,
0.09155648946762085,
0.12875467538833618,
-0.05933723598718643,
0.08037208765745163,
0.054871153086423874,
-0.06513306498527527,
-0.16375184059143066,
0.06689812988042831,
-0.027522187680006027,
0.11701495200395584,
-0.02599320188164711,
0.09883548319339752,
0.10301516205072403,
-0.17238076031208038,
0.059033364057540894,
-0.043439775705337524,
-0.09824863821268082,
-0.10290127247571945,
-0.10741869360208511,
-0.08576306700706482,
-0.1241767480969429,
0.005576684605330229,
-0.13120262324810028,
0.03222338482737541,
0.075589619576931,
-0.014134961180388927,
-0.02781789004802704,
0.1527658998966217,
-0.06780309230089188,
-0.01864500530064106,
0.07093995809555054,
-0.009928827174007893,
-0.016726266592741013,
-0.04812786728143692,
-0.04601020738482475,
0.04339104890823364,
0.03046526201069355,
0.09678702056407928,
-0.035909369587898254,
0.00945102795958519,
0.03435423597693443,
-0.024302946403622627,
-0.0973571389913559,
0.013930868357419968,
0.021192461252212524,
0.00881222914904356,
0.011324964463710785,
0.024120021611452103,
0.0026273636613041162,
-0.04767556115984917,
0.26695024967193604,
-0.06394614279270172,
-0.03714193031191826,
-0.13463094830513,
0.1502072960138321,
0.049684323370456696,
-0.03502146527171135,
0.07972369343042374,
-0.10572056472301483,
-0.00815280806273222,
0.1524346023797989,
0.10340319573879242,
-0.0338001549243927,
-0.027555812150239944,
-0.003932109568268061,
-0.023369261994957924,
-0.04449247568845749,
0.10097568482160568,
0.08867069333791733,
-0.011454681865870953,
-0.040282268077135086,
0.028105059638619423,
-0.01237039640545845,
-0.03960208222270012,
-0.08029735833406448,
0.102127306163311,
0.012183740735054016,
0.010340387932956219,
-0.022372199222445488,
0.055956147611141205,
0.023616712540388107,
-0.1472478061914444,
0.026134124025702477,
-0.146402969956398,
-0.17550702393054962,
-0.0256930161267519,
0.031705450266599655,
0.00536860479041934,
0.05930385738611221,
0.015342341735959053,
-0.0033425132278352976,
0.133302241563797,
-0.01436495129019022,
-0.08059779554605484,
-0.08648116886615753,
0.08773905038833618,
-0.07239925861358643,
0.2110091894865036,
0.0083153760060668,
0.0669490322470665,
0.109374038875103,
0.0038826267700642347,
-0.17143085598945618,
0.00949144084006548,
0.06251689046621323,
-0.018590813502669334,
0.05546526238322258,
0.16788412630558014,
-0.01354866474866867,
0.04653799906373024,
0.03382298722863197,
-0.15136723220348358,
-0.05866047739982605,
-0.0653083398938179,
0.005492920987308025,
-0.07677216827869415,
-0.013658160343766212,
-0.0643312856554985,
0.1632225662469864,
0.19954830408096313,
-0.05869267135858536,
-0.029872221872210503,
-0.066815085709095,
0.04088956490159035,
0.06487955898046494,
0.08146999776363373,
-0.007926618680357933,
-0.19925661385059357,
0.006584334187209606,
0.002180317882448435,
0.018186919391155243,
-0.2124805599451065,
-0.10768933594226837,
0.04212712123990059,
-0.062114790081977844,
-0.05566179379820824,
0.1066041812300682,
0.04034971445798874,
0.012687531299889088,
-0.034952402114868164,
-0.05280733108520508,
-0.0705915316939354,
0.14825130999088287,
-0.1524062603712082,
-0.049950011074543
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-sentiment-analysis-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2868
- Accuracy: 0.909
- Precision: 0.8900
- Recall: 0.9283
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "precision", "recall"], "model-index": [{"name": "finetuned-sentiment-analysis-model", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text"}, "metrics": [{"type": "accuracy", "value": 0.909, "name": "Accuracy"}, {"type": "precision", "value": 0.8899803536345776, "name": "Precision"}, {"type": "recall", "value": 0.9282786885245902, "name": "Recall"}]}]}]}
|
text-classification
|
federicopascual/finetuned-sentiment-analysis-model
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuned-sentiment-analysis-model
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2868
- Accuracy: 0.909
- Precision: 0.8900
- Recall: 0.9283
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
[
"# finetuned-sentiment-analysis-model\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2868\n- Accuracy: 0.909\n- Precision: 0.8900\n- Recall: 0.9283",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuned-sentiment-analysis-model\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2868\n- Accuracy: 0.909\n- Precision: 0.8900\n- Recall: 0.9283",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] |
[
67,
78,
6,
12,
8,
3,
90,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n# finetuned-sentiment-analysis-model\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.2868\n- Accuracy: 0.909\n- Precision: 0.8900\n- Recall: 0.9283## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2### Training results### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] |
[
-0.1270783692598343,
0.170096755027771,
-0.0030526837799698114,
0.0819387137889862,
0.14433085918426514,
0.03090442158281803,
0.06961461901664734,
0.16656768321990967,
-0.08910830318927765,
0.1156148612499237,
0.07501570880413055,
0.07403549551963806,
0.07571476697921753,
0.15890194475650787,
-0.03194378688931465,
-0.23221154510974884,
0.020212478935718536,
0.0053267707116901875,
-0.04135894402861595,
0.09380759298801422,
0.12398882210254669,
-0.10411890596151352,
0.07296385616064072,
0.02843886986374855,
-0.16959476470947266,
0.007505382411181927,
-0.01770075038075447,
-0.07303310930728912,
0.07569899410009384,
0.016982581466436386,
0.05973748117685318,
-0.01430006604641676,
0.0811789259314537,
-0.19378377497196198,
-0.010819612070918083,
0.06650648266077042,
0.0492192842066288,
0.09692718088626862,
0.07301533967256546,
0.009052660316228867,
0.054774731397628784,
-0.13119250535964966,
0.0781695768237114,
0.04443720728158951,
-0.08061478286981583,
-0.17818370461463928,
-0.10276322811841965,
0.10851515084505081,
0.08595635741949081,
0.09846501052379608,
-0.010500352829694748,
0.1422528475522995,
-0.02999921515583992,
0.07321528345346451,
0.17177318036556244,
-0.24344787001609802,
-0.05881299450993538,
0.05026620253920555,
0.04633385315537453,
0.02694559469819069,
-0.09853541105985641,
-0.002821505069732666,
0.046652454882860184,
0.03281155228614807,
0.0764184445142746,
0.0016993626486510038,
-0.06185462325811386,
-0.017406722530722618,
-0.11139100790023804,
-0.07822749018669128,
0.2398522049188614,
0.056894369423389435,
-0.0611775778234005,
-0.1359664350748062,
-0.05090047046542168,
-0.1081463173031807,
-0.050703778862953186,
-0.05188547074794769,
0.03756108507514,
-0.03574094548821449,
-0.054878558963537216,
-0.026802457869052887,
-0.07823412865400314,
-0.044734980911016464,
0.0076143150217831135,
0.07399642467498779,
0.04696996882557869,
0.023684639483690262,
-0.03189476579427719,
0.09903907775878906,
-0.017807308584451675,
-0.13236628472805023,
-0.05195845291018486,
-0.008430765010416508,
-0.05470503866672516,
-0.0623159296810627,
-0.03718121349811554,
0.006583737209439278,
0.010729100555181503,
0.17087779939174652,
-0.04757805913686752,
0.0661253035068512,
0.02801193669438362,
-0.0005326812388375401,
-0.02503249980509281,
0.18875637650489807,
-0.06178542971611023,
-0.04462626576423645,
0.009783916175365448,
0.10127521306276321,
-0.002405255800113082,
-0.0030856525991111994,
-0.06228937581181526,
-0.022515764459967613,
0.14763765037059784,
0.0580466166138649,
-0.00887045357376337,
0.035879965871572495,
-0.05851665139198303,
-0.03876623883843422,
0.028284229338169098,
-0.13033869862556458,
0.03457647189497948,
-0.017340177670121193,
-0.11551358550786972,
-0.00809472519904375,
0.036051079630851746,
-0.010903513990342617,
-0.05779469385743141,
0.027558941394090652,
-0.09617448598146439,
-0.0239561740309,
-0.0737084224820137,
-0.04553547501564026,
0.01745559088885784,
-0.04345454275608063,
-0.001880426425486803,
-0.06751508265733719,
-0.21558769047260284,
-0.04866451397538185,
0.03262484446167946,
-0.07393045723438263,
-0.0735933855175972,
-0.019104816019535065,
-0.0728943794965744,
0.030625006183981895,
-0.0004189092433080077,
0.10823755711317062,
-0.023761341348290443,
0.07043902575969696,
0.042590219527482986,
0.035623129457235336,
0.06719736754894257,
0.06007557734847069,
-0.093289315700531,
0.03788990154862404,
-0.12204907089471817,
0.09783819317817688,
-0.08319086581468582,
0.02641274593770504,
-0.13352574408054352,
-0.08365115523338318,
0.004665810149163008,
-0.019236234948039055,
0.06798316538333893,
0.14447049796581268,
-0.1549123227596283,
-0.035867609083652496,
0.14489832520484924,
-0.056041400879621506,
-0.11038776487112045,
0.07800135016441345,
-0.019176021218299866,
0.05392185598611832,
0.05292164906859398,
0.12827461957931519,
0.10551021248102188,
-0.08763127774000168,
-0.06733068823814392,
0.016751794144511223,
0.060877725481987,
0.019435619935393333,
0.0680282860994339,
-0.027178727090358734,
0.042422566562891006,
0.04063868150115013,
-0.04899156838655472,
-0.0277670007199049,
-0.08462349325418472,
-0.07712860405445099,
-0.06725252419710159,
-0.06336072832345963,
0.024393662810325623,
0.02584235742688179,
0.050050973892211914,
-0.05274414271116257,
-0.11003991961479187,
0.10100950300693512,
0.14481677114963531,
-0.037016142159700394,
0.00725196860730648,
-0.0816580057144165,
0.07759832590818405,
-0.0380609855055809,
-0.01468872744590044,
-0.22209659218788147,
-0.09103178977966309,
0.05570060759782791,
-0.08384256809949875,
-0.0000689636217430234,
-0.02184482105076313,
0.054896362125873566,
0.04288400709629059,
-0.019356608390808105,
-0.041335053741931915,
-0.07322533428668976,
-0.02601633220911026,
-0.1008944883942604,
-0.14787179231643677,
-0.056280579417943954,
-0.017102127894759178,
0.20906566083431244,
-0.20580342411994934,
0.018118806183338165,
-0.0020731224212795496,
0.15180853009223938,
0.002984517253935337,
-0.07642923295497894,
0.011476188898086548,
-0.0006459705764427781,
-0.014517450705170631,
-0.10554033517837524,
0.03452082350850105,
0.010762524791061878,
-0.07135862857103348,
-0.04804476723074913,
-0.14113713800907135,
0.038974493741989136,
0.09231134504079819,
0.08194608241319656,
-0.08357824385166168,
-0.02143104560673237,
-0.056806694716215134,
-0.05047456547617912,
-0.0610383115708828,
-0.03248925134539604,
0.15461814403533936,
0.01710095815360546,
0.1271991729736328,
-0.0666225403547287,
-0.07751460373401642,
0.012657160870730877,
-0.00709196925163269,
-0.04114638268947601,
0.06738172471523285,
0.006258722394704819,
-0.10338868200778961,
0.11222244799137115,
0.06240524351596832,
-0.027595622465014458,
0.1314927041530609,
-0.05834349989891052,
-0.09481437504291534,
-0.02837798185646534,
0.0011248759692534804,
0.001954426057636738,
0.10200177878141403,
-0.09482462704181671,
0.006695298012346029,
0.06427650898694992,
0.012910070829093456,
0.03227023035287857,
-0.1418197751045227,
0.025721244513988495,
0.03112597018480301,
-0.0347694493830204,
0.021576661616563797,
-0.0015809021424502134,
0.029009776189923286,
0.08619313687086105,
0.021900909021496773,
0.006644641514867544,
0.025970466434955597,
-0.01170401182025671,
-0.08333650976419449,
0.18849821388721466,
-0.11632941663265228,
-0.1881789267063141,
-0.15443332493305206,
0.05344635993242264,
-0.07955971360206604,
-0.026813112199306488,
0.009987249039113522,
-0.061393290758132935,
-0.05758630856871605,
-0.07057423889636993,
-0.016465207561850548,
-0.05364380031824112,
-0.007680555805563927,
0.07390553504228592,
-0.0018041478469967842,
0.1166287213563919,
-0.13781607151031494,
0.00028771706274710596,
-0.0065219528041779995,
-0.07060588896274567,
0.002810702659189701,
0.03809470310807228,
0.09252546727657318,
0.08922620117664337,
-0.004265605937689543,
0.030628805980086327,
-0.01910656876862049,
0.251903772354126,
-0.07576185464859009,
-0.0018763324478641152,
0.16351863741874695,
0.032830897718667984,
0.06943906843662262,
0.1036466583609581,
0.021150508895516396,
-0.0799500048160553,
0.012235935777425766,
0.05631478503346443,
0.005576827097684145,
-0.2249288558959961,
-0.03719425946474075,
-0.02620069682598114,
-0.046352386474609375,
0.12110530585050583,
0.04112519696354866,
0.05926255136728287,
0.07430174201726913,
-0.03818686679005623,
0.07665785402059555,
-0.0260075144469738,
0.0905749648809433,
0.11396609991788864,
0.04338744282722473,
0.1036611944437027,
-0.020361771807074547,
-0.004572811536490917,
0.06373867392539978,
-0.0008698551682755351,
0.237117737531662,
-0.020352425053715706,
0.13458049297332764,
0.028752105310559273,
0.14317554235458374,
-0.030049871653318405,
0.05366956815123558,
0.009782388806343079,
0.011954862624406815,
0.013172389939427376,
-0.0645953118801117,
-0.0491844080388546,
0.018089797347784042,
-0.0011647468199953437,
0.07633306086063385,
-0.1094198152422905,
0.046217821538448334,
0.03074532002210617,
0.23691776394844055,
0.05065393075346947,
-0.34012481570243835,
-0.10304377973079681,
0.02070327289402485,
-0.024473639205098152,
-0.1156497523188591,
-0.01351200696080923,
0.07145561277866364,
-0.15941348671913147,
0.05640978738665581,
-0.06810428947210312,
0.09278479218482971,
-0.04201407730579376,
-0.003903917735442519,
0.052843574434518814,
0.12225862592458725,
0.013455050997436047,
0.09818532317876816,
-0.2077060341835022,
0.18646417558193207,
0.028748231008648872,
0.09338980913162231,
-0.07009093463420868,
0.06772076338529587,
0.03420504927635193,
0.0489337295293808,
0.12543390691280365,
-0.0013689410407096148,
-0.05082927271723747,
-0.1814391016960144,
-0.10371813923120499,
0.010812239721417427,
0.09213879704475403,
-0.07129604369401932,
0.09354591369628906,
-0.0563260056078434,
0.0004961533704772592,
0.02797773852944374,
-0.04111045226454735,
-0.16037890315055847,
-0.12755489349365234,
0.035789743065834045,
0.018964294344186783,
0.0081141646951437,
-0.08881956338882446,
-0.09175696223974228,
-0.022948134690523148,
0.1674564778804779,
-0.0210715401917696,
-0.06519493460655212,
-0.15766213834285736,
0.10786005109548569,
0.15645639598369598,
-0.07873433083295822,
0.04494188353419304,
-0.010922422632575035,
0.17101694643497467,
0.037815190851688385,
-0.08328855782747269,
0.04021712392568588,
-0.07855434715747833,
-0.1693951040506363,
-0.04163286089897156,
0.1265346109867096,
0.00583358108997345,
0.04169117659330368,
0.008722556754946709,
0.038420192897319794,
-0.010183908976614475,
-0.07980302721261978,
0.017111053690314293,
0.07726863771677017,
0.09634570777416229,
0.05718572810292244,
-0.06152104213833809,
-0.012482613325119019,
-0.05912244692444801,
0.0022887892555445433,
0.11949711292982101,
0.19645501673221588,
-0.08140864223241806,
0.02797267585992813,
0.03288738429546356,
-0.07118814438581467,
-0.175088569521904,
0.0442584864795208,
0.12412780523300171,
0.026307739317417145,
0.03927209600806236,
-0.17706581950187683,
0.10407467186450958,
0.09869449585676193,
-0.032162103801965714,
0.044489312916994095,
-0.28848814964294434,
-0.130428746342659,
0.09825844317674637,
0.0848354697227478,
-0.005645320285111666,
-0.13475678861141205,
-0.06943599134683609,
-0.025319069623947144,
-0.10166867822408676,
0.07225753366947174,
-0.030574118718504906,
0.09795258939266205,
-0.02879532426595688,
0.089443638920784,
0.03625845909118652,
-0.03986082226037979,
0.1634787768125534,
0.04274160414934158,
0.06950795650482178,
-0.048392534255981445,
0.03311512619256973,
0.08596666157245636,
-0.08712387830018997,
0.10466180741786957,
-0.015103835612535477,
0.09113078564405441,
-0.1899605542421341,
-0.0008933509234338999,
-0.06807611137628555,
0.07723722606897354,
-0.06250133365392685,
-0.04171208292245865,
-0.036303285509347916,
0.04683467000722885,
0.03990870341658592,
-0.03722001239657402,
0.04323329031467438,
0.04918866232037544,
0.06709139049053192,
0.1271636039018631,
0.045274123549461365,
0.02266174554824829,
-0.1638433188199997,
-0.009990937076508999,
-0.003988169599324465,
0.05037224665284157,
-0.11659268289804459,
0.02270231395959854,
0.1088605746626854,
0.03491046279668808,
0.1414904147386551,
0.029551362618803978,
-0.08443589508533478,
-0.013736768625676632,
0.040827151387929916,
-0.1016950011253357,
-0.12447473406791687,
-0.03899994492530823,
0.0026238553691655397,
-0.1688176989555359,
-0.002438261639326811,
0.08545286953449249,
-0.051949530839920044,
-0.02096862904727459,
-0.021782515570521355,
0.021001199260354042,
0.003379266243427992,
0.19654710590839386,
0.036847151815891266,
0.0786978006362915,
-0.08374422043561935,
0.13218504190444946,
0.07718700915575027,
-0.06633970886468887,
0.05083514750003815,
0.02686842530965805,
-0.08397507667541504,
-0.02154156193137169,
0.057815615087747574,
0.12238137423992157,
-0.03250383958220482,
-0.04718440771102905,
-0.07765999436378479,
-0.07941348105669022,
0.055413320660591125,
0.04187163710594177,
0.06152435392141342,
0.005773499608039856,
-0.036143749952316284,
0.0017346451058983803,
-0.13232925534248352,
0.11457812041044235,
0.0865442156791687,
0.0712999552488327,
-0.15960559248924255,
0.09254951030015945,
0.004913884215056896,
0.03000720962882042,
-0.00728164566680789,
-0.002718553179875016,
-0.0753767266869545,
-0.03243859484791756,
-0.09632398933172226,
0.0026229468639940023,
-0.040846966207027435,
-0.011277060024440289,
-0.015655672177672386,
-0.03433194011449814,
-0.03275267034769058,
0.04813067242503166,
-0.06423940509557724,
-0.09443087875843048,
0.006045354064553976,
0.08382955193519592,
-0.1401711255311966,
-0.0022735705133527517,
0.02874939702451229,
-0.10986239463090897,
0.09045662730932236,
0.08768466114997864,
0.05293406546115875,
0.01842336542904377,
-0.020600905641913414,
-0.009036273695528507,
0.03316657617688179,
0.03243076428771019,
0.06201161816716194,
-0.11730904877185822,
0.012269027531147003,
-0.03198762238025665,
0.01426196563988924,
0.006448523141443729,
0.035770077258348465,
-0.14474794268608093,
-0.05831507220864296,
-0.05574345961213112,
-0.01476556621491909,
-0.0726812481880188,
0.05569712445139885,
0.11016277223825455,
0.027145111933350563,
0.16683949530124664,
-0.061300601810216904,
0.01648777537047863,
-0.22871822118759155,
-0.009155405685305595,
-0.023694150149822235,
-0.050482701510190964,
-0.06389768421649933,
-0.03208925947546959,
0.08075958490371704,
-0.04870879277586937,
0.0931483581662178,
-0.007910839281976223,
0.09278993308544159,
0.03885580599308014,
0.008651809766888618,
-0.016337784007191658,
0.0010077625047415495,
0.1599029153585434,
0.07239741832017899,
-0.002301911124959588,
0.1031670793890953,
0.0033619648311287165,
0.07771404087543488,
0.03948506340384483,
0.12356624752283096,
0.14522825181484222,
-0.05791010335087776,
0.09261146187782288,
0.05533505603671074,
-0.09176386892795563,
-0.15724168717861176,
0.0734696313738823,
-0.04871353507041931,
0.11311633139848709,
-0.03475816547870636,
0.09735188633203506,
0.10599575191736221,
-0.18224915862083435,
0.06137888506054878,
-0.06619945168495178,
-0.09874913841485977,
-0.10099055618047714,
-0.06962026655673981,
-0.0868711769580841,
-0.10092253983020782,
0.009660583920776844,
-0.13327139616012573,
0.03348371386528015,
0.08645276725292206,
-0.0020116306841373444,
-0.017174916341900826,
0.14938604831695557,
-0.06489231437444687,
-0.018710456788539886,
0.06865644454956055,
0.008738851174712181,
-0.01959953084588051,
-0.05435984209179878,
-0.04484948143362999,
0.036189790815114975,
0.040762029588222504,
0.10754486918449402,
-0.040889922529459,
0.01229127123951912,
0.04793374240398407,
-0.01718900352716446,
-0.09369346499443054,
0.009570296853780746,
0.00374619010835886,
0.011394968256354332,
0.005553454626351595,
0.03824378177523613,
0.013429660350084305,
-0.04798297584056854,
0.2829655706882477,
-0.07763570547103882,
-0.05182206258177757,
-0.15185272693634033,
0.1642698496580124,
0.04913260042667389,
-0.028454506769776344,
0.07198483496904373,
-0.12166080623865128,
-0.014467056840658188,
0.16225691139698029,
0.10367576032876968,
-0.030644597485661507,
-0.035523951053619385,
-0.001515102107077837,
-0.0263461135327816,
-0.058035288006067276,
0.09136735647916794,
0.09496854990720749,
0.015199221670627594,
-0.047262635082006454,
0.03886410593986511,
-0.005804550368338823,
-0.059163156896829605,
-0.07095497846603394,
0.11897477507591248,
0.007793270517140627,
0.01151123084127903,
-0.024616213515400887,
0.06687034666538239,
0.0038248745258897543,
-0.1584414690732956,
0.030945371836423874,
-0.144521564245224,
-0.1855345517396927,
-0.02857438288629055,
0.0503729023039341,
0.0016425240319222212,
0.067967489361763,
0.007849509827792645,
0.00396882276982069,
0.13826435804367065,
-0.0070071532391011715,
-0.07987892627716064,
-0.10549989342689514,
0.08216553181409836,
-0.03488989546895027,
0.24289259314537048,
0.003398394212126732,
0.06442822515964508,
0.11264173686504364,
0.004849390126764774,
-0.16412845253944397,
0.0014402623055502772,
0.06446629762649536,
-0.028768928721547127,
0.05852235481142998,
0.17024792730808258,
-0.020184513181447983,
0.054615121334791183,
0.0423574298620224,
-0.1457698494195938,
-0.04333506524562836,
-0.05237597972154617,
-0.001307385740801692,
-0.10031633824110031,
0.007257187273353338,
-0.05377275124192238,
0.15581126511096954,
0.21175777912139893,
-0.05531333386898041,
-0.02123059518635273,
-0.07334964722394943,
0.0373014435172081,
0.049571335315704346,
0.09398358315229416,
-0.012071345932781696,
-0.1978711485862732,
0.028514565899968147,
-0.025908492505550385,
0.017529983073472977,
-0.2329893410205841,
-0.08904089033603668,
0.056028321385383606,
-0.06892667710781097,
-0.051783882081508636,
0.1183767095208168,
0.05605003237724304,
0.02161254547536373,
-0.03783527761697769,
-0.0433097667992115,
-0.05845728889107704,
0.1450357437133789,
-0.14376194775104523,
-0.05645888298749924
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-analysis-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3130
- Accuracy: 0.8733
- F1: 0.8812
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-analysis-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text"}, "metrics": [{"type": "accuracy", "value": 0.8733333333333333, "name": "Accuracy"}, {"type": "f1", "value": 0.88125, "name": "F1"}]}]}]}
|
text-classification
|
federicopascual/finetuning-sentiment-analysis-model-3000-samples
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-analysis-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3130
- Accuracy: 0.8733
- F1: 0.8812
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
[
"# finetuning-sentiment-analysis-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3130\n- Accuracy: 0.8733\n- F1: 0.8812",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-analysis-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3130\n- Accuracy: 0.8733\n- F1: 0.8812",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] |
[
67,
76,
6,
12,
8,
3,
90,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n# finetuning-sentiment-analysis-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3130\n- Accuracy: 0.8733\n- F1: 0.8812## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2### Training results### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] |
[
-0.1287984997034073,
0.17650039494037628,
-0.0028625763952732086,
0.0771234929561615,
0.1348087638616562,
0.02653486095368862,
0.0715121403336525,
0.15989157557487488,
-0.10150086134672165,
0.10070181638002396,
0.08385781198740005,
0.07272189855575562,
0.06962540000677109,
0.15756991505622864,
-0.028043558821082115,
-0.25322455167770386,
0.02217739261686802,
0.013949754647910595,
-0.08133984357118607,
0.09776145219802856,
0.1350569725036621,
-0.08589690923690796,
0.07652560621500015,
0.041659433394670486,
-0.17392687499523163,
0.0011843520915135741,
-0.017021873965859413,
-0.0781206414103508,
0.07013756036758423,
0.027828065678477287,
0.04034733772277832,
-0.0027810290921479464,
0.07917917519807816,
-0.18165548145771027,
-0.010114466771483421,
0.062472689896821976,
0.04756156727671623,
0.10934101790189743,
0.0675637498497963,
0.02458919584751129,
0.08810913562774658,
-0.13162492215633392,
0.07667529582977295,
0.046886783093214035,
-0.07624167948961258,
-0.20368219912052155,
-0.10062170028686523,
0.11340036988258362,
0.07351921498775482,
0.10111386328935623,
0.0026167628820985556,
0.14784908294677734,
-0.016761429607868195,
0.07083257287740707,
0.179881289601326,
-0.2457214593887329,
-0.06387729942798615,
0.02834976278245449,
0.04289770871400833,
0.044706329703330994,
-0.10189946740865707,
-0.004317028913646936,
0.05470805987715721,
0.034509189426898956,
0.09799171984195709,
-0.007201498840004206,
-0.025035396218299866,
-0.03715192526578903,
-0.1138552725315094,
-0.07881319522857666,
0.24567417800426483,
0.0643346905708313,
-0.07387066632509232,
-0.1391211599111557,
-0.03808211907744408,
-0.10159078240394592,
-0.03508610650897026,
-0.04853583872318268,
0.029930440708994865,
-0.03768470138311386,
-0.040847230702638626,
-0.05375691503286362,
-0.0757175013422966,
-0.045141879469156265,
0.02405858226120472,
0.11840257793664932,
0.04275446757674217,
0.027002835646271706,
-0.018688201904296875,
0.08976826816797256,
-0.03443889692425728,
-0.15419018268585205,
-0.05955454707145691,
-0.016686055809259415,
-0.03533671051263809,
-0.04937651753425598,
-0.044087108224630356,
0.02480759285390377,
0.009303266182541847,
0.16375145316123962,
-0.08476805686950684,
0.0652121976017952,
0.03833627700805664,
-0.006284784059971571,
-0.014344293624162674,
0.17537279427051544,
-0.04148973152041435,
-0.03241646662354469,
0.01439922396093607,
0.10289042443037033,
0.011129014194011688,
-0.003620209638029337,
-0.06818382441997528,
-0.03362206369638443,
0.11812000721693039,
0.055252671241760254,
-0.008824738673865795,
0.030506357550621033,
-0.05077619478106499,
-0.048878997564315796,
0.06757407635450363,
-0.1332215815782547,
0.028356395661830902,
-0.028588445857167244,
-0.09835804253816605,
-0.021962592378258705,
0.03525583818554878,
-0.009794695302844048,
-0.05626325681805611,
0.019128764048218727,
-0.0897468626499176,
-0.023565365001559258,
-0.05894029885530472,
-0.0354100726544857,
0.014199919998645782,
-0.04466567933559418,
0.008351589553058147,
-0.07358890771865845,
-0.18256117403507233,
-0.03621605038642883,
0.02336021140217781,
-0.06820125877857208,
-0.1045880913734436,
0.012432191520929337,
-0.06991492211818695,
0.026197537779808044,
-0.005287636071443558,
0.09903164207935333,
-0.018703239038586617,
0.06706210225820541,
0.06600699573755264,
0.026214033365249634,
0.03894622251391411,
0.055238254368305206,
-0.09015627950429916,
0.04409890994429588,
-0.11724013090133667,
0.10507847368717194,
-0.08895648270845413,
0.035335373133420944,
-0.12818174064159393,
-0.10482622683048248,
-0.00836796686053276,
-0.03253726661205292,
0.07238946855068207,
0.14232492446899414,
-0.11050107330083847,
-0.03848796710371971,
0.12153280526399612,
-0.06608746945858002,
-0.12523368000984192,
0.07049395143985748,
-0.007436443120241165,
0.031291376799345016,
0.04400700330734253,
0.1229400783777237,
0.1433434784412384,
-0.08001215755939484,
-0.05670586973428726,
0.042988330125808716,
0.07473402470350266,
0.010990186594426632,
0.09054888784885406,
-0.02511434257030487,
0.02187291905283928,
0.0410243421792984,
-0.06424417346715927,
-0.017734570428729057,
-0.07611866295337677,
-0.08362317085266113,
-0.07635797560214996,
-0.07005484402179718,
0.02739044465124607,
0.03589097037911415,
0.03666483238339424,
-0.05263671278953552,
-0.10864503681659698,
0.07488943636417389,
0.15353500843048096,
-0.03181130811572075,
0.011307003907859325,
-0.06734418869018555,
0.06979721784591675,
-0.04791201651096344,
-0.013411613181233406,
-0.22855821251869202,
-0.09725993871688843,
0.06049966812133789,
-0.08584954589605331,
0.01497322041541338,
-0.036505259573459625,
0.0487373061478138,
0.05369367077946663,
-0.027995670214295387,
-0.03596622124314308,
-0.08775229007005692,
-0.028741754591464996,
-0.09078522026538849,
-0.14647139608860016,
-0.0702475979924202,
-0.020519420504570007,
0.19913500547409058,
-0.20475579798221588,
0.018645884469151497,
0.014887848868966103,
0.13893261551856995,
0.0013628682354465127,
-0.06769992411136627,
0.017196988686919212,
-0.0036854639183729887,
-0.019056376069784164,
-0.11094221472740173,
0.027103649452328682,
0.025756636634469032,
-0.08547963947057724,
-0.044086676090955734,
-0.12493700534105301,
0.06808479875326157,
0.08433005958795547,
0.08506083488464355,
-0.07613065093755722,
-0.024282487109303474,
-0.07000412791967392,
-0.04167706146836281,
-0.059366319328546524,
-0.020279353484511375,
0.1472584307193756,
0.006752927787601948,
0.1259244978427887,
-0.07833252102136612,
-0.07174605131149292,
0.028388014063239098,
-0.009827805683016777,
-0.047693487256765366,
0.05792635306715965,
-0.004048813600093126,
-0.11903101205825806,
0.1117522343993187,
0.0816909596323967,
-0.018250271677970886,
0.12063603848218918,
-0.0736081451177597,
-0.09142701327800751,
-0.033683083951473236,
0.01096810307353735,
0.007124944590032101,
0.09753461927175522,
-0.09781019389629364,
0.010944581590592861,
0.06764975935220718,
0.012642592191696167,
0.032982468605041504,
-0.1345730423927307,
0.02620101533830166,
0.03889000415802002,
-0.03437770530581474,
0.026668721809983253,
0.0041983770206570625,
0.013105743564665318,
0.08420836180448532,
0.034150563180446625,
0.013966541737318039,
0.03520140051841736,
-0.007929871790111065,
-0.08170721679925919,
0.18435533344745636,
-0.11704248189926147,
-0.19201259315013885,
-0.16327759623527527,
0.06400924921035767,
-0.08954194188117981,
-0.02214408479630947,
0.006579817738384008,
-0.05084070563316345,
-0.061071231961250305,
-0.06490442901849747,
-0.023832201957702637,
-0.05911099165678024,
-0.0009869454661384225,
0.08990377187728882,
0.002258527325466275,
0.1314907670021057,
-0.1250581294298172,
-0.0027965970803052187,
0.012760388664901257,
-0.0640651062130928,
-0.0253691915422678,
0.030326053500175476,
0.10032209753990173,
0.08148782700300217,
-0.008970290422439575,
0.03131721541285515,
-0.018094604834914207,
0.2747700810432434,
-0.08571917563676834,
-0.00903280358761549,
0.1700119525194168,
0.03410936892032623,
0.07452327013015747,
0.0930708721280098,
0.026115521788597107,
-0.0767102912068367,
0.017194435000419617,
0.03297899290919304,
-0.006190081592649221,
-0.222972571849823,
-0.04038655012845993,
-0.03592247888445854,
-0.05668811500072479,
0.12163569778203964,
0.053500864654779434,
0.08107531815767288,
0.0863671824336052,
-0.04930547624826431,
0.0789370909333229,
-0.020300785079598427,
0.10516621917486191,
0.1281997114419937,
0.05155396834015846,
0.10010799765586853,
-0.031604014337062836,
-0.0048774355091154575,
0.07841110229492188,
-0.012091152369976044,
0.23135805130004883,
-0.025268886238336563,
0.16744272410869598,
0.021025096997618675,
0.16546763479709625,
-0.022654736414551735,
0.054581452161073685,
0.011502181179821491,
0.012747489847242832,
0.01938582770526409,
-0.07150831073522568,
-0.054213669151067734,
0.012729388661682606,
-0.01755480282008648,
0.08345462381839752,
-0.10666051506996155,
0.044781703501939774,
0.010576753877103329,
0.23969392478466034,
0.03973253816366196,
-0.3257737457752228,
-0.10574302822351456,
0.014642034657299519,
-0.02028164081275463,
-0.11859279125928879,
-0.011778146028518677,
0.07095678150653839,
-0.1580471694469452,
0.0826665386557579,
-0.06050793081521988,
0.09339325875043869,
-0.028682144358754158,
-0.0031367396004498005,
0.06657880544662476,
0.11028570681810379,
0.01585317589342594,
0.10540156811475754,
-0.21226948499679565,
0.174493670463562,
0.020001616328954697,
0.08121564984321594,
-0.07491688430309296,
0.0753118172287941,
0.02602115273475647,
0.08926437050104141,
0.11886615306138992,
-0.001959599554538727,
-0.04180435836315155,
-0.16680306196212769,
-0.09444939345121384,
0.005992478225380182,
0.0988779217004776,
-0.07922865450382233,
0.0809139832854271,
-0.061173439025878906,
0.0040385485626757145,
0.021443968638777733,
-0.03919396921992302,
-0.15147636830806732,
-0.14638306200504303,
0.04415574297308922,
0.01921331323683262,
0.0017126629827544093,
-0.09037593007087708,
-0.09577567875385284,
-0.00425230897963047,
0.17149348556995392,
-0.004485365003347397,
-0.08414091914892197,
-0.17354470491409302,
0.10587593168020248,
0.15077456831932068,
-0.083489328622818,
0.03817359358072281,
-0.02304271049797535,
0.1835382878780365,
0.04408753290772438,
-0.0905395969748497,
0.02335946261882782,
-0.07410486042499542,
-0.1769455075263977,
-0.013250894844532013,
0.15015946328639984,
-0.003102886490523815,
0.045063696801662445,
0.0034896170254796743,
0.0320938378572464,
0.001051996136084199,
-0.09135200828313828,
-0.004806539509445429,
0.05737587809562683,
0.09545896202325821,
0.04279410094022751,
-0.047455210238695145,
0.028853990137577057,
-0.05164134502410889,
-0.0020411615259945393,
0.1119367927312851,
0.1919826716184616,
-0.08991634845733643,
0.03340096399188042,
0.03165244311094284,
-0.0696973130106926,
-0.16636021435260773,
0.02922271378338337,
0.13675686717033386,
0.024563973769545555,
0.05282693728804588,
-0.17968349158763885,
0.10447162389755249,
0.09294003248214722,
-0.03873108699917793,
0.043402403593063354,
-0.26414480805397034,
-0.12576057016849518,
0.08373097330331802,
0.08594606071710587,
-0.021127311512827873,
-0.14332234859466553,
-0.07694726437330246,
-0.033102381974458694,
-0.11269745230674744,
0.08799292147159576,
-0.020215878263115883,
0.09421153366565704,
-0.025830630213022232,
0.06703399121761322,
0.04152116924524307,
-0.022183343768119812,
0.18032389879226685,
0.03338303416967392,
0.0564497709274292,
-0.057612527161836624,
0.04152549430727959,
0.09501831233501434,
-0.07776934653520584,
0.11106394231319427,
-0.03195693716406822,
0.09635347872972488,
-0.1808571219444275,
-0.010883353650569916,
-0.06979652494192123,
0.0731680691242218,
-0.06777527183294296,
-0.04203415289521217,
-0.04226307570934296,
0.04381247237324715,
0.037682775408029556,
-0.037692829966545105,
0.09320151060819626,
0.05602775141596794,
0.07301051914691925,
0.15209686756134033,
0.07875765860080719,
0.019512619823217392,
-0.16085100173950195,
-0.01923336833715439,
-0.010710637085139751,
0.05190765857696533,
-0.13280487060546875,
0.02297084964811802,
0.10012384504079819,
0.04159275442361832,
0.12121222913265228,
0.019444886595010757,
-0.07608900219202042,
-0.02565259113907814,
0.04462658241391182,
-0.09643606096506119,
-0.14796887338161469,
-0.06933476775884628,
0.008557148277759552,
-0.17934946715831757,
0.011177069507539272,
0.08826687186956406,
-0.0607214979827404,
-0.022156234830617905,
-0.01713920570909977,
0.009277884848415852,
-0.006599648855626583,
0.18686459958553314,
0.054237574338912964,
0.07985494285821915,
-0.09056856483221054,
0.1287815123796463,
0.07925121486186981,
-0.04830923676490784,
0.042268455028533936,
0.04789001867175102,
-0.0894569680094719,
-0.019925907254219055,
0.056375421583652496,
0.08922941982746124,
-0.035363730043172836,
-0.0589156411588192,
-0.06637629866600037,
-0.08614328503608704,
0.05231295898556709,
0.0341331921517849,
0.07264727354049683,
0.0025660728570073843,
-0.02770804800093174,
-0.0004271354991942644,
-0.14066965878009796,
0.11502893269062042,
0.05513646826148033,
0.07031429558992386,
-0.17962665855884552,
0.056669097393751144,
0.0070403930731117725,
0.06615686416625977,
-0.01328952331095934,
-0.012005762197077274,
-0.06268268823623657,
-0.04528172314167023,
-0.09440913051366806,
0.0037959166802465916,
-0.04371622949838638,
-0.003770112991333008,
-0.023994848132133484,
-0.0483643114566803,
-0.03374304994940758,
0.06421365588903427,
-0.05460670217871666,
-0.09772235155105591,
0.0005651487736031413,
0.06616583466529846,
-0.12407384067773819,
0.004335694946348667,
0.026516858488321304,
-0.11116840690374374,
0.09109961241483688,
0.0703195333480835,
0.04898228496313095,
0.015169253572821617,
0.030774474143981934,
0.0003685743431560695,
0.037030890583992004,
0.03473268076777458,
0.05697609856724739,
-0.11801061779260635,
0.007983013056218624,
-0.02256016992032528,
0.014787967316806316,
0.00012540425814222544,
0.04042671620845795,
-0.142361581325531,
-0.05896095186471939,
-0.050589051097631454,
-0.013541803695261478,
-0.06957142055034637,
0.06034475937485695,
0.10764598846435547,
0.021180760115385056,
0.16751691699028015,
-0.0473005510866642,
0.017843443900346756,
-0.22914771735668182,
-0.014224731363356113,
-0.02701600268483162,
-0.05440602824091911,
-0.0672515407204628,
-0.026337316259741783,
0.06919728964567184,
-0.03826776519417763,
0.09066414088010788,
-0.014866948127746582,
0.1073230430483818,
0.04482019692659378,
0.01265926007181406,
0.02102985978126526,
-0.0028413075488060713,
0.17409861087799072,
0.08205276727676392,
-0.007719759829342365,
0.11921171098947525,
0.0005338879418559372,
0.06548776477575302,
0.0310886912047863,
0.11134705692529678,
0.12798471748828888,
-0.050328582525253296,
0.07489582151174545,
0.06398741155862808,
-0.07761964201927185,
-0.14943520724773407,
0.05975789204239845,
-0.027371924370527267,
0.10541726648807526,
-0.02925092913210392,
0.09866541624069214,
0.10962139815092087,
-0.1848665028810501,
0.06328120827674866,
-0.0500725694000721,
-0.09654095023870468,
-0.10795505344867706,
-0.09656216204166412,
-0.07839763909578323,
-0.11999915540218353,
0.004912372212857008,
-0.1386626958847046,
0.023349305614829063,
0.08797156810760498,
-0.009149263612926006,
-0.024877751246094704,
0.16137947142124176,
-0.06059243157505989,
-0.022452952340245247,
0.07628272473812103,
0.0012517442228272557,
-0.006437603384256363,
-0.047435592859983444,
-0.03806886449456215,
0.04389185458421707,
0.03188164159655571,
0.10080397874116898,
-0.041784632951021194,
-0.0018349356250837445,
0.04147540405392647,
-0.014672123827040195,
-0.09189189225435257,
0.014583069831132889,
0.004895790945738554,
0.011506350710988045,
0.015985894948244095,
0.023788711056113243,
0.012243842706084251,
-0.051109977066516876,
0.27052125334739685,
-0.06791584193706512,
-0.05209304764866829,
-0.1464902013540268,
0.1490422487258911,
0.04729076474905014,
-0.041294656693935394,
0.08714674413204193,
-0.11552684009075165,
-0.02368815243244171,
0.1506369560956955,
0.10150891542434692,
-0.02146238274872303,
-0.02861202135682106,
0.00023323053028434515,
-0.02468576468527317,
-0.0559999942779541,
0.1018783450126648,
0.09578849375247955,
0.020117375999689102,
-0.04110746085643768,
0.02955053001642227,
-0.005942649208009243,
-0.04225686565041542,
-0.08013031631708145,
0.1087358146905899,
0.0077374218963086605,
0.009988510981202126,
-0.01980726793408394,
0.060123857110738754,
0.023589426651597023,
-0.14387144148349762,
0.024339215829968452,
-0.14743681252002716,
-0.18338434398174286,
-0.030951444059610367,
0.036393843591213226,
0.005790438503026962,
0.056571006774902344,
0.01860458217561245,
0.004242892377078533,
0.14072059094905853,
-0.009481706656515598,
-0.077309250831604,
-0.09735515713691711,
0.09912986308336258,
-0.06276323646306992,
0.22124804556369781,
0.0024288420099765062,
0.06612378358840942,
0.10597328096628189,
-0.0024895810056477785,
-0.18119236826896667,
-0.0008956548990681767,
0.06945151090621948,
-0.020183837041258812,
0.04927681013941765,
0.1721046268939972,
-0.015081155113875866,
0.0369744673371315,
0.035454146564006805,
-0.14385762810707092,
-0.05988100916147232,
-0.05788576602935791,
0.010890553705394268,
-0.08944469690322876,
0.00045295216841623187,
-0.06493042409420013,
0.1546667069196701,
0.2030397653579712,
-0.06330668181180954,
-0.03351042419672012,
-0.06862574070692062,
0.04314612224698067,
0.06160518527030945,
0.097832590341568,
-0.00224286993034184,
-0.20076267421245575,
0.008050386793911457,
-0.015531172975897789,
0.011335983872413635,
-0.2181105613708496,
-0.09517190605401993,
0.05349064990878105,
-0.07030011713504791,
-0.05334519222378731,
0.10752646625041962,
0.043428581207990646,
0.015465871430933475,
-0.03450409322977066,
-0.04882777854800224,
-0.07130300998687744,
0.14431051909923553,
-0.15617313981056213,
-0.04405606538057327
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples-testcopy
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3374
- Accuracy: 0.87
- F1: 0.8762
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples-testcopy", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text"}, "metrics": [{"type": "accuracy", "value": 0.87, "name": "Accuracy"}, {"type": "f1", "value": 0.8761904761904761, "name": "F1"}]}]}]}
|
text-classification
|
federicopascual/finetuning-sentiment-model-3000-samples-testcopy
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples-testcopy
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3374
- Accuracy: 0.87
- F1: 0.8762
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
[
"# finetuning-sentiment-model-3000-samples-testcopy\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3374\n- Accuracy: 0.87\n- F1: 0.8762",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples-testcopy\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3374\n- Accuracy: 0.87\n- F1: 0.8762",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] |
[
67,
74,
6,
12,
8,
3,
90,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n# finetuning-sentiment-model-3000-samples-testcopy\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3374\n- Accuracy: 0.87\n- F1: 0.8762## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2### Training results### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] |
[
-0.11613300442695618,
0.1712411791086197,
-0.002962801605463028,
0.07953286170959473,
0.13667462766170502,
0.02600974217057228,
0.08193976432085037,
0.15857943892478943,
-0.08851674944162369,
0.08926171809434891,
0.09504853934049606,
0.08200325071811676,
0.06128288432955742,
0.14356860518455505,
-0.037074144929647446,
-0.24760614335536957,
0.03176449239253998,
0.008277587592601776,
-0.06562089920043945,
0.10475248843431473,
0.1297975778579712,
-0.07828105241060257,
0.08777596801519394,
0.047143448144197464,
-0.17269940674304962,
-0.000038576639781240374,
-0.009491443634033203,
-0.07125019282102585,
0.07540078461170197,
0.029277410358190536,
0.04161490127444267,
0.003006999148055911,
0.07456156611442566,
-0.18633227050304413,
-0.008445101790130138,
0.05703457444906235,
0.0402958020567894,
0.10602977871894836,
0.054637473076581955,
0.033336181193590164,
0.09058075398206711,
-0.12020035088062286,
0.08283115923404694,
0.046874552965164185,
-0.08133040368556976,
-0.17938998341560364,
-0.09611596167087555,
0.11376035958528519,
0.07200486212968826,
0.098477803170681,
0.0051893251948058605,
0.16561990976333618,
-0.007022080942988396,
0.07883831113576889,
0.17515850067138672,
-0.2770093083381653,
-0.0598892942070961,
0.04055660963058472,
0.0656939148902893,
0.055501583963632584,
-0.0988212451338768,
0.001310317893512547,
0.04468698427081108,
0.02924734354019165,
0.09489927440881729,
-0.015875492244958878,
0.008119898848235607,
-0.0471077486872673,
-0.1200738251209259,
-0.08392231166362762,
0.23649725317955017,
0.07085172086954117,
-0.07984814792871475,
-0.13796736299991608,
-0.04494905099272728,
-0.102249376475811,
-0.033141255378723145,
-0.06109106168150902,
0.03199775889515877,
-0.027210162952542305,
-0.03229809179902077,
-0.0663478747010231,
-0.06919846683740616,
-0.04276587814092636,
0.02928743325173855,
0.11633222550153732,
0.04208175465464592,
0.025688588619232178,
-0.013523572124540806,
0.08396362513303757,
-0.04167114198207855,
-0.14808019995689392,
-0.06111287698149681,
-0.01727326586842537,
-0.03684943914413452,
-0.05764540657401085,
-0.04563319683074951,
-0.0020891844760626554,
0.008081328123807907,
0.16844408214092255,
-0.05875258892774582,
0.06986739486455917,
0.02929988130927086,
-0.004877541679888964,
-0.021708421409130096,
0.18381507694721222,
-0.03209917992353439,
-0.024687757715582848,
0.017152929678559303,
0.10333295166492462,
0.008688820526003838,
-0.00624119071289897,
-0.07743974030017853,
-0.030350985005497932,
0.13213317096233368,
0.05389682948589325,
-0.011429150588810444,
0.03900730982422829,
-0.05336279422044754,
-0.053208962082862854,
0.07461497187614441,
-0.1237710639834404,
0.029983365908265114,
-0.03534502536058426,
-0.08516643196344376,
-0.037090372294187546,
0.03548923134803772,
-0.0073984223417937756,
-0.05300292745232582,
0.007417184766381979,
-0.09358424693346024,
-0.016759809106588364,
-0.05096058547496796,
-0.03782657906413078,
0.0016299817943945527,
-0.050085488706827164,
0.011303512379527092,
-0.08018110692501068,
-0.17614462971687317,
-0.04053983464837074,
0.0295429490506649,
-0.06512563675642014,
-0.09851124882698059,
0.01084295567125082,
-0.06982550770044327,
0.030899569392204285,
-0.005804293788969517,
0.06935726851224899,
-0.020420538261532784,
0.0642961710691452,
0.059509921818971634,
0.015344865620136261,
0.03703302517533302,
0.05250764638185501,
-0.09280270338058472,
0.050257544964551926,
-0.11756924539804459,
0.10248388350009918,
-0.08660973608493805,
0.0328725203871727,
-0.13605262339115143,
-0.10576041042804718,
0.006885205861181021,
-0.0350443534553051,
0.07130751758813858,
0.13635390996932983,
-0.09850548207759857,
-0.023827817291021347,
0.12078450620174408,
-0.0694846659898758,
-0.12271618098020554,
0.08127443492412567,
-0.013257444836199284,
0.0444723442196846,
0.04576417803764343,
0.14146140217781067,
0.13296747207641602,
-0.09484051167964935,
-0.04221484065055847,
0.04089832305908203,
0.05530404672026634,
0.015293964184820652,
0.08963442593812943,
-0.01583814062178135,
0.019842414185404778,
0.029918747022747993,
-0.06345140188932419,
-0.024212677031755447,
-0.07378203421831131,
-0.08166656643152237,
-0.07824680954217911,
-0.06311675906181335,
0.033521346747875214,
0.027447540313005447,
0.03681646287441254,
-0.05545427277684212,
-0.11523221433162689,
0.08345019072294235,
0.14423692226409912,
-0.03760511055588722,
0.012731257826089859,
-0.0821930468082428,
0.07634937018156052,
-0.05962071195244789,
-0.017074793577194214,
-0.22192880511283875,
-0.08950770646333694,
0.06675317883491516,
-0.08644358068704605,
0.028389448300004005,
-0.01066279225051403,
0.04846326261758804,
0.05453656613826752,
-0.02350577525794506,
-0.0419798269867897,
-0.08751510828733444,
-0.01789802499115467,
-0.10345298796892166,
-0.1370466947555542,
-0.06704746931791306,
-0.019322538748383522,
0.1814582496881485,
-0.2165265828371048,
0.018225187435746193,
0.01337165106087923,
0.12581199407577515,
0.005847776774317026,
-0.06570703536272049,
0.01834837719798088,
-0.008477076888084412,
-0.017085634171962738,
-0.11195190250873566,
0.03567282110452652,
0.03091549314558506,
-0.09596636146306992,
-0.04038623347878456,
-0.13242347538471222,
0.0877726748585701,
0.0755629763007164,
0.0750899538397789,
-0.07976201176643372,
-0.011567376554012299,
-0.05875258520245552,
-0.04343423247337341,
-0.04145878180861473,
-0.02020678110420704,
0.17046517133712769,
0.007698887959122658,
0.1366698294878006,
-0.07675507664680481,
-0.05908840894699097,
0.02925422415137291,
-0.018477266654372215,
-0.048131249845027924,
0.05609859153628349,
-0.02018231339752674,
-0.16029484570026398,
0.10068468749523163,
0.09573769569396973,
-0.00873007345944643,
0.10782325267791748,
-0.06613689661026001,
-0.08105768263339996,
-0.04256458953022957,
0.015022077597677708,
0.0050944131799042225,
0.08113065361976624,
-0.09618527442216873,
0.00938829779624939,
0.0628000870347023,
0.02378075383603573,
0.023632073774933815,
-0.13574181497097015,
0.02385234273970127,
0.04573320224881172,
-0.03925125673413277,
0.02653985284268856,
0.011805041693150997,
0.001166898524388671,
0.07723493129014969,
0.041003864258527756,
-0.008455735631287098,
0.040240850299596786,
-0.007651037070900202,
-0.0776955708861351,
0.17447875440120697,
-0.11080417037010193,
-0.19994433224201202,
-0.1695774495601654,
0.05146264284849167,
-0.09318115562200546,
-0.015424435958266258,
0.018912330269813538,
-0.03731629252433777,
-0.0614507831633091,
-0.07407360523939133,
-0.03866443410515785,
-0.06679429113864899,
-0.00691772298887372,
0.08821330219507217,
-0.00876407790929079,
0.12952135503292084,
-0.12786392867565155,
-0.009750453755259514,
0.012685827910900116,
-0.06438229233026505,
-0.02683863788843155,
0.023323291912674904,
0.0995580181479454,
0.07547849416732788,
-0.015716785565018654,
0.027958374470472336,
-0.015869589522480965,
0.28813761472702026,
-0.07885462045669556,
-0.013938125222921371,
0.1832314133644104,
0.02090514451265335,
0.07923804223537445,
0.0981944352388382,
0.022580735385417938,
-0.08665063977241516,
0.015900613740086555,
0.0246351957321167,
-0.004205515142530203,
-0.22156287729740143,
-0.030708275735378265,
-0.029035694897174835,
-0.06582555919885635,
0.11824168264865875,
0.056429244577884674,
0.08396564424037933,
0.0797266960144043,
-0.03725680336356163,
0.07342968881130219,
-0.01788428984582424,
0.11628450453281403,
0.1406213939189911,
0.052379023283720016,
0.10036018490791321,
-0.027991030365228653,
-0.005707101430743933,
0.07297483086585999,
-0.01603643223643303,
0.22838787734508514,
-0.005276690237224102,
0.18481028079986572,
0.021601354703307152,
0.15455316007137299,
-0.01709737256169319,
0.03808506205677986,
0.01171808410435915,
0.015019770711660385,
0.010041636414825916,
-0.0646742507815361,
-0.06281182169914246,
0.01890694536268711,
-0.0181899331510067,
0.07325513660907745,
-0.10440316051244736,
0.048140574246644974,
0.005028437823057175,
0.24829228222370148,
0.03624531999230385,
-0.325486421585083,
-0.11088427901268005,
0.006771523505449295,
-0.03368578478693962,
-0.12123370915651321,
-0.013970529660582542,
0.06343484669923782,
-0.1610705703496933,
0.0697743371129036,
-0.06757626682519913,
0.09803453832864761,
-0.03901434317231178,
0.005398046690970659,
0.06019575893878937,
0.1002703458070755,
0.012495381757616997,
0.1046389490365982,
-0.21254055202007294,
0.17963488399982452,
0.023305969312787056,
0.07821160554885864,
-0.07201352715492249,
0.06697361171245575,
0.018912162631750107,
0.10726885497570038,
0.12079279124736786,
0.0012726872228085995,
-0.032372236251831055,
-0.16997510194778442,
-0.09573151916265488,
0.0007736223051324487,
0.10114672034978867,
-0.07050998508930206,
0.07880475372076035,
-0.06967200338840485,
0.008557439781725407,
0.02418162114918232,
-0.034742992371320724,
-0.1486022025346756,
-0.13141801953315735,
0.04880513250827789,
0.017288103699684143,
0.014532060362398624,
-0.08107957243919373,
-0.087220199406147,
-0.008888950571417809,
0.19171781837940216,
0.008101050741970539,
-0.07305172085762024,
-0.16669796407222748,
0.08702073991298676,
0.13819043338298798,
-0.0854889303445816,
0.0355054996907711,
-0.02485295571386814,
0.1657973974943161,
0.05125207081437111,
-0.0864076316356659,
0.03789527714252472,
-0.06608917564153671,
-0.17509981989860535,
-0.015623224899172783,
0.14083944261074066,
-0.00908784568309784,
0.04167146235704422,
0.01629354991018772,
0.0347294807434082,
-0.004497050307691097,
-0.0895497128367424,
-0.006713565438985825,
0.04196620360016823,
0.09450867027044296,
0.038368962705135345,
-0.03810594603419304,
0.05056175962090492,
-0.05926162376999855,
-0.000346095155691728,
0.11286984384059906,
0.21752050518989563,
-0.0845596045255661,
0.0364755317568779,
0.02547639235854149,
-0.07640107721090317,
-0.1564027965068817,
0.020959898829460144,
0.1273484081029892,
0.02085367776453495,
0.060942694544792175,
-0.15954720973968506,
0.08750824630260468,
0.08825880289077759,
-0.04258133843541145,
0.039888083934783936,
-0.2715460956096649,
-0.1288096010684967,
0.07823920249938965,
0.09866765141487122,
0.008309070952236652,
-0.14586840569972992,
-0.07390520721673965,
-0.02841314673423767,
-0.1140257716178894,
0.07724986225366592,
-0.008835985325276852,
0.10539942979812622,
-0.02729262411594391,
0.05948099493980408,
0.04181371256709099,
-0.019821474328637123,
0.17499558627605438,
0.03204159066081047,
0.06415218114852905,
-0.06276538968086243,
0.04512705281376839,
0.09572317451238632,
-0.08120198547840118,
0.11019543558359146,
-0.03315193951129913,
0.09824346005916595,
-0.17860646545886993,
-0.011244461871683598,
-0.05854152515530586,
0.0681709349155426,
-0.06449927389621735,
-0.04074632748961449,
-0.040219273418188095,
0.03984547406435013,
0.043935004621744156,
-0.03349829837679863,
0.11498495191335678,
0.0471477210521698,
0.06034756824374199,
0.16465577483177185,
0.07630375027656555,
0.0171424001455307,
-0.16629242897033691,
-0.022043347358703613,
-0.008627419359982014,
0.05253826454281807,
-0.124093197286129,
0.028454726561903954,
0.09696298837661743,
0.030914995819330215,
0.1316237598657608,
0.013291122391819954,
-0.06603584438562393,
-0.020824603736400604,
0.03126217797398567,
-0.09765015542507172,
-0.15686793625354767,
-0.06580930948257446,
-0.0035363093484193087,
-0.1658785194158554,
0.011522989720106125,
0.11050331592559814,
-0.057315725833177567,
-0.021579856052994728,
-0.015224002301692963,
0.00869808904826641,
-0.005705499090254307,
0.17226922512054443,
0.04429984092712402,
0.07695621252059937,
-0.08309457451105118,
0.11397082358598709,
0.07975862175226212,
-0.04136938229203224,
0.050601404160261154,
0.034422870725393295,
-0.09351546317338943,
-0.024218246340751648,
0.05293860659003258,
0.10367385298013687,
-0.03686432167887688,
-0.04744890332221985,
-0.06714790314435959,
-0.06762365996837616,
0.04526207223534584,
0.040427833795547485,
0.07879376411437988,
0.000683710677549243,
-0.031287845224142075,
-0.011075129732489586,
-0.13415613770484924,
0.11123388260602951,
0.04424554854631424,
0.07419107854366302,
-0.19402280449867249,
0.04630622640252113,
0.006466217339038849,
0.0665312260389328,
-0.01690315641462803,
-0.013645601458847523,
-0.06978663802146912,
-0.04840969666838646,
-0.09442462027072906,
0.012959321960806847,
-0.04139931499958038,
0.003600474912673235,
-0.02291885018348694,
-0.05806406959891319,
-0.033671315759420395,
0.06358149647712708,
-0.056327953934669495,
-0.09522862732410431,
0.009985470212996006,
0.0683526024222374,
-0.11983659118413925,
-0.002967437030747533,
0.026595588773489,
-0.106247678399086,
0.0928039625287056,
0.05961785092949867,
0.03643328696489334,
0.00860385037958622,
0.03051101043820381,
0.020826159045100212,
0.03474040329456329,
0.03236950561404228,
0.04602790251374245,
-0.11640187352895737,
0.005719745997339487,
-0.02127685211598873,
0.01538328267633915,
0.00341706327162683,
0.05776320770382881,
-0.14291422069072723,
-0.06930121034383774,
-0.05813485011458397,
-0.014329362660646439,
-0.06638072431087494,
0.060318294912576675,
0.10442053526639938,
0.01934496872127056,
0.18246304988861084,
-0.05049582198262215,
0.021116307005286217,
-0.2202705442905426,
-0.018053371459245682,
-0.014287962578237057,
-0.05650031566619873,
-0.05979223921895027,
-0.04837789759039879,
0.06206318736076355,
-0.04915393143892288,
0.08878013491630554,
-0.012758920900523663,
0.11403153091669083,
0.04303010553121567,
0.012841546908020973,
0.019679224118590355,
-0.010544638149440289,
0.16577844321727753,
0.07741532474756241,
-0.007395146880298853,
0.12596216797828674,
-0.004365314729511738,
0.06166185811161995,
0.022844640538096428,
0.0973147600889206,
0.13456717133522034,
-0.06490066647529602,
0.07733244448900223,
0.05614929273724556,
-0.060781799256801605,
-0.16210974752902985,
0.05756234750151634,
-0.020409969612956047,
0.11598478257656097,
-0.02532149851322174,
0.10124224424362183,
0.10409601777791977,
-0.17547927796840668,
0.0602123998105526,
-0.045725416392087936,
-0.09863398969173431,
-0.10584088414907455,
-0.10840248316526413,
-0.08539283275604248,
-0.12130872160196304,
0.006530444137752056,
-0.12948164343833923,
0.030904751271009445,
0.0753956064581871,
-0.014103417284786701,
-0.030655302107334137,
0.1536177545785904,
-0.06974855810403824,
-0.021737515926361084,
0.06800328940153122,
-0.010775457136332989,
-0.013458569534122944,
-0.04782801494002342,
-0.04414352402091026,
0.04601417854428291,
0.031249741092324257,
0.09927137941122055,
-0.035917919129133224,
0.012228403240442276,
0.03345286101102829,
-0.028060875833034515,
-0.09796087443828583,
0.014964671805500984,
0.021822938695549965,
0.009417024441063404,
0.012510576285421848,
0.025392716750502586,
0.003022741759195924,
-0.04703736677765846,
0.26567408442497253,
-0.06218525022268295,
-0.045055460184812546,
-0.13307549059391022,
0.16052952408790588,
0.04895111173391342,
-0.03689613565802574,
0.07756303995847702,
-0.10386461019515991,
-0.0050483341328799725,
0.15393120050430298,
0.1003170758485794,
-0.03139567747712135,
-0.02906874753534794,
-0.0023676431737840176,
-0.02247479557991028,
-0.0426504984498024,
0.10513206571340561,
0.08880172669887543,
-0.011184212751686573,
-0.040609538555145264,
0.02747556008398533,
-0.010725690051913261,
-0.04042632505297661,
-0.08303698152303696,
0.09560740739107132,
0.011286035180091858,
0.009807886555790901,
-0.018275275826454163,
0.059489279985427856,
0.02030181884765625,
-0.14306898415088654,
0.021268468350172043,
-0.14158938825130463,
-0.17578427493572235,
-0.023361291736364365,
0.03248990327119827,
-0.0016902047209441662,
0.05916864052414894,
0.016209226101636887,
-0.0023622359149158,
0.1313246339559555,
-0.01571347378194332,
-0.07293768227100372,
-0.08549968898296356,
0.09097853302955627,
-0.07464075088500977,
0.21161511540412903,
0.009707484394311905,
0.06944871693849564,
0.11102605611085892,
0.0008059392566792667,
-0.17250795662403107,
0.009895172901451588,
0.06483668833971024,
-0.019056549295783043,
0.05644865334033966,
0.16656948626041412,
-0.010891983285546303,
0.047704167664051056,
0.035664305090904236,
-0.14541777968406677,
-0.05874605476856232,
-0.07158464193344116,
0.011868077330291271,
-0.08059468120336533,
-0.009086072444915771,
-0.06696999073028564,
0.1643286645412445,
0.20108641684055328,
-0.06040360778570175,
-0.029294626787304878,
-0.06638002395629883,
0.04095146805047989,
0.06554919481277466,
0.08035529404878616,
-0.01057951059192419,
-0.19900846481323242,
0.006805688142776489,
-0.0000196592700376641,
0.01644226722419262,
-0.21784768998622894,
-0.10835115611553192,
0.045817241072654724,
-0.060617875307798386,
-0.054046642035245895,
0.11055021733045578,
0.03829747810959816,
0.012435190379619598,
-0.03450653702020645,
-0.054582539945840836,
-0.07119301706552505,
0.1490342766046524,
-0.15622693300247192,
-0.049980368465185165
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3404
- Accuracy: 0.8667
- F1: 0.8734
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text"}, "metrics": [{"type": "accuracy", "value": 0.8666666666666667, "name": "Accuracy"}, {"type": "f1", "value": 0.8734177215189873, "name": "F1"}]}]}]}
|
text-classification
|
federicopascual/finetuning-sentiment-model-3000-samples
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3404
- Accuracy: 0.8667
- F1: 0.8734
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
[
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3404\n- Accuracy: 0.8667\n- F1: 0.8734",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3404\n- Accuracy: 0.8667\n- F1: 0.8734",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2",
"### Training results",
"### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] |
[
67,
72,
6,
12,
8,
3,
90,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3404\n- Accuracy: 0.8667\n- F1: 0.8734## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 2### Training results### Framework versions\n\n- Transformers 4.15.0\n- Pytorch 1.10.0+cu111\n- Datasets 1.17.0\n- Tokenizers 0.10.3"
] |
[
-0.11751808226108551,
0.1816021353006363,
-0.002757289446890354,
0.09303216636180878,
0.13826438784599304,
0.022485220804810524,
0.09529184550046921,
0.15734714269638062,
-0.08146440982818604,
0.08824820816516876,
0.09797755628824234,
0.08601271361112595,
0.0615052729845047,
0.1412891447544098,
-0.03911522403359413,
-0.24315758049488068,
0.03539805859327316,
0.0035590266343206167,
-0.042545270174741745,
0.10673762112855911,
0.12219411134719849,
-0.07641628384590149,
0.1000017449259758,
0.05528891086578369,
-0.17384091019630432,
-0.00537147605791688,
-0.009628202766180038,
-0.0687950998544693,
0.08189103752374649,
0.01563439331948757,
0.04149189963936806,
0.002588931005448103,
0.07360369712114334,
-0.17845572531223297,
-0.00775195611640811,
0.05336376652121544,
0.028388652950525284,
0.10861099511384964,
0.04827982187271118,
0.02350701577961445,
0.07867701351642609,
-0.12212257087230682,
0.08650524169206619,
0.04220205917954445,
-0.08932588994503021,
-0.15759584307670593,
-0.09131480008363724,
0.10060272365808487,
0.07353182882070541,
0.09235687553882599,
0.007788898888975382,
0.1757952868938446,
-0.002852801466360688,
0.08414890617132187,
0.17317081987857819,
-0.2860468626022339,
-0.05376049131155014,
0.02981656789779663,
0.05902973935008049,
0.05652279034256935,
-0.10370984673500061,
-0.00001360774058412062,
0.03636883571743965,
0.025272708386182785,
0.09714596718549728,
-0.01633458025753498,
0.017314616590738297,
-0.045016560703516006,
-0.11395034939050674,
-0.08205408602952957,
0.23076723515987396,
0.06701014935970306,
-0.07854688167572021,
-0.1336684226989746,
-0.05366521328687668,
-0.11954469233751297,
-0.033400531858205795,
-0.04873480647802353,
0.03009580634534359,
-0.025419490411877632,
-0.04517091065645218,
-0.05430402234196663,
-0.06510978192090988,
-0.042800091207027435,
0.028932206332683563,
0.12134949862957001,
0.033762674778699875,
0.03128904849290848,
-0.01960710436105728,
0.08509393036365509,
-0.03722432628273964,
-0.15122446417808533,
-0.05238519608974457,
-0.010800817981362343,
-0.040281396359205246,
-0.0560920275747776,
-0.04092021286487579,
-0.025693854317069054,
-0.00040761500713415444,
0.16822952032089233,
-0.04308852553367615,
0.07959794253110886,
0.01906522922217846,
0.00029006163822486997,
-0.01486220397055149,
0.1728082001209259,
-0.03288211673498154,
-0.029340216889977455,
0.019865600392222404,
0.10311715304851532,
0.025607075542211533,
-0.0173783116042614,
-0.08938327431678772,
-0.01750890165567398,
0.13408991694450378,
0.047462642192840576,
-0.019709110260009766,
0.045766811817884445,
-0.057336196303367615,
-0.05276121944189072,
0.09622021019458771,
-0.11988797783851624,
0.03657311573624611,
-0.034926701337099075,
-0.09304773062467575,
-0.052380140870809555,
0.029157480224967003,
-0.0014305785298347473,
-0.04484327509999275,
0.02185952290892601,
-0.10240904986858368,
-0.01182982325553894,
-0.05255109816789627,
-0.042677439749240875,
0.00031808196217752993,
-0.05570630356669426,
0.009187376126646996,
-0.07693202793598175,
-0.1924356371164322,
-0.03391728177666664,
0.03571595251560211,
-0.06710570305585861,
-0.0827605277299881,
-0.005242336541414261,
-0.059448789805173874,
0.0394723080098629,
0.0005945955635979772,
0.05985083803534508,
-0.0278206504881382,
0.06330664455890656,
0.049852535128593445,
0.017657380551099777,
0.021186351776123047,
0.0488196462392807,
-0.10267171263694763,
0.0524601936340332,
-0.1184334084391594,
0.09803973883390427,
-0.07423459738492966,
0.038962289690971375,
-0.1313907951116562,
-0.10197166353464127,
0.0018546434585005045,
-0.04141093045473099,
0.07199592888355255,
0.13272278010845184,
-0.10335154086351395,
-0.022165454924106598,
0.11816459894180298,
-0.07785513252019882,
-0.12071248143911362,
0.08534370362758636,
-0.022953324019908905,
0.045993104577064514,
0.046202655881643295,
0.1589670181274414,
0.11482222378253937,
-0.09405960142612457,
-0.04601092264056206,
0.023824656382203102,
0.03999439254403114,
0.011852654628455639,
0.07992444187402725,
-0.00309094344265759,
0.02800542116165161,
0.02701490744948387,
-0.04682716354727745,
-0.021443666890263557,
-0.06339383870363235,
-0.08331917971372604,
-0.07590357959270477,
-0.06402608007192612,
0.043350111693143845,
0.02541390061378479,
0.040221646428108215,
-0.061540424823760986,
-0.11145498603582382,
0.08370458334684372,
0.139164537191391,
-0.03616904467344284,
0.013012531213462353,
-0.09199543297290802,
0.07749252766370773,
-0.07083675265312195,
-0.019325491040945053,
-0.22403904795646667,
-0.08301106840372086,
0.06912441551685333,
-0.0844799056649208,
0.03631474822759628,
-0.023486437276005745,
0.05756011977791786,
0.06669508665800095,
-0.020877957344055176,
-0.04520843178033829,
-0.07576073706150055,
-0.013489354401826859,
-0.1070614606142044,
-0.14007365703582764,
-0.06415729969739914,
-0.025006959214806557,
0.1785648763179779,
-0.20162789523601532,
0.018683813512325287,
0.03474830090999603,
0.1296490728855133,
0.02043439820408821,
-0.07037334144115448,
0.021798264235258102,
-0.007155642379075289,
-0.01540442742407322,
-0.10946563631296158,
0.03500191494822502,
0.0358981117606163,
-0.0988478735089302,
-0.04068392515182495,
-0.15268602967262268,
0.1001257672905922,
0.07800216972827911,
0.07480549812316895,
-0.07787521183490753,
-0.007283263374119997,
-0.052342046052217484,
-0.04770160838961601,
-0.04090987890958786,
-0.02255544438958168,
0.1789885014295578,
0.013741696253418922,
0.13861024379730225,
-0.07114116847515106,
-0.05627784878015518,
0.03262091800570488,
-0.016391878947615623,
-0.04565364494919777,
0.05119848996400833,
-0.032233331352472305,
-0.1856033205986023,
0.09945181012153625,
0.08837785571813583,
-0.009968377649784088,
0.11120755225419998,
-0.05440429598093033,
-0.07501151412725449,
-0.04978104308247566,
0.008305763825774193,
0.009151633828878403,
0.09068036824464798,
-0.09676872938871384,
-0.0012435144744813442,
0.05805904418230057,
0.025901319459080696,
0.014793879352509975,
-0.14031444489955902,
0.02428031712770462,
0.04397774860262871,
-0.03224165737628937,
0.01261818502098322,
0.012260672636330128,
-0.0064454879611730576,
0.07525911927223206,
0.043180469423532486,
-0.023749854415655136,
0.04694293439388275,
-0.009407322853803635,
-0.07326249778270721,
0.1627616584300995,
-0.11315874755382538,
-0.19340206682682037,
-0.1698642075061798,
0.03387963026762009,
-0.09838724881410599,
-0.01237355824559927,
0.02181529439985752,
-0.035198334604501724,
-0.0654141753911972,
-0.0793607234954834,
-0.06935850530862808,
-0.06716088950634003,
-0.004725308623164892,
0.08316729217767715,
-0.01389254443347454,
0.12275215238332748,
-0.12859021127223969,
-0.01312447339296341,
0.01617579534649849,
-0.04588047042489052,
-0.024191783741116524,
0.024624288082122803,
0.1066960021853447,
0.07267232239246368,
-0.019129442051053047,
0.03396252915263176,
-0.013957099989056587,
0.28829655051231384,
-0.07700160890817642,
-0.013763348571956158,
0.16620296239852905,
0.00806343462318182,
0.0908171683549881,
0.10410254448652267,
0.02938893623650074,
-0.09005089849233627,
0.015587905421853065,
0.017387278378009796,
-0.013714615255594254,
-0.20694488286972046,
-0.02887600287795067,
-0.029988422989845276,
-0.0651637613773346,
0.11897396296262741,
0.06584492325782776,
0.08896847069263458,
0.07601267844438553,
-0.020059525966644287,
0.07728071510791779,
-0.01887539029121399,
0.12239715456962585,
0.12466126680374146,
0.06316735595464706,
0.09678853303194046,
-0.02540436014533043,
-0.008474195376038551,
0.06567256897687912,
-0.006952231749892235,
0.22003857791423798,
-0.0023755573201924562,
0.21153624355793,
0.00903434120118618,
0.15483734011650085,
-0.011572915129363537,
0.04366135597229004,
0.005354332271963358,
0.017576299607753754,
0.007074527908116579,
-0.06367988139390945,
-0.06372563540935516,
0.027870740741491318,
-0.0240074023604393,
0.07183142751455307,
-0.09497726708650589,
0.05259593203663826,
0.01469150185585022,
0.2448384165763855,
0.03316648304462433,
-0.34119167923927307,
-0.10391506552696228,
0.004619442857801914,
-0.027203358709812164,
-0.1143159493803978,
-0.018975958228111267,
0.055673129856586456,
-0.15398186445236206,
0.0669340267777443,
-0.07896468788385391,
0.09647475928068161,
-0.06532717496156693,
0.006476891692727804,
0.060297392308712006,
0.09661275148391724,
0.0028436158318072557,
0.09538515657186508,
-0.1975776106119156,
0.18235979974269867,
0.03521745651960373,
0.08307526260614395,
-0.07810354977846146,
0.05547117814421654,
0.023298660293221474,
0.10496754944324493,
0.1173199862241745,
0.0016715157544240355,
-0.05484309419989586,
-0.1739548295736313,
-0.1047971099615097,
-0.006790669169276953,
0.10034748911857605,
-0.06530170142650604,
0.06909020990133286,
-0.06220695376396179,
0.0025445199571549892,
0.020988501608371735,
-0.039113905280828476,
-0.13971185684204102,
-0.13198715448379517,
0.052037231624126434,
0.0363936685025692,
0.017986765131354332,
-0.07397470623254776,
-0.08639027923345566,
-0.00149569904897362,
0.19479872286319733,
0.024537328630685806,
-0.07276735454797745,
-0.16600382328033447,
0.06684314459562302,
0.12614817917346954,
-0.08242025226354599,
0.032370831817388535,
-0.02220471017062664,
0.16392828524112701,
0.04970354586839676,
-0.08457130193710327,
0.05361076444387436,
-0.058126747608184814,
-0.16700737178325653,
-0.027699299156665802,
0.1340915560722351,
-0.0002737696922849864,
0.039944037795066833,
0.014476556330919266,
0.04034135490655899,
-0.0063025858253240585,
-0.08248136937618256,
0.0030022431164979935,
0.028310317546129227,
0.09540396183729172,
0.026606177911162376,
-0.01637192629277706,
0.0662526935338974,
-0.05731521546840668,
0.005008238833397627,
0.11470456421375275,
0.21501398086547852,
-0.07943270355463028,
0.04944658279418945,
0.039695918560028076,
-0.06786882132291794,
-0.14887364208698273,
0.019561516121029854,
0.11102131009101868,
0.020852332934737206,
0.060156624764204025,
-0.1567920744419098,
0.07312533259391785,
0.08063869923353195,
-0.04672463610768318,
0.04950418323278427,
-0.2723522484302521,
-0.12903790175914764,
0.0661400556564331,
0.10664171725511551,
0.023467646911740303,
-0.13608182966709137,
-0.07294810563325882,
-0.030931619927287102,
-0.12698885798454285,
0.07951623946428299,
-0.014900824055075645,
0.10654163360595703,
-0.027001993730664253,
0.08066123723983765,
0.03787997364997864,
-0.026980319991707802,
0.16767479479312897,
0.037222277373075485,
0.07254593819379807,
-0.07153238356113434,
0.03524688258767128,
0.10610850900411606,
-0.08675243705511093,
0.1107497438788414,
-0.04933162406086922,
0.09062749147415161,
-0.17221352458000183,
-0.004437610972672701,
-0.05065610632300377,
0.05891958251595497,
-0.05537492781877518,
-0.04453763738274574,
-0.03741813823580742,
0.03387155383825302,
0.04616621136665344,
-0.030120328068733215,
0.12807735800743103,
0.05869285762310028,
0.0446474589407444,
0.1790764182806015,
0.07974105328321457,
-0.002389336470514536,
-0.16781608760356903,
-0.018380792811512947,
-0.010416410863399506,
0.05630705505609512,
-0.11648104339838028,
0.0316196046769619,
0.09916786849498749,
0.029079265892505646,
0.13892081379890442,
0.01750325597822666,
-0.06497553735971451,
-0.008000479079782963,
0.03231145814061165,
-0.10870011895895004,
-0.1448986679315567,
-0.055610235780477524,
0.009483187459409237,
-0.16011172533035278,
0.015096813440322876,
0.11662521213293076,
-0.05256527662277222,
-0.019629083573818207,
-0.007568194065243006,
0.008486410602927208,
-0.0026067709550261497,
0.16495724022388458,
0.03892726078629494,
0.07367552071809769,
-0.08628332614898682,
0.11713596433401108,
0.07806311547756195,
-0.0483725443482399,
0.05658234283328056,
0.03068239800632,
-0.10360906273126602,
-0.02142951264977455,
0.04275353252887726,
0.12721502780914307,
-0.0362071767449379,
-0.04982610419392586,
-0.0783441886305809,
-0.05421309918165207,
0.03842892497777939,
0.0355849489569664,
0.07357504218816757,
0.008270598948001862,
-0.04284878447651863,
-0.010046403855085373,
-0.1322612315416336,
0.10487744957208633,
0.04376409575343132,
0.07568958401679993,
-0.19076810777187347,
0.02996230684220791,
0.0038561781402677298,
0.05753655731678009,
-0.015381318517029285,
-0.016712704673409462,
-0.07048890739679337,
-0.04436721280217171,
-0.08361133933067322,
0.0173640213906765,
-0.04910724237561226,
0.004190685693174601,
-0.023993810638785362,
-0.056314799934625626,
-0.03520311787724495,
0.06032620742917061,
-0.05952071398496628,
-0.0918772891163826,
0.01963680237531662,
0.05263209715485573,
-0.12466216087341309,
-0.006763247773051262,
0.02664838545024395,
-0.10338439047336578,
0.09162961691617966,
0.06686396896839142,
0.0261430311948061,
0.0024298294447362423,
0.02693534828722477,
0.026402687653899193,
0.03278490900993347,
0.016279811039566994,
0.05319879576563835,
-0.1115935668349266,
-0.0011850069276988506,
-0.02480093389749527,
0.006671086885035038,
0.004765227437019348,
0.07459354400634766,
-0.13788799941539764,
-0.052848268300294876,
-0.05219259113073349,
-0.016972040757536888,
-0.06753328442573547,
0.059837404638528824,
0.11510132998228073,
0.018206210806965828,
0.182295560836792,
-0.048866357654333115,
0.016626330092549324,
-0.20570698380470276,
-0.016301576048135757,
0.0016723927110433578,
-0.06510930508375168,
-0.06418793648481369,
-0.053858790546655655,
0.05840928852558136,
-0.05718895420432091,
0.08878058195114136,
-0.00631486251950264,
0.10432542115449905,
0.04181194305419922,
0.03147995471954346,
0.0006292795878835022,
-0.012719977647066116,
0.15181607007980347,
0.06712552160024643,
-0.014017028734087944,
0.12651672959327698,
-0.005124935880303383,
0.061418164521455765,
0.0307196993380785,
0.09320729225873947,
0.11180897057056427,
-0.04206647723913193,
0.0822397843003273,
0.04675498977303505,
-0.050604306161403656,
-0.18723511695861816,
0.057980652898550034,
-0.02233974263072014,
0.12523086369037628,
-0.027212588116526604,
0.10901179164648056,
0.10314423590898514,
-0.16096335649490356,
0.053518861532211304,
-0.042845647782087326,
-0.10045041888952255,
-0.09668776392936707,
-0.1269230991601944,
-0.08421368896961212,
-0.11768529564142227,
0.009378396905958652,
-0.11717984825372696,
0.034857869148254395,
0.06403156369924545,
-0.01819942705333233,
-0.023168670013546944,
0.15690211951732635,
-0.07030341774225235,
-0.001918309135362506,
0.061766643077135086,
-0.012344957329332829,
-0.02542407251894474,
-0.03141219913959503,
-0.04705614596605301,
0.034906089305877686,
0.021783171221613884,
0.10194259881973267,
-0.031555041670799255,
0.0259357038885355,
0.028467902913689613,
-0.024578750133514404,
-0.101446732878685,
0.013293378055095673,
0.024447008967399597,
0.009038136340677738,
0.007424691691994667,
0.029487168416380882,
0.0033221563789993525,
-0.048928190022706985,
0.2587166428565979,
-0.06380714476108551,
-0.01906404085457325,
-0.13285745680332184,
0.14711476862430573,
0.046297669410705566,
-0.040689870715141296,
0.07303299009799957,
-0.10941654443740845,
0.005252137780189514,
0.14228485524654388,
0.11021265387535095,
-0.036765486001968384,
-0.025745028629899025,
-0.012874811887741089,
-0.021170511841773987,
-0.03974330797791481,
0.09705914556980133,
0.07048969715833664,
-0.027027642354369164,
-0.04650174826383591,
0.03157719597220421,
-0.02072541043162346,
-0.04596046730875969,
-0.07593858987092972,
0.08855843544006348,
0.005402378737926483,
0.012537558563053608,
-0.031102076172828674,
0.05880855396389961,
0.012080776505172253,
-0.14038485288619995,
0.02416440099477768,
-0.15003816783428192,
-0.1703203171491623,
-0.009068356826901436,
0.04319377243518829,
0.00287028681486845,
0.06502830982208252,
0.017203791067004204,
-0.0013508943375200033,
0.11842624843120575,
-0.015294424258172512,
-0.07846234738826752,
-0.08787348121404648,
0.08139818906784058,
-0.08273843675851822,
0.21529673039913177,
0.00782808568328619,
0.06552057713270187,
0.1129598394036293,
0.008534248918294907,
-0.173043891787529,
0.009335368871688843,
0.0605417937040329,
-0.018350442871451378,
0.045982200652360916,
0.17809125781059265,
-0.012782289646565914,
0.053035106509923935,
0.030980821698904037,
-0.13401253521442413,
-0.05501260235905647,
-0.07233331352472305,
0.026081016287207603,
-0.07409694045782089,
-0.02043278142809868,
-0.05340881273150444,
0.16891324520111084,
0.18890179693698883,
-0.05188405141234398,
-0.024962356314063072,
-0.06236255541443825,
0.03384004533290863,
0.06969189643859863,
0.053426552563905716,
-0.021914072334766388,
-0.20371845364570618,
0.005894253961741924,
0.028160609304904938,
0.017105786129832268,
-0.21917624771595,
-0.11954142153263092,
0.04244231432676315,
-0.06005863845348358,
-0.05191902816295624,
0.10326740890741348,
0.03844881430268288,
0.014313673600554466,
-0.03639879822731018,
-0.04157118499279022,
-0.06431896239519119,
0.14587903022766113,
-0.1554078608751297,
-0.054137155413627625
] |
null | null |
transformers
|
# ✨ bert-restore-punctuation
[]()
This a bert-base-uncased model finetuned for punctuation restoration on [Yelp Reviews](https://www.tensorflow.org/datasets/catalog/yelp_polarity_reviews).
The model predicts the punctuation and upper-casing of plain, lower-cased text. An example use case can be ASR output. Or other cases when text has lost punctuation.
This model is intended for direct use as a punctuation restoration model for the general English language. Alternatively, you can use this for further fine-tuning on domain-specific texts for punctuation restoration tasks.
Model restores the following punctuations -- **[! ? . , - : ; ' ]**
The model also restores the upper-casing of words.
-----------------------------------------------
## 🚋 Usage
**Below is a quick way to get up and running with the model.**
1. First, install the package.
```bash
pip install rpunct
```
2. Sample python code.
```python
from rpunct import RestorePuncts
# The default language is 'english'
rpunct = RestorePuncts()
rpunct.punctuate("""in 2018 cornell researchers built a high-powered detector that in combination with an algorithm-driven process called ptychography set a world record
by tripling the resolution of a state-of-the-art electron microscope as successful as it was that approach had a weakness it only worked with ultrathin samples that were
a few atoms thick anything thicker would cause the electrons to scatter in ways that could not be disentangled now a team again led by david muller the samuel b eckert
professor of engineering has bested its own record by a factor of two with an electron microscope pixel array detector empad that incorporates even more sophisticated
3d reconstruction algorithms the resolution is so fine-tuned the only blurring that remains is the thermal jiggling of the atoms themselves""")
# Outputs the following:
# In 2018, Cornell researchers built a high-powered detector that, in combination with an algorithm-driven process called Ptychography, set a world record by tripling the
# resolution of a state-of-the-art electron microscope. As successful as it was, that approach had a weakness. It only worked with ultrathin samples that were a few atoms
# thick. Anything thicker would cause the electrons to scatter in ways that could not be disentangled. Now, a team again led by David Muller, the Samuel B.
# Eckert Professor of Engineering, has bested its own record by a factor of two with an Electron microscope pixel array detector empad that incorporates even more
# sophisticated 3d reconstruction algorithms. The resolution is so fine-tuned the only blurring that remains is the thermal jiggling of the atoms themselves.
```
**This model works on arbitrarily large text in English language and uses GPU if available.**
-----------------------------------------------
## 📡 Training data
Here is the number of product reviews we used for finetuning the model:
| Language | Number of text samples|
| -------- | ----------------- |
| English | 560,000 |
We found the best convergence around _**3 epochs**_, which is what presented here and available via a download.
-----------------------------------------------
## 🎯 Accuracy
The fine-tuned model obtained the following accuracy on 45,990 held-out text samples:
| Accuracy | Overall F1 | Eval Support |
| -------- | ---------------------- | ------------------- |
| 91% | 90% | 45,990
Below is a breakdown of the performance of the model by each label:
| label | precision | recall | f1-score | support|
| --------- | -------------|-------- | ----------|--------|
| **!** | 0.45 | 0.17 | 0.24 | 424
| **!+Upper** | 0.43 | 0.34 | 0.38 | 98
| **'** | 0.60 | 0.27 | 0.37 | 11
| **,** | 0.59 | 0.51 | 0.55 | 1522
| **,+Upper** | 0.52 | 0.50 | 0.51 | 239
| **-** | 0.00 | 0.00 | 0.00 | 18
| **.** | 0.69 | 0.84 | 0.75 | 2488
| **.+Upper** | 0.65 | 0.52 | 0.57 | 274
| **:** | 0.52 | 0.31 | 0.39 | 39
| **:+Upper** | 0.36 | 0.62 | 0.45 | 16
| **;** | 0.00 | 0.00 | 0.00 | 17
| **?** | 0.54 | 0.48 | 0.51 | 46
| **?+Upper** | 0.40 | 0.50 | 0.44 | 4
| **none** | 0.96 | 0.96 | 0.96 |35352
| **Upper** | 0.84 | 0.82 | 0.83 | 5442
-----------------------------------------------
## ☕ Contact
Contact [Daulet Nurmanbetov]([email protected]) for questions, feedback and/or requests for similar models.
-----------------------------------------------
|
{"language": ["en"], "license": "mit", "tags": ["punctuation"], "datasets": ["yelp_polarity"], "metrics": ["f1"]}
|
token-classification
|
felflare/bert-restore-punctuation
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"punctuation",
"en",
"dataset:yelp_polarity",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bert #token-classification #punctuation #en #dataset-yelp_polarity #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
bert-restore-punctuation
========================
![forthebadge]()
This a bert-base-uncased model finetuned for punctuation restoration on Yelp Reviews.
The model predicts the punctuation and upper-casing of plain, lower-cased text. An example use case can be ASR output. Or other cases when text has lost punctuation.
This model is intended for direct use as a punctuation restoration model for the general English language. Alternatively, you can use this for further fine-tuning on domain-specific texts for punctuation restoration tasks.
Model restores the following punctuations -- [! ? . , - : ; ' ]
The model also restores the upper-casing of words.
---
Usage
-----
Below is a quick way to get up and running with the model.
1. First, install the package.
2. Sample python code.
This model works on arbitrarily large text in English language and uses GPU if available.
---
Training data
-------------
Here is the number of product reviews we used for finetuning the model:
We found the best convergence around *3 epochs*, which is what presented here and available via a download.
---
Accuracy
--------
The fine-tuned model obtained the following accuracy on 45,990 held-out text samples:
Accuracy: 91%, Overall F1: 90%, Eval Support: 45,990
Below is a breakdown of the performance of the model by each label:
---
Contact
-------
Contact Daulet Nurmanbetov for questions, feedback and/or requests for similar models.
---
|
[] |
[
"TAGS\n#transformers #pytorch #bert #token-classification #punctuation #en #dataset-yelp_polarity #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
62
] |
[
"passage: TAGS\n#transformers #pytorch #bert #token-classification #punctuation #en #dataset-yelp_polarity #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
-0.017052503302693367,
0.09520770609378815,
-0.006247104611247778,
0.03608335554599762,
0.10027153789997101,
0.026419393718242645,
0.08252007514238358,
0.08708757907152176,
0.04649297520518303,
0.00864136591553688,
0.1612905114889145,
0.19685862958431244,
-0.01279420405626297,
0.09611954540014267,
-0.08850598335266113,
-0.32733380794525146,
0.06302057206630707,
0.07246104627847672,
-0.06722714006900787,
0.10348072648048401,
0.09592738747596741,
-0.10014666616916656,
0.04474412277340889,
0.01001225970685482,
-0.13195252418518066,
-0.007174701429903507,
0.015165177173912525,
-0.11420203000307083,
0.11657465994358063,
0.030887754634022713,
0.14765645563602448,
0.07174593955278397,
-0.03882116824388504,
-0.13787099719047546,
0.027842584997415543,
0.002084980485960841,
-0.0929492935538292,
0.1009092628955841,
0.04702175781130791,
-0.04034425690770149,
0.09066689014434814,
-0.02105661667883396,
0.059591732919216156,
0.028527405112981796,
-0.12682223320007324,
-0.18508175015449524,
-0.05350063741207123,
0.0724891722202301,
0.012535333633422852,
0.004054330755025148,
0.00970517285168171,
0.18540766835212708,
-0.13817906379699707,
0.06595145165920258,
0.07000270485877991,
-0.3386085331439972,
0.0017459045629948378,
0.17090149223804474,
0.028087563812732697,
-0.02395007386803627,
-0.06906387209892273,
0.059444066137075424,
0.05163949728012085,
0.0012310156598687172,
0.06974592059850693,
-0.03243616968393326,
-0.042941365391016006,
0.08745111525058746,
-0.1202186644077301,
-0.03493266925215721,
0.1598513424396515,
-0.03167945519089699,
0.06312189251184464,
-0.0450935885310173,
-0.06150808557868004,
-0.08284781873226166,
-0.00874941237270832,
0.02545350231230259,
0.0071314796805381775,
0.028824588283896446,
0.033056192100048065,
0.034820493310689926,
-0.13603077828884125,
0.03450984135270119,
-0.21765834093093872,
0.1797371357679367,
0.018409013748168945,
0.06867332011461258,
-0.14066699147224426,
0.03670800104737282,
-0.007635926827788353,
-0.08812974393367767,
0.03350358083844185,
-0.07503020763397217,
-0.000680448894854635,
-0.03810805082321167,
-0.0975768193602562,
0.04606977477669716,
0.04718170687556267,
0.21658878028392792,
0.045234814286231995,
0.007019743788987398,
0.02531713992357254,
0.11177085340023041,
0.09899342060089111,
0.09134849905967712,
-0.0453357994556427,
0.020386993885040283,
0.0032391678541898727,
-0.1255665123462677,
0.04177700728178024,
-0.06997261941432953,
-0.15366677939891815,
-0.060437895357608795,
0.05681834742426872,
0.036361731588840485,
0.036313675343990326,
0.07274852693080902,
-0.0567549504339695,
0.0008651374955661595,
0.08535641431808472,
-0.02535160258412361,
0.07795461267232895,
-0.005000177305191755,
0.03749465197324753,
0.087350994348526,
-0.022737344726920128,
0.016390036791563034,
0.017413539811968803,
0.18368545174598694,
-0.07506217062473297,
0.018810613080859184,
-0.019823163747787476,
-0.13261422514915466,
0.08105066418647766,
-0.12108447402715683,
0.05052972584962845,
-0.15785028040409088,
-0.03240993991494179,
0.03649537265300751,
0.053107745945453644,
-0.04441491886973381,
-0.026419032365083694,
0.055353228002786636,
-0.04716981202363968,
0.06356398016214371,
-0.06391812860965729,
-0.025056440383195877,
-0.07986196875572205,
0.059760380536317825,
-0.08612527698278427,
0.0940488874912262,
-0.1547870934009552,
0.03465092182159424,
-0.09647446125745773,
0.005636624060571194,
-0.06882306188344955,
-0.030045874416828156,
-0.09286646544933319,
0.08188851922750473,
-0.002334505319595337,
-0.10403533279895782,
-0.02147003449499607,
0.038984138518571854,
-0.02056039124727249,
0.10971228033304214,
-0.17573204636573792,
-0.05884154140949249,
0.09283804148435593,
-0.12343011051416397,
-0.10423971712589264,
0.11110816150903702,
-0.02053317427635193,
-0.02537836693227291,
0.014070347882807255,
0.20206806063652039,
0.04997338727116585,
-0.07333813607692719,
0.00802295096218586,
0.08170351386070251,
-0.11029449105262756,
-0.11166347563266754,
0.053997211158275604,
0.054985638707876205,
-0.06483576446771622,
0.023242276161909103,
0.06261656433343887,
0.04735569655895233,
-0.06464584916830063,
-0.057706236839294434,
-0.032979682087898254,
0.012373345904052258,
0.09454140812158585,
0.04594910144805908,
0.0843518078327179,
-0.08362577855587006,
-0.01036791317164898,
0.07441676408052444,
-0.004574284888803959,
0.06353887915611267,
0.0440656840801239,
-0.05610973387956619,
0.15743042528629303,
-0.04072343185544014,
-0.026458529755473137,
-0.14489449560642242,
-0.06022888794541359,
-0.04900704324245453,
0.06351343542337418,
0.001108247204683721,
0.18005621433258057,
0.04425474628806114,
-0.048011165112257004,
-0.021840659901499748,
0.013892735354602337,
0.15426230430603027,
0.08436709642410278,
-0.07489760220050812,
-0.11544772982597351,
-0.012804766185581684,
-0.07383004575967789,
0.02021396905183792,
-0.055103056132793427,
0.019147444516420364,
0.12512913346290588,
0.13914735615253448,
-0.023690324276685715,
0.08276081830263138,
-0.02011517621576786,
0.05880837142467499,
-0.07641567289829254,
0.018383242189884186,
0.09366171807050705,
-0.0024114628322422504,
-0.06634227186441422,
0.16034986078739166,
-0.07681974768638611,
0.3123535215854645,
0.20629745721817017,
-0.23462557792663574,
-0.01846441812813282,
-0.09428344666957855,
-0.03348633646965027,
0.03462749347090721,
0.02515505999326706,
0.00910400040447712,
-0.062420837581157684,
-0.02368350513279438,
0.09695102274417877,
-0.010232782922685146,
-0.04006308317184448,
0.017809370532631874,
-0.07568349689245224,
-0.08872809261083603,
0.06860276311635971,
0.09699977934360504,
-0.2329404354095459,
0.19949078559875488,
0.27238836884498596,
0.006710982881486416,
0.10666251927614212,
-0.015816230326890945,
0.033891573548316956,
-0.02864457294344902,
-0.093905970454216,
-0.03941260650753975,
0.08775465190410614,
-0.0868760347366333,
-0.025954818353056908,
0.10907890647649765,
0.018008938059210777,
0.014383959583938122,
-0.14737598598003387,
-0.06304267793893814,
0.04004867747426033,
0.08511043339967728,
-0.1591048538684845,
0.10628750920295715,
0.048923127353191376,
0.11335590481758118,
0.0021437001414597034,
-0.11148430407047272,
0.08940327167510986,
0.007306851912289858,
-0.03572623059153557,
0.13764943182468414,
-0.1492760181427002,
-0.20240458846092224,
-0.090920090675354,
-0.12611335515975952,
0.008052237331867218,
0.010154278948903084,
0.08161593228578568,
-0.04109404981136322,
-0.05056684464216232,
0.006058445665985346,
0.0033485994208604097,
-0.09708388149738312,
0.0618399940431118,
-0.1318737119436264,
0.02541080303490162,
-0.07928157597780228,
-0.06804265081882477,
-0.07194342464208603,
-0.049552567303180695,
0.03937869146466255,
0.1576925814151764,
-0.01831957697868347,
0.07582658529281616,
0.12800905108451843,
-0.04998025670647621,
0.02120046876370907,
-0.05112543702125549,
0.1954456865787506,
-0.04973014071583748,
0.017778396606445312,
0.1303844302892685,
0.03458816930651665,
0.06088805943727493,
0.17842428386211395,
0.06719943135976791,
-0.021856289356946945,
-0.01910468563437462,
-0.02326134406030178,
-0.10628942400217056,
-0.13187944889068604,
-0.15299782156944275,
-0.11627069860696793,
0.053219981491565704,
0.07401326298713684,
0.05662905052304268,
0.061571504920721054,
0.07822926342487335,
0.029765570536255836,
-0.04864845797419548,
-0.05311860889196396,
0.09315965324640274,
0.2890680134296417,
-0.012515458278357983,
0.1582946628332138,
-0.04763521999120712,
-0.10007189214229584,
0.07605183124542236,
0.07540088891983032,
0.07475893944501877,
0.08719705790281296,
-0.0016584559343755245,
0.05422120541334152,
0.20207560062408447,
0.13612201809883118,
0.1085161417722702,
0.03771573305130005,
-0.02390677109360695,
-0.025375526398420334,
-0.0017527553718537092,
-0.01768111251294613,
0.047873638570308685,
0.12590746581554413,
-0.1516025811433792,
-0.03211350739002228,
-0.20598554611206055,
0.057141005992889404,
0.04739123955368996,
0.09309795498847961,
-0.2010771781206131,
-0.01771751046180725,
0.06569267809391022,
0.021052325144410133,
-0.052591145038604736,
0.06456749141216278,
0.001152255805209279,
-0.07828565686941147,
0.0956638902425766,
-0.05774400383234024,
0.09120403975248337,
0.051221154630184174,
0.0719597116112709,
-0.0015852405922487378,
-0.13437804579734802,
0.019241470843553543,
0.08398488163948059,
-0.25838613510131836,
0.23989680409431458,
-0.013521125540137291,
-0.10048364847898483,
-0.06680899858474731,
-0.026174433529376984,
0.020221376791596413,
0.1680377572774887,
0.05953015014529228,
0.02701142616569996,
-0.10166754573583603,
-0.17003120481967926,
0.022120771929621696,
-0.018709849566221237,
0.055877722799777985,
-0.01071527786552906,
-0.02021877095103264,
-0.028245912864804268,
0.019665611907839775,
0.03458893299102783,
0.08339320868253708,
0.02541285753250122,
-0.17773772776126862,
0.07460452616214752,
0.05221286043524742,
-0.055029481649398804,
0.017118029296398163,
-0.10980106890201569,
-0.20931869745254517,
0.1042759045958519,
-0.029850203543901443,
-0.05699523538351059,
-0.11639174818992615,
-0.0435652881860733,
0.08638039231300354,
-0.05628937482833862,
0.06254075467586517,
-0.05558447167277336,
0.03209233656525612,
-0.054570555686950684,
-0.16456098854541779,
0.1418415755033493,
-0.10219518840312958,
-0.054210446774959564,
-0.04477234184741974,
0.11054126918315887,
-0.09728328883647919,
0.03993242606520653,
0.02422977425158024,
0.10858326405286789,
-0.14482565224170685,
-0.1126384437084198,
0.04787854850292206,
-0.10495517402887344,
0.1076662614941597,
0.006023732479661703,
-0.005993056111037731,
0.020864062011241913,
0.051816944032907486,
0.02329612709581852,
0.209211066365242,
0.2416035681962967,
-0.1269427388906479,
0.14856186509132385,
0.08809582144021988,
-0.012591867707669735,
-0.31142351031303406,
-0.08476391434669495,
-0.15574675798416138,
-0.028706204146146774,
0.06992403417825699,
-0.08288838714361191,
0.06185591593384743,
0.013570467941462994,
-0.07316498458385468,
0.03875930234789848,
-0.14803194999694824,
-0.08412524312734604,
0.1843385249376297,
-0.020442752167582512,
0.31883567571640015,
-0.09796176105737686,
-0.0471615195274353,
-0.016936317086219788,
-0.19672773778438568,
0.2395036369562149,
-0.0037167691625654697,
0.06620495021343231,
-0.03690856695175171,
0.0864025354385376,
0.03974051773548126,
-0.06799117475748062,
0.1473151445388794,
-0.02031477354466915,
0.03130227327346802,
-0.11477240175008774,
-0.14658813178539276,
0.05132204294204712,
-0.029030483216047287,
-0.007546215318143368,
-0.03679356724023819,
0.005837121978402138,
-0.11646204441785812,
-0.0034011502284556627,
-0.08483666926622391,
0.08068259060382843,
-0.01912553422152996,
-0.06598325073719025,
-0.10285380482673645,
0.02625582553446293,
0.035591453313827515,
-0.049720365554094315,
0.29054003953933716,
0.010516535490751266,
0.14145228266716003,
0.15054243803024292,
0.0397978276014328,
-0.1455579251050949,
-0.01942584104835987,
-0.02004886418581009,
-0.08314093202352524,
0.09248757362365723,
-0.06871146708726883,
0.03471618890762329,
0.14148791134357452,
-0.02520214207470417,
0.03804798796772957,
0.12527233362197876,
0.031968094408512115,
-0.04505637288093567,
0.13684487342834473,
-0.17216038703918457,
-0.07521045207977295,
0.030765622854232788,
0.010977104306221008,
0.0907256007194519,
0.07555296272039413,
0.09095356613397598,
-0.022803835570812225,
-0.034255947917699814,
0.04227070137858391,
0.01969217136502266,
-0.043206922709941864,
0.06032513827085495,
0.07331019639968872,
0.04843904823064804,
-0.1334133744239807,
0.02478858083486557,
0.040135931223630905,
-0.10926125198602676,
-0.012690450064837933,
0.018750475719571114,
-0.10467778146266937,
-0.14495758712291718,
-0.04809579998254776,
0.005517237354069948,
-0.185426265001297,
-0.02214823290705681,
0.0028711778577417135,
-0.16852355003356934,
0.048033200204372406,
0.1487250030040741,
0.11935824900865555,
0.08200415968894958,
-0.053180843591690063,
-0.04828551411628723,
-0.013552319258451462,
-0.007123496383428574,
-0.03202727437019348,
0.05533844977617264,
-0.12613287568092346,
0.051994044333696365,
-0.043642476201057434,
0.11219724267721176,
-0.09045838564634323,
-0.025574205443263054,
-0.16335299611091614,
-0.011015851981937885,
-0.04487326741218567,
-0.06074867770075798,
-0.09986580908298492,
-0.024816615507006645,
0.03243688493967056,
-0.0871920958161354,
-0.03135332465171814,
-0.048165831714868546,
-0.12824329733848572,
0.046829916536808014,
0.00653403764590621,
0.0656915009021759,
-0.09487026929855347,
-0.053203120827674866,
0.07378028333187103,
-0.0058763655833899975,
0.09184704720973969,
0.04609520733356476,
-0.04928062856197357,
0.0572211816906929,
-0.0844237357378006,
-0.11096768081188202,
0.09570474922657013,
0.018859118223190308,
0.08239869773387909,
-0.031700409948825836,
0.004188999533653259,
0.06591116636991501,
-0.023486727848649025,
0.06805644184350967,
0.027721982449293137,
-0.09660323709249496,
-0.013448233716189861,
-0.042420826852321625,
-0.160532146692276,
-0.019326047971844673,
-0.06560764461755753,
0.08836580067873001,
-0.0041424972005188465,
0.15900778770446777,
-0.013289122842252254,
0.020212465897202492,
-0.07538894563913345,
0.0022468846291303635,
-0.04136289283633232,
-0.17440854012966156,
-0.07344232499599457,
-0.023945016786456108,
0.015190576203167439,
-0.0020063139963895082,
0.2734714448451996,
0.06634877622127533,
-0.09846014529466629,
0.05435197427868843,
0.10101072490215302,
-0.030792120844125748,
0.020903177559375763,
0.20419573783874512,
0.060127176344394684,
-0.05666561424732208,
-0.06407621502876282,
0.054010458290576935,
-0.020335432142019272,
-0.02616902068257332,
0.09233993291854858,
0.07446461915969849,
0.028210701420903206,
0.04430290311574936,
0.024826230481266975,
-0.013624567538499832,
-0.0987381786108017,
-0.10073072463274002,
-0.023801738396286964,
0.0760122612118721,
-0.029034828767180443,
0.08761580288410187,
0.0946328341960907,
-0.07520552724599838,
0.05693314969539642,
-0.09047790616750717,
-0.02294754609465599,
-0.14607417583465576,
-0.18890924751758575,
-0.057024698704481125,
-0.08590160310268402,
-0.02246403507888317,
-0.05284453183412552,
0.022516194730997086,
0.14001543819904327,
0.05936487764120102,
-0.016005825251340866,
-0.007668953854590654,
-0.04087911173701286,
0.00630803732201457,
0.0165378637611866,
0.008373870514333248,
-0.0018344521522521973,
-0.04719372093677521,
-0.02403331734240055,
-0.09913751482963562,
-0.020590560510754585,
-0.04641897976398468,
0.002877728082239628,
-0.012937764637172222,
-0.010794803500175476,
-0.11053948104381561,
-0.10175131261348724,
-0.015253097750246525,
0.03427039459347725,
0.023585457354784012,
0.12771232426166534,
-0.005976539570838213,
0.06960663944482803,
0.023000139743089676,
0.20787525177001953,
-0.06245632469654083,
-0.01754995994269848,
-0.03673392906785011,
0.23260140419006348,
0.04398464411497116,
0.09431582689285278,
0.0019583499524742365,
-0.017696745693683624,
-0.08196680247783661,
0.1811521351337433,
0.32069024443626404,
-0.05259241908788681,
0.046965572983026505,
0.022342972457408905,
0.008510691113770008,
0.09746523201465607,
0.10416863113641739,
0.0744204968214035,
0.1633078008890152,
-0.0855272188782692,
0.006124089937657118,
-0.07877311110496521,
-0.0026582484133541584,
-0.07976002246141434,
0.06325225532054901,
0.041428327560424805,
-0.07920125126838684,
-0.06210967153310776,
0.08391460031270981,
-0.1192241981625557,
0.05360282585024834,
0.0726345032453537,
-0.22351261973381042,
-0.07255446165800095,
0.02406599372625351,
0.16843058168888092,
-0.0260624960064888,
0.09792903810739517,
-0.0610494464635849,
-0.060291588306427,
0.0028974274173378944,
0.007497293874621391,
-0.14555515348911285,
-0.0015775683568790555,
0.0968908965587616,
0.022558704018592834,
0.06853623688220978,
-0.0316903218626976,
0.07154305279254913,
0.12477556616067886,
0.08262696862220764,
-0.06020593270659447,
0.025951184332370758,
0.043013036251068115,
-0.0955207347869873,
-0.032818373292684555,
-0.01866978406906128,
0.017979813739657402,
-0.10337858647108078,
0.07819679379463196,
-0.18787285685539246,
0.03943188488483429,
-0.07655808329582214,
-0.017370041459798813,
-0.01014622487127781,
0.042075056582689285,
-0.039096903055906296,
0.07061254233121872,
0.043824709951877594,
-0.034494999796152115,
-0.053333476185798645,
-0.04352997988462448,
-0.04459399729967117,
0.03334075212478638,
-0.12518741190433502,
-0.09032929688692093,
-0.026649951934814453,
-0.024515988305211067,
0.03720720857381821,
0.00400589220225811,
-0.05255270376801491,
-0.03054351545870304,
-0.0796794518828392,
0.03945191949605942,
-0.10233516246080399,
0.06459276378154755,
0.037261713296175,
-0.001456945901736617,
-0.03109007515013218,
-0.07771289348602295,
0.019812503829598427,
0.05031098797917366,
-0.10309746116399765,
-0.04274389147758484
] |
null | null |
transformers
|
# DioloGPT KaeyaBot model
|
{"tags": ["conversational"]}
|
text-generation
|
felinecity/DioloGPT-small-KaeyaBot
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DioloGPT KaeyaBot model
|
[
"# DioloGPT KaeyaBot model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DioloGPT KaeyaBot model"
] |
[
51,
9
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# DioloGPT KaeyaBot model"
] |
[
-0.013482030481100082,
-0.04944401979446411,
-0.00660965871065855,
0.018116695806384087,
0.16041694581508636,
0.005352854263037443,
0.1591426134109497,
0.113655224442482,
-0.005036589689552784,
-0.02728116884827614,
0.12488944083452225,
0.22933024168014526,
0.029869183897972107,
0.1351277083158493,
-0.07913515716791153,
-0.29110777378082275,
0.05341096594929695,
0.03645821288228035,
0.0021556243300437927,
0.08520137518644333,
0.10741303861141205,
-0.07100285589694977,
0.10141543298959732,
-0.015280923806130886,
-0.12886378169059753,
-0.034620773047208786,
0.009351576678454876,
-0.14558735489845276,
0.07823927700519562,
0.0635087788105011,
0.05051994323730469,
0.025348380208015442,
-0.03491302207112312,
-0.11841075867414474,
0.036076925694942474,
-0.010789170861244202,
-0.01633112132549286,
0.03215695172548294,
-0.006123706232756376,
-0.10275706648826599,
0.06512311846017838,
0.05040643364191055,
-0.007862668484449387,
0.01055244728922844,
-0.1442529559135437,
0.010917812585830688,
-0.01737990230321884,
0.011684340424835682,
0.13442599773406982,
0.10449829697608948,
-0.02284255623817444,
0.044014379382133484,
-0.0918123796582222,
0.10083391517400742,
0.11368492990732193,
-0.24449126422405243,
-0.03187127411365509,
0.10879965126514435,
0.03690730035305023,
0.05006536468863487,
-0.014344406314194202,
0.06345362216234207,
0.046731121838092804,
0.00035137636587023735,
0.01799810864031315,
-0.09024252742528915,
-0.0809108167886734,
0.007838817313313484,
-0.07939121127128601,
0.005723253358155489,
0.2735130786895752,
-0.06425411999225616,
0.04118042066693306,
-0.0737089291214943,
-0.07196356356143951,
0.023852720856666565,
-0.0601150244474411,
-0.008588753640651703,
-0.07469012588262558,
0.027902815490961075,
0.057888440787792206,
-0.10337091982364655,
-0.1314590871334076,
-0.008448471315205097,
-0.17960487306118011,
0.24182842671871185,
0.050672855228185654,
0.018773283809423447,
-0.20690259337425232,
0.07384129613637924,
0.017387308180332184,
-0.10685200989246368,
-0.006884794682264328,
-0.11515865474939346,
0.041213035583496094,
0.010604165494441986,
-0.015647996217012405,
-0.05562400445342064,
0.08027039468288422,
0.11201230436563492,
0.049923136830329895,
0.003928730729967356,
-0.01086176373064518,
0.03498953580856323,
0.07448742538690567,
0.1263836920261383,
-0.019534315913915634,
-0.015271554701030254,
0.044583309441804886,
-0.058608293533325195,
0.013690563850104809,
-0.06365002691745758,
-0.16729891300201416,
-0.011074299924075603,
-0.0019514053128659725,
0.06900392472743988,
0.025876125320792198,
0.12303344905376434,
-0.04554140567779541,
-0.056231752038002014,
0.029509034007787704,
-0.05098415166139603,
-0.034932199865579605,
-0.01566430553793907,
-0.03030925616621971,
0.17786185443401337,
-0.005685892887413502,
0.03192080184817314,
-0.10662221908569336,
0.057476289570331573,
-0.04604175314307213,
-0.0024113552644848824,
-0.030046630650758743,
-0.05317991226911545,
-0.007894616574048996,
-0.09116844832897186,
0.04384077712893486,
-0.18569129705429077,
-0.16892604529857635,
0.002109426073729992,
-0.0014731264673173428,
-0.06242804601788521,
-0.07764072716236115,
-0.09326117485761642,
-0.03556299954652786,
0.023548118770122528,
-0.0708107203245163,
-0.0026914088521152735,
-0.0493709072470665,
0.07582206279039383,
-0.03043040819466114,
0.12273649871349335,
-0.08936804533004761,
0.0855768695473671,
-0.09660112857818604,
-0.009370164945721626,
-0.1144319400191307,
0.13016021251678467,
-0.02309008315205574,
0.10889901965856552,
-0.01719598099589348,
-0.004806773737072945,
-0.08014009147882462,
0.0707927942276001,
-0.010947787202894688,
0.2390892058610916,
-0.09615686535835266,
-0.10527963191270828,
0.20623502135276794,
-0.03790713846683502,
-0.14620162546634674,
0.10589602589607239,
-0.004730983171612024,
0.13065624237060547,
0.12939231097698212,
0.18979355692863464,
0.0007764091715216637,
-0.02888396754860878,
0.05273909121751785,
0.08515644073486328,
-0.09192401170730591,
-0.009224558249115944,
0.006953336298465729,
0.019487295299768448,
-0.0604826994240284,
0.058031678199768066,
0.07512139528989792,
0.058956172317266464,
-0.04852375388145447,
-0.03693552687764168,
0.0054132877849042416,
-0.03053313121199608,
0.07787030935287476,
-0.03355223312973976,
0.11395929753780365,
-0.028790919110178947,
-0.009170105680823326,
-0.0036145257763564587,
0.06036003306508064,
-0.04261153191328049,
0.018190739676356316,
-0.06871908903121948,
0.13146312534809113,
-0.024455873295664787,
0.07805652916431427,
-0.11542021483182907,
-0.06328452378511429,
-0.014105372130870819,
0.11948758363723755,
0.05177495628595352,
0.06743009388446808,
0.03816762939095497,
-0.04157688468694687,
0.02144087478518486,
0.006811530329287052,
0.16115623712539673,
-0.009470009244978428,
-0.08691064268350601,
-0.094950832426548,
0.10649995505809784,
-0.05250834673643112,
0.05891750752925873,
-0.04607817530632019,
0.012430060654878616,
-0.007558582350611687,
0.08419515937566757,
-0.01676550693809986,
0.09096702188253403,
0.013472671620547771,
0.017037739977240562,
-0.08330360054969788,
0.00552785862237215,
0.06929166615009308,
-0.04137129336595535,
-0.07525984197854996,
0.24265378713607788,
-0.11155009269714355,
0.09725353121757507,
0.1959332674741745,
-0.2246531844139099,
0.017021849751472473,
-0.024936281144618988,
-0.01824372075498104,
0.003590463427826762,
0.0407932847738266,
-0.020584916695952415,
0.15661026537418365,
-0.005723661743104458,
0.1630423665046692,
-0.043110910803079605,
-0.019893556833267212,
0.020257245749235153,
-0.08925081044435501,
-0.00834382139146328,
0.09131285548210144,
0.10188426077365875,
-0.06383258104324341,
0.15723173320293427,
0.14325986802577972,
0.02455965429544449,
0.18680575489997864,
0.028767947107553482,
-0.0063820513896644115,
0.04121730849146843,
-0.006069001276046038,
-0.03843383118510246,
-0.03192035108804703,
-0.219386026263237,
-0.04428457096219063,
0.06277934461832047,
0.009671555832028389,
0.10874585062265396,
-0.06731194257736206,
-0.04561801254749298,
-0.008217640221118927,
-0.0076491497457027435,
0.08621719479560852,
0.12873822450637817,
0.03028985857963562,
0.11494994908571243,
-0.00158013217151165,
-0.027686171233654022,
0.05653297156095505,
0.031615711748600006,
-0.037328172475099564,
0.19481655955314636,
-0.11397957801818848,
-0.35038501024246216,
-0.09559032320976257,
-0.17552193999290466,
-0.07455673813819885,
0.03829096630215645,
0.07300632447004318,
-0.15407921373844147,
-0.02510600909590721,
0.014120997861027718,
0.10819751769304276,
-0.06674625724554062,
0.016306182369589806,
-0.047712475061416626,
0.0020079133100807667,
-0.15064212679862976,
-0.08720564097166061,
-0.06352168321609497,
-0.046571291983127594,
-0.057697124779224396,
0.1546059548854828,
-0.1368713676929474,
0.05739881843328476,
0.20625919103622437,
0.04664858058094978,
0.022002840414643288,
-0.022375911474227905,
0.17239011824131012,
-0.11726665496826172,
0.0457535982131958,
0.2143382728099823,
0.00867398176342249,
0.07063840329647064,
0.15636718273162842,
-0.023956511169672012,
-0.09318286180496216,
0.04038812592625618,
-0.009676158428192139,
-0.07902279496192932,
-0.2506827712059021,
-0.12741594016551971,
-0.11919914186000824,
0.11445384472608566,
0.017909526824951172,
0.021345248445868492,
0.17496436834335327,
0.12987011671066284,
-0.0325968861579895,
0.06649789959192276,
0.033899616450071335,
0.08671996742486954,
0.23215514421463013,
-0.06239953264594078,
0.15568697452545166,
-0.01785510592162609,
-0.16171787679195404,
0.0989636480808258,
0.05934997648000717,
0.1355666220188141,
0.0011738985776901245,
0.0213867649435997,
0.03273242339491844,
0.04031551629304886,
0.10453791916370392,
0.07370110601186752,
0.009527068585157394,
-0.025985941290855408,
-0.04248800128698349,
-0.03684599697589874,
-0.022087519988417625,
0.08546103537082672,
0.03424449265003204,
-0.16365528106689453,
-0.014997772872447968,
-0.010500336065888405,
0.0851941704750061,
0.08801325410604477,
0.1347058266401291,
-0.17752423882484436,
-0.021291052922606468,
0.09493374824523926,
0.00997153576463461,
-0.11472714692354202,
0.06450123339891434,
-0.03718695789575577,
-0.1397397816181183,
0.05941950902342796,
-0.014308363199234009,
0.10111919790506363,
-0.06641452759504318,
0.05838441103696823,
-0.11516855657100677,
-0.02304365299642086,
0.002995515475049615,
0.09358447790145874,
-0.393774151802063,
0.1628636121749878,
-0.00986325554549694,
-0.005545234307646751,
-0.12398351728916168,
0.010843661613762379,
0.028195470571517944,
0.05972886085510254,
0.08891104906797409,
-0.010003120638430119,
0.07640086114406586,
-0.09257785975933075,
-0.07097684592008591,
0.029322227463126183,
0.08371448516845703,
-0.05723239853978157,
-0.0016892198473215103,
-0.009944526478648186,
0.0050379810854792595,
-0.05052965134382248,
-0.09611181169748306,
-0.016050349920988083,
-0.15210463106632233,
0.08686468750238419,
0.05218008905649185,
0.05912322923541069,
0.02918119542300701,
-0.026591941714286804,
-0.06211758404970169,
0.18332351744174957,
-0.005264990031719208,
-0.13673368096351624,
-0.0789438784122467,
-0.0685691386461258,
0.06152055785059929,
-0.07190051674842834,
0.032612599432468414,
-0.0811052918434143,
0.02096245437860489,
-0.05929744988679886,
-0.19246259331703186,
0.06953182816505432,
-0.09314093738794327,
-0.05067726969718933,
-0.0079753203317523,
0.18315578997135162,
-0.01674685999751091,
-0.02609667368233204,
0.047122880816459656,
-0.01152312383055687,
-0.10806039720773697,
-0.10138577967882156,
0.011060762219130993,
0.08743880689144135,
0.036481451243162155,
0.08845022320747375,
-0.05318596586585045,
-0.08045724034309387,
-0.12577585875988007,
-0.03599841520190239,
0.21745523810386658,
0.1352667659521103,
-0.034043945372104645,
0.16965073347091675,
0.1135406419634819,
-0.07224813848733902,
-0.31420955061912537,
-0.1262809932231903,
-0.07170078158378601,
0.0005419179797172546,
-0.053622931241989136,
-0.19506920874118805,
0.09403566271066666,
-0.016184424981474876,
-0.017515968531370163,
0.0996241420507431,
-0.2860146462917328,
-0.12294992059469223,
0.1576877236366272,
-0.0020774747245013714,
0.39147499203681946,
-0.09711934626102448,
-0.05213112756609917,
-0.06704415380954742,
-0.08231525123119354,
0.14660745859146118,
0.003282658290117979,
0.11926298588514328,
-0.03223257511854172,
0.1811666190624237,
0.0640127956867218,
0.005202583037316799,
0.0986623540520668,
0.049879979342222214,
-0.03150435537099838,
-0.11892729997634888,
-0.08872099220752716,
0.054370198398828506,
-0.00361951463855803,
0.06871772557497025,
-0.045202355831861496,
0.042667657136917114,
-0.1388511061668396,
-0.06406469643115997,
-0.07063893973827362,
0.03217563033103943,
0.022150516510009766,
-0.08418355882167816,
-0.05102207511663437,
-0.01917990855872631,
-0.010927168652415276,
0.028528694063425064,
0.12718607485294342,
-0.045191530138254166,
0.061615824699401855,
0.05375410616397858,
0.10642847418785095,
-0.0852544978260994,
0.039659637957811356,
-0.06165105104446411,
-0.05476545915007591,
0.08751996606588364,
-0.15280890464782715,
0.014260325580835342,
0.10948443412780762,
-0.05449675768613815,
0.07034394145011902,
0.08002948760986328,
-0.03322891891002655,
0.042468149214982986,
0.0819685310125351,
-0.2078244686126709,
-0.059846896678209305,
-0.04982765018939972,
-0.05239968001842499,
0.11036893725395203,
0.11758973449468613,
0.20212361216545105,
-0.021785449236631393,
-0.035690389573574066,
-0.014266952872276306,
0.03772967308759689,
-0.030779385939240456,
0.10024284571409225,
-0.003108932636678219,
0.014578756876289845,
-0.1356244832277298,
0.04088431969285011,
0.018231678754091263,
-0.04935435950756073,
0.050490230321884155,
0.09342405200004578,
-0.09433169662952423,
-0.12526321411132812,
-0.05212131142616272,
0.07134290039539337,
-0.0969986766576767,
-0.0027227341197431087,
-0.01635088585317135,
-0.16709518432617188,
0.052774835377931595,
0.09144146740436554,
0.05221816152334213,
0.07960651069879532,
-0.06543218344449997,
-0.0179096981883049,
-0.055595021694898605,
0.026248406618833542,
0.06626072525978088,
0.006795559544116259,
-0.0881875529885292,
0.021433385089039803,
-0.03993553668260574,
0.16217570006847382,
-0.09396102279424667,
-0.08887333422899246,
-0.13835769891738892,
0.027371102944016457,
-0.17396841943264008,
-0.05773686245083809,
-0.1259203851222992,
-0.05451234430074692,
-0.03418267145752907,
-0.029858360067009926,
-0.039504360407590866,
-0.0435597263276577,
-0.08228189498186111,
0.05074796825647354,
-0.04999759793281555,
0.03118089772760868,
-0.05612969771027565,
-0.045256368815898895,
0.030891595408320427,
-0.050119198858737946,
0.13050159811973572,
0.1406235694885254,
-0.07488695532083511,
0.06683994084596634,
-0.07634495198726654,
-0.039566803723573685,
0.09073929488658905,
0.05351864546537399,
0.06112143024802208,
0.04633815214037895,
-0.002145530190318823,
0.04543326050043106,
0.04723939672112465,
0.04391106218099594,
0.029905904084444046,
-0.09344480186700821,
0.04646405205130577,
-0.05182987079024315,
-0.12524983286857605,
-0.03639671206474304,
0.005239246413111687,
0.041996438056230545,
0.06181972473859787,
0.11364899575710297,
-0.060029465705156326,
0.11049474775791168,
-0.04336366429924965,
0.0264158733189106,
-0.021741051226854324,
-0.17489992082118988,
0.011466334573924541,
-0.11083097755908966,
0.035859253257513046,
-0.01356759387999773,
0.2189660668373108,
0.048111531883478165,
-0.0048138173297047615,
0.008679034188389778,
0.03266594558954239,
0.04032257944345474,
0.04873806983232498,
0.1462908387184143,
0.10418044030666351,
-0.03941921517252922,
-0.14856553077697754,
0.07860119640827179,
0.04979313164949417,
0.10476244986057281,
0.0788101926445961,
-0.0032637007534503937,
-0.008567674085497856,
0.09158727526664734,
-0.0024726937990635633,
0.011867997236549854,
-0.0760711207985878,
-0.10420184582471848,
-0.11120584607124329,
0.040450215339660645,
-0.014851558953523636,
0.05407802760601044,
0.13595010340213776,
-0.022204827517271042,
0.0061662159860134125,
-0.013565451838076115,
-0.09817624092102051,
-0.14944778382778168,
-0.1978628784418106,
-0.09255565702915192,
-0.10039447247982025,
-0.004987895023077726,
-0.1292000114917755,
0.017753439024090767,
0.025326456874608994,
0.07977227866649628,
-0.0743170902132988,
0.12330517172813416,
0.08845844864845276,
-0.09819568693637848,
0.1366061568260193,
-0.02930130623281002,
0.035352397710084915,
0.0013805855996906757,
-0.02378052845597267,
-0.1260133683681488,
0.04055990278720856,
-0.014052973128855228,
0.05454232916235924,
-0.06255052983760834,
0.03133060038089752,
-0.10206601023674011,
-0.08690181374549866,
-0.04904913157224655,
0.05576784908771515,
-0.01058909296989441,
0.11049410700798035,
0.04277542978525162,
-0.0542302280664444,
0.03859201818704605,
0.18897023797035217,
-0.028784576803445816,
-0.06421303749084473,
-0.07458139210939407,
0.14758092164993286,
0.011797708459198475,
0.09122835099697113,
-0.027796631678938866,
0.0249556303024292,
-0.11141808331012726,
0.36470651626586914,
0.2769416272640228,
-0.0932321548461914,
0.0011342503130435944,
-0.02969958260655403,
0.03763050213456154,
0.07493697851896286,
0.13585630059242249,
0.08117265999317169,
0.28879058361053467,
-0.053391486406326294,
-0.03465833514928818,
-0.024943789467215538,
-0.03410155698657036,
-0.10079725086688995,
0.05028124898672104,
0.05205275118350983,
-0.07995148748159409,
-0.021969502791762352,
0.09531421959400177,
-0.2670392096042633,
0.13942958414554596,
-0.08704971522092819,
-0.18320243060588837,
-0.10686859488487244,
-0.011772154830396175,
0.054480500519275665,
0.02383100986480713,
0.10637961328029633,
-0.03152860328555107,
-0.07485074549913406,
0.08676011860370636,
0.017972063273191452,
-0.1609298288822174,
0.03648524731397629,
0.08000543713569641,
-0.010551512241363525,
-0.04312047362327576,
-0.01824316568672657,
0.0699373409152031,
0.07471390813589096,
0.04988278076052666,
-0.017278242856264114,
0.05565600469708443,
-0.006048320326954126,
-0.007254385389387608,
0.06590966880321503,
0.03585546463727951,
-0.0009652543812990189,
-0.12986749410629272,
0.1127333790063858,
-0.14744898676872253,
0.03514809533953667,
0.019453592598438263,
-0.042535971850156784,
-0.03262657672166824,
0.03626735880970955,
-0.10610075294971466,
0.05622672662138939,
0.10164473205804825,
-0.028023410588502884,
-0.01666472665965557,
-0.028365053236484528,
-0.017056241631507874,
-0.04851152002811432,
-0.05852307006716728,
-0.09714926779270172,
-0.15487539768218994,
-0.1308649480342865,
0.027045125141739845,
0.031534720212221146,
-0.16601626574993134,
0.049147821962833405,
-0.1326368749141693,
0.057243626564741135,
-0.1291460543870926,
0.09664825350046158,
0.06121774762868881,
0.014837436378002167,
-0.015610164031386375,
0.01829163357615471,
0.003466215915977955,
0.0910218134522438,
-0.10638102889060974,
-0.09066619724035263
] |
null | null |
transformers
|
# DioloGPT KaeyaBot model
|
{"tags": ["conversational"]}
|
text-generation
|
felinecity/DioloGPT-small-KaeyaBot2
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DioloGPT KaeyaBot model
|
[
"# DioloGPT KaeyaBot model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DioloGPT KaeyaBot model"
] |
[
51,
9
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# DioloGPT KaeyaBot model"
] |
[
-0.013482030481100082,
-0.04944401979446411,
-0.00660965871065855,
0.018116695806384087,
0.16041694581508636,
0.005352854263037443,
0.1591426134109497,
0.113655224442482,
-0.005036589689552784,
-0.02728116884827614,
0.12488944083452225,
0.22933024168014526,
0.029869183897972107,
0.1351277083158493,
-0.07913515716791153,
-0.29110777378082275,
0.05341096594929695,
0.03645821288228035,
0.0021556243300437927,
0.08520137518644333,
0.10741303861141205,
-0.07100285589694977,
0.10141543298959732,
-0.015280923806130886,
-0.12886378169059753,
-0.034620773047208786,
0.009351576678454876,
-0.14558735489845276,
0.07823927700519562,
0.0635087788105011,
0.05051994323730469,
0.025348380208015442,
-0.03491302207112312,
-0.11841075867414474,
0.036076925694942474,
-0.010789170861244202,
-0.01633112132549286,
0.03215695172548294,
-0.006123706232756376,
-0.10275706648826599,
0.06512311846017838,
0.05040643364191055,
-0.007862668484449387,
0.01055244728922844,
-0.1442529559135437,
0.010917812585830688,
-0.01737990230321884,
0.011684340424835682,
0.13442599773406982,
0.10449829697608948,
-0.02284255623817444,
0.044014379382133484,
-0.0918123796582222,
0.10083391517400742,
0.11368492990732193,
-0.24449126422405243,
-0.03187127411365509,
0.10879965126514435,
0.03690730035305023,
0.05006536468863487,
-0.014344406314194202,
0.06345362216234207,
0.046731121838092804,
0.00035137636587023735,
0.01799810864031315,
-0.09024252742528915,
-0.0809108167886734,
0.007838817313313484,
-0.07939121127128601,
0.005723253358155489,
0.2735130786895752,
-0.06425411999225616,
0.04118042066693306,
-0.0737089291214943,
-0.07196356356143951,
0.023852720856666565,
-0.0601150244474411,
-0.008588753640651703,
-0.07469012588262558,
0.027902815490961075,
0.057888440787792206,
-0.10337091982364655,
-0.1314590871334076,
-0.008448471315205097,
-0.17960487306118011,
0.24182842671871185,
0.050672855228185654,
0.018773283809423447,
-0.20690259337425232,
0.07384129613637924,
0.017387308180332184,
-0.10685200989246368,
-0.006884794682264328,
-0.11515865474939346,
0.041213035583496094,
0.010604165494441986,
-0.015647996217012405,
-0.05562400445342064,
0.08027039468288422,
0.11201230436563492,
0.049923136830329895,
0.003928730729967356,
-0.01086176373064518,
0.03498953580856323,
0.07448742538690567,
0.1263836920261383,
-0.019534315913915634,
-0.015271554701030254,
0.044583309441804886,
-0.058608293533325195,
0.013690563850104809,
-0.06365002691745758,
-0.16729891300201416,
-0.011074299924075603,
-0.0019514053128659725,
0.06900392472743988,
0.025876125320792198,
0.12303344905376434,
-0.04554140567779541,
-0.056231752038002014,
0.029509034007787704,
-0.05098415166139603,
-0.034932199865579605,
-0.01566430553793907,
-0.03030925616621971,
0.17786185443401337,
-0.005685892887413502,
0.03192080184817314,
-0.10662221908569336,
0.057476289570331573,
-0.04604175314307213,
-0.0024113552644848824,
-0.030046630650758743,
-0.05317991226911545,
-0.007894616574048996,
-0.09116844832897186,
0.04384077712893486,
-0.18569129705429077,
-0.16892604529857635,
0.002109426073729992,
-0.0014731264673173428,
-0.06242804601788521,
-0.07764072716236115,
-0.09326117485761642,
-0.03556299954652786,
0.023548118770122528,
-0.0708107203245163,
-0.0026914088521152735,
-0.0493709072470665,
0.07582206279039383,
-0.03043040819466114,
0.12273649871349335,
-0.08936804533004761,
0.0855768695473671,
-0.09660112857818604,
-0.009370164945721626,
-0.1144319400191307,
0.13016021251678467,
-0.02309008315205574,
0.10889901965856552,
-0.01719598099589348,
-0.004806773737072945,
-0.08014009147882462,
0.0707927942276001,
-0.010947787202894688,
0.2390892058610916,
-0.09615686535835266,
-0.10527963191270828,
0.20623502135276794,
-0.03790713846683502,
-0.14620162546634674,
0.10589602589607239,
-0.004730983171612024,
0.13065624237060547,
0.12939231097698212,
0.18979355692863464,
0.0007764091715216637,
-0.02888396754860878,
0.05273909121751785,
0.08515644073486328,
-0.09192401170730591,
-0.009224558249115944,
0.006953336298465729,
0.019487295299768448,
-0.0604826994240284,
0.058031678199768066,
0.07512139528989792,
0.058956172317266464,
-0.04852375388145447,
-0.03693552687764168,
0.0054132877849042416,
-0.03053313121199608,
0.07787030935287476,
-0.03355223312973976,
0.11395929753780365,
-0.028790919110178947,
-0.009170105680823326,
-0.0036145257763564587,
0.06036003306508064,
-0.04261153191328049,
0.018190739676356316,
-0.06871908903121948,
0.13146312534809113,
-0.024455873295664787,
0.07805652916431427,
-0.11542021483182907,
-0.06328452378511429,
-0.014105372130870819,
0.11948758363723755,
0.05177495628595352,
0.06743009388446808,
0.03816762939095497,
-0.04157688468694687,
0.02144087478518486,
0.006811530329287052,
0.16115623712539673,
-0.009470009244978428,
-0.08691064268350601,
-0.094950832426548,
0.10649995505809784,
-0.05250834673643112,
0.05891750752925873,
-0.04607817530632019,
0.012430060654878616,
-0.007558582350611687,
0.08419515937566757,
-0.01676550693809986,
0.09096702188253403,
0.013472671620547771,
0.017037739977240562,
-0.08330360054969788,
0.00552785862237215,
0.06929166615009308,
-0.04137129336595535,
-0.07525984197854996,
0.24265378713607788,
-0.11155009269714355,
0.09725353121757507,
0.1959332674741745,
-0.2246531844139099,
0.017021849751472473,
-0.024936281144618988,
-0.01824372075498104,
0.003590463427826762,
0.0407932847738266,
-0.020584916695952415,
0.15661026537418365,
-0.005723661743104458,
0.1630423665046692,
-0.043110910803079605,
-0.019893556833267212,
0.020257245749235153,
-0.08925081044435501,
-0.00834382139146328,
0.09131285548210144,
0.10188426077365875,
-0.06383258104324341,
0.15723173320293427,
0.14325986802577972,
0.02455965429544449,
0.18680575489997864,
0.028767947107553482,
-0.0063820513896644115,
0.04121730849146843,
-0.006069001276046038,
-0.03843383118510246,
-0.03192035108804703,
-0.219386026263237,
-0.04428457096219063,
0.06277934461832047,
0.009671555832028389,
0.10874585062265396,
-0.06731194257736206,
-0.04561801254749298,
-0.008217640221118927,
-0.0076491497457027435,
0.08621719479560852,
0.12873822450637817,
0.03028985857963562,
0.11494994908571243,
-0.00158013217151165,
-0.027686171233654022,
0.05653297156095505,
0.031615711748600006,
-0.037328172475099564,
0.19481655955314636,
-0.11397957801818848,
-0.35038501024246216,
-0.09559032320976257,
-0.17552193999290466,
-0.07455673813819885,
0.03829096630215645,
0.07300632447004318,
-0.15407921373844147,
-0.02510600909590721,
0.014120997861027718,
0.10819751769304276,
-0.06674625724554062,
0.016306182369589806,
-0.047712475061416626,
0.0020079133100807667,
-0.15064212679862976,
-0.08720564097166061,
-0.06352168321609497,
-0.046571291983127594,
-0.057697124779224396,
0.1546059548854828,
-0.1368713676929474,
0.05739881843328476,
0.20625919103622437,
0.04664858058094978,
0.022002840414643288,
-0.022375911474227905,
0.17239011824131012,
-0.11726665496826172,
0.0457535982131958,
0.2143382728099823,
0.00867398176342249,
0.07063840329647064,
0.15636718273162842,
-0.023956511169672012,
-0.09318286180496216,
0.04038812592625618,
-0.009676158428192139,
-0.07902279496192932,
-0.2506827712059021,
-0.12741594016551971,
-0.11919914186000824,
0.11445384472608566,
0.017909526824951172,
0.021345248445868492,
0.17496436834335327,
0.12987011671066284,
-0.0325968861579895,
0.06649789959192276,
0.033899616450071335,
0.08671996742486954,
0.23215514421463013,
-0.06239953264594078,
0.15568697452545166,
-0.01785510592162609,
-0.16171787679195404,
0.0989636480808258,
0.05934997648000717,
0.1355666220188141,
0.0011738985776901245,
0.0213867649435997,
0.03273242339491844,
0.04031551629304886,
0.10453791916370392,
0.07370110601186752,
0.009527068585157394,
-0.025985941290855408,
-0.04248800128698349,
-0.03684599697589874,
-0.022087519988417625,
0.08546103537082672,
0.03424449265003204,
-0.16365528106689453,
-0.014997772872447968,
-0.010500336065888405,
0.0851941704750061,
0.08801325410604477,
0.1347058266401291,
-0.17752423882484436,
-0.021291052922606468,
0.09493374824523926,
0.00997153576463461,
-0.11472714692354202,
0.06450123339891434,
-0.03718695789575577,
-0.1397397816181183,
0.05941950902342796,
-0.014308363199234009,
0.10111919790506363,
-0.06641452759504318,
0.05838441103696823,
-0.11516855657100677,
-0.02304365299642086,
0.002995515475049615,
0.09358447790145874,
-0.393774151802063,
0.1628636121749878,
-0.00986325554549694,
-0.005545234307646751,
-0.12398351728916168,
0.010843661613762379,
0.028195470571517944,
0.05972886085510254,
0.08891104906797409,
-0.010003120638430119,
0.07640086114406586,
-0.09257785975933075,
-0.07097684592008591,
0.029322227463126183,
0.08371448516845703,
-0.05723239853978157,
-0.0016892198473215103,
-0.009944526478648186,
0.0050379810854792595,
-0.05052965134382248,
-0.09611181169748306,
-0.016050349920988083,
-0.15210463106632233,
0.08686468750238419,
0.05218008905649185,
0.05912322923541069,
0.02918119542300701,
-0.026591941714286804,
-0.06211758404970169,
0.18332351744174957,
-0.005264990031719208,
-0.13673368096351624,
-0.0789438784122467,
-0.0685691386461258,
0.06152055785059929,
-0.07190051674842834,
0.032612599432468414,
-0.0811052918434143,
0.02096245437860489,
-0.05929744988679886,
-0.19246259331703186,
0.06953182816505432,
-0.09314093738794327,
-0.05067726969718933,
-0.0079753203317523,
0.18315578997135162,
-0.01674685999751091,
-0.02609667368233204,
0.047122880816459656,
-0.01152312383055687,
-0.10806039720773697,
-0.10138577967882156,
0.011060762219130993,
0.08743880689144135,
0.036481451243162155,
0.08845022320747375,
-0.05318596586585045,
-0.08045724034309387,
-0.12577585875988007,
-0.03599841520190239,
0.21745523810386658,
0.1352667659521103,
-0.034043945372104645,
0.16965073347091675,
0.1135406419634819,
-0.07224813848733902,
-0.31420955061912537,
-0.1262809932231903,
-0.07170078158378601,
0.0005419179797172546,
-0.053622931241989136,
-0.19506920874118805,
0.09403566271066666,
-0.016184424981474876,
-0.017515968531370163,
0.0996241420507431,
-0.2860146462917328,
-0.12294992059469223,
0.1576877236366272,
-0.0020774747245013714,
0.39147499203681946,
-0.09711934626102448,
-0.05213112756609917,
-0.06704415380954742,
-0.08231525123119354,
0.14660745859146118,
0.003282658290117979,
0.11926298588514328,
-0.03223257511854172,
0.1811666190624237,
0.0640127956867218,
0.005202583037316799,
0.0986623540520668,
0.049879979342222214,
-0.03150435537099838,
-0.11892729997634888,
-0.08872099220752716,
0.054370198398828506,
-0.00361951463855803,
0.06871772557497025,
-0.045202355831861496,
0.042667657136917114,
-0.1388511061668396,
-0.06406469643115997,
-0.07063893973827362,
0.03217563033103943,
0.022150516510009766,
-0.08418355882167816,
-0.05102207511663437,
-0.01917990855872631,
-0.010927168652415276,
0.028528694063425064,
0.12718607485294342,
-0.045191530138254166,
0.061615824699401855,
0.05375410616397858,
0.10642847418785095,
-0.0852544978260994,
0.039659637957811356,
-0.06165105104446411,
-0.05476545915007591,
0.08751996606588364,
-0.15280890464782715,
0.014260325580835342,
0.10948443412780762,
-0.05449675768613815,
0.07034394145011902,
0.08002948760986328,
-0.03322891891002655,
0.042468149214982986,
0.0819685310125351,
-0.2078244686126709,
-0.059846896678209305,
-0.04982765018939972,
-0.05239968001842499,
0.11036893725395203,
0.11758973449468613,
0.20212361216545105,
-0.021785449236631393,
-0.035690389573574066,
-0.014266952872276306,
0.03772967308759689,
-0.030779385939240456,
0.10024284571409225,
-0.003108932636678219,
0.014578756876289845,
-0.1356244832277298,
0.04088431969285011,
0.018231678754091263,
-0.04935435950756073,
0.050490230321884155,
0.09342405200004578,
-0.09433169662952423,
-0.12526321411132812,
-0.05212131142616272,
0.07134290039539337,
-0.0969986766576767,
-0.0027227341197431087,
-0.01635088585317135,
-0.16709518432617188,
0.052774835377931595,
0.09144146740436554,
0.05221816152334213,
0.07960651069879532,
-0.06543218344449997,
-0.0179096981883049,
-0.055595021694898605,
0.026248406618833542,
0.06626072525978088,
0.006795559544116259,
-0.0881875529885292,
0.021433385089039803,
-0.03993553668260574,
0.16217570006847382,
-0.09396102279424667,
-0.08887333422899246,
-0.13835769891738892,
0.027371102944016457,
-0.17396841943264008,
-0.05773686245083809,
-0.1259203851222992,
-0.05451234430074692,
-0.03418267145752907,
-0.029858360067009926,
-0.039504360407590866,
-0.0435597263276577,
-0.08228189498186111,
0.05074796825647354,
-0.04999759793281555,
0.03118089772760868,
-0.05612969771027565,
-0.045256368815898895,
0.030891595408320427,
-0.050119198858737946,
0.13050159811973572,
0.1406235694885254,
-0.07488695532083511,
0.06683994084596634,
-0.07634495198726654,
-0.039566803723573685,
0.09073929488658905,
0.05351864546537399,
0.06112143024802208,
0.04633815214037895,
-0.002145530190318823,
0.04543326050043106,
0.04723939672112465,
0.04391106218099594,
0.029905904084444046,
-0.09344480186700821,
0.04646405205130577,
-0.05182987079024315,
-0.12524983286857605,
-0.03639671206474304,
0.005239246413111687,
0.041996438056230545,
0.06181972473859787,
0.11364899575710297,
-0.060029465705156326,
0.11049474775791168,
-0.04336366429924965,
0.0264158733189106,
-0.021741051226854324,
-0.17489992082118988,
0.011466334573924541,
-0.11083097755908966,
0.035859253257513046,
-0.01356759387999773,
0.2189660668373108,
0.048111531883478165,
-0.0048138173297047615,
0.008679034188389778,
0.03266594558954239,
0.04032257944345474,
0.04873806983232498,
0.1462908387184143,
0.10418044030666351,
-0.03941921517252922,
-0.14856553077697754,
0.07860119640827179,
0.04979313164949417,
0.10476244986057281,
0.0788101926445961,
-0.0032637007534503937,
-0.008567674085497856,
0.09158727526664734,
-0.0024726937990635633,
0.011867997236549854,
-0.0760711207985878,
-0.10420184582471848,
-0.11120584607124329,
0.040450215339660645,
-0.014851558953523636,
0.05407802760601044,
0.13595010340213776,
-0.022204827517271042,
0.0061662159860134125,
-0.013565451838076115,
-0.09817624092102051,
-0.14944778382778168,
-0.1978628784418106,
-0.09255565702915192,
-0.10039447247982025,
-0.004987895023077726,
-0.1292000114917755,
0.017753439024090767,
0.025326456874608994,
0.07977227866649628,
-0.0743170902132988,
0.12330517172813416,
0.08845844864845276,
-0.09819568693637848,
0.1366061568260193,
-0.02930130623281002,
0.035352397710084915,
0.0013805855996906757,
-0.02378052845597267,
-0.1260133683681488,
0.04055990278720856,
-0.014052973128855228,
0.05454232916235924,
-0.06255052983760834,
0.03133060038089752,
-0.10206601023674011,
-0.08690181374549866,
-0.04904913157224655,
0.05576784908771515,
-0.01058909296989441,
0.11049410700798035,
0.04277542978525162,
-0.0542302280664444,
0.03859201818704605,
0.18897023797035217,
-0.028784576803445816,
-0.06421303749084473,
-0.07458139210939407,
0.14758092164993286,
0.011797708459198475,
0.09122835099697113,
-0.027796631678938866,
0.0249556303024292,
-0.11141808331012726,
0.36470651626586914,
0.2769416272640228,
-0.0932321548461914,
0.0011342503130435944,
-0.02969958260655403,
0.03763050213456154,
0.07493697851896286,
0.13585630059242249,
0.08117265999317169,
0.28879058361053467,
-0.053391486406326294,
-0.03465833514928818,
-0.024943789467215538,
-0.03410155698657036,
-0.10079725086688995,
0.05028124898672104,
0.05205275118350983,
-0.07995148748159409,
-0.021969502791762352,
0.09531421959400177,
-0.2670392096042633,
0.13942958414554596,
-0.08704971522092819,
-0.18320243060588837,
-0.10686859488487244,
-0.011772154830396175,
0.054480500519275665,
0.02383100986480713,
0.10637961328029633,
-0.03152860328555107,
-0.07485074549913406,
0.08676011860370636,
0.017972063273191452,
-0.1609298288822174,
0.03648524731397629,
0.08000543713569641,
-0.010551512241363525,
-0.04312047362327576,
-0.01824316568672657,
0.0699373409152031,
0.07471390813589096,
0.04988278076052666,
-0.017278242856264114,
0.05565600469708443,
-0.006048320326954126,
-0.007254385389387608,
0.06590966880321503,
0.03585546463727951,
-0.0009652543812990189,
-0.12986749410629272,
0.1127333790063858,
-0.14744898676872253,
0.03514809533953667,
0.019453592598438263,
-0.042535971850156784,
-0.03262657672166824,
0.03626735880970955,
-0.10610075294971466,
0.05622672662138939,
0.10164473205804825,
-0.028023410588502884,
-0.01666472665965557,
-0.028365053236484528,
-0.017056241631507874,
-0.04851152002811432,
-0.05852307006716728,
-0.09714926779270172,
-0.15487539768218994,
-0.1308649480342865,
0.027045125141739845,
0.031534720212221146,
-0.16601626574993134,
0.049147821962833405,
-0.1326368749141693,
0.057243626564741135,
-0.1291460543870926,
0.09664825350046158,
0.06121774762868881,
0.014837436378002167,
-0.015610164031386375,
0.01829163357615471,
0.003466215915977955,
0.0910218134522438,
-0.10638102889060974,
-0.09066619724035263
] |
null | null |
transformers
|
# DioloGPT LisaBot model
|
{"tags": ["conversational"]}
|
text-generation
|
felinecity/DioloGPT-small-LisaBot
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DioloGPT LisaBot model
|
[
"# DioloGPT LisaBot model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DioloGPT LisaBot model"
] |
[
51,
8
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# DioloGPT LisaBot model"
] |
[
-0.029328245669603348,
0.03723815828561783,
-0.006895450409501791,
0.04889634996652603,
0.14895035326480865,
0.011883338913321495,
0.18311049044132233,
0.11943424493074417,
0.016406463459134102,
-0.04449348524212837,
0.13111895322799683,
0.20789767801761627,
-0.007189597003161907,
0.10930775851011276,
-0.07690312713384628,
-0.3252317011356354,
0.050289642065763474,
0.039966024458408356,
-0.043235696852207184,
0.08937612920999527,
0.1125669926404953,
-0.05250434949994087,
0.10541144013404846,
-0.01817232556641102,
-0.16798274219036102,
-0.01912146620452404,
-0.003156219609081745,
-0.10860709846019745,
0.0807017832994461,
0.0664425864815712,
0.0651884451508522,
0.03904646262526512,
-0.022599807009100914,
-0.0782502070069313,
0.030250338837504387,
-0.0013531785225495696,
-0.013569631613790989,
0.05846495181322098,
0.0021010912023484707,
-0.10364317148923874,
0.0955217182636261,
0.04475487768650055,
0.011700058355927467,
0.022704096511006355,
-0.15814244747161865,
0.03986551985144615,
-0.009011158719658852,
-0.03857630863785744,
0.08329746872186661,
0.11513280123472214,
-0.013509880751371384,
0.08637033402919769,
-0.09660452604293823,
0.11082640290260315,
0.14374354481697083,
-0.2736108601093292,
-0.0333932600915432,
0.1316474974155426,
0.0014306249795481563,
0.03414762765169144,
-0.0006292964681051672,
0.05321236327290535,
0.02901291847229004,
0.021492712199687958,
-0.030763672664761543,
-0.07305711507797241,
-0.06811391562223434,
-0.0035538466181606054,
-0.07215484231710434,
-0.010293906554579735,
0.2924296259880066,
-0.04940566420555115,
0.03532072901725769,
-0.07024267315864563,
-0.09191200137138367,
0.008452476002275944,
-0.04876045510172844,
-0.03136386349797249,
-0.06502240151166916,
0.05245746299624443,
0.018965627998113632,
-0.09600221365690231,
-0.10828667134046555,
-0.018839769065380096,
-0.162322998046875,
0.18976624310016632,
0.022241136059165,
0.03648676350712776,
-0.22765123844146729,
0.08548924326896667,
0.018568141385912895,
-0.10558510571718216,
0.001871575484983623,
-0.10700592398643494,
0.0543266162276268,
0.0028644076082855463,
-0.04493405669927597,
-0.0672609731554985,
0.061313383281230927,
0.12189832329750061,
0.015669548884034157,
-0.00040804027230478823,
-0.028488848358392715,
0.03569619730114937,
0.07285302132368088,
0.09100448340177536,
-0.012112108990550041,
-0.03155976161360741,
0.03680747002363205,
-0.09935184568166733,
-0.010885883122682571,
-0.05244247987866402,
-0.1707736700773239,
-0.015808958560228348,
0.006480935029685497,
0.06544920057058334,
0.04539652168750763,
0.12768065929412842,
-0.0614190474152565,
-0.06447000056505203,
0.06650754809379578,
-0.05465654283761978,
-0.007555694319307804,
-0.011759641580283642,
-0.032091014087200165,
0.1556423008441925,
0.023399123921990395,
0.016510792076587677,
-0.11394122242927551,
0.04684578627347946,
-0.05298810824751854,
-0.004111834801733494,
-0.05765104666352272,
-0.04745332524180412,
-0.015261505730450153,
-0.04245674982666969,
0.03313618525862694,
-0.15648072957992554,
-0.19743779301643372,
-0.004618766717612743,
0.0063469791784882545,
-0.08496898412704468,
-0.07072534412145615,
-0.10597080737352371,
-0.03714630380272865,
0.03529529646039009,
-0.058053888380527496,
-0.021311650052666664,
-0.04197255149483681,
0.07204986363649368,
-0.01978432573378086,
0.10103687644004822,
-0.14080685377120972,
0.08326230943202972,
-0.10657190531492233,
-0.017607903108000755,
-0.10631078481674194,
0.1399596780538559,
0.01639265939593315,
0.12174445390701294,
-0.018155479803681374,
-0.01947285607457161,
-0.0979805439710617,
0.07973404973745346,
-0.016247684136033058,
0.26703155040740967,
-0.14957906305789948,
-0.08640493452548981,
0.21982432901859283,
-0.04766673594713211,
-0.11975882947444916,
0.10443239659070969,
-0.010852615348994732,
0.10889342427253723,
0.13001975417137146,
0.14211255311965942,
0.016910318285226822,
0.003616544883698225,
0.0890187993645668,
0.0921565368771553,
-0.0913369283080101,
0.003929294180124998,
-0.016294218599796295,
0.012157018296420574,
-0.05037248879671097,
0.06453073024749756,
0.07614035904407501,
0.04521912708878517,
-0.05181271582841873,
-0.038874536752700806,
-0.01960822381079197,
-0.019774090498685837,
0.0579492561519146,
-0.015295184217393398,
0.1124684289097786,
-0.03190203011035919,
-0.029047509655356407,
-0.001268048188649118,
0.035614024847745895,
-0.04596944525837898,
0.006015157792717218,
-0.06469520181417465,
0.08704603463411331,
0.007005276158452034,
0.06870530545711517,
-0.16074246168136597,
-0.07456262409687042,
-0.024646446108818054,
0.1859603226184845,
0.06171246990561485,
0.12458490580320358,
0.05969439819455147,
-0.028478698804974556,
0.02469014748930931,
0.004784136079251766,
0.16775494813919067,
-0.0217563696205616,
-0.06523340195417404,
-0.10004142671823502,
0.07941178232431412,
-0.07156039029359818,
0.03872760012745857,
-0.04045075923204422,
0.01761152222752571,
0.029499925673007965,
0.11049078404903412,
0.011326950043439865,
0.053087394684553146,
0.002181092044338584,
0.009439605288207531,
-0.07019832730293274,
-0.017854485660791397,
0.07975471019744873,
-0.02419431321322918,
-0.0518796369433403,
0.17911815643310547,
-0.10394753515720367,
0.11938143521547318,
0.19722236692905426,
-0.23980706930160522,
0.011709686368703842,
-0.029900360852479935,
-0.03316936641931534,
0.03433942794799805,
0.05051653832197189,
-0.033179424703121185,
0.15759620070457458,
-0.01345332246273756,
0.1525457352399826,
-0.0320751927793026,
-0.013479908928275108,
-0.009387596510350704,
-0.06116515398025513,
-0.023092057555913925,
0.07077686488628387,
0.10918232798576355,
-0.06681957095861435,
0.17251652479171753,
0.12455529719591141,
0.033532701432704926,
0.17275840044021606,
0.04018564522266388,
0.009206144139170647,
0.04883567988872528,
0.0011404067045077682,
-0.03174205124378204,
-0.062111083418130875,
-0.22378267347812653,
-0.037767570465803146,
0.06516052782535553,
0.027117392048239708,
0.09936538338661194,
-0.07091864198446274,
-0.03324853628873825,
-0.005588016007095575,
-0.01720249094069004,
0.08236531168222427,
0.08248358219861984,
0.04030303657054901,
0.12026672065258026,
-0.01257975772023201,
-0.062292519956827164,
0.06384793668985367,
0.023545363917946815,
-0.05764133483171463,
0.18055981397628784,
-0.11536531895399094,
-0.3679669201374054,
-0.10600132495164871,
-0.1268586665391922,
-0.0801299437880516,
0.06812874972820282,
0.07285431772470474,
-0.1376919448375702,
-0.03320015221834183,
0.014042738825082779,
0.1282191127538681,
-0.06355020403862,
0.033497974276542664,
-0.061721865087747574,
0.0254905316978693,
-0.10997317731380463,
-0.11603692919015884,
-0.07344507426023483,
-0.03562313690781593,
-0.061827100813388824,
0.15000444650650024,
-0.1394326090812683,
0.034454166889190674,
0.17137554287910461,
0.05042339116334915,
0.0466831810772419,
-0.0034055719152092934,
0.2326716184616089,
-0.10744582861661911,
0.029558314010500908,
0.1954965591430664,
-0.003776682773604989,
0.039349768310785294,
0.159366637468338,
-0.0004405127256177366,
-0.1355820745229721,
0.03295709937810898,
-0.021703729405999184,
-0.0780366063117981,
-0.2132139801979065,
-0.15655606985092163,
-0.12239070981740952,
0.10196684300899506,
0.059776462614536285,
0.03645845130085945,
0.17203155159950256,
0.09710431843996048,
-0.04531267657876015,
0.0693298727273941,
0.04805470630526543,
0.08374835550785065,
0.1797250211238861,
-0.062022119760513306,
0.15569792687892914,
-0.017404386773705482,
-0.17779505252838135,
0.0826643630862236,
0.05476132780313492,
0.13946041464805603,
0.02026558294892311,
0.05783287063241005,
0.016617126762866974,
0.026054853573441505,
0.12065419554710388,
0.10330135375261307,
0.02679803967475891,
-0.03137991204857826,
-0.04592600464820862,
-0.04544096067547798,
-0.050997279584407806,
0.06792062520980835,
0.03195817396044731,
-0.14016731083393097,
-0.02626923844218254,
-0.022649670019745827,
0.07838907092809677,
0.10879790782928467,
0.1311095952987671,
-0.22993257641792297,
-0.018177006393671036,
0.07750948518514633,
0.01380266435444355,
-0.1133960410952568,
0.06935802847146988,
-0.03933881223201752,
-0.12841691076755524,
0.033285703510046005,
-0.024671919643878937,
0.11001930385828018,
-0.10688246786594391,
0.08170825988054276,
-0.08598823100328445,
-0.02947520650923252,
0.02083451859652996,
0.08769021928310394,
-0.4155300557613373,
0.17136959731578827,
-0.0028397401329129934,
-0.010652957484126091,
-0.11393999308347702,
0.02341303788125515,
0.00957039836794138,
0.09979651868343353,
0.10026714205741882,
-0.017088761553168297,
0.10056046396493912,
-0.040410224348306656,
-0.053241461515426636,
0.021868539974093437,
0.08025902509689331,
-0.04339143633842468,
-0.009067812003195286,
-0.01626693829894066,
-0.005357156042009592,
-0.022745361551642418,
-0.07246052473783493,
-0.025547878816723824,
-0.18719080090522766,
0.09235312044620514,
0.027513938024640083,
0.07983490824699402,
0.032102979719638824,
-0.012125144712626934,
-0.014014055021107197,
0.2620481252670288,
0.017862198874354362,
-0.11942107230424881,
-0.0999886766076088,
0.022636644542217255,
0.055908575654029846,
-0.045603007078170776,
0.00659693218767643,
-0.06790260225534439,
0.03325832635164261,
-0.05723269656300545,
-0.2149651050567627,
0.08400005847215652,
-0.10023558884859085,
-0.037675946950912476,
-0.028931772336363792,
0.17939595878124237,
-0.01579897850751877,
-0.0202646441757679,
0.05214685946702957,
-0.0023455743212252855,
-0.0930907279253006,
-0.09471689164638519,
-0.0014590051723644137,
0.07170749455690384,
0.028751065954566002,
0.061922837048769,
-0.04346413165330887,
-0.02971756085753441,
-0.07804159075021744,
0.01759967766702175,
0.23147441446781158,
0.09695100784301758,
-0.03633489832282066,
0.16121940314769745,
0.0696209967136383,
-0.06318257749080658,
-0.2922605574131012,
-0.07825800776481628,
-0.06788463145494461,
-0.02415655180811882,
-0.05849893391132355,
-0.20682021975517273,
0.11679048091173172,
-0.027748052030801773,
-0.0034161508083343506,
0.09470835328102112,
-0.27453699707984924,
-0.10052988678216934,
0.17825189232826233,
-0.009373466484248638,
0.3862219750881195,
-0.10375989228487015,
-0.0658525750041008,
-0.08681701868772507,
-0.16159115731716156,
0.18966032564640045,
0.03590280935168266,
0.08856327086687088,
-0.02286359667778015,
0.1957816183567047,
0.07653509080410004,
-0.00011590548820095137,
0.08372047543525696,
0.040449678897857666,
-0.014453429728746414,
-0.11105240881443024,
-0.06641056388616562,
-0.010886232368648052,
0.017607709392905235,
0.0772450789809227,
-0.048635538667440414,
0.03654354438185692,
-0.11528810113668442,
-0.06704042851924896,
-0.06825733184814453,
0.05138803645968437,
0.011639050208032131,
-0.08690837025642395,
-0.010470828041434288,
-0.03270682692527771,
-0.016293589025735855,
0.03173070028424263,
0.14301708340644836,
-0.04513125494122505,
0.06420530378818512,
0.03176986053586006,
0.12464249134063721,
-0.16548354923725128,
0.024609988555312157,
-0.031447023153305054,
-0.03449559211730957,
0.09566409140825272,
-0.13100557029247284,
0.034142155200242996,
0.09994935244321823,
-0.06078726053237915,
0.08567981421947479,
0.10689770430326462,
0.0008646309142932296,
-0.012657948769629002,
0.09084746986627579,
-0.24090035259723663,
-0.05544648692011833,
-0.05918146297335625,
-0.018603218719363213,
0.10920432209968567,
0.11404559761285782,
0.22877946496009827,
-0.015639323741197586,
-0.030029263347387314,
0.002932126633822918,
0.031081972643733025,
-0.0450381375849247,
0.08835586905479431,
-0.018310153856873512,
0.012093662284314632,
-0.15789571404457092,
0.04433852434158325,
0.02244865708053112,
-0.0707460343837738,
0.039262257516384125,
0.11037477850914001,
-0.09846869856119156,
-0.13594451546669006,
-0.04708186164498329,
0.09709841012954712,
-0.096428781747818,
0.012995309196412563,
-0.0523790642619133,
-0.14515894651412964,
0.06072337180376053,
0.017306145280599594,
0.06055498868227005,
0.09657725691795349,
-0.09191824495792389,
-0.024184856563806534,
-0.05204879865050316,
-0.005891706794500351,
0.02405719645321369,
0.02162100374698639,
-0.0630398616194725,
0.06672002375125885,
-0.05940939113497734,
0.10763068497180939,
-0.09084988385438919,
-0.08303232491016388,
-0.15293340384960175,
0.026662325486540794,
-0.11924871057271957,
-0.08327414095401764,
-0.12774458527565002,
-0.03937143832445145,
-0.011084392666816711,
-0.02469954639673233,
-0.023838724941015244,
-0.03479287028312683,
-0.08508969843387604,
0.022697657346725464,
-0.04802407696843147,
0.0041303476318717,
-0.06598515063524246,
-0.00935723539441824,
0.02200583927333355,
-0.06122900918126106,
0.11967559158802032,
0.13688738644123077,
-0.09367017447948456,
0.04697304591536522,
-0.13659369945526123,
-0.06506028026342392,
0.09950483590364456,
0.03830504044890404,
0.07414589077234268,
0.06787694245576859,
0.005343320779502392,
0.07992826402187347,
0.05187946557998657,
0.0344943031668663,
0.06912799924612045,
-0.09244941920042038,
0.0658605694770813,
-0.02900785766541958,
-0.1294308304786682,
-0.05894545093178749,
-0.006244589574635029,
0.07104294002056122,
0.04180511087179184,
0.1220419779419899,
-0.0498996302485466,
0.09186876565217972,
-0.06217348203063011,
0.017349546775221825,
0.006175586488097906,
-0.1530129313468933,
0.007450266275554895,
-0.10926893353462219,
0.05219777673482895,
-0.004620085470378399,
0.19237548112869263,
0.07745229452848434,
0.0010993537725880742,
0.017758850008249283,
0.07199726998806,
0.03930443152785301,
0.04195794463157654,
0.11341273784637451,
0.10655054450035095,
-0.03603072091937065,
-0.13982895016670227,
0.0903310775756836,
0.06373830884695053,
0.1524895578622818,
0.1152951791882515,
0.013661934994161129,
-0.027940701693296432,
0.08077488094568253,
-0.003302662167698145,
0.020844755694270134,
-0.09067203849554062,
-0.10712030529975891,
-0.04167025536298752,
0.05574262887239456,
-0.025057220831513405,
0.01499838288873434,
0.09863069653511047,
-0.020017394796013832,
0.03213697299361229,
-0.021219294518232346,
-0.08396103978157043,
-0.15123461186885834,
-0.2017359882593155,
-0.08672753721475601,
-0.13552598655223846,
-0.008164982311427593,
-0.14829589426517487,
0.025607747957110405,
0.005115304607897997,
0.07170896232128143,
-0.07334594428539276,
0.08434181660413742,
0.04011553153395653,
-0.08079126477241516,
0.0826503336429596,
-0.02742615155875683,
0.0634450912475586,
-0.021856343373656273,
-0.0072669596411287785,
-0.1320638507604599,
0.07470422983169556,
-0.013501787558197975,
0.05481485277414322,
-0.04567525163292885,
0.00996420718729496,
-0.09791994094848633,
-0.08780521899461746,
-0.05149204283952713,
0.04040034860372543,
-0.05197697505354881,
0.1783996969461441,
0.02675895392894745,
-0.05794303119182587,
0.04526633396744728,
0.197531595826149,
-0.04246615990996361,
-0.09518314152956009,
-0.11401218920946121,
0.14335015416145325,
0.008522981777787209,
0.10336427390575409,
-0.03119117207825184,
0.008374445140361786,
-0.10785622149705887,
0.3159852921962738,
0.3454977869987488,
-0.10033400356769562,
0.010934093967080116,
-0.0023077947553247213,
0.030782446265220642,
0.08879277110099792,
0.1244446188211441,
0.10082508623600006,
0.3241177201271057,
-0.054311320185661316,
-0.03129411116242409,
-0.017128048464655876,
-0.04109000042080879,
-0.08236774802207947,
0.06314016133546829,
0.038076888769865036,
-0.059701621532440186,
-0.008216362446546555,
0.08916931599378586,
-0.2314404845237732,
0.1254129707813263,
-0.05804092809557915,
-0.20983493328094482,
-0.0852634459733963,
-0.014475858770310879,
0.02941286750137806,
0.01720484159886837,
0.1127777174115181,
-0.04030393436551094,
-0.08143871277570724,
0.10755171626806259,
0.005435420665889978,
-0.1961628496646881,
-0.00031151421717368066,
0.06759988516569138,
-0.0792757123708725,
-0.038009703159332275,
-0.017896179109811783,
0.055265624076128006,
0.06909351050853729,
0.0564793162047863,
-0.04164296016097069,
0.03333113342523575,
-0.016823936253786087,
-0.01986556500196457,
0.06435897946357727,
0.04644544795155525,
-0.006870954763144255,
-0.0894399955868721,
0.0946667492389679,
-0.1775442659854889,
0.020637469366192818,
0.053979430347681046,
-0.024212993681430817,
-0.036186333745718,
0.030745983123779297,
-0.09573786705732346,
0.048446789383888245,
0.11576225608587265,
-0.010237271897494793,
0.004423218779265881,
-0.03030877187848091,
0.015428856946527958,
-0.03862288221716881,
-0.08859021216630936,
-0.1236775666475296,
-0.17528457939624786,
-0.12474242597818375,
0.06890276074409485,
0.00874523539096117,
-0.1622752994298935,
0.014656917192041874,
-0.12388904392719269,
0.039198122918605804,
-0.132217675447464,
0.09370765835046768,
0.0809192955493927,
0.02019476145505905,
-0.013686512596905231,
0.022850895300507545,
0.01967916078865528,
0.09122931957244873,
-0.10017788410186768,
-0.10062745213508606
] |
null | null |
transformers
|
# DioloGPT KaeyaBot model
|
{"tags": ["conversational"]}
|
text-generation
|
felinecity/ScaraBot
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DioloGPT KaeyaBot model
|
[
"# DioloGPT KaeyaBot model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DioloGPT KaeyaBot model"
] |
[
51,
9
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# DioloGPT KaeyaBot model"
] |
[
-0.013482030481100082,
-0.04944401979446411,
-0.00660965871065855,
0.018116695806384087,
0.16041694581508636,
0.005352854263037443,
0.1591426134109497,
0.113655224442482,
-0.005036589689552784,
-0.02728116884827614,
0.12488944083452225,
0.22933024168014526,
0.029869183897972107,
0.1351277083158493,
-0.07913515716791153,
-0.29110777378082275,
0.05341096594929695,
0.03645821288228035,
0.0021556243300437927,
0.08520137518644333,
0.10741303861141205,
-0.07100285589694977,
0.10141543298959732,
-0.015280923806130886,
-0.12886378169059753,
-0.034620773047208786,
0.009351576678454876,
-0.14558735489845276,
0.07823927700519562,
0.0635087788105011,
0.05051994323730469,
0.025348380208015442,
-0.03491302207112312,
-0.11841075867414474,
0.036076925694942474,
-0.010789170861244202,
-0.01633112132549286,
0.03215695172548294,
-0.006123706232756376,
-0.10275706648826599,
0.06512311846017838,
0.05040643364191055,
-0.007862668484449387,
0.01055244728922844,
-0.1442529559135437,
0.010917812585830688,
-0.01737990230321884,
0.011684340424835682,
0.13442599773406982,
0.10449829697608948,
-0.02284255623817444,
0.044014379382133484,
-0.0918123796582222,
0.10083391517400742,
0.11368492990732193,
-0.24449126422405243,
-0.03187127411365509,
0.10879965126514435,
0.03690730035305023,
0.05006536468863487,
-0.014344406314194202,
0.06345362216234207,
0.046731121838092804,
0.00035137636587023735,
0.01799810864031315,
-0.09024252742528915,
-0.0809108167886734,
0.007838817313313484,
-0.07939121127128601,
0.005723253358155489,
0.2735130786895752,
-0.06425411999225616,
0.04118042066693306,
-0.0737089291214943,
-0.07196356356143951,
0.023852720856666565,
-0.0601150244474411,
-0.008588753640651703,
-0.07469012588262558,
0.027902815490961075,
0.057888440787792206,
-0.10337091982364655,
-0.1314590871334076,
-0.008448471315205097,
-0.17960487306118011,
0.24182842671871185,
0.050672855228185654,
0.018773283809423447,
-0.20690259337425232,
0.07384129613637924,
0.017387308180332184,
-0.10685200989246368,
-0.006884794682264328,
-0.11515865474939346,
0.041213035583496094,
0.010604165494441986,
-0.015647996217012405,
-0.05562400445342064,
0.08027039468288422,
0.11201230436563492,
0.049923136830329895,
0.003928730729967356,
-0.01086176373064518,
0.03498953580856323,
0.07448742538690567,
0.1263836920261383,
-0.019534315913915634,
-0.015271554701030254,
0.044583309441804886,
-0.058608293533325195,
0.013690563850104809,
-0.06365002691745758,
-0.16729891300201416,
-0.011074299924075603,
-0.0019514053128659725,
0.06900392472743988,
0.025876125320792198,
0.12303344905376434,
-0.04554140567779541,
-0.056231752038002014,
0.029509034007787704,
-0.05098415166139603,
-0.034932199865579605,
-0.01566430553793907,
-0.03030925616621971,
0.17786185443401337,
-0.005685892887413502,
0.03192080184817314,
-0.10662221908569336,
0.057476289570331573,
-0.04604175314307213,
-0.0024113552644848824,
-0.030046630650758743,
-0.05317991226911545,
-0.007894616574048996,
-0.09116844832897186,
0.04384077712893486,
-0.18569129705429077,
-0.16892604529857635,
0.002109426073729992,
-0.0014731264673173428,
-0.06242804601788521,
-0.07764072716236115,
-0.09326117485761642,
-0.03556299954652786,
0.023548118770122528,
-0.0708107203245163,
-0.0026914088521152735,
-0.0493709072470665,
0.07582206279039383,
-0.03043040819466114,
0.12273649871349335,
-0.08936804533004761,
0.0855768695473671,
-0.09660112857818604,
-0.009370164945721626,
-0.1144319400191307,
0.13016021251678467,
-0.02309008315205574,
0.10889901965856552,
-0.01719598099589348,
-0.004806773737072945,
-0.08014009147882462,
0.0707927942276001,
-0.010947787202894688,
0.2390892058610916,
-0.09615686535835266,
-0.10527963191270828,
0.20623502135276794,
-0.03790713846683502,
-0.14620162546634674,
0.10589602589607239,
-0.004730983171612024,
0.13065624237060547,
0.12939231097698212,
0.18979355692863464,
0.0007764091715216637,
-0.02888396754860878,
0.05273909121751785,
0.08515644073486328,
-0.09192401170730591,
-0.009224558249115944,
0.006953336298465729,
0.019487295299768448,
-0.0604826994240284,
0.058031678199768066,
0.07512139528989792,
0.058956172317266464,
-0.04852375388145447,
-0.03693552687764168,
0.0054132877849042416,
-0.03053313121199608,
0.07787030935287476,
-0.03355223312973976,
0.11395929753780365,
-0.028790919110178947,
-0.009170105680823326,
-0.0036145257763564587,
0.06036003306508064,
-0.04261153191328049,
0.018190739676356316,
-0.06871908903121948,
0.13146312534809113,
-0.024455873295664787,
0.07805652916431427,
-0.11542021483182907,
-0.06328452378511429,
-0.014105372130870819,
0.11948758363723755,
0.05177495628595352,
0.06743009388446808,
0.03816762939095497,
-0.04157688468694687,
0.02144087478518486,
0.006811530329287052,
0.16115623712539673,
-0.009470009244978428,
-0.08691064268350601,
-0.094950832426548,
0.10649995505809784,
-0.05250834673643112,
0.05891750752925873,
-0.04607817530632019,
0.012430060654878616,
-0.007558582350611687,
0.08419515937566757,
-0.01676550693809986,
0.09096702188253403,
0.013472671620547771,
0.017037739977240562,
-0.08330360054969788,
0.00552785862237215,
0.06929166615009308,
-0.04137129336595535,
-0.07525984197854996,
0.24265378713607788,
-0.11155009269714355,
0.09725353121757507,
0.1959332674741745,
-0.2246531844139099,
0.017021849751472473,
-0.024936281144618988,
-0.01824372075498104,
0.003590463427826762,
0.0407932847738266,
-0.020584916695952415,
0.15661026537418365,
-0.005723661743104458,
0.1630423665046692,
-0.043110910803079605,
-0.019893556833267212,
0.020257245749235153,
-0.08925081044435501,
-0.00834382139146328,
0.09131285548210144,
0.10188426077365875,
-0.06383258104324341,
0.15723173320293427,
0.14325986802577972,
0.02455965429544449,
0.18680575489997864,
0.028767947107553482,
-0.0063820513896644115,
0.04121730849146843,
-0.006069001276046038,
-0.03843383118510246,
-0.03192035108804703,
-0.219386026263237,
-0.04428457096219063,
0.06277934461832047,
0.009671555832028389,
0.10874585062265396,
-0.06731194257736206,
-0.04561801254749298,
-0.008217640221118927,
-0.0076491497457027435,
0.08621719479560852,
0.12873822450637817,
0.03028985857963562,
0.11494994908571243,
-0.00158013217151165,
-0.027686171233654022,
0.05653297156095505,
0.031615711748600006,
-0.037328172475099564,
0.19481655955314636,
-0.11397957801818848,
-0.35038501024246216,
-0.09559032320976257,
-0.17552193999290466,
-0.07455673813819885,
0.03829096630215645,
0.07300632447004318,
-0.15407921373844147,
-0.02510600909590721,
0.014120997861027718,
0.10819751769304276,
-0.06674625724554062,
0.016306182369589806,
-0.047712475061416626,
0.0020079133100807667,
-0.15064212679862976,
-0.08720564097166061,
-0.06352168321609497,
-0.046571291983127594,
-0.057697124779224396,
0.1546059548854828,
-0.1368713676929474,
0.05739881843328476,
0.20625919103622437,
0.04664858058094978,
0.022002840414643288,
-0.022375911474227905,
0.17239011824131012,
-0.11726665496826172,
0.0457535982131958,
0.2143382728099823,
0.00867398176342249,
0.07063840329647064,
0.15636718273162842,
-0.023956511169672012,
-0.09318286180496216,
0.04038812592625618,
-0.009676158428192139,
-0.07902279496192932,
-0.2506827712059021,
-0.12741594016551971,
-0.11919914186000824,
0.11445384472608566,
0.017909526824951172,
0.021345248445868492,
0.17496436834335327,
0.12987011671066284,
-0.0325968861579895,
0.06649789959192276,
0.033899616450071335,
0.08671996742486954,
0.23215514421463013,
-0.06239953264594078,
0.15568697452545166,
-0.01785510592162609,
-0.16171787679195404,
0.0989636480808258,
0.05934997648000717,
0.1355666220188141,
0.0011738985776901245,
0.0213867649435997,
0.03273242339491844,
0.04031551629304886,
0.10453791916370392,
0.07370110601186752,
0.009527068585157394,
-0.025985941290855408,
-0.04248800128698349,
-0.03684599697589874,
-0.022087519988417625,
0.08546103537082672,
0.03424449265003204,
-0.16365528106689453,
-0.014997772872447968,
-0.010500336065888405,
0.0851941704750061,
0.08801325410604477,
0.1347058266401291,
-0.17752423882484436,
-0.021291052922606468,
0.09493374824523926,
0.00997153576463461,
-0.11472714692354202,
0.06450123339891434,
-0.03718695789575577,
-0.1397397816181183,
0.05941950902342796,
-0.014308363199234009,
0.10111919790506363,
-0.06641452759504318,
0.05838441103696823,
-0.11516855657100677,
-0.02304365299642086,
0.002995515475049615,
0.09358447790145874,
-0.393774151802063,
0.1628636121749878,
-0.00986325554549694,
-0.005545234307646751,
-0.12398351728916168,
0.010843661613762379,
0.028195470571517944,
0.05972886085510254,
0.08891104906797409,
-0.010003120638430119,
0.07640086114406586,
-0.09257785975933075,
-0.07097684592008591,
0.029322227463126183,
0.08371448516845703,
-0.05723239853978157,
-0.0016892198473215103,
-0.009944526478648186,
0.0050379810854792595,
-0.05052965134382248,
-0.09611181169748306,
-0.016050349920988083,
-0.15210463106632233,
0.08686468750238419,
0.05218008905649185,
0.05912322923541069,
0.02918119542300701,
-0.026591941714286804,
-0.06211758404970169,
0.18332351744174957,
-0.005264990031719208,
-0.13673368096351624,
-0.0789438784122467,
-0.0685691386461258,
0.06152055785059929,
-0.07190051674842834,
0.032612599432468414,
-0.0811052918434143,
0.02096245437860489,
-0.05929744988679886,
-0.19246259331703186,
0.06953182816505432,
-0.09314093738794327,
-0.05067726969718933,
-0.0079753203317523,
0.18315578997135162,
-0.01674685999751091,
-0.02609667368233204,
0.047122880816459656,
-0.01152312383055687,
-0.10806039720773697,
-0.10138577967882156,
0.011060762219130993,
0.08743880689144135,
0.036481451243162155,
0.08845022320747375,
-0.05318596586585045,
-0.08045724034309387,
-0.12577585875988007,
-0.03599841520190239,
0.21745523810386658,
0.1352667659521103,
-0.034043945372104645,
0.16965073347091675,
0.1135406419634819,
-0.07224813848733902,
-0.31420955061912537,
-0.1262809932231903,
-0.07170078158378601,
0.0005419179797172546,
-0.053622931241989136,
-0.19506920874118805,
0.09403566271066666,
-0.016184424981474876,
-0.017515968531370163,
0.0996241420507431,
-0.2860146462917328,
-0.12294992059469223,
0.1576877236366272,
-0.0020774747245013714,
0.39147499203681946,
-0.09711934626102448,
-0.05213112756609917,
-0.06704415380954742,
-0.08231525123119354,
0.14660745859146118,
0.003282658290117979,
0.11926298588514328,
-0.03223257511854172,
0.1811666190624237,
0.0640127956867218,
0.005202583037316799,
0.0986623540520668,
0.049879979342222214,
-0.03150435537099838,
-0.11892729997634888,
-0.08872099220752716,
0.054370198398828506,
-0.00361951463855803,
0.06871772557497025,
-0.045202355831861496,
0.042667657136917114,
-0.1388511061668396,
-0.06406469643115997,
-0.07063893973827362,
0.03217563033103943,
0.022150516510009766,
-0.08418355882167816,
-0.05102207511663437,
-0.01917990855872631,
-0.010927168652415276,
0.028528694063425064,
0.12718607485294342,
-0.045191530138254166,
0.061615824699401855,
0.05375410616397858,
0.10642847418785095,
-0.0852544978260994,
0.039659637957811356,
-0.06165105104446411,
-0.05476545915007591,
0.08751996606588364,
-0.15280890464782715,
0.014260325580835342,
0.10948443412780762,
-0.05449675768613815,
0.07034394145011902,
0.08002948760986328,
-0.03322891891002655,
0.042468149214982986,
0.0819685310125351,
-0.2078244686126709,
-0.059846896678209305,
-0.04982765018939972,
-0.05239968001842499,
0.11036893725395203,
0.11758973449468613,
0.20212361216545105,
-0.021785449236631393,
-0.035690389573574066,
-0.014266952872276306,
0.03772967308759689,
-0.030779385939240456,
0.10024284571409225,
-0.003108932636678219,
0.014578756876289845,
-0.1356244832277298,
0.04088431969285011,
0.018231678754091263,
-0.04935435950756073,
0.050490230321884155,
0.09342405200004578,
-0.09433169662952423,
-0.12526321411132812,
-0.05212131142616272,
0.07134290039539337,
-0.0969986766576767,
-0.0027227341197431087,
-0.01635088585317135,
-0.16709518432617188,
0.052774835377931595,
0.09144146740436554,
0.05221816152334213,
0.07960651069879532,
-0.06543218344449997,
-0.0179096981883049,
-0.055595021694898605,
0.026248406618833542,
0.06626072525978088,
0.006795559544116259,
-0.0881875529885292,
0.021433385089039803,
-0.03993553668260574,
0.16217570006847382,
-0.09396102279424667,
-0.08887333422899246,
-0.13835769891738892,
0.027371102944016457,
-0.17396841943264008,
-0.05773686245083809,
-0.1259203851222992,
-0.05451234430074692,
-0.03418267145752907,
-0.029858360067009926,
-0.039504360407590866,
-0.0435597263276577,
-0.08228189498186111,
0.05074796825647354,
-0.04999759793281555,
0.03118089772760868,
-0.05612969771027565,
-0.045256368815898895,
0.030891595408320427,
-0.050119198858737946,
0.13050159811973572,
0.1406235694885254,
-0.07488695532083511,
0.06683994084596634,
-0.07634495198726654,
-0.039566803723573685,
0.09073929488658905,
0.05351864546537399,
0.06112143024802208,
0.04633815214037895,
-0.002145530190318823,
0.04543326050043106,
0.04723939672112465,
0.04391106218099594,
0.029905904084444046,
-0.09344480186700821,
0.04646405205130577,
-0.05182987079024315,
-0.12524983286857605,
-0.03639671206474304,
0.005239246413111687,
0.041996438056230545,
0.06181972473859787,
0.11364899575710297,
-0.060029465705156326,
0.11049474775791168,
-0.04336366429924965,
0.0264158733189106,
-0.021741051226854324,
-0.17489992082118988,
0.011466334573924541,
-0.11083097755908966,
0.035859253257513046,
-0.01356759387999773,
0.2189660668373108,
0.048111531883478165,
-0.0048138173297047615,
0.008679034188389778,
0.03266594558954239,
0.04032257944345474,
0.04873806983232498,
0.1462908387184143,
0.10418044030666351,
-0.03941921517252922,
-0.14856553077697754,
0.07860119640827179,
0.04979313164949417,
0.10476244986057281,
0.0788101926445961,
-0.0032637007534503937,
-0.008567674085497856,
0.09158727526664734,
-0.0024726937990635633,
0.011867997236549854,
-0.0760711207985878,
-0.10420184582471848,
-0.11120584607124329,
0.040450215339660645,
-0.014851558953523636,
0.05407802760601044,
0.13595010340213776,
-0.022204827517271042,
0.0061662159860134125,
-0.013565451838076115,
-0.09817624092102051,
-0.14944778382778168,
-0.1978628784418106,
-0.09255565702915192,
-0.10039447247982025,
-0.004987895023077726,
-0.1292000114917755,
0.017753439024090767,
0.025326456874608994,
0.07977227866649628,
-0.0743170902132988,
0.12330517172813416,
0.08845844864845276,
-0.09819568693637848,
0.1366061568260193,
-0.02930130623281002,
0.035352397710084915,
0.0013805855996906757,
-0.02378052845597267,
-0.1260133683681488,
0.04055990278720856,
-0.014052973128855228,
0.05454232916235924,
-0.06255052983760834,
0.03133060038089752,
-0.10206601023674011,
-0.08690181374549866,
-0.04904913157224655,
0.05576784908771515,
-0.01058909296989441,
0.11049410700798035,
0.04277542978525162,
-0.0542302280664444,
0.03859201818704605,
0.18897023797035217,
-0.028784576803445816,
-0.06421303749084473,
-0.07458139210939407,
0.14758092164993286,
0.011797708459198475,
0.09122835099697113,
-0.027796631678938866,
0.0249556303024292,
-0.11141808331012726,
0.36470651626586914,
0.2769416272640228,
-0.0932321548461914,
0.0011342503130435944,
-0.02969958260655403,
0.03763050213456154,
0.07493697851896286,
0.13585630059242249,
0.08117265999317169,
0.28879058361053467,
-0.053391486406326294,
-0.03465833514928818,
-0.024943789467215538,
-0.03410155698657036,
-0.10079725086688995,
0.05028124898672104,
0.05205275118350983,
-0.07995148748159409,
-0.021969502791762352,
0.09531421959400177,
-0.2670392096042633,
0.13942958414554596,
-0.08704971522092819,
-0.18320243060588837,
-0.10686859488487244,
-0.011772154830396175,
0.054480500519275665,
0.02383100986480713,
0.10637961328029633,
-0.03152860328555107,
-0.07485074549913406,
0.08676011860370636,
0.017972063273191452,
-0.1609298288822174,
0.03648524731397629,
0.08000543713569641,
-0.010551512241363525,
-0.04312047362327576,
-0.01824316568672657,
0.0699373409152031,
0.07471390813589096,
0.04988278076052666,
-0.017278242856264114,
0.05565600469708443,
-0.006048320326954126,
-0.007254385389387608,
0.06590966880321503,
0.03585546463727951,
-0.0009652543812990189,
-0.12986749410629272,
0.1127333790063858,
-0.14744898676872253,
0.03514809533953667,
0.019453592598438263,
-0.042535971850156784,
-0.03262657672166824,
0.03626735880970955,
-0.10610075294971466,
0.05622672662138939,
0.10164473205804825,
-0.028023410588502884,
-0.01666472665965557,
-0.028365053236484528,
-0.017056241631507874,
-0.04851152002811432,
-0.05852307006716728,
-0.09714926779270172,
-0.15487539768218994,
-0.1308649480342865,
0.027045125141739845,
0.031534720212221146,
-0.16601626574993134,
0.049147821962833405,
-0.1326368749141693,
0.057243626564741135,
-0.1291460543870926,
0.09664825350046158,
0.06121774762868881,
0.014837436378002167,
-0.015610164031386375,
0.01829163357615471,
0.003466215915977955,
0.0910218134522438,
-0.10638102889060974,
-0.09066619724035263
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-de-en-finetuned-de-to-en-second
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-de-en](https://huggingface.co/Helsinki-NLP/opus-mt-de-en) on the wmt16 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2282
- Bleu: 37.9762
- Gen Len: 25.3696
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 157 | 1.1837 | 38.8278 | 25.22 |
| No log | 2.0 | 314 | 1.2057 | 38.3047 | 25.2908 |
| No log | 3.0 | 471 | 1.2167 | 38.231 | 25.316 |
| 1.4808 | 4.0 | 628 | 1.2256 | 37.9871 | 25.3556 |
| 1.4808 | 5.0 | 785 | 1.2282 | 37.9762 | 25.3696 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-de-en-finetuned-de-to-en-second", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "args": "de-en"}, "metrics": [{"type": "bleu", "value": 37.9762, "name": "Bleu"}]}]}]}
|
text2text-generation
|
felipetanios/opus-mt-de-en-finetuned-de-to-en-second
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
opus-mt-de-en-finetuned-de-to-en-second
=======================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-de-en on the wmt16 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2282
* Bleu: 37.9762
* Gen Len: 25.3696
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu111
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
69,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
-0.10624333471059799,
0.07806745916604996,
-0.0026847387198358774,
0.1246161162853241,
0.1471657156944275,
0.027972735464572906,
0.13686558604240417,
0.12347269803285599,
-0.09508732706308365,
0.024209782481193542,
0.13771513104438782,
0.15107424557209015,
0.01696319319307804,
0.10587067902088165,
-0.04728548601269722,
-0.2438245415687561,
-0.012300528585910797,
0.0469089150428772,
-0.06706249713897705,
0.13539668917655945,
0.0899498462677002,
-0.1274656057357788,
0.09885881841182709,
0.008835035376250744,
-0.18603600561618805,
0.0017271469114348292,
-0.00640947837382555,
-0.046327780932188034,
0.1452941745519638,
0.023918602615594864,
0.11343810707330704,
0.004095644224435091,
0.08415603637695312,
-0.18689918518066406,
0.01045252662152052,
0.03868924826383591,
0.00870944932103157,
0.0905226543545723,
0.04740118607878685,
0.008789327926933765,
0.1286650002002716,
-0.06326135247945786,
0.04229431599378586,
0.020519282668828964,
-0.12857484817504883,
-0.1936672329902649,
-0.08285903930664062,
0.03201901912689209,
0.07020982354879379,
0.1122099980711937,
-0.0057212901301681995,
0.13123472034931183,
-0.08095180243253708,
0.08899672329425812,
0.23428693413734436,
-0.2772238552570343,
-0.0656919777393341,
0.040469300001859665,
0.030802669003605843,
0.06065039336681366,
-0.08844535797834396,
-0.023034904152154922,
0.04646942391991615,
0.049859996885061264,
0.11436253041028976,
-0.025212211534380913,
-0.08864949643611908,
0.022045735269784927,
-0.1378270536661148,
-0.0442851223051548,
0.1871890276670456,
0.04917928948998451,
-0.023441661149263382,
-0.04119861498475075,
-0.06193077564239502,
-0.14908017218112946,
-0.024270515888929367,
-0.015684522688388824,
0.04031725227832794,
-0.025182930752635002,
-0.07211671024560928,
-0.011044886894524097,
-0.10956957936286926,
-0.07064551115036011,
-0.06579877436161041,
0.12021487206220627,
0.029448220506310463,
0.01500458363443613,
-0.049799349159002304,
0.11330915987491608,
-0.0014499182580038905,
-0.13395169377326965,
0.02365082874894142,
0.024220312014222145,
0.02762713097035885,
-0.034106314182281494,
-0.06294132769107819,
-0.08023654669523239,
0.006823976524174213,
0.10927707701921463,
-0.050572916865348816,
0.039402592927217484,
0.0527624636888504,
0.047845255583524704,
-0.09036706387996674,
0.1651281714439392,
-0.03958650305867195,
-0.04513408988714218,
-0.0006341070402413607,
0.05989210307598114,
0.01855846308171749,
-0.011498781852424145,
-0.12110251933336258,
0.0028259945102036,
0.08406369388103485,
0.007796582765877247,
-0.05550406873226166,
0.06725793331861496,
-0.05470459535717964,
-0.026469849050045013,
-0.007426395080983639,
-0.0804273933172226,
0.024914903566241264,
-0.0025971964932978153,
-0.08355296403169632,
-0.017205270007252693,
0.031726304441690445,
0.021225029602646828,
-0.016274388879537582,
0.10262485593557358,
-0.09101160615682602,
0.020911209285259247,
-0.09426376223564148,
-0.09852413088083267,
0.019575461745262146,
-0.08347029983997345,
0.03087248094379902,
-0.08980461955070496,
-0.2059880644083023,
-0.012731341645121574,
0.06327962875366211,
-0.02803385816514492,
-0.0567544549703598,
-0.058821067214012146,
-0.07118762284517288,
0.015808437019586563,
-0.008151859045028687,
0.14619608223438263,
-0.06093943864107132,
0.1019357293844223,
0.03764098882675171,
0.06520857661962509,
-0.03903718665242195,
0.05127754807472229,
-0.10024280101060867,
0.01006454136222601,
-0.1440439522266388,
0.04135357588529587,
-0.03059815615415573,
0.06647466868162155,
-0.07895239442586899,
-0.0962895080447197,
-0.01840386539697647,
-0.0023279287852346897,
0.07613850384950638,
0.11065979301929474,
-0.17931152880191803,
-0.074381522834301,
0.1672659069299698,
-0.0600106380879879,
-0.12874601781368256,
0.12245785444974899,
-0.061823442578315735,
0.06732068955898285,
0.06273268163204193,
0.16169822216033936,
0.049081459641456604,
-0.07658139616250992,
0.019840704277157784,
-0.004577424842864275,
0.05514132231473923,
-0.0532940998673439,
0.07398417592048645,
0.0037604174576699734,
0.019116941839456558,
0.023599453270435333,
-0.026080798357725143,
0.05238062143325806,
-0.09585674852132797,
-0.10124392807483673,
-0.043637655675411224,
-0.10451988130807877,
0.036362871527671814,
0.0645357146859169,
0.07324264943599701,
-0.10548129677772522,
-0.08006016165018082,
0.034252095967531204,
0.0822068452835083,
-0.05621802806854248,
0.02541123703122139,
-0.06033840402960777,
0.06867313385009766,
-0.03513285890221596,
-0.01541160512715578,
-0.1682693213224411,
0.00038578035309910774,
0.002772791078314185,
0.006018455605953932,
0.027480972930788994,
0.034812986850738525,
0.07671476155519485,
0.07359424978494644,
-0.04821432754397392,
-0.013993104919791222,
-0.044124484062194824,
-0.0028353321831673384,
-0.12608574330806732,
-0.1994713544845581,
-0.029376957565546036,
-0.017326172441244125,
0.14368294179439545,
-0.21779057383537292,
0.04183819517493248,
-0.020403824746608734,
0.07370135933160782,
0.010646134614944458,
-0.01952945627272129,
-0.041316669434309006,
0.07058677077293396,
-0.04332353174686432,
-0.04220760613679886,
0.0706971064209938,
0.00984268169850111,
-0.0962906926870346,
-0.04628867655992508,
-0.11224640160799026,
0.1606275886297226,
0.13503187894821167,
-0.12852250039577484,
-0.05956530198454857,
-0.0002462649717926979,
-0.05853443220257759,
-0.039339907467365265,
-0.051874928176403046,
0.018932679668068886,
0.17654183506965637,
-0.0072119226679205894,
0.14587384462356567,
-0.07722223550081253,
-0.04177303612232208,
0.025849059224128723,
-0.03749316558241844,
0.02148856781423092,
0.12736931443214417,
0.12066736817359924,
-0.07979803532361984,
0.13842670619487762,
0.15774595737457275,
-0.08184291422367096,
0.1503305584192276,
-0.03513462096452713,
-0.0709647610783577,
-0.026398316025733948,
-0.02755776047706604,
-0.008474316447973251,
0.103703074157238,
-0.16937704384326935,
0.002394914161413908,
0.027132101356983185,
0.024112612009048462,
0.024004360660910606,
-0.22615014016628265,
-0.04623258486390114,
0.04639080539345741,
-0.037915267050266266,
-0.026300137862563133,
-0.006589734926819801,
-0.0032882257364690304,
0.10017489641904831,
-0.004160253331065178,
-0.0767209455370903,
0.03525527939200401,
0.0035008571576327085,
-0.08172447979450226,
0.20575347542762756,
-0.07233186066150665,
-0.1471085101366043,
-0.13010422885417938,
-0.08125895261764526,
-0.04682854190468788,
0.011643537320196629,
0.07116058468818665,
-0.08437880873680115,
-0.02522558532655239,
-0.07165402919054031,
0.04258987307548523,
-0.008616885170340538,
0.0167219340801239,
0.03265415504574776,
0.002859178464859724,
0.06244874745607376,
-0.11036261916160583,
-0.009742358699440956,
-0.04569846764206886,
-0.052130527794361115,
0.03923463448882103,
0.025100981816649437,
0.12303885817527771,
0.14213216304779053,
-0.010386514477431774,
0.013688411563634872,
-0.018923867493867874,
0.25025659799575806,
-0.06591328233480453,
-0.01850191317498684,
0.1527075171470642,
-0.0051827626302838326,
0.05033169314265251,
0.09767386317253113,
0.07492422312498093,
-0.08470603823661804,
-0.004937117453664541,
0.02545689232647419,
-0.039869118481874466,
-0.2300322949886322,
-0.04346197843551636,
-0.05245925486087799,
0.0061734579503536224,
0.0864645317196846,
0.01870207116007805,
0.04066898301243782,
0.08028218150138855,
0.03396929055452347,
0.08624771982431412,
-0.0375768207013607,
0.05671008676290512,
0.1158643439412117,
0.043482016772031784,
0.12290572375059128,
-0.044615041464567184,
-0.060206759721040726,
0.04353388398885727,
0.0046261209063231945,
0.21889151632785797,
0.01650458201766014,
0.13596783578395844,
0.06745550781488419,
0.15591828525066376,
-0.013097036629915237,
0.077749103307724,
-0.0032278229482471943,
-0.04307049140334129,
-0.02557292766869068,
-0.04590129479765892,
-0.03430257365107536,
0.02801785245537758,
-0.07427618652582169,
0.06425555050373077,
-0.1221156194806099,
0.02850964292883873,
0.04894611984491348,
0.23947595059871674,
0.03536003828048706,
-0.32024243474006653,
-0.09829245507717133,
0.005446299910545349,
-0.029103994369506836,
-0.016687646508216858,
0.03489646315574646,
0.09652964770793915,
-0.09452309459447861,
0.0276954248547554,
-0.06358115375041962,
0.10399508476257324,
-0.04747457429766655,
0.05508390814065933,
0.05688343569636345,
0.08530346304178238,
0.007181981112807989,
0.091334268450737,
-0.29900386929512024,
0.28452232480049133,
0.004067650996148586,
0.06885473430156708,
-0.08388353884220123,
-0.004483846016228199,
0.027986625209450722,
0.0557597354054451,
0.06561791151762009,
-0.008266119286417961,
-0.02464044652879238,
-0.16977417469024658,
-0.05097714066505432,
0.026202267035841942,
0.08062072843313217,
-0.03027254343032837,
0.09513580799102783,
-0.023561690002679825,
0.015234217047691345,
0.06712079048156738,
0.03324263170361519,
-0.06674318015575409,
-0.10276306420564651,
-0.004520912654697895,
0.03515361249446869,
-0.04547571390867233,
-0.061142049729824066,
-0.10727165639400482,
-0.11540930718183517,
0.12918730080127716,
-0.010169184766709805,
-0.024036165326833725,
-0.09783580899238586,
0.07594382762908936,
0.07400140166282654,
-0.08646830916404724,
0.032338161021471024,
0.008865300565958023,
0.06466817110776901,
0.0196726992726326,
-0.05772050842642784,
0.10528942197561264,
-0.08084769546985626,
-0.16530822217464447,
-0.07009667158126831,
0.10733997821807861,
0.03701900318264961,
0.0680900290608406,
-0.011153467930853367,
0.009003950282931328,
-0.05969119444489479,
-0.08133267611265182,
0.03458701819181442,
-0.017047738656401634,
0.07316652685403824,
0.008714748546481133,
-0.04722818732261658,
0.03029933199286461,
-0.062475647777318954,
-0.056825652718544006,
0.1893990933895111,
0.2393544763326645,
-0.09004295617341995,
0.01990298368036747,
0.04507823660969734,
-0.0691816434264183,
-0.18316268920898438,
0.025727177038788795,
0.047554127871990204,
0.00956587865948677,
0.04822542518377304,
-0.18956252932548523,
0.11676836758852005,
0.10114551335573196,
-0.012631410732865334,
0.11187462508678436,
-0.35086527466773987,
-0.12218939512968063,
0.10994945466518402,
0.13913756608963013,
0.13481493294239044,
-0.14324454963207245,
-0.02284303866326809,
-0.029726102948188782,
-0.16866518557071686,
0.106343112885952,
-0.09632949531078339,
0.12271274626255035,
-0.03337335214018822,
0.1141027957201004,
0.005695724859833717,
-0.061566874384880066,
0.12249676883220673,
0.015864385291934013,
0.09177111089229584,
-0.06537538021802902,
-0.018176767975091934,
0.05019437149167061,
-0.0389213003218174,
0.01949484832584858,
-0.10823563486337662,
0.03212869539856911,
-0.10261905193328857,
-0.020697716623544693,
-0.07278269529342651,
0.03216016665101051,
-0.04292260482907295,
-0.06419209390878677,
-0.0279899500310421,
0.012973738834261894,
0.05181066691875458,
-0.005832187365740538,
0.11925255507230759,
0.02441655844449997,
0.13544189929962158,
0.0999901294708252,
0.08135821670293808,
-0.08269812166690826,
-0.06143157556653023,
-0.030541516840457916,
-0.01569085754454136,
0.053918588906526566,
-0.14678804576396942,
0.018730642274022102,
0.1311543732881546,
0.024645064026117325,
0.1384451538324356,
0.08302182704210281,
-0.033024270087480545,
0.00980584230273962,
0.06246161088347435,
-0.1730741411447525,
-0.09109684079885483,
-0.024701643735170364,
-0.03403407335281372,
-0.10434114933013916,
0.05261564254760742,
0.10101568698883057,
-0.0649624913930893,
-0.0047539109364151955,
-0.0146296676248312,
0.01042401883751154,
-0.05938832834362984,
0.19745369255542755,
0.05071590095758438,
0.04968269169330597,
-0.09603080153465271,
0.07830528914928436,
0.04149392619729042,
-0.05965464189648628,
0.012151017785072327,
0.07967221736907959,
-0.07589860260486603,
-0.04783656448125839,
0.05329577252268791,
0.1896236389875412,
-0.06861604005098343,
-0.046161018311977386,
-0.1435944139957428,
-0.11142081767320633,
0.08034311980009079,
0.14370816946029663,
0.10451076179742813,
0.004676085896790028,
-0.06288141757249832,
0.004666488151997328,
-0.11496265977621078,
0.09362637251615524,
0.05593739077448845,
0.06392709165811539,
-0.1415705680847168,
0.14068971574306488,
0.005604010075330734,
0.035553738474845886,
-0.014294374734163284,
0.0273720845580101,
-0.09101203083992004,
0.015419894829392433,
-0.118554025888443,
-0.037896111607551575,
-0.02361542358994484,
-0.00274658203125,
-0.017730914056301117,
-0.054477520287036896,
-0.06128585711121559,
0.018218044191598892,
-0.10862161219120026,
-0.020093316212296486,
0.029134975746273994,
0.055151958018541336,
-0.10753678530454636,
-0.029432710260152817,
0.03351130709052086,
-0.062243953347206116,
0.06290514767169952,
0.053884223103523254,
0.014461261220276356,
0.04911995306611061,
-0.14145337045192719,
0.022970598191022873,
0.05175848305225372,
0.023853542283177376,
0.06047973781824112,
-0.09082359820604324,
-0.014864620752632618,
-0.0053275590762495995,
0.05682501196861267,
0.014440094120800495,
0.07442010194063187,
-0.1280810832977295,
-0.00030133678228594363,
-0.0076990858651697636,
-0.08645270764827728,
-0.06328076869249344,
0.025812475010752678,
0.08818896859884262,
0.007970710285007954,
0.19714166224002838,
-0.06484730541706085,
0.049539703875780106,
-0.2133236676454544,
0.010722736828029156,
0.004395260475575924,
-0.10798349976539612,
-0.12747406959533691,
-0.0701686218380928,
0.06069780886173248,
-0.06385453045368195,
0.14660723507404327,
0.020184936001896858,
0.018564073368906975,
0.02212054282426834,
-0.004356988240033388,
0.01305472757667303,
-0.002717613009735942,
0.20963835716247559,
0.03648258000612259,
-0.03304186090826988,
0.047217488288879395,
0.055550385266542435,
0.10443304479122162,
0.12877924740314484,
0.19615735113620758,
0.1441223919391632,
0.002379127312451601,
0.09814969450235367,
0.03983467072248459,
-0.04669048637151718,
-0.16531431674957275,
0.028255971148610115,
-0.02322797104716301,
0.10283520817756653,
-0.029659822583198547,
0.21211691200733185,
0.07951273769140244,
-0.16170097887516022,
0.03934573382139206,
-0.06225811317563057,
-0.07792330533266068,
-0.11040978133678436,
-0.07317480444908142,
-0.07836231589317322,
-0.13702574372291565,
0.004183078650385141,
-0.11554816365242004,
0.022122856229543686,
0.1030067577958107,
0.005219617392867804,
-0.03226274251937866,
0.15506111085414886,
0.02355988323688507,
0.02542177401483059,
0.04567556083202362,
0.0006645179819315672,
-0.027774646878242493,
-0.10656283795833588,
-0.0631115734577179,
-0.014061656780540943,
-0.01937052421271801,
0.03207935765385628,
-0.057561665773391724,
-0.06276938319206238,
0.025586364790797234,
-0.012413124553859234,
-0.09906536340713501,
0.001886314363218844,
0.007908323779702187,
0.0674385353922844,
0.05240970104932785,
-0.00044939442886970937,
0.023233378306031227,
-0.006918375845998526,
0.21677739918231964,
-0.07256411761045456,
-0.08007853478193283,
-0.09262605011463165,
0.20669898390769958,
0.03707428649067879,
-0.01727042905986309,
0.02886890061199665,
-0.0642564669251442,
0.011150621809065342,
0.2620165944099426,
0.20915158092975616,
-0.09759026020765305,
-0.01354790385812521,
0.019256088882684708,
-0.004872651770710945,
-0.024642210453748703,
0.09260037541389465,
0.1305469274520874,
0.051055487245321274,
-0.09291112422943115,
-0.03133433312177658,
-0.0695333406329155,
-0.014704016037285328,
-0.03559606149792671,
0.07551994919776917,
0.04333168640732765,
-0.0011194167891517282,
-0.031356628984212875,
0.05582214146852493,
-0.06372059136629105,
-0.07945653051137924,
0.032788343727588654,
-0.21873411536216736,
-0.15323957800865173,
-0.01915874145925045,
0.11796282976865768,
0.006616602186113596,
0.06953156739473343,
-0.02809479832649231,
0.0007086072000674903,
0.07208989560604095,
-0.01572396419942379,
-0.09769819676876068,
-0.07797207683324814,
0.08971355110406876,
-0.11094043403863907,
0.2078281193971634,
-0.048102639615535736,
0.05914730578660965,
0.12413325160741806,
0.06247631087899208,
-0.07642737776041031,
0.07069544494152069,
0.04625409469008446,
-0.045684974640607834,
0.01538063120096922,
0.08449843525886536,
-0.028688596561551094,
0.06964082270860672,
0.04838111996650696,
-0.13198286294937134,
0.02069452963769436,
-0.02149025723338127,
-0.04727916419506073,
-0.0319603756070137,
-0.032445989549160004,
-0.052974361926317215,
0.1335410177707672,
0.2120964676141739,
-0.03609080985188484,
-0.00668561365455389,
-0.07293707132339478,
0.024918273091316223,
0.06497760117053986,
0.022006604820489883,
-0.0638643205165863,
-0.2261056751012802,
0.001259823446162045,
0.05893371254205704,
-0.009926946833729744,
-0.2504805028438568,
-0.09800540655851364,
-0.0003476119600236416,
-0.07714854925870895,
-0.08977741748094559,
0.07144946604967117,
0.10762341320514679,
0.05058607831597328,
-0.05376794561743736,
-0.045206695795059204,
-0.07347717881202698,
0.15391094982624054,
-0.14071427285671234,
-0.09476979076862335
] |
null | null |
transformers
|
# mbart for 9-3
|
{}
|
text2text-generation
|
felixai/distilmbart-9-3
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
# mbart for 9-3
|
[
"# mbart for 9-3"
] |
[
"TAGS\n#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# mbart for 9-3"
] |
[
39,
6
] |
[
"passage: TAGS\n#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n# mbart for 9-3"
] |
[
-0.06892195343971252,
-0.0357501357793808,
-0.006704540923237801,
0.017345275729894638,
0.13324514031410217,
0.006342276465147734,
0.13286231458187103,
0.12973016500473022,
-0.0037631425075232983,
-0.029139557853341103,
0.14235307276248932,
0.2483941912651062,
-0.0292444359511137,
0.21427693963050842,
-0.10631557554006577,
-0.2154942750930786,
0.03711683303117752,
0.07098500430583954,
0.04702899232506752,
0.14072096347808838,
0.07514137029647827,
-0.06867991387844086,
0.0706714391708374,
-0.07902637869119644,
-0.1733202040195465,
0.07657776027917862,
-0.004330298863351345,
-0.12543068826198578,
0.061872448772192,
0.024009699001908302,
0.09237861633300781,
0.08397229015827179,
-0.074364073574543,
-0.10011713951826096,
0.03037085570394993,
-0.03221787139773369,
-0.07087177783250809,
0.049601126462221146,
0.08234962821006775,
-0.07596161216497421,
0.030767900869250298,
0.0786045715212822,
0.012745031155645847,
0.03696799650788307,
-0.13217203319072723,
0.0432780459523201,
-0.06980327516794205,
0.051804300397634506,
0.12488479912281036,
0.06100426986813545,
-0.0006172880530357361,
0.17010019719600677,
-0.14147713780403137,
0.13161732256412506,
0.15741963684558868,
-0.28923743963241577,
-0.006636971142143011,
0.08096438646316528,
0.08093610405921936,
0.09333431720733643,
0.006916667800396681,
0.012813614681363106,
0.007595224771648645,
0.04454715549945831,
0.01697036437690258,
-0.08139965683221817,
-0.04935417324304581,
0.01488359086215496,
-0.11334867775440216,
-0.0025399820879101753,
0.27085089683532715,
-0.053221698850393295,
0.03855035454034805,
-0.034314170479774475,
-0.05513360723853111,
-0.02787320502102375,
-0.04067210480570793,
0.024412304162979126,
-0.07045668363571167,
0.03951268643140793,
-0.05797678977251053,
-0.03611243888735771,
-0.11178737133741379,
-0.0017716004513204098,
-0.18979881703853607,
0.14518949389457703,
0.004413645714521408,
0.04730971157550812,
-0.16622653603553772,
0.07066140323877335,
-0.018767239525914192,
-0.11387725919485092,
0.05222970247268677,
-0.11515887081623077,
0.04926449805498123,
-0.03252324461936951,
-0.05351362004876137,
-0.0845504179596901,
0.04861194267868996,
0.13538607954978943,
0.1336207389831543,
0.05717906355857849,
-0.03677193820476532,
0.08650927990674973,
-0.005029279738664627,
0.03732757270336151,
0.003264747792854905,
-0.11399243772029877,
0.08194351196289062,
-0.02691015601158142,
0.0319354385137558,
-0.04588611423969269,
-0.16756698489189148,
-0.02758615091443062,
0.04780244454741478,
0.10636278241872787,
0.034125395119190216,
0.0672144889831543,
-0.034913238137960434,
-0.0477626770734787,
0.2203088253736496,
-0.07415910065174103,
-0.013760053552687168,
-0.010769687592983246,
-0.013822868466377258,
0.07587393373250961,
0.022074062377214432,
0.021391969174146652,
-0.08449149876832962,
0.10734229534864426,
-0.040207214653491974,
-0.0007793199620209634,
-0.02903200127184391,
-0.08297862112522125,
0.0364564023911953,
-0.07667698711156845,
0.027778012678027153,
-0.17420125007629395,
-0.18071100115776062,
0.02197543904185295,
-0.014243606477975845,
-0.02001969888806343,
-0.030139034613966942,
-0.03295483812689781,
-0.04941084608435631,
0.037148214876651764,
-0.08735071122646332,
0.06911299377679825,
-0.04194499924778938,
0.08546009659767151,
0.04160251468420029,
0.07574684917926788,
-0.15339474380016327,
0.06638901680707932,
-0.08914528042078018,
-0.024839168414473534,
-0.1312083750963211,
0.08140639215707779,
-0.042335256934165955,
0.15891258418560028,
-0.032059445977211,
-0.0342683345079422,
-0.09001702070236206,
0.015483261086046696,
0.008634235709905624,
0.1781369000673294,
-0.08140730857849121,
-0.09603609144687653,
0.17626550793647766,
-0.059563323855400085,
-0.15728859603405,
0.05599348992109299,
-0.0009301382233388722,
0.03552549332380295,
0.10514320433139801,
0.1641346663236618,
0.0649726390838623,
0.0018691073637455702,
0.04972320795059204,
0.06384453922510147,
-0.06952519714832306,
-0.18891938030719757,
0.02195475623011589,
0.03546638786792755,
-0.08944331109523773,
0.05297580361366272,
0.0810946598649025,
0.06924840807914734,
-0.047621652483940125,
-0.026391174644231796,
-0.0010561887174844742,
-0.03948511928319931,
0.08698241412639618,
0.013928789645433426,
0.1100696250796318,
-0.11728952080011368,
0.00007450440898537636,
0.05475778877735138,
0.005684461444616318,
0.01409239787608385,
0.020562442019581795,
-0.049348849803209305,
0.07961037755012512,
-0.11963512748479843,
0.0309338066726923,
-0.1679556518793106,
-0.03821905329823494,
-0.0014068132732063532,
0.1427135318517685,
0.04355057328939438,
0.06429655849933624,
0.07538696378469467,
-0.017610738053917885,
-0.030538374558091164,
-0.03903988376259804,
0.163905531167984,
0.014318660832941532,
-0.09213759005069733,
-0.06644332408905029,
0.025722669437527657,
-0.08461961150169373,
-0.037021148949861526,
0.015467241406440735,
0.04147464036941528,
0.020186571404337883,
0.10353270173072815,
0.01177149172872305,
0.039621442556381226,
0.03441974148154259,
0.04541851580142975,
-0.05849530175328255,
0.034178439527750015,
0.09524329006671906,
0.006046784110367298,
-0.0988755151629448,
0.2022186815738678,
-0.2152528166770935,
0.24009577929973602,
0.20975840091705322,
-0.18109749257564545,
0.021628662943840027,
-0.06027401611208916,
-0.013669949024915695,
-0.011917613446712494,
0.0875815600156784,
-0.02826622501015663,
0.0399562269449234,
0.008899268694221973,
0.17163461446762085,
-0.06446391344070435,
-0.09201055020093918,
-0.012690135277807713,
-0.05915465205907822,
-0.023913325741887093,
0.05193888023495674,
0.06145758554339409,
-0.2106339931488037,
0.14223498106002808,
0.2633437216281891,
0.006162608042359352,
0.16761603951454163,
0.050884395837783813,
-0.016986144706606865,
0.036405567079782486,
-0.025723442435264587,
-0.022538065910339355,
-0.08520691841840744,
-0.17789243161678314,
-0.04207925498485565,
0.05505353584885597,
0.052201006561517715,
0.11662750691175461,
-0.0891476646065712,
-0.027280349284410477,
-0.01739487238228321,
0.018333859741687775,
-0.021792994812130928,
0.08891071379184723,
0.035917215049266815,
0.08603506535291672,
-0.0016619378002360463,
-0.017324967309832573,
0.10384973883628845,
0.007511509116739035,
-0.09858398139476776,
0.15407219529151917,
-0.1483912616968155,
-0.2982812821865082,
-0.16642123460769653,
-0.22832411527633667,
-0.05460157245397568,
0.03332815319299698,
0.11365236341953278,
-0.0826638787984848,
-0.022659966722130775,
-0.006816021166741848,
0.03497041389346123,
-0.054827846586704254,
0.03762606903910637,
-0.023071184754371643,
0.049868833273649216,
-0.05240403488278389,
-0.0784095749258995,
-0.045768264681100845,
-0.022693729028105736,
-0.03780993074178696,
0.142952099442482,
-0.1545613706111908,
0.05837426334619522,
0.1423107087612152,
0.019195767119526863,
0.057236529886722565,
-0.019982265308499336,
0.1661379337310791,
-0.027691245079040527,
0.021809101104736328,
0.2083650529384613,
-0.04527096822857857,
0.0851840004324913,
0.14810340106487274,
0.017033465206623077,
-0.06805652379989624,
0.047988411039114,
-0.06307290494441986,
-0.08999951928853989,
-0.22644475102424622,
-0.09234552085399628,
-0.11748892813920975,
0.05430649220943451,
0.043524567037820816,
0.03922058269381523,
0.09818235784769058,
0.09734170138835907,
-0.0072397030889987946,
-0.007345980033278465,
0.05307123064994812,
0.0983942374587059,
0.12842367589473724,
0.0018710544100031257,
0.18229269981384277,
-0.05682768672704697,
-0.09386905282735825,
0.10824626684188843,
0.0929219201207161,
0.08567119389772415,
0.03694918379187584,
-0.018571747466921806,
0.041065528988838196,
0.06813081353902817,
0.12662631273269653,
0.1654113233089447,
0.008779439143836498,
-0.025278935208916664,
-0.026281341910362244,
-0.02169492468237877,
-0.03058900311589241,
0.01279663760215044,
0.06718304008245468,
-0.1145641878247261,
-0.048539258539676666,
-0.12763889133930206,
0.08114700019359589,
0.05703360587358475,
0.10089785605669022,
-0.2409328669309616,
0.01479456014931202,
0.07827139645814896,
-0.05397266894578934,
-0.0529097244143486,
0.048174142837524414,
-0.03543861210346222,
-0.08895765990018845,
0.11353711038827896,
-0.007807196583598852,
0.1246558204293251,
0.0112066138535738,
0.043699201196432114,
-0.06345543265342712,
-0.06846676766872406,
0.013941876590251923,
0.09125172346830368,
-0.3355643153190613,
0.14600183069705963,
0.009516443125903606,
-0.027767017483711243,
-0.08313923329114914,
-0.008642846718430519,
0.04082600399851799,
0.11096683889627457,
0.015537845902144909,
-0.0076188333332538605,
-0.02508750557899475,
-0.11342304944992065,
-0.08916491270065308,
-0.007492721080780029,
0.10704606771469116,
0.016990698873996735,
0.023488037288188934,
0.0007054278394207358,
-0.04257650673389435,
-0.024695133790373802,
-0.004867002833634615,
-0.010353750549256802,
-0.1378449946641922,
0.07297720015048981,
0.03730929270386696,
-0.01777709275484085,
0.0024654425214976072,
-0.04082099720835686,
-0.03199842572212219,
0.20975114405155182,
-0.010968330316245556,
-0.10766109824180603,
-0.09560222178697586,
-0.0870378389954567,
0.09274730831384659,
-0.07733339071273804,
0.04222572222352028,
-0.06573361903429031,
0.021542629227042198,
-0.05517560988664627,
-0.19614984095096588,
0.12914271652698517,
-0.12438622117042542,
-0.024951979517936707,
-0.05538006126880646,
0.18804344534873962,
-0.09066951274871826,
0.01806395873427391,
0.019666066393256187,
0.0019392459653317928,
-0.1038392037153244,
-0.07023093104362488,
-0.0021482736337929964,
-0.04186651483178139,
0.05061221122741699,
0.07254479080438614,
-0.05403662845492363,
-0.07795659452676773,
-0.04850710555911064,
-0.025008291006088257,
0.2434644103050232,
0.2090003937482834,
-0.031335096806287766,
0.13671380281448364,
0.17776526510715485,
-0.03637063130736351,
-0.307149738073349,
-0.07875126600265503,
-0.06518107652664185,
-0.03173458203673363,
-0.05247603729367256,
-0.08416710048913956,
0.07702014595270157,
0.0038967239670455456,
-0.016817089170217514,
0.1026173010468483,
-0.22321979701519012,
-0.09226659685373306,
0.12550920248031616,
0.023317819461226463,
0.34226658940315247,
-0.12908394634723663,
-0.09391581267118454,
-0.08057352155447006,
-0.12808339297771454,
0.08406434953212738,
-0.01971336454153061,
0.09802018105983734,
-0.03931561857461929,
0.11033771187067032,
0.009466622956097126,
-0.06485925614833832,
0.06565568596124649,
0.02760336548089981,
-0.005445214454084635,
-0.10910406708717346,
-0.02000551111996174,
0.09587042778730392,
-0.039514973759651184,
0.050592705607414246,
-0.06817664206027985,
0.02610795758664608,
-0.13054467737674713,
-0.03855583071708679,
-0.10764570534229279,
0.014216066338121891,
0.018902111798524857,
-0.02796626277267933,
0.04237893223762512,
-0.06326029449701309,
-0.013471550308167934,
-0.006003708112984896,
0.10830570757389069,
-0.0868203192949295,
0.1238512247800827,
0.18668608367443085,
0.1187034547328949,
-0.1656518280506134,
0.056921716779470444,
-0.04855036363005638,
-0.09664583951234818,
0.02745654806494713,
-0.08896069973707199,
0.06970543414354324,
0.1021953821182251,
-0.06434483826160431,
0.06226677447557449,
0.09339962154626846,
0.0038661169819533825,
0.012243512086570263,
0.16803665459156036,
-0.1989738494157791,
0.16167378425598145,
-0.056125130504369736,
0.09356138855218887,
0.025010615587234497,
0.09462772309780121,
0.15107865631580353,
0.05131053552031517,
-0.060200463980436325,
-0.017701376229524612,
0.0025520927738398314,
-0.07747155427932739,
0.1514032483100891,
0.05351497605443001,
0.028740961104631424,
-0.15363462269306183,
0.058778923004865646,
-0.01591380313038826,
-0.0799158364534378,
-0.02607942372560501,
0.20585548877716064,
-0.15551231801509857,
-0.11766470968723297,
-0.00015093204274307936,
0.11866717785596848,
-0.09403695166110992,
-0.007866195403039455,
-0.07724249362945557,
-0.09415620565414429,
0.10930551588535309,
0.1789552867412567,
0.06800340861082077,
0.036092620342969894,
-0.034996964037418365,
-0.02947748452425003,
-0.03633831441402435,
-0.001147251226939261,
0.037652235478162766,
0.0934113934636116,
-0.07317007333040237,
0.03963937610387802,
-0.03810274973511696,
0.12118025869131088,
-0.08862695842981339,
-0.01958727277815342,
-0.1200343444943428,
0.03886738792061806,
-0.13264766335487366,
-0.05616142973303795,
-0.11544762551784515,
-0.040267929434776306,
-0.027662839740514755,
-0.04886013641953468,
-0.07235387712717056,
-0.03912399336695671,
-0.1174139678478241,
0.02741110324859619,
-0.05689988285303116,
-0.0018149887910112739,
-0.052256569266319275,
-0.047386009246110916,
0.06491245329380035,
-0.05860578268766403,
0.10569623857736588,
0.14099343121051788,
-0.07501338422298431,
0.07900919020175934,
-0.013645244762301445,
-0.12081965804100037,
0.11583693325519562,
0.03449335694313049,
0.04797589033842087,
0.07060566544532776,
0.0209041740745306,
0.09713207185268402,
0.04310305416584015,
0.046732235699892044,
0.10602668672800064,
-0.10537201166152954,
-0.0001775141863618046,
-0.08038479089736938,
-0.15970414876937866,
-0.050942957401275635,
-0.0019782271701842546,
0.10579844564199448,
0.04856609180569649,
0.14094722270965576,
-0.05353757366538048,
0.0770760253071785,
-0.06340640783309937,
-0.0009513967088423669,
-0.010035114362835884,
-0.21176573634147644,
-0.06484410166740417,
-0.05612938106060028,
0.03016454167664051,
0.011338278651237488,
0.241689532995224,
-0.040884412825107574,
-0.0027520996518433094,
0.021778585389256477,
0.0534219890832901,
0.04297066852450371,
0.011858968064188957,
0.20910874009132385,
0.05513913929462433,
-0.04754401743412018,
-0.10364016890525818,
0.09846355766057968,
0.012446790002286434,
-0.051400020718574524,
0.08647236227989197,
0.07491733133792877,
0.031630974262952805,
0.08599795401096344,
0.018488720059394836,
0.07649217545986176,
-0.09436121582984924,
-0.17753098905086517,
-0.01891496032476425,
0.05269602686166763,
0.012353511527180672,
0.07462600618600845,
0.1407892107963562,
-0.03147793933749199,
0.014510243199765682,
-0.009967437013983727,
-0.054968833923339844,
-0.14711220562458038,
-0.08360100537538528,
-0.09713605791330338,
-0.09966355562210083,
0.008228031918406487,
-0.0881221815943718,
0.024025248363614082,
0.03744838014245033,
0.07694751024246216,
-0.020474299788475037,
0.10616232454776764,
0.05026464909315109,
-0.08345168083906174,
0.04642145335674286,
-0.07843214273452759,
0.009464265778660774,
0.0642533004283905,
0.01794266700744629,
-0.07600889354944229,
-0.0012557149166241288,
-0.004481921903789043,
0.03268194943666458,
-0.10845696181058884,
-0.006692146882414818,
-0.12197476625442505,
-0.1321103572845459,
-0.04821602255105972,
0.03744411841034889,
0.0147593654692173,
0.1667894423007965,
0.006421511061489582,
-0.024230970069766045,
0.03768453374505043,
0.15283605456352234,
-0.07844193279743195,
-0.10639483481645584,
-0.04707916080951691,
0.15303704142570496,
0.05920162796974182,
0.08979368209838867,
-0.039127103984355927,
0.00923154503107071,
-0.05839163064956665,
0.30567479133605957,
0.2941461503505707,
-0.07487822324037552,
0.02979397587478161,
0.053869277238845825,
0.03605571389198303,
0.09729649126529694,
0.13363489508628845,
0.08549845963716507,
0.1696004420518875,
-0.061922039836645126,
-0.0032773283310234547,
-0.018166858702898026,
-0.020119959488511086,
-0.06946394592523575,
0.05767068266868591,
0.027663923799991608,
-0.07544992119073868,
-0.035940490663051605,
0.10344897210597992,
-0.12220442295074463,
0.1281331479549408,
-0.08859247714281082,
-0.19203129410743713,
-0.07137063890695572,
-0.014119947329163551,
0.1328325718641281,
0.007326135877519846,
0.08666878193616867,
0.002362459897994995,
-0.0461600124835968,
-0.017756033688783646,
-0.0017005010740831494,
-0.20506201684474945,
-0.0374620258808136,
0.06705210357904434,
-0.04991445317864418,
-0.05072595551609993,
-0.03433799743652344,
0.04867485538125038,
0.08569800853729248,
0.03147657960653305,
-0.03787043318152428,
0.1071709394454956,
0.0023655444383621216,
-0.0006135308649390936,
0.034802716225385666,
0.08813365548849106,
-0.024139875546097755,
-0.047538042068481445,
0.04958384484052658,
-0.19865301251411438,
0.02344624139368534,
-0.07416210323572159,
-0.05572020635008812,
-0.020441414788365364,
0.035624779760837555,
-0.01722699962556362,
0.0811632052063942,
0.07163990288972855,
-0.030791157856583595,
-0.005192028824239969,
-0.044116947799921036,
-0.012780101038515568,
0.022233638912439346,
-0.04267401248216629,
-0.11395188421010971,
-0.15986555814743042,
-0.11158245801925659,
0.08805301040410995,
0.043099939823150635,
-0.2814752161502838,
0.010410896502435207,
-0.13490693271160126,
0.016184337437152863,
-0.20859172940254211,
0.13412529230117798,
0.040788158774375916,
0.010107075795531273,
-0.007146431598812342,
-0.083252914249897,
0.017186742275953293,
0.0798274502158165,
-0.17566820979118347,
-0.09651437401771545
] |
null | null |
transformers
|
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### corgi
#### samoyed
#### shiba inu
|
{"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]}
|
image-classification
|
ferdinand/rare-puppers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rare-puppers
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### corgi
!corgi
#### samoyed
!samoyed
#### shiba inu
!shiba inu
|
[
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### corgi\n\n!corgi",
"#### samoyed\n\n!samoyed",
"#### shiba inu\n\n!shiba inu"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### corgi\n\n!corgi",
"#### samoyed\n\n!samoyed",
"#### shiba inu\n\n!shiba inu"
] |
[
49,
44,
4,
7,
9,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n# rare-puppers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.## Example Images#### corgi\n\n!corgi#### samoyed\n\n!samoyed#### shiba inu\n\n!shiba inu"
] |
[
-0.08725201338529587,
0.03195034712553024,
-0.0021389354951679707,
0.1135053038597107,
0.15519317984580994,
0.03239595144987106,
0.13578292727470398,
0.12030287832021713,
0.05626533553004265,
0.03964082524180412,
0.10228189080953598,
0.11883898079395294,
0.028155656531453133,
0.17724762856960297,
0.012100371532142162,
-0.2828732430934906,
0.034176383167505264,
0.05366837605834007,
0.031914111226797104,
0.057901784777641296,
0.023022184148430824,
-0.0998450219631195,
0.15487806499004364,
-0.0027328983414918184,
-0.1814582347869873,
-0.06354904174804688,
-0.011302858591079712,
-0.026495302096009254,
0.062415964901447296,
0.02706352062523365,
0.07643365859985352,
0.04628670588135719,
0.048448216170072556,
-0.037812162190675735,
0.06485318392515182,
0.02091336064040661,
-0.03882516920566559,
0.08166497945785522,
0.09309375286102295,
0.05058153718709946,
-0.0022560437209904194,
0.06809859722852707,
-0.03961021453142166,
0.037191152572631836,
-0.10592982918024063,
0.016338806599378586,
-0.046722013503313065,
0.041327252984046936,
0.09235234558582306,
0.05342043563723564,
-0.0159174632281065,
0.18937605619430542,
-0.07387242466211319,
0.06267139315605164,
0.204153373837471,
-0.008847134187817574,
-0.1255074292421341,
0.10155905038118362,
0.022742880508303642,
-0.010586855001747608,
-0.04176018014550209,
0.07217155396938324,
0.06993623822927475,
-0.023803625255823135,
-0.08407008647918701,
-0.07971884310245514,
-0.10400845110416412,
-0.0659603402018547,
-0.11390291899442673,
-0.0005772259319201112,
-0.036161940544843674,
0.007211155258119106,
-0.026158908382058144,
-0.011855215765535831,
-0.06617917120456696,
-0.03847353532910347,
-0.07845224440097809,
0.046847131103277206,
0.011522369459271431,
-0.002245101844891906,
-0.08106949180364609,
-0.02939991094172001,
-0.11006252467632294,
-0.07804960012435913,
-0.040562357753515244,
0.07844525575637817,
0.06363581120967865,
0.06238023564219475,
-0.09889709204435349,
0.034971993416547775,
-0.060022495687007904,
-0.06362511217594147,
-0.02836483158171177,
-0.04747164621949196,
-0.04073799401521683,
-0.03706582635641098,
0.006928959395736456,
-0.02729734592139721,
0.1591191291809082,
0.17127059400081635,
0.019826671108603477,
0.053793616592884064,
0.0022218883968889713,
0.07772756367921829,
0.05695729702711105,
0.10798590630292892,
-0.08507385104894638,
0.05391492694616318,
0.10528440773487091,
0.044559672474861145,
0.03493637964129448,
-0.02846892736852169,
-0.10629629343748093,
-0.04619873687624931,
0.005445396527647972,
-0.004112243186682463,
0.07416379451751709,
0.07932920753955841,
-0.06106899678707123,
-0.040247976779937744,
0.12962009012699127,
0.033938441425561905,
-0.0067568556405603886,
-0.029184821993112564,
-0.017937632277607918,
-0.03950744494795799,
0.07358310371637344,
-0.00608476297929883,
-0.009796147234737873,
0.019999604672193527,
-0.0709942877292633,
0.05375722050666809,
-0.021510133519768715,
0.041401349008083344,
0.03244863077998161,
-0.19535957276821136,
0.002194782951846719,
-0.18268167972564697,
0.0064466861076653,
-0.008419123478233814,
0.03258252888917923,
-0.04353305697441101,
-0.059357188642024994,
0.008648096583783627,
0.0038042874075472355,
-0.05518633499741554,
0.008929149247705936,
-0.05204879119992256,
-0.049876581877470016,
0.03365376591682434,
0.03086325339972973,
0.08409526944160461,
-0.12796184420585632,
0.02025497891008854,
-0.14339636266231537,
0.01808703877031803,
-0.22358684241771698,
0.07740803807973862,
-0.0807129368185997,
0.13639891147613525,
-0.05171693116426468,
-0.018077053129673004,
0.026494668796658516,
0.003394173691049218,
0.03545491024851799,
0.1802806854248047,
-0.10681743174791336,
-0.08012638241052628,
0.09299447387456894,
-0.13464754819869995,
-0.08361375331878662,
0.1784285008907318,
-0.009829333052039146,
-0.004474638029932976,
0.04090268909931183,
0.14485524594783783,
0.049888018518686295,
-0.12506426870822906,
-0.0023372904397547245,
-0.03928179293870926,
-0.10563148558139801,
0.001297077862545848,
0.02361970581114292,
0.07307205349206924,
-0.03278478607535362,
0.028870759531855583,
-0.057325270026922226,
0.13522130250930786,
-0.0756012573838234,
-0.06212741509079933,
-0.011922481469810009,
-0.07765046507120132,
0.010072366334497929,
0.10895247757434845,
0.08522842079401016,
0.03529075160622597,
0.006655340548604727,
-0.11329551786184311,
0.06300210952758789,
0.003479685401543975,
-0.010017212480306625,
-0.07677577435970306,
0.20370084047317505,
-0.026291143149137497,
0.01377155166119337,
-0.0799843817949295,
-0.03747062012553215,
0.050133541226387024,
-0.037256646901369095,
0.15824776887893677,
-0.010255233384668827,
0.03251718729734421,
0.03872392326593399,
-0.012551270425319672,
-0.006375012919306755,
0.06509717553853989,
-0.041594523936510086,
-0.1060938611626625,
-0.10735425353050232,
0.0698326975107193,
-0.03683536499738693,
0.12558099627494812,
-0.08935602009296417,
0.0005720042972825468,
0.03784332051873207,
0.13630113005638123,
0.04155619814991951,
-0.05775643512606621,
0.06386461853981018,
-0.013183949515223503,
-0.052448321133852005,
-0.03119824267923832,
0.10777141898870468,
-0.023843131959438324,
-0.028976907953619957,
0.0922992154955864,
-0.03028225526213646,
-0.053854260593652725,
0.1724037230014801,
-0.17595380544662476,
-0.08996876329183578,
-0.07063911855220795,
-0.021678511053323746,
0.0257002804428339,
-0.03513510897755623,
0.09859878569841385,
0.12328615039587021,
-0.02315390482544899,
0.11330175399780273,
-0.025448214262723923,
0.07668901979923248,
0.04546011611819267,
-0.03618212789297104,
-0.037199001759290695,
0.07456471771001816,
0.1732560098171234,
-0.13867461681365967,
0.09609217196702957,
0.014329317957162857,
-0.08134523779153824,
0.12503774464130402,
0.017924558371305466,
-0.013553149066865444,
0.0364948995411396,
-0.015502866357564926,
0.06963246315717697,
0.16218173503875732,
-0.1248825266957283,
-0.04629707336425781,
0.04180235043168068,
-0.12308557331562042,
0.012266606092453003,
-0.13019578158855438,
0.00032581883715465665,
-0.059241216629743576,
0.004582630470395088,
0.058693598955869675,
0.056561533361673355,
-0.02150285243988037,
0.06558628380298615,
-0.016758570447564125,
-0.03748732805252075,
-0.007656690198928118,
0.04019112512469292,
-0.022498898208141327,
0.12266936153173447,
-0.018671948462724686,
-0.23571515083312988,
-0.11232282221317291,
-0.004492770880460739,
-0.05782869830727577,
0.08241916447877884,
0.04560401663184166,
-0.1352987140417099,
-0.028466781601309776,
-0.0029495072085410357,
0.07264716923236847,
0.08223062753677368,
0.06170215457677841,
-0.07660193741321564,
0.03016502410173416,
0.02640322595834732,
-0.029735982418060303,
-0.015808120369911194,
-0.07721602916717529,
-0.041204772889614105,
0.1743878871202469,
-0.04482881724834442,
0.11662738770246506,
0.04770622402429581,
0.0002567560295574367,
0.017374958842992783,
0.0006751101464033127,
0.17181523144245148,
-0.13390152156352997,
0.0354495644569397,
0.19081208109855652,
-0.015049438923597336,
0.039994172751903534,
0.125303715467453,
0.004979373887181282,
-0.08107107877731323,
-0.010994499549269676,
0.025968365371227264,
-0.15241122245788574,
-0.05767630413174629,
-0.05720720812678337,
-0.0671539455652237,
0.16645075380802155,
0.13435012102127075,
0.055542655289173126,
0.049172159284353256,
0.20927931368350983,
-0.023016836494207382,
0.06522751599550247,
-0.017073944211006165,
0.058385107666254044,
-0.04346790909767151,
0.0064426143653690815,
0.06281419843435287,
0.0005436323117464781,
-0.08646364510059357,
0.10815723985433578,
-0.009162385016679764,
0.11316092312335968,
-0.026063119992613792,
0.03861460089683533,
-0.004421021323651075,
0.1333499252796173,
0.1291249841451645,
-0.03633374720811844,
0.03913675248622894,
-0.034587886184453964,
-0.022632235661149025,
-0.04384814575314522,
-0.06627126783132553,
0.04909754544496536,
0.08852285146713257,
-0.11463501304388046,
0.02870742790400982,
0.023283440619707108,
0.036439232528209686,
0.17827972769737244,
0.04621202126145363,
-0.3211063742637634,
-0.014094782061874866,
-0.039162687957286835,
0.022455396130681038,
-0.023093489930033684,
0.012418294325470924,
-0.010800427757203579,
-0.0842115506529808,
0.07306312769651413,
-0.10376513749361038,
0.11075038462877274,
-0.1286194920539856,
0.02667526714503765,
0.034889865666627884,
0.06627020984888077,
-0.00503558898344636,
-0.009011751972138882,
-0.09946633875370026,
0.10092965513467789,
-0.02858249843120575,
-0.06153140589594841,
-0.05412408336997032,
0.004426621366292238,
0.12417545169591904,
0.14550656080245972,
0.09538534283638,
0.007453936152160168,
0.07573871314525604,
-0.20739008486270905,
-0.10569656640291214,
0.006672629155218601,
0.0209259781986475,
-0.05289652198553085,
-0.007929849438369274,
-0.005339838098734617,
-0.022439075633883476,
-0.04532014951109886,
0.043954893946647644,
-0.1144966408610344,
-0.07773924618959427,
0.029495103284716606,
0.0043275728821754456,
0.03984550014138222,
-0.008062648586928844,
-0.05335452780127525,
-0.11182335764169693,
0.04794478043913841,
0.14780905842781067,
-0.011666921898722649,
-0.09672053903341293,
0.11132102459669113,
0.035195332020521164,
-0.08826787024736404,
0.02265583910048008,
-0.10907873511314392,
0.05252985656261444,
-0.025499023497104645,
-0.15687888860702515,
0.08956512808799744,
-0.10042647272348404,
-0.13593590259552002,
-0.07579820603132248,
-0.001924236654303968,
0.041143544018268585,
0.009296742267906666,
0.018196964636445045,
0.03443411365151405,
-0.012468704022467136,
-0.05671745538711548,
0.059189241379499435,
0.028822725638747215,
0.02986554056406021,
0.1001846119761467,
0.029619254171848297,
-0.07286301255226135,
-0.08809434622526169,
0.007607334293425083,
0.04963431879878044,
0.17585845291614532,
-0.054766152054071426,
0.039655692875385284,
0.07079920917749405,
0.015472592785954475,
-0.3493516147136688,
-0.013201026245951653,
0.03561287000775337,
-0.048122551292181015,
-0.05820423737168312,
-0.12167651951313019,
0.15705633163452148,
0.08948065340518951,
-0.03899039328098297,
0.08654363453388214,
-0.07273750007152557,
-0.13459128141403198,
0.08251696079969406,
0.1053558811545372,
0.15190021693706512,
-0.1989029198884964,
-0.035685472190380096,
-0.010895918123424053,
0.04725129157304764,
0.13657422363758087,
-0.04451083764433861,
0.07869797199964523,
-0.04333565756678581,
-0.009525101631879807,
0.02575378306210041,
-0.013180339708924294,
0.1126987561583519,
-0.08153390884399414,
-0.003806003136560321,
-0.11173462122678757,
-0.1275569498538971,
-0.030610211193561554,
-0.028784172609448433,
-0.03528527542948723,
0.025452205911278725,
0.02299712970852852,
-0.03399757668375969,
0.005726407282054424,
-0.052121832966804504,
0.08704721182584763,
0.0508025623857975,
-0.040398649871349335,
-0.12360955774784088,
0.057384323328733444,
-0.08068276941776276,
0.062280766665935516,
0.24721577763557434,
-0.015316469594836235,
0.011376569978892803,
0.14206595718860626,
0.030055245384573936,
-0.06631238758563995,
-0.00721421092748642,
-0.051209401339292526,
-0.04404282569885254,
0.12108639627695084,
-0.04142024368047714,
0.020760687068104744,
0.05812057852745056,
0.02079555206000805,
0.028378119692206383,
0.03908412531018257,
-0.003966422751545906,
0.0966523140668869,
0.12638217210769653,
-0.10604514181613922,
-0.11415927112102509,
-0.02408481575548649,
-0.07823611050844193,
0.04854125156998634,
0.06240178644657135,
0.1317335069179535,
-0.0403314009308815,
-0.05524204671382904,
0.019972680136561394,
0.0285005085170269,
-0.015163728967308998,
0.10260289907455444,
0.05561532825231552,
-0.03574158623814583,
-0.13584718108177185,
0.06593596935272217,
0.04068996384739876,
-0.11389065533876419,
-0.06260333955287933,
0.12008363753557205,
-0.08999498933553696,
-0.13304714858531952,
0.07044947892427444,
0.11229751259088516,
-0.17939569056034088,
0.03147571533918381,
-0.08238337188959122,
-0.019431615248322487,
0.0027325269766151905,
0.018985729664564133,
0.10295498371124268,
-0.040888141840696335,
0.0607030875980854,
0.05882333219051361,
-0.08289425820112228,
-0.003656163811683655,
0.01958155445754528,
0.14842434227466583,
-0.20882852375507355,
0.009651273488998413,
-0.011176757514476776,
0.10227029770612717,
-0.11131701618432999,
-0.02373182401061058,
-0.1929367184638977,
-0.024856941774487495,
-0.03278624638915062,
0.16526728868484497,
-0.1437751203775406,
0.00010059722262667492,
-0.0007877603638917208,
-0.05140140280127525,
-0.019004421308636665,
-0.02863585203886032,
-0.10821327567100525,
-0.012270847335457802,
-0.000684377970173955,
0.06372399628162384,
-0.0017814041348174214,
0.0002054373180726543,
0.08945772051811218,
-0.01676907017827034,
0.15503482520580292,
0.044239673763513565,
-0.02895471639931202,
-0.006449585780501366,
-0.18732605874538422,
-0.06998784095048904,
0.09405958652496338,
0.004841608460992575,
0.022439563646912575,
0.11194804310798645,
0.04371132701635361,
-0.03388566896319389,
0.023277083411812782,
0.005538814701139927,
0.20068097114562988,
-0.07086087763309479,
-0.002188601763918996,
-0.041933923959732056,
-0.06707032769918442,
-0.06389482319355011,
0.04217404127120972,
0.09274961054325104,
0.04379455745220184,
0.054257262498140335,
-0.1059599220752716,
0.07182063162326813,
-0.10689008980989456,
0.022997181862592697,
0.00436578132212162,
-0.12181895971298218,
-0.05255858600139618,
-0.10454263538122177,
0.057460565119981766,
0.002079826081171632,
0.0405784510076046,
0.07822473347187042,
0.014956562779843807,
-0.017391670495271683,
0.1237729862332344,
0.10886748880147934,
0.05331963673233986,
0.03983170911669731,
0.019532421603798866,
0.019626639783382416,
-0.0523393452167511,
0.07320230454206467,
0.0911930724978447,
-0.055890075862407684,
0.034207191318273544,
0.08102597296237946,
-0.06335224211215973,
0.06631726771593094,
0.07132592797279358,
0.009764610789716244,
-0.02630438841879368,
0.05446895584464073,
-0.13079112768173218,
0.07842826098203659,
-0.032619405537843704,
0.08821578323841095,
0.06923332810401917,
-0.04353935644030571,
0.016186915338039398,
-0.04772103950381279,
-0.01853223703801632,
-0.037967365235090256,
-0.13838975131511688,
-0.08448541164398193,
-0.21566998958587646,
0.06285329163074493,
0.017504384741187096,
-0.06989763677120209,
0.0664452537894249,
0.0007161907269619405,
-0.04861297085881233,
0.14067216217517853,
0.020421603694558144,
-0.028630387037992477,
0.06892839074134827,
0.00839083082973957,
-0.12336155027151108,
0.06620859354734421,
-0.14866822957992554,
0.004483657423406839,
0.027446625754237175,
0.01047604251652956,
-0.022959185764193535,
0.012766623869538307,
0.06751374155282974,
-0.02648531273007393,
-0.07218021154403687,
0.018889764323830605,
-0.020737793296575546,
0.0167922955006361,
0.029956558719277382,
-0.040787506848573685,
0.034757260233163834,
0.016432933509349823,
0.11644991487264633,
0.002297893399372697,
0.1180640161037445,
-0.1241452693939209,
0.09324660897254944,
-0.16601592302322388,
-0.002099323319271207,
-0.052609313279390335,
-0.03613383695483208,
-0.04724516719579697,
0.2724369168281555,
0.20673854649066925,
-0.09205078333616257,
0.011794310063123703,
-0.004787789657711983,
-0.010113546624779701,
-0.028441956266760826,
0.11468766629695892,
-0.0010977567872032523,
0.06403674930334091,
-0.05773759260773659,
-0.04239797219634056,
0.022904792800545692,
-0.04402057081460953,
-0.09063120186328888,
-0.06883646547794342,
0.06888104975223541,
0.017044290900230408,
-0.12659882009029388,
0.06993076205253601,
-0.15111567080020905,
-0.07410997897386551,
0.14538536965847015,
-0.16738663613796234,
-0.0761028453707695,
-0.04550480842590332,
0.01064881682395935,
0.05035661160945892,
0.06608270853757858,
-0.05546850711107254,
0.07120561599731445,
-0.1258363276720047,
-0.02713467925786972,
-0.17326048016548157,
-0.05071635544300079,
0.03572816029191017,
-0.1299186497926712,
0.20968447625637054,
-0.07654013484716415,
-0.02401120588183403,
0.07507874071598053,
0.01623411290347576,
-0.07579199224710464,
-0.05201580002903938,
-0.049096111208200455,
-0.16179879009723663,
-0.010975295677781105,
0.14950203895568848,
-0.024331487715244293,
-0.08826322853565216,
0.036256734281778336,
-0.02044050209224224,
-0.005074082873761654,
-0.07827994227409363,
0.0521697998046875,
-0.06823016703128815,
0.08491257578134537,
-0.1509498655796051,
0.11561037600040436,
0.061694223433732986,
0.005649690981954336,
-0.043289005756378174,
-0.05039950832724571,
0.04929303377866745,
0.02856380119919777,
-0.08540303260087967,
-0.08779382705688477,
-0.11135072261095047,
-0.04295514523983002,
-0.07339957356452942,
-0.020091285929083824,
-0.06009935215115547,
-0.018592648208141327,
-0.15128980576992035,
0.011191206984221935,
-0.03268490359187126,
0.12019525468349457,
0.12039797753095627,
-0.020448220893740654,
0.04011854529380798,
-0.1739470660686493,
0.013399132527410984,
0.05131117254495621,
-0.04280964657664299,
-0.09528150409460068
] |
null | null |
transformers
|
# FinBERT fine-tuned with the FinnSentiment dataset
This is a FinBERT model fine-tuned with the [FinnSentiment dataset](https://arxiv.org/pdf/2012.02613.pdf). 90% of sentences were used for training and 10% for evaluation.
## Evaluation results
|Metric|Score|
|--|--|
|Accuracy|0.8639028475711893|
|F1-score|0.8643024701696561|
|Precision|0.8653866541244811|
|Recall|0.8639028475711893|
|Matthews|0.6764924917164834|
## License
FinBERT-FinnSentiment is licensed under the [CC BY 4.0 License](https://creativecommons.org/licenses/by/4.0/deed.en) (same as FinBERT and the FinnSentiment dataset).
|
{"language": "fi", "license": "cc-by-4.0"}
|
text-classification
|
fergusq/finbert-finnsentiment
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"fi",
"arxiv:2012.02613",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2012.02613"
] |
[
"fi"
] |
TAGS
#transformers #pytorch #safetensors #bert #text-classification #fi #arxiv-2012.02613 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
FinBERT fine-tuned with the FinnSentiment dataset
=================================================
This is a FinBERT model fine-tuned with the FinnSentiment dataset. 90% of sentences were used for training and 10% for evaluation.
Evaluation results
------------------
!URL
License
-------
FinBERT-FinnSentiment is licensed under the CC BY 4.0 License (same as FinBERT and the FinnSentiment dataset).
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #fi #arxiv-2012.02613 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
61
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bert #text-classification #fi #arxiv-2012.02613 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
-0.07074271887540817,
0.1043623685836792,
-0.006278748158365488,
0.046398576349020004,
0.12059628963470459,
0.005458899307996035,
0.1450931876897812,
0.0793006643652916,
0.04170950502157211,
-0.01655121147632599,
0.15116415917873383,
0.24215225875377655,
-0.017093593254685402,
0.14331147074699402,
-0.13538865745067596,
-0.1949196308851242,
0.06379783898591995,
0.047832317650318146,
0.03236619383096695,
0.11127027124166489,
0.12005003541707993,
-0.07536625117063522,
0.06056512892246246,
-0.029500572010874748,
-0.12850743532180786,
0.00561158312484622,
0.08329801261425018,
-0.1338895559310913,
0.12008535861968994,
0.058767758309841156,
0.1431434601545334,
0.09094791859388351,
-0.010634674690663815,
-0.14248427748680115,
0.02540016360580921,
-0.015264094807207584,
-0.10475098341703415,
0.06461168080568314,
0.09635281562805176,
-0.040653910487890244,
0.041922617703676224,
0.047876521944999695,
-0.004216212313622236,
0.05420336499810219,
-0.11650311946868896,
-0.0849558487534523,
-0.07212138175964355,
0.10645899176597595,
0.08085799217224121,
0.08065704256296158,
0.019003113731741905,
0.16506046056747437,
-0.08007381856441498,
0.10076391696929932,
0.08353759348392487,
-0.367742121219635,
-0.0010109146824106574,
0.0865483507514,
0.03220474347472191,
0.016638409346342087,
-0.044925421476364136,
0.04744303971529007,
0.06987771391868591,
-0.002614782890304923,
0.031174035742878914,
-0.06526535004377365,
-0.10202645510435104,
0.007784956134855747,
-0.07830189168453217,
-0.03890659287571907,
0.1972433179616928,
-0.029916996136307716,
0.007616093382239342,
-0.0230194590985775,
-0.07168407738208771,
-0.015436410903930664,
-0.008619073778390884,
0.024051489308476448,
-0.01561423484236002,
0.046218276023864746,
0.04904455319046974,
0.026302753016352654,
-0.149550661444664,
0.0009570135152898729,
-0.20752619206905365,
0.16446882486343384,
0.008243273943662643,
0.0581035278737545,
-0.11698577553033829,
0.05432150885462761,
-0.016618339344859123,
-0.09272322058677673,
0.02226351574063301,
-0.09104248881340027,
0.10162078589200974,
-0.0296144001185894,
-0.04442151263356209,
0.049238573759794235,
0.08648194372653961,
0.15019384026527405,
-0.02954678051173687,
0.007587640080600977,
-0.046393752098083496,
0.11180588603019714,
-0.03409462422132492,
0.029780440032482147,
0.03360237926244736,
0.03211415186524391,
0.1029139831662178,
-0.051492657512426376,
0.05909283086657524,
-0.03227897360920906,
-0.160806342959404,
-0.02416400983929634,
0.03862007334828377,
0.11454067379236221,
0.013662733137607574,
0.07560654729604721,
-0.050086382776498795,
0.03292026370763779,
0.17201471328735352,
-0.06302247196435928,
0.011886829510331154,
0.01810828037559986,
0.03580337017774582,
-0.007519731298089027,
0.015913011506199837,
0.02070789411664009,
-0.055859629064798355,
0.15250791609287262,
-0.05811484530568123,
0.003306036349385977,
-0.0012286456767469645,
-0.07226062566041946,
0.07183679938316345,
-0.09328675270080566,
0.04678503796458244,
-0.21567828953266144,
-0.09686805307865143,
0.024833980947732925,
0.0026878754142671824,
0.011270358227193356,
-0.06840067356824875,
0.019906094297766685,
-0.02765078842639923,
0.02270451933145523,
-0.08095479756593704,
-0.07456734776496887,
-0.0829448476433754,
0.11050869524478912,
-0.020342599600553513,
0.026764987036585808,
-0.1626507192850113,
0.023332245647907257,
-0.09384893625974655,
-0.02557964436709881,
-0.060497112572193146,
-0.04713434353470802,
-0.07784012705087662,
0.13766400516033173,
-0.02137025073170662,
-0.05278102308511734,
0.0031754670199006796,
0.028838537633419037,
-0.03299179673194885,
0.16266986727714539,
-0.0868707001209259,
-0.05809537321329117,
0.18100100755691528,
-0.1518547385931015,
-0.1919063925743103,
0.09247097373008728,
0.011101177893579006,
-0.02235986851155758,
0.08443167060613632,
0.1607116013765335,
0.05271334573626518,
-0.0923633798956871,
0.012953861616551876,
0.11074459552764893,
-0.06790196895599365,
-0.15456199645996094,
0.03254792094230652,
-0.004554698709398508,
-0.13852038979530334,
0.03427949920296669,
-0.02525174245238304,
0.05428009852766991,
-0.03685225918889046,
-0.05746304243803024,
-0.03719807043671608,
-0.040577951818704605,
0.06566020101308823,
0.02562335878610611,
0.06511855870485306,
-0.09443122148513794,
0.0006612924044020474,
-0.04245160520076752,
0.012977326288819313,
0.04419251158833504,
0.024824021384119987,
-0.09312403202056885,
0.12862488627433777,
-0.0033105737529695034,
0.007930737920105457,
-0.15072953701019287,
-0.030441511422395706,
-0.014350167475640774,
0.09509500116109848,
0.0034827941562980413,
0.053466763347387314,
0.04051684960722923,
-0.001198116224259138,
-0.02736065909266472,
-0.033005278557538986,
0.15303315222263336,
0.03889263793826103,
-0.06022843345999718,
-0.11146719008684158,
0.058771345764398575,
-0.045946795493364334,
0.05297522246837616,
-0.14451907575130463,
0.021906739100813866,
0.09077957272529602,
0.08814112097024918,
-0.025080738589167595,
0.08553198724985123,
-0.03476230055093765,
0.04380197823047638,
-0.06335697323083878,
0.033393848687410355,
0.11940270662307739,
0.038626957684755325,
-0.06747191399335861,
0.1906302124261856,
-0.13983391225337982,
0.34616753458976746,
0.2098294049501419,
-0.2077997326850891,
-0.016497092321515083,
-0.048250991851091385,
-0.006027663126587868,
0.018800344318151474,
0.01270280685275793,
0.0321851409971714,
0.04516378790140152,
-0.019824495539069176,
0.19192181527614594,
-0.06772179156541824,
-0.01730082929134369,
0.020897675305604935,
-0.05794063210487366,
-0.03782336041331291,
0.08757883310317993,
0.0818096399307251,
-0.2161003053188324,
0.17733970284461975,
0.2593190371990204,
-0.008451641537249088,
0.07160479575395584,
-0.023319965228438377,
0.032290540635585785,
0.03903978690505028,
0.0014638458378612995,
0.005997702479362488,
0.003866740269586444,
-0.060137178748846054,
-0.0005446984432637691,
0.07461974769830704,
-0.0006178183248266578,
0.028414001688361168,
-0.16043105721473694,
-0.059043679386377335,
-0.009853385388851166,
-0.008207723498344421,
-0.0860285609960556,
0.06482779234647751,
0.021338675171136856,
0.14313249289989471,
-0.026306888088583946,
-0.12025167793035507,
0.10904137045145035,
0.0014181324513629079,
-0.12205129861831665,
0.18732154369354248,
-0.13257120549678802,
-0.2709187865257263,
-0.1454779952764511,
-0.1559039205312729,
-0.012640021741390228,
0.04545939341187477,
0.11590591818094254,
-0.04738107696175575,
-0.060243524610996246,
-0.026645855978131294,
-0.10022860020399094,
-0.036776021122932434,
0.022239724174141884,
-0.04039403423666954,
0.06483953446149826,
0.002380412071943283,
-0.0855664312839508,
-0.05747024342417717,
-0.014821469783782959,
-0.04336372762918472,
0.13557758927345276,
-0.06439600139856339,
0.09430009871721268,
0.11445273458957672,
-0.024682529270648956,
0.024962928146123886,
-0.06922992318868637,
0.1531110256910324,
-0.05286826565861702,
-0.020942213013768196,
0.21189601719379425,
-0.07360640168190002,
0.08038653433322906,
0.16571952402591705,
0.042770709842443466,
-0.060152072459459305,
0.02010812982916832,
-0.08552411943674088,
-0.06052431836724281,
-0.23212295770645142,
-0.12768390774726868,
-0.07596899569034576,
0.07871754467487335,
0.07908094674348831,
0.08218871057033539,
0.09549518674612045,
0.08522160351276398,
-0.01475648581981659,
-0.011011523194611073,
0.03502928838133812,
0.09675823897123337,
0.2549041509628296,
0.008415330201387405,
0.13837824761867523,
-0.08943170309066772,
-0.06633943319320679,
0.09999057650566101,
0.03290081024169922,
0.09425829350948334,
0.09376215934753418,
-0.03880556672811508,
0.05608048662543297,
0.13479351997375488,
0.15850131213665009,
0.11507951468229294,
0.03629238158464432,
-0.030595501884818077,
-0.009794050827622414,
-0.02979508973658085,
-0.03639188036322594,
0.024150235578417778,
-0.0535786896944046,
-0.11122114211320877,
-0.07944049686193466,
-0.13139793276786804,
0.06366061419248581,
0.07915858179330826,
0.05906464159488678,
-0.23843058943748474,
-0.0036411266773939133,
0.07024072855710983,
0.019116220995783806,
-0.07351277023553848,
0.08438655734062195,
-0.029017271474003792,
-0.0842670202255249,
0.12047035992145538,
-0.04799588397145271,
0.09997634589672089,
-0.03173407167196274,
0.0544683001935482,
-0.033220384269952774,
-0.12217731773853302,
0.022433390840888023,
0.10845091193914413,
-0.28616034984588623,
0.2501465678215027,
0.01887289062142372,
-0.015688011422753334,
-0.052815936505794525,
-0.026078278198838234,
0.03746016323566437,
0.2570573091506958,
0.12802380323410034,
0.0018820639234036207,
-0.08989229053258896,
-0.15901924669742584,
-0.04272191971540451,
0.01906212791800499,
0.07694466412067413,
0.006811900995671749,
-0.019293952733278275,
-0.04611637070775032,
-0.01628873497247696,
-0.0002959085686597973,
-0.039737019687891006,
-0.02853742614388466,
-0.13824236392974854,
0.055993951857089996,
0.09454870223999023,
0.08258339017629623,
-0.04205092042684555,
-0.05612349510192871,
-0.1655026227235794,
0.1851685792207718,
-0.1789713203907013,
-0.06623032689094543,
-0.0984569564461708,
-0.11111850291490555,
0.0021514897234737873,
-0.06707938760519028,
0.07625589519739151,
-0.06768288463354111,
0.008889100514352322,
-0.08463508635759354,
-0.16253946721553802,
0.11786206066608429,
-0.131697416305542,
-0.07285914570093155,
-0.05026162788271904,
0.1447104811668396,
-0.1044854074716568,
0.012092161923646927,
0.04254868999123573,
0.02915591187775135,
-0.07835763692855835,
-0.1044110581278801,
0.0042512137442827225,
-0.010304361581802368,
0.11747129261493683,
-0.009443403221666813,
-0.11466637998819351,
-0.13373416662216187,
-0.026460712775588036,
-0.04503853619098663,
0.21952031552791595,
0.29248958826065063,
-0.08967375755310059,
0.11645007133483887,
0.18010616302490234,
-0.060415346175432205,
-0.3239750862121582,
-0.11100344359874725,
-0.13418544828891754,
-0.06286675482988358,
-0.030471673235297203,
-0.07674804329872131,
0.06616607308387756,
0.043063934892416,
-0.07719185203313828,
0.12089245766401291,
-0.14207503199577332,
-0.09851471334695816,
0.1856117695569992,
0.02154366672039032,
0.3327651619911194,
-0.14202052354812622,
-0.08768591284751892,
-0.06000358611345291,
-0.14044471085071564,
0.13343439996242523,
-0.030364444479346275,
0.051103442907333374,
-0.010798578150570393,
0.007267214357852936,
0.001266229897737503,
-0.053431592881679535,
0.10807426273822784,
-0.0529869981110096,
0.0500190369784832,
-0.12663285434246063,
-0.08860290050506592,
0.029455559328198433,
-0.015837816521525383,
0.01744050532579422,
-0.08183266222476959,
0.040383100509643555,
-0.09137121587991714,
-0.027667731046676636,
-0.052566614001989365,
0.08446726948022842,
-0.004200867377221584,
-0.0431387685239315,
-0.021306628361344337,
-0.017233258113265038,
-0.01843353733420372,
-0.031064586713910103,
0.22153760492801666,
-0.043152742087841034,
0.16993960738182068,
0.1667640507221222,
0.14269854128360748,
-0.12350665032863617,
0.03828267753124237,
-0.0517355315387249,
-0.10582006722688675,
0.060462016612291336,
-0.08783143758773804,
0.048980049788951874,
0.1250205636024475,
-0.04182390496134758,
0.058756861835718155,
0.09579753875732422,
0.04777585342526436,
-0.019564593210816383,
0.15586210787296295,
-0.23489080369472504,
0.04034769535064697,
-0.01866963505744934,
0.09536218643188477,
0.061074595898389816,
0.09409862011671066,
0.1352885663509369,
-0.021350812166929245,
-0.028106406331062317,
0.006970398128032684,
0.013317417353391647,
-0.015213350765407085,
0.05960509181022644,
0.06603699922561646,
0.027226410806179047,
-0.11097204685211182,
0.06174527853727341,
0.0259476900100708,
-0.15335947275161743,
-0.03369821608066559,
0.07917152345180511,
-0.17124788463115692,
-0.1315019577741623,
-0.026862312108278275,
0.040110137313604355,
-0.15074406564235687,
-0.1091003268957138,
-0.05394360050559044,
-0.15110981464385986,
0.053122133016586304,
0.23138107359409332,
0.09241433441638947,
0.06283695250749588,
0.009724192321300507,
-0.06628880649805069,
-0.021596848964691162,
0.028893722221255302,
-0.05994907766580582,
0.045507341623306274,
-0.14319956302642822,
0.05433565005660057,
-0.007015409413725138,
0.09501933306455612,
-0.07515900582075119,
0.009730958379805088,
-0.1367054432630539,
0.028605233877897263,
-0.05751592665910721,
0.04076853394508362,
-0.07442336529493332,
-0.007184928748756647,
-0.011079986579716206,
-0.06485582143068314,
-0.03568093478679657,
-0.023853430524468422,
-0.09216051548719406,
0.037606753408908844,
-0.0016493572620674968,
0.0416400283575058,
-0.09563501179218292,
-0.055580150336027145,
0.047098513692617416,
-0.016783343628048897,
0.1046050563454628,
0.0761178731918335,
-0.07771732658147812,
0.07718051970005035,
-0.18059512972831726,
-0.07068981975317001,
0.12822763621807098,
0.020390082150697708,
0.029548360034823418,
0.01571624167263508,
0.02687649615108967,
0.12220696359872818,
-0.029411928728222847,
0.07056375592947006,
0.09191861003637314,
-0.1116727814078331,
0.004895064979791641,
-0.010901281610131264,
-0.12495690584182739,
-0.032781124114990234,
-0.05979784205555916,
0.12262355536222458,
-0.014485474675893784,
0.1938188225030899,
-0.08400819450616837,
0.03366199880838394,
-0.05087275430560112,
0.015400512143969536,
-0.034510452300310135,
-0.19307594001293182,
-0.16412897408008575,
-0.05822133272886276,
0.011124669574201107,
-0.010898958891630173,
0.24157097935676575,
0.008662634529173374,
-0.03354019671678543,
0.08519979566335678,
0.041845060884952545,
-0.01015971228480339,
0.011599669232964516,
0.22613704204559326,
0.05419141426682472,
-0.03267159312963486,
-0.09817115217447281,
0.03861822560429573,
-0.0005756395403295755,
-0.0932866781949997,
0.10415002703666687,
0.0978325679898262,
-0.011069783940911293,
0.04893701151013374,
0.04369695112109184,
-0.0033345618285238743,
-0.08591315895318985,
-0.16225600242614746,
-0.06266317516565323,
0.05599246919155121,
0.001544453320093453,
0.09742910414934158,
0.1609269380569458,
-0.017049066722393036,
-0.01410964410752058,
-0.07340028882026672,
-0.03104553185403347,
-0.17870011925697327,
-0.0990028977394104,
-0.10505956411361694,
-0.08347249776124954,
0.014799927361309528,
-0.05242210254073143,
0.0072674560360610485,
0.07010377943515778,
0.04756230115890503,
-0.05620381608605385,
0.018834928050637245,
-0.027263134717941284,
-0.03969673067331314,
0.06221161037683487,
-0.005308530759066343,
0.02500002458691597,
-0.028787752613425255,
-0.047788962721824646,
-0.07610227167606354,
-0.07444087415933609,
-0.02228902094066143,
0.029648112133145332,
-0.0012608757242560387,
0.037685003131628036,
-0.10004530102014542,
-0.08407430350780487,
-0.026890529319643974,
0.07492098212242126,
0.019489526748657227,
0.16594499349594116,
0.003541095880791545,
0.03328508138656616,
0.07509125024080276,
0.1824493259191513,
-0.05120851844549179,
-0.11478231102228165,
-0.01763494499027729,
0.268440306186676,
0.03534314036369324,
0.09742670506238937,
0.001563991536386311,
-0.005011184141039848,
-0.002137098927050829,
0.295202374458313,
0.26746663451194763,
-0.060166627168655396,
0.05805198848247528,
-0.013511033728718758,
0.027767842635512352,
0.09496545791625977,
0.11780855059623718,
0.10530523210763931,
0.24225181341171265,
-0.06580852717161179,
-0.04093404486775398,
-0.04104244336485863,
0.05279866233468056,
-0.10842593014240265,
0.08861760795116425,
-0.01660950668156147,
-0.053763601928949356,
-0.056596625596284866,
0.09285510331392288,
-0.08235899358987808,
0.08920599520206451,
0.00867441389709711,
-0.14257103204727173,
-0.03611725941300392,
0.012819601222872734,
0.1446944624185562,
-0.008330028504133224,
0.03979303315281868,
-0.04337704926729202,
-0.05812786892056465,
0.012551575899124146,
-0.005660370923578739,
-0.16105471551418304,
-0.003661832306534052,
0.035952646285295486,
-0.002821034286171198,
0.09291094541549683,
0.0076299309730529785,
0.11849743127822876,
0.0803941860795021,
0.0528525784611702,
-0.07474478334188461,
0.11828083544969559,
0.021677520126104355,
-0.08450602740049362,
0.032245200127363205,
-0.06709827482700348,
-0.006505848374217749,
-0.03576981648802757,
0.05608944594860077,
-0.11308006197214127,
0.07059606164693832,
-0.041230663657188416,
-0.10353749245405197,
-0.044698480516672134,
0.06768415123224258,
-0.05590090528130531,
0.06255866587162018,
0.03887259587645531,
-0.025135301053524017,
-0.04550786316394806,
-0.04817911610007286,
-0.01671639084815979,
0.024267880246043205,
-0.1349005550146103,
-0.028970913961529732,
-0.09170505404472351,
-0.020892305299639702,
0.1045571118593216,
0.04662511870265007,
-0.1726902574300766,
-0.002053714357316494,
-0.11377237737178802,
0.029541870579123497,
-0.18153497576713562,
0.04947223886847496,
0.07548246532678604,
-0.005252008326351643,
-0.025395112112164497,
-0.08556065708398819,
0.033699631690979004,
0.050686173141002655,
-0.07823415100574493,
-0.10458377748727798
] |
null | null | null |
<br />
<p align="center">
<a href="https://github.com/FernandoPerezLara/image-preprocessing-layer">
<img src="https://huggingface.co/fernandoperlar/preprocessing_image/resolve/main/duck.png" alt="Logo" width="100" height="146">
</a>
<h3 align="center">Image Preprocessing Model</h3>
<p align="center">
Image preprocessing in a convolutional model
<br />
<a href="https://github.com/FernandoPerezLara/image-preprocessing-layer"><strong>Read more about the model »</strong></a>
<br />
<br />
<a href="https://github.com/FernandoPerezLara/image-preprocessing-layer">View Code</a>
·
<a href="https://github.com/FernandoPerezLara/image-preprocessing-layer/issues">Report Bug</a>
·
<a href="https://github.com/FernandoPerezLara/image-preprocessing-layer/discussions">Start a discussion</a>
</p>
</p>
<br />
The main objective of this project is to apply preprocessing to an image dataset while the model is being trained.
The solution has been taken because we do not want to apply preprocessing to the data before training (i.e. create a copy of the data but already preprocessed) because we want to apply data augmentation while the model trains.
The use of `Lambda` layers has been discarded because they do not allow the use of external libraries that do not work with tensors, since we want to use the functions provided by *OpenCV* and *NumPy*.
## Preprocessing
In this example found in this repository we wanted to divide the images from HSV color masks, where it is divided into:
* **Warm zones**: red and white colors are obtained.
* **Warm zones**: The green color is obtained.
* **Cold zones**: The color blue is obtained.
Within the code you can find the declaration of these filters as:
```python
filters = {
"original": lambda x: x,
"red": lambda x: data.getImageTensor(x, (330, 0, 0), (360, 255, 255)) + data.getImageTensor(x, (0, 0, 0), (50, 255, 255)),
"green": lambda x: data.getImageTensor(x, (60, 0, 0), (130, 255, 255)),
"blue": lambda x: data.getImageTensor(x, (180, 0, 0), (270, 255, 255)),
}
```
On the other hand, the preprocessing functions are located inside `scripts/Data.py` file as follows:
```python
def detectColor(self, image, lower, upper):
if tf.is_tensor(image):
temp_image = image.numpy().copy() # Used for training
else:
temp_image = image.copy() # Used for displaying the image
hsv_image = temp_image.copy()
hsv_image = cv.cvtColor(hsv_image, cv.COLOR_RGB2HSV)
mask = cv.inRange(hsv_image, lower, upper)
result = temp_image.copy()
result[np.where(mask == 0)] = 0
return result
def getImageTensor(self, images, lower, upper):
results = []
for img in images:
results.append(np.expand_dims(self.detectColor(img, lower, upper), axis=0))
return np.concatenate(results, axis=0)
```
## Model
The model used to solve our problem was a *CNN* with a preprocessing layer:
This model can be found in the `scripts/Model.py` file in the following function:
```python
def create_model():
class FilterLayer(layers.Layer):
def __init__(self, filter, **kwargs):
self.filter = filter
super(FilterLayer, self).__init__(name="filter_layer", **kwargs)
def call(self, image):
shape = image.shape
[image, ] = tf.py_function(self.filter, [image], [tf.float32])
image = backend.stop_gradient(image)
image.set_shape(shape)
return image
def get_config(self):
return super().get_config()
model = models.Sequential()
model.add(layers.Input(shape=(215, 538, 3)))
model.add(FilterLayer(filter=self.filter))
model.add(layers.Conv2D(32, (3, 3), activation="relu"))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Conv2D(32, (3, 3), activation="relu"))
model.add(layers.GlobalAveragePooling2D())
model.add(layers.Dropout(rate=0.4))
model.add(layers.Dense(32, activation="relu"))
model.add(layers.Dropout(rate=0.4))
model.add(layers.Dense(2, activation="softmax"))
return model
```
## Contributors
This work has been possible thanks to:
- [Fernando Pérez Lara](https://www.linkedin.com/in/fernandoperezlara/) ([**@FernandoPerezLara**](https://github.com/FernandoPerezLara)) for having developed the model to make this idea come true.
## License
Copyright (c) 2021 Fernando Pérez Lara.
Licensed and distributed under the [MIT](LICENSE.txt) license.
|
{}
| null |
fernandoperlar/preprocessing_image
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
<br />
<p align="center">
<a href="URL
<img src="URL alt="Logo" width="100" height="146">
</a>
<h3 align="center">Image Preprocessing Model</h3>
<p align="center">
Image preprocessing in a convolutional model
<br />
<a href="URL more about the model »</strong></a>
<br />
<br />
<a href="URL Code</a>
·
<a href="URL Bug</a>
·
<a href="URL a discussion</a>
</p>
</p>
<br />
The main objective of this project is to apply preprocessing to an image dataset while the model is being trained.
The solution has been taken because we do not want to apply preprocessing to the data before training (i.e. create a copy of the data but already preprocessed) because we want to apply data augmentation while the model trains.
The use of 'Lambda' layers has been discarded because they do not allow the use of external libraries that do not work with tensors, since we want to use the functions provided by *OpenCV* and *NumPy*.
## Preprocessing
In this example found in this repository we wanted to divide the images from HSV color masks, where it is divided into:
* Warm zones: red and white colors are obtained.
* Warm zones: The green color is obtained.
* Cold zones: The color blue is obtained.
Within the code you can find the declaration of these filters as:
On the other hand, the preprocessing functions are located inside 'scripts/URL' file as follows:
## Model
The model used to solve our problem was a *CNN* with a preprocessing layer:
!Model
This model can be found in the 'scripts/URL' file in the following function:
## Contributors
This work has been possible thanks to:
- Fernando Pérez Lara (@FernandoPerezLara) for having developed the model to make this idea come true.
## License
Copyright (c) 2021 Fernando Pérez Lara.
Licensed and distributed under the MIT license.
|
[
"## Preprocessing\nIn this example found in this repository we wanted to divide the images from HSV color masks, where it is divided into:\n* Warm zones: red and white colors are obtained.\n* Warm zones: The green color is obtained.\n* Cold zones: The color blue is obtained.\n\nWithin the code you can find the declaration of these filters as:\n\n\nOn the other hand, the preprocessing functions are located inside 'scripts/URL' file as follows:",
"## Model\nThe model used to solve our problem was a *CNN* with a preprocessing layer:\n\n!Model\n\nThis model can be found in the 'scripts/URL' file in the following function:",
"## Contributors\nThis work has been possible thanks to:\n- Fernando Pérez Lara (@FernandoPerezLara) for having developed the model to make this idea come true.",
"## License\nCopyright (c) 2021 Fernando Pérez Lara.\n\nLicensed and distributed under the MIT license."
] |
[
"TAGS\n#region-us \n",
"## Preprocessing\nIn this example found in this repository we wanted to divide the images from HSV color masks, where it is divided into:\n* Warm zones: red and white colors are obtained.\n* Warm zones: The green color is obtained.\n* Cold zones: The color blue is obtained.\n\nWithin the code you can find the declaration of these filters as:\n\n\nOn the other hand, the preprocessing functions are located inside 'scripts/URL' file as follows:",
"## Model\nThe model used to solve our problem was a *CNN* with a preprocessing layer:\n\n!Model\n\nThis model can be found in the 'scripts/URL' file in the following function:",
"## Contributors\nThis work has been possible thanks to:\n- Fernando Pérez Lara (@FernandoPerezLara) for having developed the model to make this idea come true.",
"## License\nCopyright (c) 2021 Fernando Pérez Lara.\n\nLicensed and distributed under the MIT license."
] |
[
6,
110,
43,
37,
21
] |
[
"passage: TAGS\n#region-us \n## Preprocessing\nIn this example found in this repository we wanted to divide the images from HSV color masks, where it is divided into:\n* Warm zones: red and white colors are obtained.\n* Warm zones: The green color is obtained.\n* Cold zones: The color blue is obtained.\n\nWithin the code you can find the declaration of these filters as:\n\n\nOn the other hand, the preprocessing functions are located inside 'scripts/URL' file as follows:## Model\nThe model used to solve our problem was a *CNN* with a preprocessing layer:\n\n!Model\n\nThis model can be found in the 'scripts/URL' file in the following function:## Contributors\nThis work has been possible thanks to:\n- Fernando Pérez Lara (@FernandoPerezLara) for having developed the model to make this idea come true.## License\nCopyright (c) 2021 Fernando Pérez Lara.\n\nLicensed and distributed under the MIT license."
] |
[
-0.15608875453472137,
-0.05257842317223549,
0.002983200130984187,
0.05010061711072922,
0.08410165458917618,
0.011202559806406498,
0.15150055289268494,
0.03484790399670601,
0.10241308063268661,
0.0698520615696907,
0.08416642993688583,
0.09638826549053192,
0.0587843656539917,
-0.003764333901926875,
0.040134113281965256,
-0.23202061653137207,
0.0165336262434721,
-0.06554718315601349,
-0.1101100817322731,
0.03815435618162155,
0.05381498858332634,
-0.036982301622629166,
0.11271597445011139,
0.0359090194106102,
-0.1439070999622345,
-0.07059735059738159,
-0.04551935940980911,
-0.012105877511203289,
0.1503628045320511,
0.049955714493989944,
0.06130620837211609,
0.07192687690258026,
0.11755283176898956,
-0.18668414652347565,
0.027757925912737846,
-0.010570020414888859,
-0.0073732915334403515,
0.09430015087127686,
0.23540768027305603,
-0.011878647841513157,
0.17577655613422394,
0.016698531806468964,
-0.06013065204024315,
0.06667982786893845,
-0.11298277229070663,
-0.1328621357679367,
-0.07501159608364105,
-0.011106760241091251,
0.05675945058465004,
-0.016375986859202385,
0.0247428510338068,
0.09360361844301224,
-0.10194287449121475,
0.016516895964741707,
0.07369983941316605,
-0.23602502048015594,
0.015825549140572548,
0.07671736180782318,
0.045952193439006805,
-0.003553721820935607,
0.05921065807342529,
0.15034818649291992,
0.13119088113307953,
0.015327886678278446,
-0.06187457591295242,
-0.03766298294067383,
0.04902670159935951,
-0.004494286607950926,
-0.1262129694223404,
-0.034880541265010834,
0.054464250802993774,
-0.029552627354860306,
-0.06492898613214493,
-0.00770873436704278,
-0.106983482837677,
0.07477639615535736,
0.07237004488706589,
0.09684217721223831,
0.020649341866374016,
0.07600695639848709,
0.02147473208606243,
-0.030085129663348198,
-0.13344943523406982,
-0.09240406006574631,
0.010342893190681934,
0.25863325595855713,
0.021187806501984596,
0.08512406796216965,
-0.11536043137311935,
0.14570866525173187,
-0.021637337282299995,
-0.1048131063580513,
0.0062009356915950775,
-0.12746837735176086,
0.024520650506019592,
0.05630608648061752,
-0.009118457324802876,
-0.0994531512260437,
0.06422662734985352,
0.14708800613880157,
0.038683634251356125,
0.019319264218211174,
0.01507234014570713,
0.09842685610055923,
0.12965261936187744,
0.02431226707994938,
-0.1390441507101059,
0.015358022414147854,
-0.0049966671504080296,
-0.14161179959774017,
0.06455998122692108,
-0.09913589805364609,
-0.11122612655162811,
-0.06965946406126022,
-0.08865076303482056,
0.10259503871202469,
0.05794985964894295,
-0.008532960899174213,
-0.06527259200811386,
-0.04966222867369652,
0.18343321979045868,
-0.013750186190009117,
-0.04148247465491295,
0.008118392899632454,
0.0038949043955653906,
-0.011432185769081116,
0.03468545153737068,
0.0029793984722346067,
-0.002025796566158533,
0.06616903841495514,
-0.1083894819021225,
-0.007679058704525232,
-0.08524268865585327,
-0.07999736815690994,
-0.02396630123257637,
-0.06239556148648262,
-0.015688257291913033,
-0.07612384855747223,
-0.045518696308135986,
-0.007771106902509928,
0.12042676657438278,
0.0027776893693953753,
0.13852424919605255,
0.0733596682548523,
0.008942368440330029,
-0.014168520458042622,
0.017556605860590935,
-0.04274026304483414,
-0.07613123953342438,
0.023590488359332085,
0.006795584689825773,
0.12913942337036133,
-0.11871732771396637,
-0.0019520909991115332,
-0.037975311279296875,
0.11745419353246689,
-0.11106002330780029,
0.023911578580737114,
-0.04768642783164978,
0.09620419889688492,
-0.012743519619107246,
-0.10006589442491531,
0.009020775556564331,
0.04305444285273552,
0.003523119492456317,
0.16009078919887543,
-0.1700749695301056,
-0.03617604821920395,
0.08831323683261871,
-0.18900027871131897,
-0.0015014918753877282,
0.06123030185699463,
0.0012652457226067781,
-0.03106321208178997,
-0.0013251688797026873,
0.29032450914382935,
-0.01935613341629505,
-0.1107148602604866,
0.09495431929826736,
0.1054232195019722,
-0.06752736121416092,
-0.11266612261533737,
0.1139724925160408,
-0.06968261301517487,
-0.17576192319393158,
0.006927344016730785,
-0.21015702188014984,
0.0604667104780674,
-0.0740148052573204,
-0.03691904619336128,
0.022670211270451546,
-0.03765453025698662,
-0.00047887390246614814,
-0.03619914501905441,
0.0019314831588417292,
0.007311596535146236,
0.031193198636174202,
-0.18935826420783997,
0.0997028723359108,
-0.05952144414186478,
0.034856028854846954,
-0.03936963900923729,
0.12326579540967941,
-0.021508095785975456,
-0.06621742248535156,
-0.14118066430091858,
0.04449068754911423,
0.05517728254199028,
-0.09100606292486191,
0.03341588005423546,
0.032861270010471344,
-0.04649105668067932,
0.1988869160413742,
0.014555179513990879,
0.00711626885458827,
0.021929483860731125,
-0.03953960910439491,
0.03900153189897537,
-0.15285935997962952,
-0.1020546406507492,
-0.04151378199458122,
0.04570554941892624,
-0.05199158191680908,
-0.026499107480049133,
0.007862874306738377,
0.020396795123815536,
0.006034775637090206,
0.024410834535956383,
0.0643361359834671,
0.0030839003156870604,
-0.06802685558795929,
-0.06748819351196289,
0.03473362699151039,
-0.006771004758775234,
-0.03640889748930931,
0.13368169963359833,
-0.1350698173046112,
-0.13736797869205475,
0.0968429446220398,
-0.07828913629055023,
-0.014094545505940914,
-0.02578899823129177,
-0.03871842473745346,
0.054920680820941925,
0.05994999036192894,
0.07713302224874496,
0.07075128704309464,
-0.031637296080589294,
0.015260443091392517,
-0.07998525351285934,
0.08438251167535782,
-0.030998360365629196,
-0.11480177193880081,
-0.05297097936272621,
0.0009111440158449113,
0.11286157369613647,
-0.14099125564098358,
0.036428604274988174,
0.024870198220014572,
0.13334141671657562,
0.1237599179148674,
0.05357810854911804,
-0.04242938384413719,
-0.040276627987623215,
0.073459692299366,
0.0166925135999918,
0.19189050793647766,
-0.003340918105095625,
-0.017226701602339745,
0.008059385232627392,
-0.012107573449611664,
0.10530222952365875,
-0.15546618402004242,
-0.016217101365327835,
0.0009706161799840629,
-0.011316969990730286,
-0.019853224977850914,
0.006000257097184658,
-0.16630442440509796,
0.003705225884914398,
0.0014453039038926363,
-0.003967061173170805,
0.04188895598053932,
-0.05011659115552902,
-0.08290208876132965,
0.19509650766849518,
-0.17282621562480927,
-0.11149559170007706,
-0.18591853976249695,
0.07030872255563736,
-0.002532635349780321,
0.12666253745555878,
0.038801103830337524,
-0.13096512854099274,
-0.08026330918073654,
-0.03647198900580406,
0.10419375449419022,
-0.04541783779859543,
-0.04511350393295288,
-0.010067390277981758,
0.003339262679219246,
-0.09442855417728424,
-0.10355164855718613,
-0.017379280179739,
-0.03733748942613602,
0.023924482986330986,
0.12338186055421829,
-0.04151933267712593,
0.14072534441947937,
0.16908764839172363,
0.06603918224573135,
-0.03399158641695976,
0.028052644804120064,
0.13067547976970673,
-0.05485004931688309,
0.01533570047467947,
0.16611698269844055,
-0.014470157213509083,
0.0564555861055851,
0.11213655024766922,
0.018995041027665138,
-0.07870938628911972,
0.010907291434705257,
-0.13808919489383698,
-0.11469916254281998,
-0.21529772877693176,
-0.16789212822914124,
-0.06767553836107254,
-0.028709949925541878,
-0.012672358192503452,
0.032318469136953354,
-0.0397307351231575,
0.12403053790330887,
-0.05302343890070915,
-0.05739552527666092,
-0.06657514721155167,
0.06848105043172836,
-0.07590451091527939,
-0.013744217343628407,
-0.01217624731361866,
-0.03198807314038277,
-0.00020607348415069282,
0.15289759635925293,
0.06244262680411339,
0.2129417210817337,
0.06758511811494827,
0.19087791442871094,
0.12084983289241791,
0.17593936622142792,
0.12183880805969238,
0.0898672416806221,
0.08036186546087265,
0.042961012572050095,
-0.05756775662302971,
-0.07236181944608688,
-0.046076346188783646,
0.04153301939368248,
-0.04265141114592552,
-0.16983994841575623,
-0.0009337240480817854,
0.07415991276502609,
0.03431585431098938,
0.11233759671449661,
0.06735099852085114,
-0.24630193412303925,
0.02470717392861843,
0.06620530039072037,
0.1458635777235031,
-0.06645827740430832,
0.025834446772933006,
0.23310276865959167,
-0.0735003873705864,
0.04207071289420128,
0.007522535510361195,
0.06310690939426422,
-0.11596221476793289,
0.04300765320658684,
-0.01084220688790083,
-0.0008725532097741961,
-0.01802910305559635,
0.06577762216329575,
-0.03809117153286934,
0.19944362342357635,
0.024434538558125496,
0.002303351880982518,
-0.05242849141359329,
0.008876734413206577,
0.021633896976709366,
0.17042827606201172,
0.0881384015083313,
0.03517650440335274,
-0.18213213980197906,
-0.0474303774535656,
-0.07102327048778534,
0.038265377283096313,
0.14508382976055145,
-0.034306712448596954,
-0.04950826242566109,
0.04617562144994736,
0.030362624675035477,
-0.04906973987817764,
0.07306662946939468,
-0.16350829601287842,
-0.07169809192419052,
0.07361026853322983,
-0.1506967544555664,
0.05886683985590935,
-0.08161421120166779,
0.029823536053299904,
0.0747302919626236,
0.05776919797062874,
-0.05676089972257614,
-0.101817786693573,
-0.1330377608537674,
-0.15597453713417053,
0.07267502695322037,
-0.07469165325164795,
0.05391982942819595,
-0.011829937808215618,
0.05375641584396362,
-0.06852688640356064,
-0.10454915463924408,
0.11309660226106644,
-0.09952617436647415,
-0.06306784600019455,
-0.062082719057798386,
0.032842233777046204,
0.08038774877786636,
-0.00807640515267849,
0.025376779958605766,
0.01320675853639841,
-0.06785602122545242,
-0.15678860247135162,
0.02728920802474022,
0.02226218953728676,
-0.0019488377729430795,
0.04076233506202698,
-0.08599475771188736,
-0.09031430631875992,
0.01588382199406624,
0.0005615045083686709,
0.017567304894328117,
0.25026172399520874,
-0.06577648222446442,
0.04760514944791794,
0.37802642583847046,
-0.08790574967861176,
-0.28474846482276917,
-0.01414487324655056,
-0.09686288982629776,
0.029546083882451057,
0.08196886628866196,
-0.25025463104248047,
0.04260833188891411,
0.0620511956512928,
-0.01994960382580757,
0.18269219994544983,
-0.2398918718099594,
-0.058708786964416504,
0.08552046865224838,
0.09880297631025314,
0.16509877145290375,
-0.13262130320072174,
-0.0954379066824913,
-0.12552610039710999,
-0.13610437512397766,
0.050933897495269775,
-0.1238045021891594,
0.12144887447357178,
-0.03394762799143791,
-0.02380731701850891,
-0.0023296144790947437,
-0.07544893026351929,
0.2251737266778946,
0.05948355048894882,
0.15991921722888947,
-0.1184946671128273,
-0.015166209079325199,
0.08722526580095291,
-0.003706136951223016,
0.05304466560482979,
-0.020944764837622643,
0.12169284373521805,
0.0386502705514431,
-0.027681173756718636,
-0.05373337119817734,
0.08162801712751389,
-0.03977401927113533,
-0.145080104470253,
-0.0948801189661026,
0.039984025061130524,
-0.03787657618522644,
0.04556124284863472,
0.013875873759388924,
0.006791275925934315,
-0.004141450859606266,
0.09354035556316376,
0.08380629122257233,
0.007198995910584927,
0.023347824811935425,
-0.020132357254624367,
-0.00042045064037665725,
0.13319775462150574,
-0.07988400757312775,
-0.04547632858157158,
0.0775633454322815,
0.043565478175878525,
0.0346941240131855,
0.04010724276304245,
-0.12016472965478897,
0.05663728341460228,
0.10440011322498322,
-0.1367848664522171,
-0.06231900304555893,
0.011378878727555275,
0.029180185869336128,
0.13187913596630096,
0.0559687502682209,
0.09871829301118851,
-0.010266127064824104,
-0.013132537715137005,
-0.02309010922908783,
0.048806313425302505,
-0.07159397751092911,
0.06918945163488388,
0.13152627646923065,
-0.02098464034497738,
-0.07957547903060913,
0.12666530907154083,
0.007485362235456705,
0.13188251852989197,
-0.09555870294570923,
0.04519771784543991,
-0.1523445099592209,
-0.13141204416751862,
-0.007478979881852865,
0.11100469529628754,
-0.15938808023929596,
-0.08134986460208893,
-0.013510080054402351,
-0.05311669036746025,
-0.09939263761043549,
-0.008999600075185299,
0.04239894077181816,
0.034736424684524536,
-0.0030733037274330854,
-0.031610313802957535,
0.030260320752859116,
-0.09930427372455597,
-0.10812927782535553,
0.07046426087617874,
-0.029019851237535477,
0.05621107667684555,
-0.03353060781955719,
0.06005215272307396,
-0.06389733403921127,
-0.05862661451101303,
-0.09379933029413223,
-0.003034069901332259,
-0.213337704539299,
0.04119051620364189,
-0.08938388526439667,
-0.024565821513533592,
-0.02932608313858509,
0.03097205050289631,
0.029603540897369385,
0.06851613521575928,
-0.09041989594697952,
-0.021913081407546997,
-0.07919340580701828,
-0.008432631380856037,
-0.07048165053129196,
0.008567321114242077,
0.06749049574136734,
-0.03093068115413189,
0.08292106539011002,
-0.027205372229218483,
-0.05263017863035202,
-0.0008162196027114987,
-0.20513999462127686,
-0.12438099831342697,
0.008467698469758034,
-0.0028265563305467367,
0.015434427186846733,
0.04333624988794327,
-0.0437716469168663,
-0.02534310705959797,
0.005831714253872633,
-0.04964029788970947,
0.09542132914066315,
-0.07047805190086365,
0.08451008051633835,
-0.019916603341698647,
-0.07772374153137207,
-0.02293551340699196,
0.005081453360617161,
0.06613844633102417,
0.07087922841310501,
0.004163635428994894,
0.029336752369999886,
0.034661103039979935,
-0.024143481627106667,
-0.04405203089118004,
0.031736183911561966,
0.03751912713050842,
-0.09362741559743881,
-0.009320275858044624,
0.0028999745845794678,
-0.023719418793916702,
0.18117347359657288,
0.045445479452610016,
-0.14875321090221405,
-0.05708452686667442,
0.2046167105436325,
0.04549335688352585,
0.017300685867667198,
0.14028318226337433,
-0.015256124548614025,
0.07428038865327835,
-0.001544001861475408,
0.06279364973306656,
-0.023897560313344002,
-0.07221926748752594,
0.20030391216278076,
0.03794487938284874,
0.06188388913869858,
0.03204542025923729,
0.008266218937933445,
-0.08931268751621246,
-0.163443461060524,
-0.20353148877620697,
0.05309334397315979,
0.062377166002988815,
-0.0059588816948235035,
-0.03462173789739609,
0.14744135737419128,
-0.1243322417140007,
0.03047114983201027,
0.07634896785020828,
-0.0852881446480751,
-0.12610311806201935,
-0.21334242820739746,
0.012618577107787132,
-0.06891199946403503,
0.026641016826033592,
-0.020790619775652885,
-0.021016700193285942,
0.25246065855026245,
0.014330422505736351,
-0.04368991032242775,
0.15072552859783173,
0.014834551140666008,
-0.12237747758626938,
-0.02811424620449543,
0.028312798589468002,
0.005654714070260525,
-0.01819869875907898,
-0.04743362218141556,
-0.04244891181588173,
0.016652749851346016,
0.0031776358373463154,
0.013318216428160667,
0.025870855897665024,
0.04821256175637245,
-0.008674866519868374,
-0.0323990099132061,
-0.04005221650004387,
0.013369143940508366,
-0.03149249404668808,
0.16322149336338043,
-0.020159905776381493,
-0.06700272858142853,
0.05255056172609329,
0.0733109563589096,
0.013525590300559998,
-0.01746608316898346,
0.011595120653510094,
0.2043246477842331,
-0.1155272051692009,
0.08226317167282104,
-0.0408550500869751,
-0.08999022096395493,
-0.01599196344614029,
0.15209899842739105,
0.2524440884590149,
-0.0941653847694397,
-0.01958494447171688,
-0.06941749155521393,
-0.0006034171092323959,
0.02525743469595909,
0.17437659204006195,
-0.04166391119360924,
0.19905945658683777,
-0.07235518842935562,
0.02080940268933773,
-0.05361953377723694,
-0.051330070942640305,
-0.15174701809883118,
-0.010317628271877766,
-0.04439663514494896,
-0.05682707577943802,
-0.04944697767496109,
0.1372140794992447,
-0.08582142740488052,
-0.11569245904684067,
0.2000715285539627,
0.06466659903526306,
-0.006045263726264238,
0.030060121789574623,
0.09548894315958023,
-0.011721797287464142,
0.13267989456653595,
-0.10226956754922867,
0.0022191961761564016,
0.03250768780708313,
0.04928377643227577,
-0.17302322387695312,
-0.06578003615140915,
0.033766649663448334,
-0.0832807794213295,
0.17375634610652924,
0.034700412303209305,
-0.0028272115159779787,
0.02494698390364647,
0.033741582185029984,
-0.11323077231645584,
0.07010751217603683,
0.006787893362343311,
-0.10662472248077393,
-0.051601190119981766,
-0.02843325026333332,
-0.04724963381886482,
-0.08897155523300171,
-0.017964914441108704,
-0.07622845470905304,
0.040025968104600906,
0.12096083909273148,
0.03909454867243767,
-0.11201304197311401,
-0.09217272698879242,
-0.15236856043338776,
0.09748721867799759,
-0.0034185086842626333,
-0.026883769780397415,
-0.07257121801376343,
-0.008313623256981373,
0.038295380771160126,
-0.021952977403998375,
-0.037201423197984695,
-0.01519891619682312,
-0.10679473727941513,
-0.023104693740606308,
0.006975912023335695,
0.044304974377155304,
-0.04437784478068352,
0.044226884841918945,
-0.058123763650655746,
-0.01113586314022541,
-0.0042368629947304726,
0.04331446439027786,
0.11343951523303986,
-0.02225923351943493,
-0.0026579650584608316,
-0.0679413303732872,
0.0405062735080719,
0.02166692353785038,
-0.05509445071220398,
-0.121132493019104
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2108
- Accuracy: 0.9265
- F1: 0.9265
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8108 | 1.0 | 250 | 0.3101 | 0.903 | 0.8995 |
| 0.2423 | 2.0 | 500 | 0.2108 | 0.9265 | 0.9265 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.9265, "name": "Accuracy"}, {"type": "f1", "value": 0.9264826040883781, "name": "F1"}]}]}]}
|
text-classification
|
ffalcao/distilbert-base-uncased-finetuned-emotion
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2108
* Accuracy: 0.9265
* F1: 0.9265
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.13.0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.13.1+cu116\n* Datasets 2.8.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.13.1+cu116\n* Datasets 2.8.0\n* Tokenizers 0.10.3"
] |
[
72,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.13.1+cu116\n* Datasets 2.8.0\n* Tokenizers 0.10.3"
] |
[
-0.10738013684749603,
0.12037812173366547,
-0.003153255209326744,
0.1326982080936432,
0.15002381801605225,
0.02951776422560215,
0.1262800693511963,
0.1218746155500412,
-0.06491266191005707,
0.039960283786058426,
0.10552217066287994,
0.15024977922439575,
0.029286423698067665,
0.11231227219104767,
-0.07817020267248154,
-0.24861617386341095,
-0.007724636700004339,
0.03708606958389282,
-0.010069441981613636,
0.13520842790603638,
0.09634879231452942,
-0.13117188215255737,
0.09391900151968002,
-0.008931620046496391,
-0.14541161060333252,
-0.0017114082584157586,
0.012582090683281422,
-0.048663102090358734,
0.1410035490989685,
0.00944233126938343,
0.10318908840417862,
0.023198334500193596,
0.08706836402416229,
-0.2165239304304123,
0.018166648223996162,
0.04777665436267853,
0.0031746358145028353,
0.08763284236192703,
0.055753957480192184,
-0.023306939750909805,
0.11809997260570526,
-0.07967203110456467,
0.05498460680246353,
0.015601394698023796,
-0.11547456681728363,
-0.25516316294670105,
-0.0840914249420166,
0.04780462384223938,
0.07551255822181702,
0.1094307154417038,
-0.023223524913191795,
0.15445543825626373,
-0.08217428624629974,
0.10310891270637512,
0.237522691488266,
-0.25629961490631104,
-0.0627451092004776,
0.028756586834788322,
0.027023211121559143,
0.06155195087194443,
-0.12160498648881912,
-0.03170251101255417,
0.04410795494914055,
0.04403066262602806,
0.13819263875484467,
-0.030917851254343987,
-0.07354148477315903,
-0.0009101519826799631,
-0.1330411434173584,
-0.04713287204504013,
0.15973150730133057,
0.06199865788221359,
-0.03261520713567734,
-0.06254356354475021,
-0.06499145925045013,
-0.13330763578414917,
-0.03132476285099983,
-0.006702800747007132,
0.05669199675321579,
-0.025019584223628044,
-0.07246871292591095,
0.012257851660251617,
-0.1197022944688797,
-0.04126187413930893,
-0.047979556024074554,
0.12115919589996338,
0.02106955647468567,
0.011509185656905174,
-0.013582732528448105,
0.10747323930263519,
0.006222587078809738,
-0.1396915763616562,
0.026790224015712738,
0.018921758979558945,
0.02717369794845581,
-0.028102705255150795,
-0.05952167138457298,
-0.06622409075498581,
0.0033474767114967108,
0.10761664807796478,
-0.08736157417297363,
0.04585705325007439,
0.028024472296237946,
0.040354400873184204,
-0.07636317610740662,
0.18481169641017914,
-0.04317011684179306,
-0.03289242833852768,
0.0011324422666803002,
0.09089276194572449,
0.05265146121382713,
-0.004149126820266247,
-0.11027292162179947,
0.03734550252556801,
0.10026814043521881,
0.01654667966067791,
-0.07126529514789581,
0.0772046223282814,
-0.08336299657821655,
-0.0178986843675375,
-0.000735446868930012,
-0.08097123354673386,
0.02359742857515812,
0.024546580389142036,
-0.07694234699010849,
-0.02195565216243267,
0.018656332045793533,
0.013177206739783287,
-0.010094692930579185,
0.11104919016361237,
-0.07081261277198792,
0.018783608451485634,
-0.08207854628562927,
-0.11113880574703217,
0.033186301589012146,
-0.08138547837734222,
0.029754262417554855,
-0.0984206274151802,
-0.20685920119285583,
-0.020144041627645493,
0.06542225182056427,
-0.018268711864948273,
-0.040020037442445755,
-0.0573602132499218,
-0.06467879563570023,
0.012154403142631054,
-0.010844796895980835,
0.08125416189432144,
-0.06735078990459442,
0.09785182774066925,
0.02294820174574852,
0.08270080387592316,
-0.01892436482012272,
0.04191906377673149,
-0.12780946493148804,
0.017943762242794037,
-0.13850148022174835,
0.036643754690885544,
-0.05332427844405174,
0.07444900274276733,
-0.06473563611507416,
-0.1026984229683876,
0.02704141102731228,
-0.018425948917865753,
0.062454890459775925,
0.11823844164609909,
-0.18955378234386444,
-0.08593228459358215,
0.17446842789649963,
-0.07870082557201385,
-0.1261122077703476,
0.14950068295001984,
-0.058255549520254135,
0.0401092953979969,
0.07549437880516052,
0.19948290288448334,
0.048918891698122025,
-0.0800500139594078,
-0.01546560600399971,
0.011783279478549957,
0.05713983252644539,
-0.028022857382893562,
0.07074547559022903,
0.023854954168200493,
0.03508080169558525,
0.028752511367201805,
-0.03309422358870506,
0.06542623043060303,
-0.08911225199699402,
-0.0964989960193634,
-0.030252955853939056,
-0.10231538861989975,
0.06348754465579987,
0.08671025186777115,
0.048831209540367126,
-0.11964574456214905,
-0.078431636095047,
-0.0007229573675431311,
0.09430715441703796,
-0.06335091590881348,
0.02560480684041977,
-0.06312396377325058,
0.07856052368879318,
-0.00803186371922493,
-0.013806653209030628,
-0.16547515988349915,
-0.00025345777976326644,
0.013531169854104519,
0.012597477063536644,
-0.008225331082940102,
0.008640298619866371,
0.07249526679515839,
0.05988670885562897,
-0.06955114006996155,
-0.042277999222278595,
-0.031904660165309906,
0.00013912607391830534,
-0.10697627067565918,
-0.22450529038906097,
-0.014900698326528072,
-0.03084101714193821,
0.1789993792772293,
-0.21338386833667755,
0.04850100725889206,
-0.0032907791901379824,
0.07510723173618317,
0.029655301943421364,
-0.02584785781800747,
-0.02252284437417984,
0.054337162524461746,
-0.046715863049030304,
-0.05366230756044388,
0.07381673902273178,
0.00793092604726553,
-0.09500080347061157,
-0.02472624182701111,
-0.14067336916923523,
0.1711043268442154,
0.1318063884973526,
-0.08554276078939438,
-0.06124741584062576,
0.00010591503814794123,
-0.05654057487845421,
-0.013289240188896656,
-0.043974850326776505,
0.027796776965260506,
0.15437054634094238,
0.005344624165445566,
0.14236098527908325,
-0.07850074768066406,
-0.02889401838183403,
0.022390656173229218,
-0.056242503225803375,
0.0028597263153642416,
0.13540928065776825,
0.07433552294969559,
-0.08041377365589142,
0.15081864595413208,
0.1712636649608612,
-0.08767783641815186,
0.14514374732971191,
-0.04316583275794983,
-0.04757386073470116,
-0.02947305701673031,
-0.02521711029112339,
-0.00546006765216589,
0.10953806340694427,
-0.13613945245742798,
0.004148281179368496,
0.02241726964712143,
-0.0037554344162344933,
-0.009225415997207165,
-0.22285281121730804,
-0.05126520246267319,
0.04775461181998253,
-0.044692039489746094,
-0.004424051847308874,
-0.011690959334373474,
0.0069193351082503796,
0.11150737851858139,
-0.007837783545255661,
-0.08906052261590958,
0.03225323557853699,
-0.006934234872460365,
-0.08391449600458145,
0.20748741924762726,
-0.0937250554561615,
-0.1885107159614563,
-0.10593202710151672,
-0.058404240757226944,
-0.05301670730113983,
0.013206051662564278,
0.06912534683942795,
-0.10520486533641815,
-0.026268254965543747,
-0.10035684704780579,
0.008472384884953499,
0.024951541796326637,
0.010454241186380386,
0.033621884882450104,
-0.009799627587199211,
0.06674747169017792,
-0.10191746056079865,
-0.018702268600463867,
-0.05685834586620331,
-0.04128672555088997,
0.061994265764951706,
0.03265887498855591,
0.11460454016923904,
0.15965910255908966,
-0.01042711641639471,
0.003484437009319663,
-0.037683747708797455,
0.22628140449523926,
-0.07145828008651733,
-0.015763575211167336,
0.14970622956752777,
-0.024509722366929054,
0.06351570039987564,
0.12856224179267883,
0.06516996026039124,
-0.09621702879667282,
0.016218267381191254,
0.04032311215996742,
-0.03838679566979408,
-0.21909739077091217,
-0.040575072169303894,
-0.03351905569434166,
0.0386095866560936,
0.06351953744888306,
0.023883381858468056,
0.03143133595585823,
0.07166815549135208,
0.019514616578817368,
0.02573566883802414,
-0.02952040545642376,
0.05931525304913521,
0.11396495997905731,
0.02074858359992504,
0.11041425913572311,
-0.032439224421978,
-0.051776278764009476,
0.05885699763894081,
-0.021928695961833,
0.20053409039974213,
-0.008747171610593796,
0.13898831605911255,
0.05160636082291603,
0.17712163925170898,
-0.029722891747951508,
0.071148581802845,
-0.018414292484521866,
-0.03751150146126747,
-0.030652105808258057,
-0.03812199831008911,
-0.05431320518255234,
0.025635410100221634,
-0.06157097592949867,
0.08580240607261658,
-0.12982039153575897,
0.0027094625402241945,
0.07161881774663925,
0.25926923751831055,
0.03836555778980255,
-0.3252430260181427,
-0.1033373773097992,
0.01746140979230404,
-0.02640632539987564,
-0.0004996988573111594,
0.021190525963902473,
0.10317878425121307,
-0.08734596520662308,
0.050169482827186584,
-0.06900926679372787,
0.08083584159612656,
-0.06841132044792175,
0.06383111327886581,
0.0343250036239624,
0.06725509464740753,
-0.0006044135079719126,
0.07967722415924072,
-0.2738777697086334,
0.2684120237827301,
-0.002520986134186387,
0.061079539358615875,
-0.07433506846427917,
-0.006549430079758167,
0.06516318768262863,
0.0760224387049675,
0.07716395705938339,
-0.006319473031908274,
0.013318191282451153,
-0.19533054530620575,
-0.048702821135520935,
0.0244325939565897,
0.07467348873615265,
-0.03669372573494911,
0.09170855581760406,
-0.023628195747733116,
0.0013500787317752838,
0.07312533259391785,
0.02603452280163765,
-0.05950009077787399,
-0.08302415907382965,
-0.007087198551744223,
0.05184769257903099,
-0.04751817509531975,
-0.07046545296907425,
-0.12624719738960266,
-0.11691436171531677,
0.1346554011106491,
-0.006356689613312483,
-0.03741643950343132,
-0.10334764420986176,
0.06629548966884613,
0.04730217158794403,
-0.08870925009250641,
0.018650127574801445,
0.004131474066525698,
0.08785171806812286,
0.017820967361330986,
-0.06537182629108429,
0.11033626645803452,
-0.08806706964969635,
-0.19294407963752747,
-0.06691883504390717,
0.11359626799821854,
0.041155193001031876,
0.06932346522808075,
-0.003752838121727109,
-0.002032366581261158,
-0.04304051026701927,
-0.08039100468158722,
0.04708993434906006,
0.023878104984760284,
0.0540541410446167,
0.02402973175048828,
-0.025128215551376343,
-0.01970686763525009,
-0.07235625386238098,
-0.03590741753578186,
0.17233505845069885,
0.2765439748764038,
-0.07973001897335052,
0.02975377067923546,
0.05791657790541649,
-0.06067396700382233,
-0.2012566179037094,
0.037933215498924255,
0.040234677493572235,
-0.003969583194702864,
0.07056132704019547,
-0.15809661149978638,
0.10529280453920364,
0.08042293041944504,
-0.029356079176068306,
0.09196817874908447,
-0.29406121373176575,
-0.11933547258377075,
0.13450835645198822,
0.1556558907032013,
0.11756180971860886,
-0.15321114659309387,
-0.0145959397777915,
-0.03019106760621071,
-0.10941160470247269,
0.11603622138500214,
-0.11586499959230423,
0.112421914935112,
-0.020291801542043686,
0.09621621668338776,
0.01785537786781788,
-0.053377434611320496,
0.12818114459514618,
-0.0023059186059981585,
0.1058083027601242,
-0.07386764883995056,
-0.013059407472610474,
0.0473051555454731,
-0.04666903242468834,
0.0308030154556036,
-0.1241644099354744,
0.030894111841917038,
-0.09954892843961716,
-0.028886590152978897,
-0.0900978073477745,
0.032225556671619415,
-0.03998291864991188,
-0.07150743901729584,
-0.04851265996694565,
0.03974129259586334,
0.07763869315385818,
-0.006157561670988798,
0.1023707464337349,
0.026913728564977646,
0.12513503432273865,
0.1412152796983719,
0.09708569198846817,
-0.053394902497529984,
-0.058022934943437576,
-0.025750471279025078,
-0.021645763888955116,
0.04708586633205414,
-0.14358749985694885,
0.025062020868062973,
0.1340748518705368,
0.014271227642893791,
0.15553757548332214,
0.07572933286428452,
-0.047549162060022354,
0.01470349170267582,
0.06181354448199272,
-0.16021232306957245,
-0.10559000074863434,
-0.017840785905718803,
-0.0469122938811779,
-0.12981821596622467,
0.03864598646759987,
0.09418358653783798,
-0.07159127295017242,
0.0016896713059395552,
-0.018101895228028297,
0.027956025674939156,
-0.019163066521286964,
0.18026293814182281,
0.05227549746632576,
0.03549981862306595,
-0.10539087653160095,
0.09119581431150436,
0.03190138190984726,
-0.11368244141340256,
0.020912881940603256,
0.07004209607839584,
-0.0834701657295227,
-0.05490371584892273,
0.06460746377706528,
0.19754746556282043,
-0.04763536527752876,
-0.056359950453042984,
-0.15829403698444366,
-0.13200967013835907,
0.06908898800611496,
0.13708283007144928,
0.10735714435577393,
0.006597604136914015,
-0.06494376808404922,
0.014836554415524006,
-0.1136559247970581,
0.117315374314785,
0.07447594404220581,
0.05268869921565056,
-0.14841759204864502,
0.1148848831653595,
0.003247949993237853,
0.03167227655649185,
-0.02133387327194214,
0.02511807531118393,
-0.09128063172101974,
0.005571509711444378,
-0.13476049900054932,
-0.019282404333353043,
-0.03536384552717209,
0.009085833095014095,
-0.00745523814111948,
-0.04634622484445572,
-0.03782188147306442,
0.00983763113617897,
-0.11083492636680603,
-0.017869630828499794,
0.037801384925842285,
0.062426116317510605,
-0.12920522689819336,
-0.058162204921245575,
0.0234682597219944,
-0.06441442668437958,
0.09897768497467041,
0.04559144005179405,
0.013262161053717136,
0.05453994870185852,
-0.18676890432834625,
0.020589660853147507,
0.08140170574188232,
0.014802796766161919,
0.0449494943022728,
-0.07800853252410889,
-0.01867101900279522,
-0.00910028163343668,
0.025842491537332535,
0.01822701096534729,
0.08591962605714798,
-0.12416549026966095,
0.031040837988257408,
0.009499761275947094,
-0.07697191089391708,
-0.06275857985019684,
0.030774621292948723,
0.06775809824466705,
0.011832303367555141,
0.20853044092655182,
-0.08388349413871765,
0.042012471705675125,
-0.21961669623851776,
0.004216327797621489,
0.0020531846676021814,
-0.11958099901676178,
-0.1540694683790207,
-0.05120823159813881,
0.06165393069386482,
-0.05620124563574791,
0.11759550869464874,
0.04582194238901138,
0.017272446304559708,
0.01675061695277691,
-0.004731167573481798,
0.04780687764286995,
0.0019947579130530357,
0.17721763253211975,
0.021816140040755272,
-0.056988656520843506,
0.04797830060124397,
0.03260938823223114,
0.11721111834049225,
0.08933580666780472,
0.1886584311723709,
0.15622109174728394,
0.02981962077319622,
0.09189388900995255,
0.02813323773443699,
-0.031156154349446297,
-0.16229967772960663,
0.00432564876973629,
-0.05390536040067673,
0.11433552950620651,
-0.010693995282053947,
0.23532900214195251,
0.07936564087867737,
-0.15368826687335968,
0.05050675943493843,
-0.06795287877321243,
-0.07332437485456467,
-0.10287248343229294,
-0.07431399077177048,
-0.0832594633102417,
-0.13521558046340942,
-0.0009195648599416018,
-0.1332794576883316,
0.010511270724236965,
0.09945814311504364,
0.007528843358159065,
-0.042415037751197815,
0.14689233899116516,
0.013448726385831833,
0.02429973892867565,
0.09087087213993073,
0.009900511242449284,
-0.06708618998527527,
-0.08655992150306702,
-0.06875015795230865,
-0.0068365284241735935,
-0.013168963603675365,
0.03683122619986534,
-0.04911878705024719,
-0.06444915384054184,
0.029101835563778877,
-0.017534881830215454,
-0.11305786669254303,
0.014851273968815804,
0.011090103536844254,
0.06791515648365021,
0.045740287750959396,
0.0032669028732925653,
0.024636946618556976,
-0.00015511750825680792,
0.21607159078121185,
-0.07622655481100082,
-0.030628353357315063,
-0.08508682250976562,
0.23128584027290344,
0.009267394430935383,
-0.018446270376443863,
0.03587211295962334,
-0.08570235967636108,
0.0035302513279020786,
0.2270987629890442,
0.20721586048603058,
-0.09580633044242859,
0.0037647325079888105,
-0.01957179792225361,
0.0023524698335677385,
-0.050390880554914474,
0.09620951116085052,
0.1416652798652649,
0.01995927467942238,
-0.09922285377979279,
0.0005028511513955891,
-0.053904928267002106,
-0.01814708299934864,
-0.032905496656894684,
0.050679806619882584,
0.05186834931373596,
0.013971772976219654,
-0.05090099573135376,
0.06255252659320831,
-0.06880387663841248,
-0.10963638126850128,
0.06411782652139664,
-0.2171366959810257,
-0.15080052614212036,
-0.02332746610045433,
0.1129748672246933,
0.033197250217199326,
0.06499268114566803,
-0.016172580420970917,
0.009849218651652336,
0.08137096464633942,
-0.01975616067647934,
-0.09997548907995224,
-0.06772688776254654,
0.09057573229074478,
-0.13697165250778198,
0.22004394233226776,
-0.057056576013565063,
0.042246196419000626,
0.12145630270242691,
0.04681579768657684,
-0.07037979364395142,
0.0866442397236824,
0.05757230147719383,
-0.06143627688288689,
0.01537763886153698,
0.11174119263887405,
-0.02652757801115513,
0.09448489546775818,
0.052946437150239944,
-0.14834070205688477,
0.011008336208760738,
-0.01731264404952526,
-0.06894208490848541,
-0.04691863805055618,
-0.006483388599008322,
-0.058587756007909775,
0.11787135899066925,
0.20219439268112183,
-0.036150433123111725,
0.006827424746006727,
-0.06121744588017464,
0.003329202067106962,
0.05887424200773239,
0.005951235070824623,
-0.036282237619161606,
-0.2111884504556656,
0.022500867024064064,
0.07111190259456635,
-0.00145635474473238,
-0.2740030884742737,
-0.09300485253334045,
-0.017257453873753548,
-0.059886012226343155,
-0.09825723618268967,
0.06390213966369629,
0.07416994124650955,
0.051391515880823135,
-0.043416306376457214,
-0.05303296074271202,
-0.06403885781764984,
0.16994766891002655,
-0.13369248807430267,
-0.08018191903829575
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro
This model is a fine-tuned version of [patrickvonplaten/t5-tiny-random](https://huggingface.co/patrickvonplaten/t5-tiny-random) on the wmt16_en_ro_pre_processed dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "model-index": [{"name": "t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro", "results": []}]}
|
text2text-generation
|
ffsouza/t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro
This model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
|
[
"# t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro\n\nThis model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1",
"### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.10.0+cu102\n- Datasets 1.15.1\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro\n\nThis model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1",
"### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.10.0+cu102\n- Datasets 1.15.1\n- Tokenizers 0.10.3"
] |
[
75,
76,
6,
12,
8,
3,
90,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# t5-tiny-random-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro\n\nThis model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.10.0+cu102\n- Datasets 1.15.1\n- Tokenizers 0.10.3"
] |
[
-0.07454276829957962,
0.1281173974275589,
-0.0035724544432014227,
0.06385056674480438,
0.13110652565956116,
0.04022363945841789,
0.09290546923875809,
0.1334718018770218,
-0.11183986067771912,
0.11996877193450928,
0.097145214676857,
0.04726020246744156,
0.08105902373790741,
0.12456467747688293,
-0.023203421384096146,
-0.23168502748012543,
0.009665986523032188,
-0.017003944143652916,
-0.04044090956449509,
0.09548240154981613,
0.0997786894440651,
-0.0971563383936882,
0.08274980634450912,
0.01621328666806221,
-0.13660019636154175,
0.009984185919165611,
-0.040057554841041565,
-0.06856904923915863,
0.07590329647064209,
0.04518725350499153,
0.06451524049043655,
0.021036427468061447,
0.11217986047267914,
-0.22439219057559967,
-0.010052218101918697,
0.07330816984176636,
0.031702686101198196,
0.0992278903722763,
0.09974756836891174,
0.01659388840198517,
0.06922155618667603,
-0.13946498930454254,
0.06316259503364563,
0.039692703634500504,
-0.06948382407426834,
-0.11120614409446716,
-0.07066610455513,
0.07930659502744675,
0.039581336081027985,
0.09800612181425095,
-0.005668694619089365,
0.16185332834720612,
-0.05978386849164963,
0.05719210207462311,
0.20782732963562012,
-0.24187837541103363,
-0.07313869893550873,
0.035403575748205185,
0.07014771550893784,
0.048007335513830185,
-0.08955694735050201,
-0.017885910347104073,
0.06278960406780243,
0.02731972001492977,
0.06749500334262848,
0.0011714135762304068,
-0.03099711798131466,
-0.014876344241201878,
-0.12232816964387894,
-0.032879192382097244,
0.1477474421262741,
0.049415815621614456,
-0.023097453638911247,
-0.1126399114727974,
-0.06920094043016434,
-0.13940401375293732,
-0.022993672639131546,
-0.009523802436888218,
0.008611752651631832,
-0.05042267590761185,
-0.07349778711795807,
-0.004771319217979908,
-0.05438019335269928,
-0.02691512368619442,
0.01826009340584278,
0.10002445429563522,
0.046002406626939774,
0.008141597732901573,
-0.032650839537382126,
0.10313309729099274,
0.00846299808472395,
-0.13939377665519714,
-0.02705674059689045,
-0.007680670823901892,
-0.08955111354589462,
-0.05816037952899933,
-0.0323595330119133,
0.032827816903591156,
0.01341953594237566,
0.1874500811100006,
-0.034105077385902405,
0.06312210112810135,
0.03319365903735161,
-0.019175847992300987,
-0.0054336488246917725,
0.12793640792369843,
-0.08201392740011215,
-0.08895238488912582,
-0.011040911078453064,
0.10612201690673828,
-0.004317318554967642,
-0.009807267226278782,
-0.07739191502332687,
-0.03002704307436943,
0.09819963574409485,
0.044546667486429214,
0.004836533218622208,
0.04465894028544426,
-0.019490009173750877,
-0.0431574285030365,
0.0475081205368042,
-0.12791702151298523,
0.03233201429247856,
0.013558711856603622,
-0.08226199448108673,
0.026061076670885086,
-0.005136311054229736,
-0.01669127494096756,
-0.06675735116004944,
0.06309164315462112,
-0.08335854113101959,
-0.025763841345906258,
-0.06748280674219131,
-0.07326909154653549,
0.01428950298577547,
-0.04747939482331276,
-0.0008971152710728347,
-0.0832446739077568,
-0.14598838984966278,
-0.04381003975868225,
0.023497380316257477,
-0.08984711021184921,
-0.09095502644777298,
-0.020338458940386772,
-0.07062370330095291,
0.0366177037358284,
-0.008258968591690063,
0.16511224210262299,
-0.03165020793676376,
0.05549130216240883,
0.03631480783224106,
0.03224122151732445,
0.08234734833240509,
0.033872541040182114,
-0.08221295475959778,
0.03672229126095772,
-0.049800168722867966,
0.09725311398506165,
-0.07104319334030151,
0.03278814256191254,
-0.13370707631111145,
-0.1128527969121933,
-0.018819155171513557,
-0.032867033034563065,
0.06343622505664825,
0.11185434460639954,
-0.11852974444627762,
-0.033632177859544754,
0.12442225217819214,
-0.01952170394361019,
-0.06671302020549774,
0.08962526172399521,
-0.021538058295845985,
-0.0007393707637675107,
0.027881087735295296,
0.07933014631271362,
0.1307353973388672,
-0.09472878277301788,
-0.052765898406505585,
0.024594126269221306,
0.07755153626203537,
0.03892584517598152,
0.06239605322480202,
0.0029310910031199455,
-0.009436027146875858,
0.019202163442969322,
-0.06350677460432053,
-0.005388021934777498,
-0.07849761098623276,
-0.08752522617578506,
-0.0505049042403698,
-0.06703957915306091,
0.03874104470014572,
0.02810736931860447,
0.04491012170910835,
-0.053467653691768646,
-0.1396329253911972,
0.04190180078148842,
0.14062969386577606,
-0.054520364850759506,
0.012388771399855614,
-0.08069714903831482,
0.04099108278751373,
-0.04151654243469238,
-0.005952579900622368,
-0.19984710216522217,
-0.0868861973285675,
0.05628005415201187,
-0.07075992971658707,
0.022720688953995705,
0.007160317152738571,
0.05800807103514671,
0.06310420483350754,
-0.016110777854919434,
-0.036203786730766296,
-0.05408757925033569,
-0.032267116010189056,
-0.08410939574241638,
-0.16084490716457367,
-0.0858842059969902,
-0.013532549142837524,
0.17428477108478546,
-0.22170892357826233,
0.004784273914992809,
-0.0061203595250844955,
0.1370016485452652,
-0.005958054214715958,
-0.043756745755672455,
-0.014803528785705566,
0.02638228051364422,
-0.05096063390374184,
-0.0920969620347023,
0.02241384983062744,
-0.009224624373018742,
-0.06693210452795029,
-0.05032765865325928,
-0.15367986261844635,
-0.005999053828418255,
0.08991897106170654,
0.053544990718364716,
-0.09515836089849472,
0.008988126181066036,
-0.07635308057069778,
-0.052035655826330185,
-0.062377188354730606,
-0.02187621220946312,
0.15814514458179474,
0.011650738306343555,
0.11276214569807053,
-0.051640067249536514,
-0.07667968422174454,
0.026253122836351395,
0.02741653099656105,
-0.03548818081617355,
0.09434208273887634,
0.08703813701868057,
-0.08514811098575592,
0.08241724967956543,
0.031110534444451332,
-0.024205902591347694,
0.11563852429389954,
-0.03395579755306244,
-0.10459302365779877,
-0.02140820398926735,
0.005381499417126179,
0.015332914888858795,
0.06080399081110954,
-0.0876418948173523,
0.0023228994105011225,
0.06360289454460144,
-0.006782783195376396,
0.03265239670872688,
-0.1336480677127838,
0.01747988723218441,
0.03316371887922287,
-0.019046040251851082,
0.03261500969529152,
-0.028521938249468803,
0.029454706236720085,
0.07569146901369095,
0.037176430225372314,
-0.0192697923630476,
0.017953457310795784,
-0.014677918516099453,
-0.08316139131784439,
0.17254410684108734,
-0.07870761305093765,
-0.15407127141952515,
-0.14119009673595428,
-0.021490322425961494,
-0.055365849286317825,
-0.028429370373487473,
-0.0029447744600474834,
-0.06766727566719055,
-0.07939556241035461,
-0.08276593685150146,
0.015882957726716995,
-0.05603795126080513,
0.012136983685195446,
0.08840256184339523,
0.021376224234700203,
0.10172140598297119,
-0.11071527749300003,
0.03253448009490967,
0.01197353471070528,
-0.07031121850013733,
0.009446430020034313,
0.04384922236204147,
0.10953625291585922,
0.1257028430700302,
-0.0022023192141205072,
0.01119438000023365,
-0.017224391922354698,
0.2426493614912033,
-0.06805239617824554,
0.003986455500125885,
0.10899815708398819,
0.010672018863260746,
0.06465302407741547,
0.12782540917396545,
0.03174369037151337,
-0.061227086931467056,
0.03629739210009575,
0.04859725758433342,
-0.00925416499376297,
-0.24122875928878784,
-0.04955125227570534,
-0.027535725384950638,
-0.02699650451540947,
0.16321682929992676,
0.03773116320371628,
0.00898232962936163,
0.0673220083117485,
-0.05599473789334297,
0.10478878021240234,
-0.01834850199520588,
0.08790374547243118,
0.07602834701538086,
0.06886841356754303,
0.09934758394956589,
-0.015257810242474079,
-0.035494621843099594,
0.062118493020534515,
0.0005468972958624363,
0.2203683704137802,
-0.039522022008895874,
0.18836545944213867,
-0.00907787773758173,
0.15730756521224976,
-0.007884849794209003,
0.04409375041723251,
0.020966313779354095,
0.017189716920256615,
0.02644365467131138,
-0.07177909463644028,
-0.013923313468694687,
0.03675602748990059,
-0.009209002368152142,
0.05586869269609451,
-0.07492313534021378,
0.0715084820985794,
0.006770663894712925,
0.23997460305690765,
0.0559757798910141,
-0.3196845054626465,
-0.07569017261266708,
0.014031252823770046,
-0.024150650948286057,
-0.07710672169923782,
0.01167986448854208,
0.08628935366868973,
-0.13240265846252441,
0.07047048211097717,
-0.06917230039834976,
0.09198751300573349,
-0.03461515158414841,
0.0036614465061575174,
0.08872142434120178,
0.15283292531967163,
0.018803775310516357,
0.10305394977331161,
-0.16117292642593384,
0.16771450638771057,
0.018468564376235008,
0.08243131637573242,
-0.08982641249895096,
0.05332348495721817,
-0.017305882647633553,
0.02732934057712555,
0.10212162882089615,
-0.012180114164948463,
-0.0532962828874588,
-0.15164941549301147,
-0.11880657076835632,
0.006800312083214521,
0.12034857273101807,
-0.04497985169291496,
0.12083374708890915,
-0.06570697575807571,
-0.018328476697206497,
0.03061620146036148,
-0.07019559293985367,
-0.13332237303256989,
-0.16299648582935333,
0.055059678852558136,
-0.011917597614228725,
-0.04044617712497711,
-0.07797136902809143,
-0.10588666051626205,
-0.0788196250796318,
0.20987971127033234,
-0.04106149449944496,
-0.03671550005674362,
-0.1407472938299179,
0.10757769644260406,
0.15468938648700714,
-0.0839855819940567,
0.0025932842399924994,
-0.0000019463134321995312,
0.16604597866535187,
0.009819341823458672,
-0.10552242398262024,
0.05319526791572571,
-0.06786823272705078,
-0.18727096915245056,
-0.03712758049368858,
0.13553497195243835,
0.025124043226242065,
0.05711159110069275,
-0.023982631042599678,
0.011451606638729572,
-0.005601155571639538,
-0.08200626820325851,
-0.012945604510605335,
0.06621203571557999,
0.03914692997932434,
0.03402990102767944,
-0.03979012742638588,
-0.008002583868801594,
-0.06346467137336731,
0.0046472810208797455,
0.1138807013630867,
0.18834006786346436,
-0.09708474576473236,
0.040018606930971146,
0.04331229627132416,
-0.061329882591962814,
-0.18166585266590118,
0.03616723418235779,
0.1188635602593422,
0.014518240466713905,
0.02182491309940815,
-0.1780472993850708,
0.09745516628026962,
0.07948439568281174,
-0.0022839445155113935,
0.061649784445762634,
-0.33148375153541565,
-0.12980122864246368,
0.06158037483692169,
0.055659666657447815,
0.008509373292326927,
-0.12941019237041473,
-0.06555692851543427,
-0.042627908289432526,
-0.13826198875904083,
0.07425348460674286,
-0.02700718306005001,
0.09766485542058945,
-0.015368839725852013,
0.08223474025726318,
0.032394569367170334,
-0.051901720464229584,
0.14249460399150848,
0.03776979446411133,
0.031200673431158066,
-0.05553652346134186,
0.016505630686879158,
0.08124452829360962,
-0.06926850229501724,
0.04156417399644852,
-0.026452496647834778,
0.06412360817193985,
-0.14053674042224884,
-0.03484141454100609,
-0.0640365406870842,
0.06674246490001678,
-0.06183619424700737,
-0.04484335705637932,
-0.044243376702070236,
0.04134710878133774,
0.07106628268957138,
-0.023620717227458954,
0.06638357043266296,
0.04532261937856674,
0.041820503771305084,
0.1503549963235855,
0.08801033347845078,
0.019386926665902138,
-0.11338572949171066,
0.0009362888522446156,
-0.007218612357974052,
0.05453440546989441,
-0.09135064482688904,
0.01824173703789711,
0.1289403885602951,
0.04372129216790199,
0.10923436284065247,
0.03105846606194973,
-0.07823703438043594,
-0.021642837673425674,
0.030418772250413895,
-0.10797200351953506,
-0.15875943005084991,
-0.046624500304460526,
-0.008601175621151924,
-0.1813557744026184,
-0.02268386445939541,
0.10987656563520432,
-0.0544714629650116,
-0.019624270498752594,
-0.008732952177524567,
0.02881857194006443,
0.0005883442354388535,
0.19456599652767181,
0.025691935792565346,
0.07772994041442871,
-0.09509331732988358,
0.09305880218744278,
0.10207235813140869,
-0.0504712238907814,
0.034450918436050415,
0.09120277315378189,
-0.0820384994149208,
-0.014993010088801384,
0.055453088134527206,
0.07107751071453094,
-0.04796278104186058,
-0.02169143036007881,
-0.05293455347418785,
-0.09580382704734802,
0.07400291413068771,
0.02057354524731636,
0.04292243719100952,
-0.014896637760102749,
-0.015237732790410519,
0.0021268408745527267,
-0.15201520919799805,
0.10145584493875504,
0.06739237159490585,
0.09269959479570389,
-0.13711190223693848,
0.11946100741624832,
-0.0007092011510394514,
0.05894780531525612,
-0.009864820167422295,
0.02046786993741989,
-0.07552451640367508,
-0.03572665527462959,
-0.13438750803470612,
0.015750179067254066,
-0.05765724927186966,
0.005485298577696085,
-0.015843991190195084,
-0.023392733186483383,
-0.04911153018474579,
0.0420541986823082,
-0.04915963113307953,
-0.09016533941030502,
-0.01496781688183546,
0.052467722445726395,
-0.10487958043813705,
0.018960237503051758,
0.023809760808944702,
-0.1230500265955925,
0.07998013496398926,
0.047134190797805786,
0.030565502122044563,
0.007472021505236626,
-0.018102064728736877,
-0.03107317164540291,
0.019193893298506737,
0.05604720488190651,
0.07286497950553894,
-0.09906810522079468,
0.004384736064821482,
-0.0384114608168602,
0.028530947864055634,
-0.005540662910789251,
0.019164927303791046,
-0.14489246904850006,
-0.005654293578118086,
-0.0677919015288353,
-0.029260626062750816,
-0.06287636607885361,
0.04636619612574577,
0.06284093111753464,
-0.011830740608274937,
0.15517552196979523,
-0.06509847193956375,
0.0635448768734932,
-0.219806507229805,
-0.01677549071609974,
-0.008428421802818775,
-0.01993701234459877,
-0.03719929978251457,
-0.010631093755364418,
0.0886334627866745,
-0.04970794916152954,
0.11137082427740097,
-0.02983943559229374,
0.07857246696949005,
0.03608296811580658,
-0.030764171853661537,
-0.024320129305124283,
0.01979132741689682,
0.17697884142398834,
0.09365644305944443,
-0.009662983007729053,
0.06913501769304276,
-0.02521488629281521,
0.0830264762043953,
0.06446246802806854,
0.15357452630996704,
0.12896651029586792,
-0.0056044477969408035,
0.0742572769522667,
0.06741838157176971,
-0.12660643458366394,
-0.14442752301692963,
0.13567280769348145,
-0.08041811734437943,
0.12024681270122528,
-0.03494255244731903,
0.10609891265630722,
0.11717426776885986,
-0.15922874212265015,
0.011385674588382244,
-0.062084659934043884,
-0.09927724301815033,
-0.11012730002403259,
-0.09674537926912308,
-0.09981565177440643,
-0.10126007348299026,
0.02855408936738968,
-0.11523651331663132,
0.026166534051299095,
0.061394304037094116,
0.014431821182370186,
0.009990076534450054,
0.12924911081790924,
-0.0051550427451729774,
-0.011651528999209404,
0.051366761326789856,
0.027467671781778336,
0.0019250990590080619,
-0.03338927775621414,
-0.053653329610824585,
0.04970654100179672,
0.015756798908114433,
0.0944725051522255,
-0.04079728573560715,
0.02286725677549839,
0.06586373597383499,
0.005101245827972889,
-0.08484868705272675,
0.015098812058568,
0.008161063306033611,
0.012701671570539474,
0.04929252341389656,
0.037484560161828995,
0.025830978527665138,
-0.05922163650393486,
0.21058249473571777,
-0.07380843907594681,
-0.07648251950740814,
-0.14079490303993225,
0.1520812213420868,
0.02918452024459839,
-0.007396283093839884,
0.08116990327835083,
-0.09745726734399796,
-0.028750810772180557,
0.20364519953727722,
0.16537724435329437,
-0.037946753203868866,
-0.03757253661751747,
0.022256283089518547,
-0.014414447359740734,
-0.04072245955467224,
0.10161290317773819,
0.10323749482631683,
0.016555292531847954,
-0.054341819137334824,
-0.030285704880952835,
0.005877992138266563,
-0.0316244475543499,
-0.07398621737957001,
0.0805978775024414,
0.011109080165624619,
0.016517600044608116,
-0.024775361642241478,
0.034952156245708466,
-0.022209657356142998,
-0.17012391984462738,
0.04460611194372177,
-0.1340268850326538,
-0.18354102969169617,
-0.018222393468022346,
0.0857880637049675,
-0.01358551625162363,
0.06704103946685791,
-0.004651995375752449,
-0.026018129661679268,
0.14363688230514526,
-0.013825691305100918,
-0.09078910946846008,
-0.05325369909405708,
0.06943486630916595,
-0.06533253937959671,
0.19909830391407013,
-0.0029357646126300097,
0.046251073479652405,
0.11071157455444336,
0.020207369700074196,
-0.18261058628559113,
0.009586961939930916,
0.09024257957935333,
-0.04834793135523796,
0.05286119878292084,
0.17097412049770355,
-0.03212078660726547,
0.08486053347587585,
0.051558397710323334,
-0.13128605484962463,
-0.036742713302373886,
0.006887665018439293,
0.033459994941949844,
-0.08887848258018494,
-0.021551573649048805,
-0.04441196471452713,
0.16202585399150848,
0.22187449038028717,
-0.05168595910072327,
-0.023739513009786606,
-0.059897612780332565,
0.013050926849246025,
0.01762017235159874,
0.13050399720668793,
-0.019051656126976013,
-0.21773867309093475,
-0.004682816565036774,
-0.044174011796712875,
0.05632476881146431,
-0.22040626406669617,
-0.073118656873703,
0.046381086111068726,
-0.05398676171898842,
-0.07014645636081696,
0.14311657845973969,
0.04911119118332863,
0.03164202347397804,
-0.03750263899564743,
-0.06146903708577156,
-0.05925631523132324,
0.13470786809921265,
-0.1719033271074295,
-0.0624619759619236
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-tiny-random-length-96-learning_rate-0.0002-weight_decay-0.01-finetuned-en-to-ro
This model is a fine-tuned version of [patrickvonplaten/t5-tiny-random](https://huggingface.co/patrickvonplaten/t5-tiny-random) on the wmt16_en_ro_pre_processed dataset.
It achieves the following results on the evaluation set:
- Loss: 4.6426
- Bleu: 0.0617
- Gen Len: 8.9895
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:------:|:-------:|
| 4.5828 | 1.0 | 76290 | 5.5397 | 0.0089 | 8.981 |
| 4.187 | 2.0 | 152580 | 5.2241 | 0.0172 | 8.989 |
| 3.9612 | 3.0 | 228870 | 5.0092 | 0.034 | 8.988 |
| 3.8151 | 4.0 | 305160 | 4.8688 | 0.0365 | 8.9865 |
| 3.7162 | 5.0 | 381450 | 4.7656 | 0.0469 | 8.9865 |
| 3.6498 | 6.0 | 457740 | 4.6874 | 0.0531 | 8.9885 |
| 3.6147 | 7.0 | 534030 | 4.6612 | 0.0585 | 8.9875 |
| 3.5972 | 8.0 | 610320 | 4.6426 | 0.0617 | 8.9895 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "metrics": ["bleu"], "model-index": [{"name": "t5-tiny-random-length-96-learning_rate-0.0002-weight_decay-0.01-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16_en_ro_pre_processed", "type": "wmt16_en_ro_pre_processed", "args": "enro"}, "metrics": [{"type": "bleu", "value": 0.0617, "name": "Bleu"}]}]}]}
|
text2text-generation
|
ffsouza/t5-tiny-random-length-96-learning_rate-0.0002-weight_decay-0.01-finetuned-en-to-ro
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
t5-tiny-random-length-96-learning\_rate-0.0002-weight\_decay-0.01-finetuned-en-to-ro
====================================================================================
This model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16\_en\_ro\_pre\_processed dataset.
It achieves the following results on the evaluation set:
* Loss: 4.6426
* Bleu: 0.0617
* Gen Len: 8.9895
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu102
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu102\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu102\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
79,
97,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu102\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
-0.11129482090473175,
0.08685921877622604,
-0.0019008017843589187,
0.1210261657834053,
0.14959387481212616,
0.021479889750480652,
0.13989385962486267,
0.13551859557628632,
-0.0606486015021801,
0.037123966962099075,
0.1452626734972,
0.12729692459106445,
0.02874220535159111,
0.10761258006095886,
-0.04405386373400688,
-0.2482037991285324,
-0.007995142601430416,
0.061650633811950684,
-0.036537010222673416,
0.15019957721233368,
0.0797363668680191,
-0.1301073282957077,
0.10767052322626114,
-0.006576516665518284,
-0.19651693105697632,
-0.006081182509660721,
0.00554992537945509,
-0.03884774446487427,
0.13952550292015076,
0.026141176000237465,
0.10299208015203476,
0.02175716869533062,
0.06772463023662567,
-0.16168303787708282,
0.011475127190351486,
0.045628730207681656,
0.018104426562786102,
0.10245069116353989,
0.06251989305019379,
-0.005448930896818638,
0.08260294049978256,
-0.06530297547578812,
0.03612416237592697,
0.01916002482175827,
-0.13478617370128632,
-0.21275638043880463,
-0.045997150242328644,
0.031168172135949135,
0.05455317348241806,
0.11096958816051483,
-0.024920545518398285,
0.1282678246498108,
-0.034558143466711044,
0.10980916023254395,
0.2241102159023285,
-0.2695293426513672,
-0.06273896992206573,
0.03556695953011513,
0.03931974992156029,
0.1122829020023346,
-0.09568601101636887,
-0.0203696358948946,
0.044046979397535324,
0.041817985475063324,
0.12553012371063232,
-0.02948448434472084,
-0.021478643640875816,
0.010867977514863014,
-0.1388722062110901,
-0.0503593310713768,
0.1779460459947586,
0.04712356626987457,
-0.02765977941453457,
-0.04853586480021477,
-0.09261561185121536,
-0.1845214068889618,
-0.018543466925621033,
-0.004119635093957186,
0.02533983811736107,
-0.03527422994375229,
-0.09917336702346802,
-0.033515237271785736,
-0.10603202134370804,
-0.061659909784793854,
-0.032555967569351196,
0.14196909964084625,
0.029648011550307274,
0.0023994748480618,
-0.04960209131240845,
0.12417323142290115,
0.009536376222968102,
-0.15036380290985107,
0.033998530358076096,
0.021037869155406952,
0.0011753244325518608,
-0.0293295718729496,
-0.061307236552238464,
-0.11105137318372726,
-0.018665416166186333,
0.09206307679414749,
-0.043777160346508026,
0.05108170583844185,
0.03486951068043709,
0.04030550271272659,
-0.07852241396903992,
0.18859583139419556,
-0.06047699972987175,
-0.05662250146269798,
-0.021431751549243927,
0.09131195396184921,
0.009117735549807549,
-0.026442984119057655,
-0.13609834015369415,
-0.009921104647219181,
0.12812486290931702,
-0.011518249288201332,
-0.04509327933192253,
0.07824814319610596,
-0.04442282021045685,
-0.06101405620574951,
-0.02407834865152836,
-0.0847381055355072,
-0.001449243980459869,
-0.009097795933485031,
-0.07676529139280319,
-0.0011566028697416186,
0.022409798577427864,
0.006128526292741299,
-0.027562445029616356,
0.081508107483387,
-0.1173551008105278,
0.01525898091495037,
-0.07669254392385483,
-0.1224713996052742,
-0.010257628746330738,
-0.08423690497875214,
0.018277473747730255,
-0.09586714953184128,
-0.18395371735095978,
-0.015308857895433903,
0.05399496853351593,
-0.034455884248018265,
-0.047746676951646805,
-0.07184713333845139,
-0.05470087379217148,
0.021843841299414635,
-0.00817794632166624,
0.1215696856379509,
-0.0556861087679863,
0.12181766331195831,
0.05138289928436279,
0.06578737497329712,
-0.03622157871723175,
0.05891212821006775,
-0.10192742198705673,
0.029560817405581474,
-0.12927882373332977,
0.06914499402046204,
-0.010102389380335808,
0.055833473801612854,
-0.10595555603504181,
-0.10386954247951508,
-0.025067349895834923,
-0.010514556430280209,
0.07984087616205215,
0.11024698615074158,
-0.14627398550510406,
-0.09091447293758392,
0.18390220403671265,
-0.04385499656200409,
-0.125582754611969,
0.1473132073879242,
-0.05992583930492401,
0.07971242815256119,
0.06254217773675919,
0.21478764712810516,
0.07076668739318848,
-0.05057637393474579,
0.017250504344701767,
0.005246484186500311,
0.09135492891073227,
-0.05109844729304314,
0.09531278163194656,
0.007194054313004017,
-0.011691847816109657,
0.016495870426297188,
-0.04557368531823158,
0.06020643934607506,
-0.09820611774921417,
-0.08236289024353027,
-0.02155541069805622,
-0.0992109552025795,
0.08183135837316513,
0.0605379119515419,
0.08620022982358932,
-0.09088705480098724,
-0.09571415930986404,
0.025034746155142784,
0.07948452234268188,
-0.0945788100361824,
0.030454343184828758,
-0.0653187707066536,
0.07882210612297058,
-0.10116099566221237,
-0.01807030662894249,
-0.17955178022384644,
-0.007650430779904127,
0.01404951699078083,
0.030111826956272125,
0.021472079679369926,
0.03843677043914795,
0.0824122205376625,
0.0719778835773468,
-0.05631662532687187,
-0.040043119341135025,
-0.03546709939837456,
-0.01212724857032299,
-0.1426432728767395,
-0.16650746762752533,
-0.04934372752904892,
-0.019319185987114906,
0.16602735221385956,
-0.21011324226856232,
0.03896033391356468,
-0.01744273491203785,
0.06421886384487152,
0.022563062608242035,
-0.03561697155237198,
-0.027341531589627266,
0.057942915707826614,
-0.05066772177815437,
-0.04959757253527641,
0.07873088121414185,
0.006656716112047434,
-0.1131027489900589,
-0.03283371031284332,
-0.15751534700393677,
0.14594940841197968,
0.1169838234782219,
-0.10979779064655304,
-0.06776002049446106,
-0.0007647479069419205,
-0.04844418913125992,
-0.03825066611170769,
-0.020527860149741173,
-0.028047336265444756,
0.17930945754051208,
0.005923958029597998,
0.1664733737707138,
-0.08781255781650543,
-0.04237636178731918,
0.025958117097616196,
-0.02935734950006008,
0.0137011194601655,
0.14746977388858795,
0.07550208270549774,
-0.10782855749130249,
0.13676957786083221,
0.15014037489891052,
-0.0603259801864624,
0.1641107201576233,
-0.01452576369047165,
-0.08505077660083771,
-0.03407925367355347,
-0.012186839245259762,
-0.006428622640669346,
0.08348101377487183,
-0.160430446267128,
-0.006495638284832239,
0.029980506747961044,
0.019886475056409836,
0.03048672527074814,
-0.19566494226455688,
-0.03214646503329277,
0.060776595026254654,
-0.028426144272089005,
-0.010689237155020237,
-0.002882651286199689,
-0.011068331077694893,
0.1041257306933403,
0.019159674644470215,
-0.06481349468231201,
0.03623358905315399,
-0.0023412322625517845,
-0.07933124899864197,
0.21002481877803802,
-0.051610067486763,
-0.17904849350452423,
-0.14629283547401428,
-0.0981934517621994,
-0.06641706079244614,
0.02154536359012127,
0.05405288189649582,
-0.07840787619352341,
-0.013316973112523556,
-0.08774561434984207,
0.055168937891721725,
-0.04507259652018547,
0.019661828875541687,
0.02686798758804798,
-0.02759852074086666,
0.05778733640909195,
-0.0976761132478714,
-0.005192176904529333,
-0.03058706969022751,
-0.03250386565923691,
0.05406416952610016,
-0.010812480002641678,
0.1208207756280899,
0.15754888951778412,
-0.005376092158257961,
0.02624027617275715,
-0.02023119106888771,
0.25612345337867737,
-0.0688457116484642,
-0.02485201321542263,
0.1504194587469101,
-0.02221675217151642,
0.06264086812734604,
0.09631248563528061,
0.053010422736406326,
-0.08236527442932129,
0.011875933036208153,
0.02107503078877926,
-0.043893855065107346,
-0.20206905901432037,
-0.02455109916627407,
-0.055745434015989304,
0.024452706798911095,
0.11532095819711685,
0.007761124521493912,
0.035049326717853546,
0.08070503175258636,
0.034222863614559174,
0.09296494722366333,
-0.03978166729211807,
0.06705260276794434,
0.09173208475112915,
0.040664687752723694,
0.1285676807165146,
-0.02663811668753624,
-0.05917946994304657,
0.03242690861225128,
0.011330103501677513,
0.19615039229393005,
-0.009373449720442295,
0.20156331360340118,
0.032291460782289505,
0.15498697757720947,
-0.0010564557742327452,
0.08369356393814087,
-0.014710722491145134,
-0.0214210394769907,
-0.02648720145225525,
-0.04048586264252663,
-0.05702728405594826,
0.013919324614107609,
-0.032597292214632034,
0.06492257863283157,
-0.12426115572452545,
0.007493156474083662,
0.03129393979907036,
0.23850788176059723,
0.05887012183666229,
-0.36489030718803406,
-0.1106652170419693,
0.0065041594207286835,
-0.026808777824044228,
-0.035205431282520294,
0.022671692073345184,
0.13791674375534058,
-0.09658671170473099,
0.007798857521265745,
-0.08875349909067154,
0.0957459881901741,
-0.07007870078086853,
0.06062912195920944,
0.05597538501024246,
0.07270423322916031,
-0.009864714927971363,
0.08510639518499374,
-0.2646436393260956,
0.26567530632019043,
0.010244968347251415,
0.024983251467347145,
-0.07139675319194794,
-0.020740801468491554,
0.005712062120437622,
0.044399309903383255,
0.08061598241329193,
-0.0028259011451154947,
0.026348065584897995,
-0.20926323533058167,
-0.07175976037979126,
0.019231561571359634,
0.1060052365064621,
-0.0852772668004036,
0.1252749115228653,
-0.03626513481140137,
0.012466403655707836,
0.06012581288814545,
0.0294849444180727,
-0.021669352427124977,
-0.10336462408304214,
0.022995183244347572,
0.022509099915623665,
-0.02344200387597084,
-0.053027890622615814,
-0.10989664494991302,
-0.09556565433740616,
0.13688647747039795,
-0.011782022193074226,
-0.05062823370099068,
-0.11239292472600937,
0.08722682297229767,
0.07554320991039276,
-0.10337405651807785,
0.0033031993079930544,
0.004759280011057854,
0.08320991694927216,
0.012824726291000843,
-0.06869152933359146,
0.11388908326625824,
-0.04748409241437912,
-0.1659952849149704,
-0.045753564685583115,
0.13247646391391754,
0.03850533813238144,
0.06085497885942459,
-0.023980602622032166,
0.009089206345379353,
-0.06291390210390091,
-0.06886947900056839,
0.030844353139400482,
-0.016694484278559685,
0.05612017586827278,
0.01364199910312891,
-0.019094640389084816,
0.052622802555561066,
-0.0765843316912651,
-0.019563013687729836,
0.20178432762622833,
0.2359534353017807,
-0.07618433982133865,
-0.005636718589812517,
0.054743047803640366,
-0.062000781297683716,
-0.17917783558368683,
0.03499739617109299,
0.06646290421485901,
0.016469575464725494,
0.051147568970918655,
-0.17624080181121826,
0.07445341348648071,
0.07482858747243881,
0.004213227890431881,
0.104732945561409,
-0.35477378964424133,
-0.12407789379358292,
0.08845802396535873,
0.1507720947265625,
0.12904518842697144,
-0.14049115777015686,
-0.03426070138812065,
-0.019631842151284218,
-0.15300865471363068,
0.11324676126241684,
-0.0660524070262909,
0.1397533118724823,
-0.022951459512114525,
0.11222439259290695,
0.025674832984805107,
-0.06091678887605667,
0.12254446744918823,
0.018481770530343056,
0.07058332860469818,
-0.057890936732292175,
-0.03235486149787903,
0.06357522308826447,
-0.04974477365612984,
0.013858463615179062,
-0.08485979586839676,
0.032363295555114746,
-0.12633977830410004,
-0.03725280240178108,
-0.08917655795812607,
0.010101281106472015,
-0.03475666046142578,
-0.07591066509485245,
-0.01850280724465847,
0.02512645721435547,
0.05800189822912216,
-0.0038036732003092766,
0.13983456790447235,
-0.0041191792115569115,
0.1327277421951294,
0.14394435286521912,
0.08411548286676407,
-0.047292470932006836,
-0.06776440143585205,
-0.027899879962205887,
-0.01165557000786066,
0.049793291836977005,
-0.14211437106132507,
0.023204324766993523,
0.14460144937038422,
0.022188011556863785,
0.14698119461536407,
0.07972295582294464,
-0.03799492120742798,
0.01344555988907814,
0.04415521025657654,
-0.1898542046546936,
-0.13568857312202454,
-0.05381710082292557,
-0.06256797909736633,
-0.14456912875175476,
0.034538108855485916,
0.13479746878147125,
-0.06627839803695679,
-0.005813077557832003,
-0.01361168920993805,
0.005463586188852787,
-0.04325440898537636,
0.19285036623477936,
0.049665335565805435,
0.059440575540065765,
-0.0901356041431427,
0.06587804853916168,
0.05376181751489639,
-0.07427607476711273,
0.011483541689813137,
0.07216204702854156,
-0.08689289540052414,
-0.04113250598311424,
0.0004796424473170191,
0.17255167663097382,
-0.06949758529663086,
-0.00782988965511322,
-0.15394414961338043,
-0.09244858473539352,
0.06321394443511963,
0.12065427750349045,
0.093609519302845,
0.013792373239994049,
-0.06757332384586334,
-0.0014389370335265994,
-0.12796510756015778,
0.10911466181278229,
0.06661643087863922,
0.0743437185883522,
-0.13960669934749603,
0.1669340431690216,
-0.01472312118858099,
0.06309062242507935,
-0.018414989113807678,
0.00526881217956543,
-0.0942457765340805,
0.016482926905155182,
-0.11979763209819794,
-0.028711194172501564,
-0.017412666231393814,
-0.007925674319267273,
-0.02963428944349289,
-0.056428369134664536,
-0.0513606034219265,
0.0021314327605068684,
-0.1183808296918869,
-0.027786053717136383,
0.037159740924835205,
0.03171360865235329,
-0.08544643968343735,
-0.02448243461549282,
0.027139663696289062,
-0.06882205605506897,
0.08638905733823776,
0.04110212251543999,
-0.00012752260954584926,
0.043659619987010956,
-0.08062781393527985,
0.032736074179410934,
0.024610891938209534,
0.01429933961480856,
0.04740913584828377,
-0.05832435563206673,
-0.010674666613340378,
-0.031749505549669266,
0.05245659872889519,
0.018067283555865288,
0.09071727842092514,
-0.12808115780353546,
0.024396151304244995,
-0.01971321739256382,
-0.04723051190376282,
-0.0873897448182106,
0.0522678941488266,
0.03237037733197212,
0.05411618575453758,
0.17966414988040924,
-0.06380388885736465,
0.05598807707428932,
-0.21672235429286957,
-0.0009439538116566837,
0.017880966886878014,
-0.12175183743238449,
-0.07429616898298264,
-0.08526328951120377,
0.06577122956514359,
-0.07165410369634628,
0.08928274363279343,
0.007163198199123144,
0.04599053040146828,
0.015425685793161392,
-0.013273127377033234,
0.029242413118481636,
-0.0021599854808300734,
0.20797015726566315,
0.021039988845586777,
-0.045666489750146866,
0.06148066371679306,
0.053232643753290176,
0.10892506688833237,
0.18480288982391357,
0.19697199761867523,
0.14888998866081238,
-0.01180803682655096,
0.08532299101352692,
0.012032845057547092,
-0.026215296238660812,
-0.19024434685707092,
0.02526647038757801,
-0.029139967635273933,
0.10761931538581848,
-0.019621150568127632,
0.22047936916351318,
0.08585403859615326,
-0.15253011882305145,
0.02652900107204914,
-0.04342740401625633,
-0.09028571099042892,
-0.09434922784566879,
-0.1054535061120987,
-0.07622504234313965,
-0.12466257065534592,
-0.00924481451511383,
-0.1200873851776123,
0.018467430025339127,
0.12350839376449585,
0.01847468689084053,
-0.03345026820898056,
0.17971186339855194,
0.023012014105916023,
-0.01612667925655842,
0.06983274966478348,
0.0005830178270116448,
-0.01168929785490036,
-0.09141409397125244,
-0.07643794268369675,
0.00873758364468813,
0.011849329806864262,
0.045003872364759445,
-0.0571669302880764,
-0.053561195731163025,
0.021158600226044655,
-0.02375832013785839,
-0.12179611623287201,
-0.008990729227662086,
0.028691109269857407,
0.06521307677030563,
0.029601536691188812,
-0.0025359257124364376,
-0.0027129645459353924,
-0.029080107808113098,
0.2381495237350464,
-0.08546233177185059,
-0.03052346035838127,
-0.08457199484109879,
0.20631572604179382,
0.01742284744977951,
-0.0184982530772686,
0.02886461466550827,
-0.07630789279937744,
0.016031304374337196,
0.2628134787082672,
0.19123020768165588,
-0.11069875955581665,
-0.009602580219507217,
0.02004328928887844,
0.003959952853620052,
0.003056860761716962,
0.10381925851106644,
0.0952906534075737,
0.03010847605764866,
-0.0985950455069542,
-0.01877722702920437,
-0.05483366921544075,
-0.010615571402013302,
-0.010334235616028309,
0.08158329129219055,
0.04801483079791069,
0.01809796877205372,
-0.06115364283323288,
0.057133302092552185,
-0.07882513850927353,
-0.10489687323570251,
0.015047763474285603,
-0.21812622249126434,
-0.15430127084255219,
-0.026992086321115494,
0.0862409695982933,
0.0009460148285143077,
0.07524008303880692,
-0.032421525567770004,
0.017369817942380905,
0.06236458569765091,
-0.0030545194167643785,
-0.08221832662820816,
-0.06251957267522812,
0.09354276955127716,
-0.11502611637115479,
0.17406325042247772,
-0.050186894834041595,
0.028856346383690834,
0.12190888077020645,
0.04716724157333374,
-0.07578524947166443,
0.06421569734811783,
0.04703047499060631,
-0.01750980131328106,
0.02627057395875454,
0.124296173453331,
-0.019520321860909462,
0.09641355276107788,
0.04136328771710396,
-0.14797455072402954,
0.004058438818901777,
-0.04112648963928223,
-0.02636597864329815,
-0.03798500448465347,
-0.06659182906150818,
-0.045060768723487854,
0.14509902894496918,
0.22133317589759827,
-0.05027059465646744,
-0.01862996816635132,
-0.06728777289390564,
0.00022357363195624202,
0.06746768951416016,
0.04599805921316147,
-0.05905519798398018,
-0.24511601030826569,
-0.023602943867444992,
0.03520466014742851,
-0.004474869463592768,
-0.2902645170688629,
-0.07854700088500977,
0.0017739227041602135,
-0.055823903530836105,
-0.08007501810789108,
0.1018914058804512,
0.0882430300116539,
0.04882802441716194,
-0.054092857986688614,
-0.00949146132916212,
-0.07811802625656128,
0.16588880121707916,
-0.1573590487241745,
-0.09565908461809158
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro
This model is a fine-tuned version of [patrickvonplaten/t5-tiny-random](https://huggingface.co/patrickvonplaten/t5-tiny-random) on the wmt16_en_ro_pre_processed dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "model-index": [{"name": "t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro", "results": []}]}
|
text2text-generation
|
ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro
This model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
|
[
"# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro\n\nThis model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1",
"### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.10.0+cu102\n- Datasets 1.15.1\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro\n\nThis model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1",
"### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.10.0+cu102\n- Datasets 1.15.1\n- Tokenizers 0.10.3"
] |
[
75,
76,
6,
12,
8,
3,
90,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro\n\nThis model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16_en_ro_pre_processed dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.10.0+cu102\n- Datasets 1.15.1\n- Tokenizers 0.10.3"
] |
[
-0.07418181002140045,
0.12743863463401794,
-0.00356648163869977,
0.06326070427894592,
0.13032041490077972,
0.04047324135899544,
0.09316591918468475,
0.1340050995349884,
-0.11097250878810883,
0.120153047144413,
0.09673666954040527,
0.04853234440088272,
0.08074729889631271,
0.12553581595420837,
-0.0236589964479208,
-0.23097194731235504,
0.01008811965584755,
-0.01663970574736595,
-0.03954813629388809,
0.09594252705574036,
0.09988157451152802,
-0.09760366380214691,
0.08205656707286835,
0.01597023755311966,
-0.138411283493042,
0.009813698008656502,
-0.04016759619116783,
-0.0680353119969368,
0.07562119513750076,
0.04428306594491005,
0.06475557386875153,
0.021186724305152893,
0.11133276671171188,
-0.22338995337486267,
-0.010042744688689709,
0.07364706695079803,
0.03161165490746498,
0.09939579665660858,
0.09961281716823578,
0.016189245507121086,
0.07015498727560043,
-0.13938596844673157,
0.06354567408561707,
0.03972258046269417,
-0.0699368342757225,
-0.11338455229997635,
-0.07076487690210342,
0.08011937141418457,
0.04098479077219963,
0.09782573580741882,
-0.0061334893107414246,
0.1627500206232071,
-0.060032304376363754,
0.057182930409908295,
0.20695017278194427,
-0.24249766767024994,
-0.07314903289079666,
0.034531522542238235,
0.07007107883691788,
0.047561392188072205,
-0.09048281610012054,
-0.017405908554792404,
0.0621376596391201,
0.026094399392604828,
0.06708939373493195,
0.0006017988780513406,
-0.03146769851446152,
-0.015506349503993988,
-0.12239054590463638,
-0.03223931044340134,
0.1490137279033661,
0.04911011457443237,
-0.022885944694280624,
-0.11340128630399704,
-0.06934042274951935,
-0.13799405097961426,
-0.02266167290508747,
-0.009799162857234478,
0.0088186739012599,
-0.050364065915346146,
-0.07263348996639252,
-0.004844512324780226,
-0.05490795150399208,
-0.027646834030747414,
0.01747892051935196,
0.10004136711359024,
0.04559042304754257,
0.007825921289622784,
-0.03303985297679901,
0.10328050702810287,
0.008754858747124672,
-0.13964426517486572,
-0.026708856225013733,
-0.007394325453788042,
-0.09020762890577316,
-0.05835678055882454,
-0.032734766602516174,
0.032611455768346786,
0.013623115606606007,
0.18710733950138092,
-0.03273444622755051,
0.06299522519111633,
0.031595733016729355,
-0.018826929852366447,
-0.0051434226334095,
0.128192737698555,
-0.08171398192644119,
-0.08820155262947083,
-0.011304512619972229,
0.10627055168151855,
-0.004672045819461346,
-0.009565997868776321,
-0.07754439115524292,
-0.02940073050558567,
0.09914620965719223,
0.04510268196463585,
0.0055306460708379745,
0.04430462419986725,
-0.020244065672159195,
-0.04351089149713516,
0.04559120535850525,
-0.12775805592536926,
0.032773226499557495,
0.013585792854428291,
-0.08118696510791779,
0.025997048243880272,
-0.0053815217688679695,
-0.016443170607089996,
-0.06636925786733627,
0.06304613500833511,
-0.08394178003072739,
-0.025694862008094788,
-0.06745991855859756,
-0.07279670983552933,
0.013870961032807827,
-0.04865380376577377,
-0.001223134808242321,
-0.0820317417383194,
-0.1456955075263977,
-0.04446931555867195,
0.02364944852888584,
-0.08965185284614563,
-0.09049931913614273,
-0.02030479907989502,
-0.07050300389528275,
0.03730315715074539,
-0.008392253890633583,
0.16649635136127472,
-0.03143835440278053,
0.05573509261012077,
0.03597058728337288,
0.03221584111452103,
0.08298374712467194,
0.03473685681819916,
-0.0818534716963768,
0.0365743413567543,
-0.05046025291085243,
0.09699501097202301,
-0.07107671350240707,
0.03194189816713333,
-0.13426576554775238,
-0.11203917860984802,
-0.017597895115613937,
-0.03298012167215347,
0.06275356560945511,
0.1119319349527359,
-0.11893519759178162,
-0.034151531755924225,
0.12526831030845642,
-0.019920246675610542,
-0.06646004319190979,
0.08826473355293274,
-0.021628880873322487,
0.0003178598126396537,
0.02736332081258297,
0.08113457262516022,
0.12954272329807281,
-0.09518667310476303,
-0.052185047417879105,
0.023403076454997063,
0.07781252264976501,
0.03934044763445854,
0.0624811127781868,
0.0018280921503901482,
-0.008816225454211235,
0.018609825521707535,
-0.06549667567014694,
-0.005801456980407238,
-0.07840068638324738,
-0.08725499361753464,
-0.05035070702433586,
-0.06674111634492874,
0.03860640898346901,
0.028551243245601654,
0.04484712705016136,
-0.053192902356386185,
-0.13935424387454987,
0.0415179543197155,
0.1405262053012848,
-0.054143305867910385,
0.011948841623961926,
-0.08023544400930405,
0.040147602558135986,
-0.04151606187224388,
-0.005709582474082708,
-0.19999702274799347,
-0.08732941001653671,
0.05664153769612312,
-0.07153180986642838,
0.022754771634936333,
0.007709439843893051,
0.057923343032598495,
0.062029965221881866,
-0.01580214500427246,
-0.03684321045875549,
-0.05422186106443405,
-0.03234400972723961,
-0.08406432718038559,
-0.16227418184280396,
-0.08486375212669373,
-0.014120729640126228,
0.17601971328258514,
-0.2203109860420227,
0.004243309143930674,
-0.0052683399990201,
0.1379135549068451,
-0.005586687009781599,
-0.04404732957482338,
-0.014010963030159473,
0.025996381416916847,
-0.05044574290513992,
-0.09229596704244614,
0.023299112915992737,
-0.009920947253704071,
-0.06651455163955688,
-0.04988516494631767,
-0.15391938388347626,
-0.0054778908379375935,
0.09011385589838028,
0.05489550530910492,
-0.09547069668769836,
0.008363653905689716,
-0.07622798532247543,
-0.05164499953389168,
-0.0622459277510643,
-0.021924979984760284,
0.15931279957294464,
0.012444806285202503,
0.11249688267707825,
-0.051296133548021317,
-0.07756978273391724,
0.025775916874408722,
0.028047895058989525,
-0.035445939749479294,
0.09379537403583527,
0.08629599213600159,
-0.08764080703258514,
0.08276248723268509,
0.03347956761717796,
-0.02311287261545658,
0.11637216806411743,
-0.03443247079849243,
-0.1046655923128128,
-0.021715277805924416,
0.005822309758514166,
0.015407344326376915,
0.06093396246433258,
-0.08842355012893677,
0.0014797371113672853,
0.06399517506361008,
-0.007040772121399641,
0.03204115107655525,
-0.13346007466316223,
0.017546288669109344,
0.03217848390340805,
-0.018653182312846184,
0.03356495127081871,
-0.027898449450731277,
0.02948947437107563,
0.07612942159175873,
0.03747326880693436,
-0.019404614344239235,
0.017307231202721596,
-0.014600570313632488,
-0.0826447606086731,
0.17266122996807098,
-0.0788344070315361,
-0.15374226868152618,
-0.14060451090335846,
-0.020948002114892006,
-0.05573057383298874,
-0.02867957577109337,
-0.002963604172691703,
-0.0680164098739624,
-0.07973427325487137,
-0.08152998238801956,
0.01612657494843006,
-0.05564262345433235,
0.012188361026346684,
0.08839647471904755,
0.020875809714198112,
0.10083343088626862,
-0.11103734374046326,
0.032457828521728516,
0.011362293735146523,
-0.07097893953323364,
0.009389606304466724,
0.04414244368672371,
0.10919036716222763,
0.1256340891122818,
-0.0008258925518020988,
0.011875634081661701,
-0.017013829201459885,
0.24065004289150238,
-0.0691148191690445,
0.00410713255405426,
0.1087549477815628,
0.010778593830764294,
0.06424201279878616,
0.12822693586349487,
0.03108612820506096,
-0.06171835586428642,
0.035767048597335815,
0.0484420582652092,
-0.008411379531025887,
-0.24128128588199615,
-0.04962819069623947,
-0.027945145964622498,
-0.029467113316059113,
0.16368407011032104,
0.03757195547223091,
0.009025441482663155,
0.06716599315404892,
-0.05610543116927147,
0.10361802577972412,
-0.01816912740468979,
0.08827796578407288,
0.07687577605247498,
0.06793363392353058,
0.10026531666517258,
-0.014918338507413864,
-0.03585238382220268,
0.06182241067290306,
-0.0006296408828347921,
0.22199572622776031,
-0.038389626890420914,
0.18712952733039856,
-0.008429111912846565,
0.15538783371448517,
-0.008692358620464802,
0.04440055415034294,
0.020818686112761497,
0.017223086208105087,
0.026179272681474686,
-0.07179425656795502,
-0.01357956137508154,
0.03667986020445824,
-0.007977474480867386,
0.05514978989958763,
-0.0748305469751358,
0.06894850730895996,
0.007007031701505184,
0.24034208059310913,
0.05654946714639664,
-0.3187890946865082,
-0.07468138635158539,
0.014191573485732079,
-0.023613421246409416,
-0.07782180607318878,
0.010659796185791492,
0.08548946678638458,
-0.13244949281215668,
0.07142260670661926,
-0.06885404884815216,
0.09199321269989014,
-0.03508928790688515,
0.003306146478280425,
0.08750665932893753,
0.1518699675798416,
0.019173281267285347,
0.10234366357326508,
-0.1606743037700653,
0.1694195568561554,
0.019257867708802223,
0.0821535512804985,
-0.08973142504692078,
0.05343689024448395,
-0.016948893666267395,
0.027221858501434326,
0.10281180590391159,
-0.012193537317216396,
-0.051175955682992935,
-0.15047839283943176,
-0.11889006197452545,
0.006645054556429386,
0.12129858136177063,
-0.046689458191394806,
0.12056874483823776,
-0.06581755727529526,
-0.018435314297676086,
0.03066038154065609,
-0.06916983425617218,
-0.13278928399085999,
-0.1625521034002304,
0.05597778782248497,
-0.011415330693125725,
-0.040472667664289474,
-0.07831451296806335,
-0.10598127543926239,
-0.0771765485405922,
0.2105577141046524,
-0.042181797325611115,
-0.036761436611413956,
-0.14082497358322144,
0.10708753764629364,
0.15522506833076477,
-0.08377961814403534,
0.002293901052325964,
-0.0008664841298013926,
0.16612695157527924,
0.009776060469448566,
-0.10533087700605392,
0.05353928729891777,
-0.06780482083559036,
-0.1872951090335846,
-0.03757864609360695,
0.13614852726459503,
0.024559490382671356,
0.056567490100860596,
-0.023291274905204773,
0.012704006396234035,
-0.006541674956679344,
-0.0820838138461113,
-0.011698796413838863,
0.06695874035358429,
0.03975372016429901,
0.03396221622824669,
-0.040649790316820145,
-0.008962134830653667,
-0.06324262917041779,
0.004388037603348494,
0.11429882049560547,
0.18836210668087006,
-0.09662864357233047,
0.040995776653289795,
0.043330349028110504,
-0.061448197811841965,
-0.1817743182182312,
0.036562446504831314,
0.11946088820695877,
0.014114661142230034,
0.020509691908955574,
-0.17891372740268707,
0.09732168912887573,
0.07944983243942261,
-0.0027222889475524426,
0.06188669800758362,
-0.3293774127960205,
-0.12965713441371918,
0.06273695826530457,
0.05552852526307106,
0.008916239254176617,
-0.1301513910293579,
-0.06609053164720535,
-0.041755713522434235,
-0.13690564036369324,
0.07517316192388535,
-0.02951027825474739,
0.0982368066906929,
-0.016027705743908882,
0.08214469999074936,
0.0323435477912426,
-0.051456864923238754,
0.14201174676418304,
0.037779275327920914,
0.03137131780385971,
-0.05509347468614578,
0.016241295263171196,
0.08169806003570557,
-0.06947203725576401,
0.0411139652132988,
-0.026649830862879753,
0.06387108564376831,
-0.1404699981212616,
-0.03429039195179939,
-0.06461954861879349,
0.06755191087722778,
-0.06164070591330528,
-0.044864851981401443,
-0.044262927025556564,
0.04132939875125885,
0.07083117216825485,
-0.024036090821027756,
0.06589724123477936,
0.045250896364450455,
0.04306233301758766,
0.1497763693332672,
0.08774790167808533,
0.019221346825361252,
-0.11359357088804245,
0.0005660988972522318,
-0.0076027195900678635,
0.054767899215221405,
-0.09163688123226166,
0.017556915059685707,
0.12834468483924866,
0.04409150406718254,
0.10971633344888687,
0.03143804520368576,
-0.07819853723049164,
-0.022266652435064316,
0.030767589807510376,
-0.10817781835794449,
-0.15834921598434448,
-0.04605187848210335,
-0.007232364267110825,
-0.18097898364067078,
-0.0228995680809021,
0.10984302312135696,
-0.05294066667556763,
-0.01975387893617153,
-0.008713576011359692,
0.028635531663894653,
0.0012321521062403917,
0.19443555176258087,
0.025026312097907066,
0.07787635177373886,
-0.09553048759698868,
0.09207576513290405,
0.10201539099216461,
-0.05066460371017456,
0.03501662611961365,
0.09071191400289536,
-0.081886425614357,
-0.015289386734366417,
0.05622285231947899,
0.07359595596790314,
-0.04754742607474327,
-0.020814819261431694,
-0.05212448537349701,
-0.09592626988887787,
0.07381751388311386,
0.021497247740626335,
0.04268088564276695,
-0.014974446035921574,
-0.015023821033537388,
0.001653366256505251,
-0.15106698870658875,
0.10117831081151962,
0.0661628469824791,
0.09249754250049591,
-0.13643452525138855,
0.1194264218211174,
-0.0006586590898223221,
0.0580153726041317,
-0.00994501356035471,
0.0202805008739233,
-0.0756177008152008,
-0.035833101719617844,
-0.1313917338848114,
0.01531313732266426,
-0.05698670819401741,
0.005207360722124577,
-0.015663521364331245,
-0.023979248479008675,
-0.04911723732948303,
0.04180021956562996,
-0.04995500296354294,
-0.09079337865114212,
-0.015369709581136703,
0.05279660224914551,
-0.10603249818086624,
0.018662551417946815,
0.02375922165811062,
-0.12352988123893738,
0.08060982823371887,
0.047349780797958374,
0.031213412061333656,
0.007696637883782387,
-0.018956277519464493,
-0.03159578889608383,
0.01892700605094433,
0.05599585920572281,
0.07301251590251923,
-0.10063803941011429,
0.004644182510674,
-0.038783516734838486,
0.02902538888156414,
-0.005112755112349987,
0.02019549533724785,
-0.1446962058544159,
-0.006531673017889261,
-0.06809398531913757,
-0.029806245118379593,
-0.06263609230518341,
0.04706573486328125,
0.06342793256044388,
-0.011169638484716415,
0.15515197813510895,
-0.06494840979576111,
0.06363890320062637,
-0.21963545680046082,
-0.01651011034846306,
-0.008863434195518494,
-0.02028803341090679,
-0.036094553768634796,
-0.011042412370443344,
0.08827535808086395,
-0.05048205703496933,
0.11114952713251114,
-0.02984660491347313,
0.0786038339138031,
0.03618514910340309,
-0.02959398925304413,
-0.024951301515102386,
0.01916293241083622,
0.17685620486736298,
0.09403638541698456,
-0.00952405110001564,
0.06943081319332123,
-0.024842334911227226,
0.08234786987304688,
0.06426641345024109,
0.1540626734495163,
0.1287890374660492,
-0.006258445791900158,
0.07353080064058304,
0.06776217371225357,
-0.12801721692085266,
-0.14458538591861725,
0.13585186004638672,
-0.07969573140144348,
0.12013901770114899,
-0.0347411148250103,
0.10686350613832474,
0.11692016571760178,
-0.15981332957744598,
0.011673422530293465,
-0.06199651584029198,
-0.09860962629318237,
-0.10974232107400894,
-0.09438035637140274,
-0.10033067315816879,
-0.10078784823417664,
0.028502890840172768,
-0.11510713398456573,
0.02643214538693428,
0.05819004029035568,
0.014603934250772,
0.010110881179571152,
0.12985283136367798,
-0.005043017212301493,
-0.012275430373847485,
0.05230393260717392,
0.027823040261864662,
0.0005674578133039176,
-0.03406054154038429,
-0.05348987132310867,
0.04943283647298813,
0.015342378988862038,
0.09448172897100449,
-0.040379468351602554,
0.021871598437428474,
0.06511974334716797,
0.004069601651281118,
-0.0853264182806015,
0.015379414893686771,
0.008129558525979519,
0.012764993123710155,
0.04849393665790558,
0.03744901716709137,
0.02562381885945797,
-0.05953989923000336,
0.21017098426818848,
-0.07375449687242508,
-0.075953409075737,
-0.1397092193365097,
0.15326528251171112,
0.02882455661892891,
-0.00778298219665885,
0.08070196956396103,
-0.09701081365346909,
-0.02925402671098709,
0.20297187566757202,
0.164110466837883,
-0.03754974156618118,
-0.03726063296198845,
0.022705689072608948,
-0.014215352013707161,
-0.04007003828883171,
0.10151498764753342,
0.10333559662103653,
0.01676959916949272,
-0.05418608337640762,
-0.0289749875664711,
0.00603037653490901,
-0.03227294236421585,
-0.07356574386358261,
0.08055533468723297,
0.011927317827939987,
0.01607964001595974,
-0.0245932936668396,
0.035373132675886154,
-0.02094629965722561,
-0.16873924434185028,
0.045716799795627594,
-0.13541458547115326,
-0.1842414289712906,
-0.01857091300189495,
0.08616283535957336,
-0.014447744935750961,
0.0671057403087616,
-0.004475873429328203,
-0.02636061981320381,
0.14274567365646362,
-0.01367342472076416,
-0.09069297462701797,
-0.05468224734067917,
0.06964804977178574,
-0.06741304695606232,
0.19863082468509674,
-0.003127173986285925,
0.046243153512477875,
0.11094433069229126,
0.019602058455348015,
-0.18273566663265228,
0.010192499496042728,
0.08989924192428589,
-0.04916628450155258,
0.053228266537189484,
0.1720985323190689,
-0.03242483735084534,
0.08345486223697662,
0.05155443027615547,
-0.1312558501958847,
-0.03615700453519821,
0.006474698428064585,
0.03280748426914215,
-0.08905409276485443,
-0.02105553261935711,
-0.0447988323867321,
0.16260389983654022,
0.22200174629688263,
-0.05104714259505272,
-0.02415650337934494,
-0.06051218509674072,
0.013531175442039967,
0.01869555190205574,
0.13112694025039673,
-0.019703056663274765,
-0.21707351505756378,
-0.004496349021792412,
-0.04290616512298584,
0.05673912912607193,
-0.22065535187721252,
-0.07282544672489166,
0.04584036394953728,
-0.053826507180929184,
-0.07083562016487122,
0.14242050051689148,
0.04824373871088028,
0.031555306166410446,
-0.03739641606807709,
-0.06304434686899185,
-0.05854129046201706,
0.13423623144626617,
-0.17234298586845398,
-0.061630044132471085
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.02-finetuned-en-to-ro
This model is a fine-tuned version of [patrickvonplaten/t5-tiny-random](https://huggingface.co/patrickvonplaten/t5-tiny-random) on the wmt16_en_ro_pre_processed dataset.
It achieves the following results on the evaluation set:
- Loss: 6.4854
- Bleu: 0.0002
- Gen Len: 9.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 6.2568 | 1.0 | 76290 | 6.4854 | 0.0002 | 9.0 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "metrics": ["bleu"], "model-index": [{"name": "t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.02-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16_en_ro_pre_processed", "type": "wmt16_en_ro_pre_processed", "args": "enro"}, "metrics": [{"type": "bleu", "value": 0.0002, "name": "Bleu"}]}]}]}
|
text2text-generation
|
ffsouza/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.02-finetuned-en-to-ro
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
t5-tiny-random-length-96-learning\_rate-2e-05-weight\_decay-0.02-finetuned-en-to-ro
===================================================================================
This model is a fine-tuned version of patrickvonplaten/t5-tiny-random on the wmt16\_en\_ro\_pre\_processed dataset.
It achieves the following results on the evaluation set:
* Loss: 6.4854
* Bleu: 0.0002
* Gen Len: 9.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu102
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu102\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu102\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
79,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu102\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
-0.10774723440408707,
0.08628731220960617,
-0.001870359992608428,
0.11920102685689926,
0.14882314205169678,
0.02130948193371296,
0.14098460972309113,
0.12969866394996643,
-0.06249110400676727,
0.0388583280146122,
0.1491231918334961,
0.13375863432884216,
0.02812032401561737,
0.12197306752204895,
-0.04938492923974991,
-0.24534083902835846,
-0.005457524675875902,
0.05262937396764755,
-0.037154871970415115,
0.15240593254566193,
0.08033008873462677,
-0.12989507615566254,
0.10782449692487717,
-0.005903293844312429,
-0.1886114776134491,
-0.012816695496439934,
0.003559398464858532,
-0.03731314465403557,
0.14121417701244354,
0.0262891985476017,
0.10258346796035767,
0.02521291747689247,
0.06673301756381989,
-0.16061021387577057,
0.011788610368967056,
0.042155418545007706,
0.016526076942682266,
0.10150386393070221,
0.05959181487560272,
-0.00511342054232955,
0.08273180574178696,
-0.07050734758377075,
0.035208940505981445,
0.013182499445974827,
-0.13450658321380615,
-0.19015681743621826,
-0.05159010738134384,
0.018215501680970192,
0.05385340005159378,
0.10720022767782211,
-0.02583475597202778,
0.13096685707569122,
-0.0367567203938961,
0.10871986299753189,
0.21426399052143097,
-0.272760808467865,
-0.06669247895479202,
0.021960493177175522,
0.04315323382616043,
0.11682489514350891,
-0.09002728015184402,
-0.01871895045042038,
0.04285798221826553,
0.03973958268761635,
0.12394102662801743,
-0.0284748412668705,
-0.023888489231467247,
0.007423250004649162,
-0.13884533941745758,
-0.04406731203198433,
0.17523221671581268,
0.04865052551031113,
-0.02777033858001232,
-0.050285592675209045,
-0.08898360282182693,
-0.1863611936569214,
-0.011553222313523293,
-0.002635424956679344,
0.023610278964042664,
-0.03352053835988045,
-0.10198689252138138,
-0.03804132714867592,
-0.103229820728302,
-0.062406379729509354,
-0.029041122645139694,
0.1368304044008255,
0.022753313183784485,
0.004293069709092379,
-0.04102432355284691,
0.12000657618045807,
0.004691520240157843,
-0.14708617329597473,
0.03361368551850319,
0.020406421273946762,
0.0003251024172641337,
-0.03370055928826332,
-0.060449786484241486,
-0.11632466316223145,
-0.01761735789477825,
0.09170252829790115,
-0.034017208963632584,
0.04675908386707306,
0.033480074256658554,
0.03902272507548332,
-0.0737721174955368,
0.18502630293369293,
-0.049542851746082306,
-0.06402894109487534,
-0.018068863078951836,
0.09790397435426712,
0.013162719085812569,
-0.029897315427660942,
-0.13339664041996002,
-0.0053167203441262245,
0.12896905839443207,
-0.009364540688693523,
-0.04509549215435982,
0.0744059607386589,
-0.051193851977586746,
-0.057931166142225266,
-0.01662680134177208,
-0.08285773545503616,
-0.0003395781386643648,
-0.01206312794238329,
-0.07701395452022552,
-0.014723146334290504,
0.01876666396856308,
0.013187197968363762,
-0.02162221446633339,
0.08588190376758575,
-0.11482267081737518,
0.014977783896028996,
-0.07675927877426147,
-0.12095070630311966,
-0.011725707910954952,
-0.09122127294540405,
0.016112180426716805,
-0.0896715298295021,
-0.19030073285102844,
-0.016972217708826065,
0.05427630618214607,
-0.03608277812600136,
-0.0574197918176651,
-0.07396691292524338,
-0.0533733107149601,
0.02010693959891796,
-0.008175469003617764,
0.12689027190208435,
-0.059430502355098724,
0.11810141056776047,
0.04683571308851242,
0.06695125997066498,
-0.043724898248910904,
0.057768139988183975,
-0.10209804773330688,
0.029261110350489616,
-0.13367193937301636,
0.07102397829294205,
-0.010290442034602165,
0.05914675444364548,
-0.10255616903305054,
-0.10174454748630524,
-0.022925028577446938,
-0.013626717031002045,
0.0746566578745842,
0.10308433324098587,
-0.1571042686700821,
-0.08619727939367294,
0.17436569929122925,
-0.045112814754247665,
-0.12421849370002747,
0.14652688801288605,
-0.06280937045812607,
0.07533766329288483,
0.0630154088139534,
0.20517362654209137,
0.05327320471405983,
-0.048405442386865616,
0.0197143517434597,
0.0011833757162094116,
0.0877043828368187,
-0.044013481587171555,
0.10025973618030548,
0.005348844453692436,
-0.005279725883156061,
0.010656893253326416,
-0.03898818418383598,
0.05500559136271477,
-0.09780627489089966,
-0.08607520908117294,
-0.01811862178146839,
-0.10186000913381577,
0.0732976496219635,
0.05400490388274193,
0.08287518471479416,
-0.09522616118192673,
-0.09692344814538956,
0.022586438804864883,
0.07647441327571869,
-0.08911005407571793,
0.024221371859312057,
-0.06453760713338852,
0.07425585389137268,
-0.09637979418039322,
-0.022269582375884056,
-0.17849101126194,
-0.008913354948163033,
0.016279814764857292,
0.030681060627102852,
0.025591345503926277,
0.039917297661304474,
0.07837951928377151,
0.07755965739488602,
-0.049786314368247986,
-0.03269043192267418,
-0.034031301736831665,
-0.010211145505309105,
-0.13873407244682312,
-0.16902174055576324,
-0.043915316462516785,
-0.022279683500528336,
0.18226222693920135,
-0.21647904813289642,
0.032854363322257996,
-0.014207706786692142,
0.05987383797764778,
0.024662600830197334,
-0.03712032362818718,
-0.025545410811901093,
0.05942387506365776,
-0.051046259701251984,
-0.049001339823007584,
0.07638950645923615,
0.00989341177046299,
-0.11827360093593597,
-0.03318874537944794,
-0.15964670479297638,
0.14098739624023438,
0.11633137613534927,
-0.11130397766828537,
-0.0664505660533905,
-0.0002133355737896636,
-0.04710477218031883,
-0.0373370386660099,
-0.024506045505404472,
-0.022135237231850624,
0.18290172517299652,
0.009438975714147091,
0.16548773646354675,
-0.0854150652885437,
-0.04303021356463432,
0.021292874589562416,
-0.02792905457317829,
0.014003908261656761,
0.14203451573848724,
0.07807327061891556,
-0.12720945477485657,
0.13396447896957397,
0.15381291508674622,
-0.0624454990029335,
0.1684192568063736,
-0.010685895569622517,
-0.08137813955545425,
-0.034922730177640915,
-0.012009073980152607,
-0.005393679719418287,
0.07928093522787094,
-0.15248213708400726,
-0.007558057084679604,
0.030449621379375458,
0.015675822272896767,
0.0268548596650362,
-0.19868753850460052,
-0.03191935271024704,
0.06008246913552284,
-0.028934704139828682,
-0.011707316152751446,
-0.008340663276612759,
-0.007458898704499006,
0.10249648988246918,
0.023371202871203423,
-0.0643196776509285,
0.03806531801819801,
-0.0035566750448197126,
-0.07688045501708984,
0.20436513423919678,
-0.05327022448182106,
-0.17928729951381683,
-0.14668089151382446,
-0.09422823041677475,
-0.06242665648460388,
0.02406477928161621,
0.05030794441699982,
-0.07218629121780396,
-0.01645067147910595,
-0.08790940791368484,
0.053258124738931656,
-0.0392012819647789,
0.01812429167330265,
0.031749654561281204,
-0.026748117059469223,
0.058091070502996445,
-0.09266697615385056,
-0.007064655423164368,
-0.031056411564350128,
-0.035527415573596954,
0.057029690593481064,
-0.004624802619218826,
0.12071084976196289,
0.15104196965694427,
-0.007931860163807869,
0.02231030911207199,
-0.019529711455106735,
0.26309239864349365,
-0.07133061438798904,
-0.023551203310489655,
0.1394638866186142,
-0.032084278762340546,
0.06362183392047882,
0.10074799507856369,
0.0527925081551075,
-0.08505625277757645,
0.014647670090198517,
0.020610447973012924,
-0.043524619191884995,
-0.1985921561717987,
-0.025299029424786568,
-0.055112455040216446,
0.013589435257017612,
0.11410301178693771,
0.008727239444851875,
0.03946748003363609,
0.07959052175283432,
0.035921886563301086,
0.09563974291086197,
-0.03733958303928375,
0.06419390439987183,
0.0884065255522728,
0.04582539200782776,
0.13073225319385529,
-0.022594748064875603,
-0.062313538044691086,
0.036969877779483795,
0.004549635574221611,
0.19371940195560455,
-0.0062346076592803,
0.21120691299438477,
0.029419703409075737,
0.146567702293396,
0.0006074634729884565,
0.08088073134422302,
-0.007997007109224796,
-0.020186565816402435,
-0.03248445689678192,
-0.043229103088378906,
-0.06393326818943024,
0.02082190476357937,
-0.030479898676276207,
0.060008201748132706,
-0.11452192068099976,
0.02001105807721615,
0.033499374985694885,
0.24142764508724213,
0.05505506321787834,
-0.36730292439460754,
-0.10524879395961761,
0.008617457002401352,
-0.025427451357245445,
-0.03373785689473152,
0.01874992996454239,
0.13601338863372803,
-0.09514268487691879,
0.015488062053918839,
-0.08975418657064438,
0.09731888025999069,
-0.07740909606218338,
0.06115658953785896,
0.055142488330602646,
0.07405675947666168,
-0.009166877716779709,
0.0825689285993576,
-0.25992628931999207,
0.26444369554519653,
0.01094553992152214,
0.030266771093010902,
-0.07329799979925156,
-0.01840299181640148,
0.007375625893473625,
0.03484370559453964,
0.0791439637541771,
-0.0050027091056108475,
0.021083783358335495,
-0.2003200650215149,
-0.07694539427757263,
0.018995966762304306,
0.10391358286142349,
-0.08437260240316391,
0.1230710968375206,
-0.03859550878405571,
0.01140495017170906,
0.05552784726023674,
0.028621677309274673,
-0.023119643330574036,
-0.1033669039607048,
0.024801483377814293,
0.019575882703065872,
-0.024079790338873863,
-0.051339659839868546,
-0.1068825051188469,
-0.09427852928638458,
0.14668011665344238,
-0.005597354844212532,
-0.045632172375917435,
-0.1093110665678978,
0.08571308106184006,
0.0771670788526535,
-0.09937753528356552,
0.005838858429342508,
0.0036207446828484535,
0.08220444619655609,
0.013866214081645012,
-0.05913907662034035,
0.1134570986032486,
-0.047871485352516174,
-0.1686278134584427,
-0.04876172915101051,
0.1271866261959076,
0.03455254063010216,
0.0634649470448494,
-0.022397847846150398,
0.014753150753676891,
-0.0656997337937355,
-0.06686975061893463,
0.030460715293884277,
-0.023626932874321938,
0.05268034711480141,
0.011320429854094982,
-0.011761435307562351,
0.05621881037950516,
-0.07575412094593048,
-0.02380026876926422,
0.20333519577980042,
0.24215252697467804,
-0.0735136866569519,
-0.004147804342210293,
0.05102333799004555,
-0.05719854310154915,
-0.17386846244335175,
0.031804703176021576,
0.0655498206615448,
0.018029730767011642,
0.04812759533524513,
-0.1747165322303772,
0.07470221072435379,
0.08199149370193481,
0.00024597602896392345,
0.10374852269887924,
-0.3384709656238556,
-0.12595698237419128,
0.08452210575342178,
0.14844149351119995,
0.12713798880577087,
-0.13721174001693726,
-0.0370728075504303,
-0.02315114624798298,
-0.14604716002941132,
0.11531157046556473,
-0.06732795387506485,
0.13639362156391144,
-0.016764117404818535,
0.10659976303577423,
0.024486370384693146,
-0.0587138831615448,
0.12324017286300659,
0.015569906681776047,
0.06764480471611023,
-0.06226402893662453,
-0.03754077106714249,
0.051746632903814316,
-0.04931196570396423,
0.010488116182386875,
-0.09121342748403549,
0.02894054539501667,
-0.12213355302810669,
-0.036082033067941666,
-0.08101921528577805,
0.009510315954685211,
-0.031118325889110565,
-0.07170649617910385,
-0.01379915326833725,
0.025546645745635033,
0.055017393082380295,
0.0005571246729232371,
0.1420118361711502,
-0.00312669575214386,
0.1266428828239441,
0.14019662141799927,
0.08945295214653015,
-0.048322323709726334,
-0.05362031236290932,
-0.025913633406162262,
-0.011068700812757015,
0.04784908890724182,
-0.12903743982315063,
0.02272189036011696,
0.14405164122581482,
0.019697299227118492,
0.15277403593063354,
0.07422136515378952,
-0.03326917067170143,
0.017823578789830208,
0.05028131231665611,
-0.19570331275463104,
-0.12924817204475403,
-0.04952366650104523,
-0.06647192686796188,
-0.13838033378124237,
0.025166451930999756,
0.13828212022781372,
-0.057244136929512024,
-0.0047295959666371346,
-0.015112821012735367,
0.007801898289471865,
-0.04119333624839783,
0.18764230608940125,
0.04601430892944336,
0.05987510085105896,
-0.09114173799753189,
0.06319890916347504,
0.05905727669596672,
-0.06659964472055435,
0.011389750987291336,
0.07449010014533997,
-0.08754391968250275,
-0.03822055459022522,
0.0032389697153121233,
0.1709159016609192,
-0.06200855225324631,
-0.008119192905724049,
-0.15103937685489655,
-0.0889555886387825,
0.06269938498735428,
0.12178625911474228,
0.09208327531814575,
0.010235460475087166,
-0.06509846448898315,
-0.0008313562721014023,
-0.12204422056674957,
0.10657289624214172,
0.06349905580282211,
0.07072440534830093,
-0.13631437718868256,
0.16072171926498413,
-0.015331716276705265,
0.06197306886315346,
-0.01972285844385624,
0.00865804310888052,
-0.08754665404558182,
0.01272586639970541,
-0.11101242154836655,
-0.023725247010588646,
-0.01785249635577202,
-0.00868644192814827,
-0.02627519518136978,
-0.05933867394924164,
-0.05338694155216217,
-0.0024670155253261328,
-0.11701902002096176,
-0.030917005613446236,
0.031858209520578384,
0.0302273016422987,
-0.08978244662284851,
-0.02704700268805027,
0.030164476484060287,
-0.06934206932783127,
0.08637258410453796,
0.04106311872601509,
0.002000498352572322,
0.03931432589888573,
-0.08168420195579529,
0.032070010900497437,
0.03163016214966774,
0.016009662300348282,
0.0515790618956089,
-0.06100895255804062,
-0.015789559110999107,
-0.028670713305473328,
0.05550919100642204,
0.01630225032567978,
0.0889183059334755,
-0.12651139497756958,
0.02457459829747677,
-0.009918969124555588,
-0.05548012629151344,
-0.08445757627487183,
0.05188159644603729,
0.039866846054792404,
0.04719993472099304,
0.18390096724033356,
-0.061876025050878525,
0.05359366908669472,
-0.2133646160364151,
0.0007854033610783517,
0.016962680965662003,
-0.12023309618234634,
-0.08264097571372986,
-0.08522967994213104,
0.0658491998910904,
-0.07355677336454391,
0.09584120661020279,
0.01129030343145132,
0.04469893127679825,
0.01541848573833704,
-0.002570240292698145,
0.02358938194811344,
0.0032581943087279797,
0.20298710465431213,
0.027081476524472237,
-0.04570033401250839,
0.05732863396406174,
0.04609213024377823,
0.11063463240861893,
0.17067186534404755,
0.20386597514152527,
0.1450435072183609,
0.0009683159296400845,
0.08365187048912048,
0.017259865999221802,
-0.025860024616122246,
-0.193107470870018,
0.030700894072651863,
-0.029039137065410614,
0.108987957239151,
-0.014365826733410358,
0.2233346551656723,
0.07529316842556,
-0.1535068154335022,
0.02056664600968361,
-0.043660733848810196,
-0.09200834482908249,
-0.09545472264289856,
-0.09842363744974136,
-0.08147936314344406,
-0.11390361934900284,
-0.00841597095131874,
-0.1199558824300766,
0.024104299023747444,
0.12347037345170975,
0.017037851735949516,
-0.03237475827336311,
0.17887653410434723,
0.020827779546380043,
-0.011651583015918732,
0.06675811856985092,
0.000053970212320564315,
-0.01130143366754055,
-0.09482696652412415,
-0.0797174796462059,
0.0041549052111804485,
0.014392019249498844,
0.047783542424440384,
-0.057813018560409546,
-0.05304551124572754,
0.017956960946321487,
-0.021528758108615875,
-0.12406821548938751,
-0.007896034978330135,
0.02899930253624916,
0.06320537626743317,
0.02823418751358986,
-0.0014825233956798911,
0.0005586107145063579,
-0.02478548139333725,
0.23216953873634338,
-0.08002237975597382,
-0.035330500453710556,
-0.08701420575380325,
0.19853505492210388,
0.011500914581120014,
-0.01312739122658968,
0.03108958899974823,
-0.07508767396211624,
0.019435502588748932,
0.24979852139949799,
0.1974736601114273,
-0.12000972032546997,
-0.009687304496765137,
0.017022693529725075,
0.004451688379049301,
0.00001412888741469942,
0.09610389918088913,
0.09158646315336227,
0.021937591955065727,
-0.09505988657474518,
-0.014149785973131657,
-0.05811506137251854,
-0.010553604923188686,
-0.011251507326960564,
0.07141318917274475,
0.05280524864792824,
0.02236715890467167,
-0.05966295674443245,
0.05810315161943436,
-0.07839485257863998,
-0.10698241740465164,
0.004257900174707174,
-0.22247152030467987,
-0.15952274203300476,
-0.024651911109685898,
0.08373211324214935,
-0.0013113528257235885,
0.07391777634620667,
-0.032230887562036514,
0.008458024822175503,
0.05409688502550125,
-0.005318291950970888,
-0.07965769618749619,
-0.055248651653528214,
0.09545480459928513,
-0.11907234787940979,
0.1757233738899231,
-0.04987962171435356,
0.03335264325141907,
0.12132002413272858,
0.043050285428762436,
-0.07173530012369156,
0.06888142228126526,
0.04918712005019188,
-0.01865323632955551,
0.02974170632660389,
0.12666423618793488,
-0.02482725866138935,
0.10208860784769058,
0.048766326159238815,
-0.142535999417305,
0.006210108753293753,
-0.04249749332666397,
-0.029285594820976257,
-0.03342651575803757,
-0.06838587671518326,
-0.04681166633963585,
0.14824479818344116,
0.22052203118801117,
-0.04596797749400139,
-0.011306090280413628,
-0.06329084932804108,
0.0014323149807751179,
0.06678219884634018,
0.05267966538667679,
-0.06452994793653488,
-0.24504896998405457,
-0.027361733838915825,
0.042944032698869705,
-0.0015450266655534506,
-0.28886082768440247,
-0.0810607373714447,
-0.005549087654799223,
-0.050026796758174896,
-0.0789799615740776,
0.10008943825960159,
0.08507188409566879,
0.04422440752387047,
-0.05414404720067978,
-0.008911618031561375,
-0.0789276659488678,
0.15971319377422333,
-0.15547896921634674,
-0.09321779757738113
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mbart-finetuned-en-to-ro
This model is a fine-tuned version of [sshleifer/tiny-mbart](https://huggingface.co/sshleifer/tiny-mbart) on the wmt16_en_ro_pre_processed dataset.
It achieves the following results on the evaluation set:
- Loss: 8.4792
- Bleu: 0.0
- Gen Len: 20.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:----:|:-------:|
| 8.2425 | 1.0 | 76290 | 8.4792 | 0.0 | 20.0 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "metrics": ["bleu"], "model-index": [{"name": "tiny-mbart-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16_en_ro_pre_processed", "type": "wmt16_en_ro_pre_processed", "args": "enro"}, "metrics": [{"type": "bleu", "value": 0.0, "name": "Bleu"}]}]}]}
|
text2text-generation
|
ffsouza/tiny-mbart-finetuned-en-to-ro
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us
|
tiny-mbart-finetuned-en-to-ro
=============================
This model is a fine-tuned version of sshleifer/tiny-mbart on the wmt16\_en\_ro\_pre\_processed dataset.
It achieves the following results on the evaluation set:
* Loss: 8.4792
* Bleu: 0.0
* Gen Len: 20.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu102
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu102\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu102\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
70,
113,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu102\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
-0.09897331148386002,
0.06212788075208664,
-0.003706732066348195,
0.09771396219730377,
0.12977059185504913,
0.016780540347099304,
0.14437679946422577,
0.14073237776756287,
-0.08298103511333466,
0.062452223151922226,
0.1347084641456604,
0.12738986313343048,
0.033955857157707214,
0.1455448418855667,
-0.061345331370830536,
-0.2700382173061371,
0.015838416293263435,
0.04176711663603783,
-0.020830843597650528,
0.13514454662799835,
0.08256837725639343,
-0.1272425651550293,
0.09110759943723679,
0.012491918168962002,
-0.17030619084835052,
0.004715313669294119,
0.007411223370581865,
-0.06403713673353195,
0.11829172819852829,
0.021498162299394608,
0.11776174604892731,
0.02810792811214924,
0.07449494302272797,
-0.17686888575553894,
0.010149477049708366,
0.05981271713972092,
0.012346141040325165,
0.09417496621608734,
0.05924791470170021,
-0.025981128215789795,
0.13035725057125092,
-0.07199026644229889,
0.06958475708961487,
0.026622101664543152,
-0.13616736233234406,
-0.2300657033920288,
-0.07671408355236053,
0.03188447654247284,
0.06817036122083664,
0.09032867103815079,
-0.018115881830453873,
0.13553471863269806,
-0.05925557389855385,
0.10849904268980026,
0.2665161192417145,
-0.2656686007976532,
-0.0680430456995964,
0.0003758948005270213,
0.036514852195978165,
0.08781422674655914,
-0.09227053076028824,
-0.021631114184856415,
0.0399169959127903,
0.04925042763352394,
0.12586063146591187,
-0.014902619644999504,
-0.019858496263623238,
-0.006383994594216347,
-0.14112256467342377,
-0.04904001206159592,
0.1369747668504715,
0.01968200132250786,
-0.03747319057583809,
-0.053346987813711166,
-0.08219780772924423,
-0.19726026058197021,
-0.03892681375145912,
-0.005787925329059362,
0.03019120916724205,
-0.032652173191308975,
-0.0971122458577156,
-0.02164529263973236,
-0.08096976578235626,
-0.07066786289215088,
-0.044155098497867584,
0.1711454838514328,
0.04740401729941368,
0.013811664655804634,
-0.059161365032196045,
0.09809694439172745,
-0.040094006806612015,
-0.1551884561777115,
0.0017892863834276795,
0.015752552077174187,
0.008973302319645882,
-0.03354664519429207,
-0.052260126918554306,
-0.08670564740896225,
-0.00891318079084158,
0.15838342905044556,
-0.09441167861223221,
0.07004296779632568,
-0.0007745604962110519,
0.033766698092222214,
-0.08887085318565369,
0.17045439779758453,
-0.005641345400363207,
-0.022173307836055756,
-0.008819859474897385,
0.07767505198717117,
0.024242892861366272,
-0.03651906177401543,
-0.1008463054895401,
0.015505928546190262,
0.10183527320623398,
0.013532215729355812,
-0.058871980756521225,
0.058584168553352356,
-0.04463495686650276,
-0.028801249340176582,
0.003722079796716571,
-0.10512274503707886,
0.03360525518655777,
0.00046991228009574115,
-0.07601293921470642,
0.005893405992537737,
0.020226383581757545,
0.001245943596586585,
-0.027393624186515808,
0.11483702808618546,
-0.09088370203971863,
0.028041165322065353,
-0.09183994680643082,
-0.13952599465847015,
0.008919195272028446,
-0.10776178538799286,
-0.004726531449705362,
-0.08515720814466476,
-0.16538454592227936,
-0.018863758072257042,
0.04859016463160515,
-0.04007485881447792,
-0.02362024039030075,
-0.05925755202770233,
-0.07198629528284073,
0.04349669814109802,
-0.005864326376467943,
0.09406362473964691,
-0.05822135880589485,
0.0967564806342125,
0.0523277223110199,
0.08290351182222366,
-0.03045186772942543,
0.052985984832048416,
-0.0939209833741188,
0.030807625502347946,
-0.189560204744339,
0.055618081241846085,
-0.04160178825259209,
0.052005816251039505,
-0.09559907764196396,
-0.1049751415848732,
-0.021989313885569572,
-0.005252784583717585,
0.09694705158472061,
0.10337795317173004,
-0.16564467549324036,
-0.09803105890750885,
0.19548283517360687,
-0.09156331419944763,
-0.08803698420524597,
0.13098502159118652,
-0.05640183389186859,
0.03229216858744621,
0.05906382203102112,
0.19082733988761902,
0.053000107407569885,
-0.09028912335634232,
0.0008257119916379452,
-0.036213260143995285,
0.06710461527109146,
-0.01768403686583042,
0.07082512229681015,
-0.007854596711695194,
0.03042711690068245,
0.01279536448419094,
0.011400350369513035,
0.022588388994336128,
-0.1027488186955452,
-0.08268097788095474,
-0.04528224095702171,
-0.07259701937437057,
0.05388937518000603,
0.04641779884696007,
0.06711675971746445,
-0.12081582099199295,
-0.10754842311143875,
0.03710031136870384,
0.07944260537624359,
-0.07615503668785095,
0.04221436008810997,
-0.08097509294748306,
0.07488633692264557,
-0.057539910078048706,
-0.011388523504137993,
-0.18591858446598053,
-0.008659601211547852,
0.027429502457380295,
-0.005654398817569017,
0.02947145700454712,
-0.01144387200474739,
0.08002542704343796,
0.07656674832105637,
-0.040178120136260986,
-0.025620529428124428,
-0.050042133778333664,
-0.0133230434730649,
-0.12913113832473755,
-0.19846084713935852,
-0.03727271035313606,
-0.031159045174717903,
0.10351687669754028,
-0.1881095916032791,
0.049517884850502014,
0.032958611845970154,
0.09978260099887848,
0.03585890308022499,
-0.03247595950961113,
-0.03661888837814331,
0.08518461138010025,
-0.0468377061188221,
-0.04949519410729408,
0.06455367803573608,
-0.002717884723097086,
-0.09404245018959045,
-0.036270011216402054,
-0.1606486737728119,
0.12960852682590485,
0.12691175937652588,
-0.07250460237264633,
-0.0801360234618187,
-0.005777307320386171,
-0.05533416569232941,
-0.027757564559578896,
-0.04621627554297447,
0.008728882297873497,
0.18741783499717712,
0.014111834578216076,
0.1541779339313507,
-0.08541134744882584,
-0.04517417773604393,
0.03729621693491936,
-0.032241497188806534,
0.011563410982489586,
0.12550711631774902,
0.08346886187791824,
-0.07943731546401978,
0.1286662369966507,
0.15001778304576874,
-0.07589004933834076,
0.16245101392269135,
-0.0276210755109787,
-0.07903272658586502,
-0.02905009686946869,
-0.02359064482152462,
0.008488981984555721,
0.08919837325811386,
-0.14964929223060608,
-0.020823676139116287,
0.02298814244568348,
0.0323663093149662,
0.019109414890408516,
-0.20615029335021973,
-0.008192394860088825,
0.05286870524287224,
-0.04676259681582451,
-0.03564172238111496,
-0.006425959523767233,
0.01712467335164547,
0.10805532336235046,
0.014356794767081738,
-0.055772826075553894,
0.01912580616772175,
0.0013025213265791535,
-0.07421378046274185,
0.19140633940696716,
-0.08041026443243027,
-0.15355464816093445,
-0.12810978293418884,
-0.07750896364450455,
-0.05934322997927666,
0.007653649430721998,
0.07414048165082932,
-0.10382970422506332,
-0.0303182415664196,
-0.08576316386461258,
0.04537983611226082,
-0.026873646304011345,
0.035240061581134796,
0.03577860817313194,
-0.012945204973220825,
0.05770459026098251,
-0.11058571189641953,
-0.026673320680856705,
-0.042492855340242386,
-0.047141339629888535,
0.057113975286483765,
0.019417457282543182,
0.11496564000844955,
0.13916468620300293,
-0.025600196793675423,
0.03728015720844269,
-0.029960913583636284,
0.23755085468292236,
-0.06917169690132141,
-0.02159510925412178,
0.1374811828136444,
-0.013543147593736649,
0.07307475060224533,
0.11251846700906754,
0.06206585466861725,
-0.08854836970567703,
0.002491636434569955,
0.03828795999288559,
-0.05313871055841446,
-0.21302050352096558,
-0.025031443685293198,
-0.046295635402202606,
0.006638544145971537,
0.11425517499446869,
0.023960055783391,
0.03596076741814613,
0.06408103555440903,
0.013804965652525425,
0.06680312752723694,
-0.027523597702383995,
0.09577387571334839,
0.10529199987649918,
0.05187077075242996,
0.1472768932580948,
-0.03736725449562073,
-0.04750921204686165,
0.047170549631118774,
-0.014692303724586964,
0.2279309183359146,
0.00916081853210926,
0.16695517301559448,
0.04793957248330116,
0.1477092206478119,
0.028381098061800003,
0.06994063407182693,
-0.01023856457322836,
-0.022295773029327393,
-0.01703610084950924,
-0.04907051846385002,
-0.047272372990846634,
0.024296307936310768,
-0.06058097630739212,
0.057248249650001526,
-0.1365516036748886,
0.022970369085669518,
0.04555764049291611,
0.2618778944015503,
0.03172305226325989,
-0.3472556471824646,
-0.1002122238278389,
0.005614013411104679,
-0.04552006348967552,
-0.01979408599436283,
0.025378236547112465,
0.09719720482826233,
-0.08744892477989197,
0.05507215857505798,
-0.07326015084981918,
0.1096811443567276,
-0.05308644473552704,
0.052249111235141754,
0.047453828155994415,
0.11214492470026016,
-0.014100826345384121,
0.05030013993382454,
-0.288224995136261,
0.2722267210483551,
0.019022956490516663,
0.0682285949587822,
-0.07044826447963715,
0.0049875411204993725,
0.02197965979576111,
0.03519957885146141,
0.05684145539999008,
-0.02115144208073616,
-0.09044095128774643,
-0.19563722610473633,
-0.07211947441101074,
0.02061229944229126,
0.12921065092086792,
-0.03277742862701416,
0.12908430397510529,
-0.028881454840302467,
0.013250868767499924,
0.05412276089191437,
-0.02039189450442791,
-0.07947266101837158,
-0.09611770510673523,
0.014761912636458874,
0.0392737090587616,
-0.03864405304193497,
-0.06719017773866653,
-0.10296200960874557,
-0.05826228857040405,
0.1507227122783661,
-0.023013252764940262,
-0.049378182739019394,
-0.1265472024679184,
0.060623038560152054,
0.08652881532907486,
-0.09682231396436691,
0.019612397998571396,
-0.00677607674151659,
0.10144510120153427,
0.000033204618375748396,
-0.06559253484010696,
0.11147180944681168,
-0.06208471208810806,
-0.1905837208032608,
-0.05349297076463699,
0.12378795444965363,
0.03527543693780899,
0.06882178038358688,
-0.021461280062794685,
0.031822413206100464,
-0.03452833741903305,
-0.08555588126182556,
0.03168082982301712,
0.013114294037222862,
0.042442917823791504,
0.005525163374841213,
-0.032829780131578445,
0.035109858959913254,
-0.06799621134996414,
-0.023313008248806,
0.16705676913261414,
0.253177285194397,
-0.08621221035718918,
0.0457674004137516,
0.05577586591243744,
-0.06555614620447159,
-0.17025873064994812,
0.0317862369120121,
0.06199970468878746,
0.005771013908088207,
0.006295657251030207,
-0.19181957840919495,
0.054887887090444565,
0.10014249384403229,
-0.005545844789594412,
0.09365241229534149,
-0.33491507172584534,
-0.13188865780830383,
0.09214653074741364,
0.13055038452148438,
0.11579543352127075,
-0.15492169559001923,
-0.03848681598901749,
-0.024924738332629204,
-0.1326456367969513,
0.1070517748594284,
-0.09742625802755356,
0.12233110517263412,
-0.034661851823329926,
0.1080123782157898,
0.01697089523077011,
-0.05630628019571304,
0.12142326682806015,
0.005665711592882872,
0.0844845324754715,
-0.06153843179345131,
-0.0003355967055540532,
0.05488954856991768,
-0.06049172207713127,
0.025878962129354477,
-0.08941352367401123,
0.02535502053797245,
-0.0977565199136734,
-0.030583426356315613,
-0.07521884143352509,
0.016741951927542686,
-0.03561945632100105,
-0.055432043969631195,
-0.03732994943857193,
0.003659334499388933,
0.05146348103880882,
-0.015113680623471737,
0.16142582893371582,
-0.008942115120589733,
0.16667746007442474,
0.13008330762386322,
0.09046386927366257,
-0.08709797263145447,
-0.05286401882767677,
0.007389794569462538,
-0.017894109711050987,
0.0669548436999321,
-0.1501956284046173,
0.03140188381075859,
0.14251182973384857,
0.022716309875249863,
0.14328432083129883,
0.08243858814239502,
-0.04297526553273201,
0.02871651202440262,
0.06030705198645592,
-0.16529060900211334,
-0.10059066116809845,
-0.011212041601538658,
-0.008844096213579178,
-0.12138297408819199,
0.05150214955210686,
0.13593029975891113,
-0.06492121517658234,
-0.007893775589764118,
-0.013955570757389069,
0.01040569692850113,
-0.03322751075029373,
0.19329720735549927,
0.03833417966961861,
0.056444793939590454,
-0.10130307823419571,
0.07405608147382736,
0.04188966378569603,
-0.09132052212953568,
0.03690518066287041,
0.11921681463718414,
-0.07762625068426132,
-0.027952129021286964,
0.01797192543745041,
0.19576384127140045,
-0.07224312424659729,
-0.036419034004211426,
-0.15915817022323608,
-0.11084987223148346,
0.09304860234260559,
0.17858274281024933,
0.07654240727424622,
0.001896882662549615,
-0.055322859436273575,
0.023635530844330788,
-0.1283886432647705,
0.08650189638137817,
0.05727207288146019,
0.0689699649810791,
-0.11763856559991837,
0.16589808464050293,
0.0030598528683185577,
0.028483815491199493,
-0.019085491076111794,
0.010289139114320278,
-0.10780223459005356,
0.010127787478268147,
-0.12655365467071533,
-0.016169201582670212,
-0.04053298383951187,
-0.008490345440804958,
-0.007096101995557547,
-0.05092569813132286,
-0.06673354655504227,
0.01096253376454115,
-0.11350123584270477,
-0.03338075056672096,
0.009239702485501766,
0.03322991728782654,
-0.11520674079656601,
-0.009718482382595539,
0.026040710508823395,
-0.0786505863070488,
0.06999355554580688,
0.05358589068055153,
0.0039702593348920345,
0.037376660853624344,
-0.09264218062162399,
0.02270856872200966,
0.04235853627324104,
-0.0039061398711055517,
0.05572621151804924,
-0.07320421189069748,
-0.007380381226539612,
-0.018949760124087334,
0.04972876235842705,
0.025697629898786545,
0.08340973407030106,
-0.12937885522842407,
0.028631607070565224,
-0.0007026239763945341,
-0.07893063873052597,
-0.07002419978380203,
0.05332111567258835,
0.07876299321651459,
0.029989425092935562,
0.17034710943698883,
-0.08417264372110367,
0.05757184326648712,
-0.2092028260231018,
0.001348562422208488,
0.014885411597788334,
-0.10898508131504059,
-0.07022164762020111,
-0.06361772865056992,
0.06974434852600098,
-0.06662110984325409,
0.11821982264518738,
0.0203534048050642,
0.04339184612035751,
0.037538718432188034,
-0.03650867938995361,
-0.02670113369822502,
0.003734608180820942,
0.1751812845468521,
0.0519544743001461,
-0.04838751628994942,
0.05552903935313225,
0.02939252369105816,
0.10127724707126617,
0.12266045063734055,
0.21777822077274323,
0.14708946645259857,
0.03894691914319992,
0.09670494496822357,
0.025432433933019638,
-0.04332306981086731,
-0.1820172220468521,
0.05073332414031029,
-0.02292722649872303,
0.10894192010164261,
-0.014743849635124207,
0.19512467086315155,
0.11514171957969666,
-0.15208296477794647,
0.04726603254675865,
-0.022747209295630455,
-0.07941539585590363,
-0.13579393923282623,
-0.07232115417718887,
-0.0922468900680542,
-0.13861379027366638,
0.004212976433336735,
-0.12077376246452332,
0.05685197934508324,
0.07082255184650421,
0.02051025815308094,
0.0013020924525335431,
0.14730580151081085,
0.014355231076478958,
0.00989349652081728,
0.06532898545265198,
0.003620221745222807,
-0.018465198576450348,
-0.07471563667058945,
-0.08226261287927628,
0.010024570859968662,
-0.016671497374773026,
0.04766174405813217,
-0.02732744812965393,
-0.053787775337696075,
0.02179073914885521,
-0.019136536866426468,
-0.11611424386501312,
0.00983885582536459,
0.021756017580628395,
0.06563956290483475,
0.05766556039452553,
0.005821282975375652,
-0.0004568534786812961,
-0.02220609225332737,
0.2086041420698166,
-0.08150071650743484,
-0.0535026453435421,
-0.10256025940179825,
0.24900439381599426,
0.03225886821746826,
-0.0135867390781641,
0.022436169907450676,
-0.06903573125600815,
-0.009539050050079823,
0.2153550088405609,
0.18664447963237762,
-0.05461517721414566,
0.002710554050281644,
-0.0009269415168091655,
-0.00434150779619813,
-0.007245565298944712,
0.0899820327758789,
0.10326799005270004,
0.05670422315597534,
-0.07672057300806046,
-0.01859072782099247,
-0.05025395378470421,
-0.01381755992770195,
-0.04435155168175697,
0.0676790252327919,
0.03190368413925171,
0.009576192125678062,
-0.04894528165459633,
0.044215984642505646,
-0.05627136304974556,
-0.08281057327985764,
0.03792152926325798,
-0.21512971818447113,
-0.14885850250720978,
-0.0048071835190057755,
0.09268360584974289,
0.004511278122663498,
0.07173781841993332,
-0.013976434245705605,
-0.018985044211149216,
0.08164069056510925,
-0.014499678276479244,
-0.0801888257265091,
-0.0925775021314621,
0.10104835778474808,
-0.12905697524547577,
0.18619291484355927,
-0.04216248542070389,
0.03285389021039009,
0.1279362589120865,
0.04956747218966484,
-0.07652949541807175,
0.0697784274816513,
0.048110898584127426,
-0.059095364063978195,
-0.001592571148648858,
0.15283943712711334,
-0.03610047698020935,
0.09230336546897888,
0.05328521504998207,
-0.15848761796951294,
0.005759235471487045,
-0.05225745216012001,
-0.05274975299835205,
-0.02561980113387108,
-0.044041525572538376,
-0.04756252467632294,
0.12820927798748016,
0.22180987894535065,
-0.031032932922244072,
-0.0030736145563423634,
-0.05981820821762085,
0.019438501447439194,
0.06280802190303802,
0.03888566046953201,
-0.05669305473566055,
-0.25345441699028015,
-0.0005731135024689138,
0.09300336986780167,
-0.011422841809689999,
-0.2805338203907013,
-0.09892770648002625,
0.009529856964945793,
-0.04487624019384384,
-0.09818173944950104,
0.08877147734165192,
0.07851417362689972,
0.057035110890865326,
-0.051057592034339905,
-0.0773637443780899,
-0.057304684072732925,
0.1693039834499359,
-0.14952437579631805,
-0.07335790991783142
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro
This model is a fine-tuned version of [sshleifer/tiny-mbart](https://huggingface.co/sshleifer/tiny-mbart) on the wmt16_en_ro_pre_processed dataset.
It achieves the following results on the evaluation set:
- Loss: 8.4656
- Bleu: 0.0
- Gen Len: 20.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:----:|:-------:|
| 8.2268 | 1.0 | 76290 | 8.4656 | 0.0 | 20.0 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "metrics": ["bleu"], "model-index": [{"name": "tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16_en_ro_pre_processed", "type": "wmt16_en_ro_pre_processed", "args": "enro"}, "metrics": [{"type": "bleu", "value": 0.0, "name": "Bleu"}]}]}]}
|
text2text-generation
|
ffsouza/tiny-mbart-length-128-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us
|
tiny-mbart-length-128-learning\_rate-2e-05-weight\_decay-0.01-finetuned-en-to-ro
================================================================================
This model is a fine-tuned version of sshleifer/tiny-mbart on the wmt16\_en\_ro\_pre\_processed dataset.
It achieves the following results on the evaluation set:
* Loss: 8.4656
* Bleu: 0.0
* Gen Len: 20.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu102
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu102\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu102\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
70,
113,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu102\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
-0.09897331148386002,
0.06212788075208664,
-0.003706732066348195,
0.09771396219730377,
0.12977059185504913,
0.016780540347099304,
0.14437679946422577,
0.14073237776756287,
-0.08298103511333466,
0.062452223151922226,
0.1347084641456604,
0.12738986313343048,
0.033955857157707214,
0.1455448418855667,
-0.061345331370830536,
-0.2700382173061371,
0.015838416293263435,
0.04176711663603783,
-0.020830843597650528,
0.13514454662799835,
0.08256837725639343,
-0.1272425651550293,
0.09110759943723679,
0.012491918168962002,
-0.17030619084835052,
0.004715313669294119,
0.007411223370581865,
-0.06403713673353195,
0.11829172819852829,
0.021498162299394608,
0.11776174604892731,
0.02810792811214924,
0.07449494302272797,
-0.17686888575553894,
0.010149477049708366,
0.05981271713972092,
0.012346141040325165,
0.09417496621608734,
0.05924791470170021,
-0.025981128215789795,
0.13035725057125092,
-0.07199026644229889,
0.06958475708961487,
0.026622101664543152,
-0.13616736233234406,
-0.2300657033920288,
-0.07671408355236053,
0.03188447654247284,
0.06817036122083664,
0.09032867103815079,
-0.018115881830453873,
0.13553471863269806,
-0.05925557389855385,
0.10849904268980026,
0.2665161192417145,
-0.2656686007976532,
-0.0680430456995964,
0.0003758948005270213,
0.036514852195978165,
0.08781422674655914,
-0.09227053076028824,
-0.021631114184856415,
0.0399169959127903,
0.04925042763352394,
0.12586063146591187,
-0.014902619644999504,
-0.019858496263623238,
-0.006383994594216347,
-0.14112256467342377,
-0.04904001206159592,
0.1369747668504715,
0.01968200132250786,
-0.03747319057583809,
-0.053346987813711166,
-0.08219780772924423,
-0.19726026058197021,
-0.03892681375145912,
-0.005787925329059362,
0.03019120916724205,
-0.032652173191308975,
-0.0971122458577156,
-0.02164529263973236,
-0.08096976578235626,
-0.07066786289215088,
-0.044155098497867584,
0.1711454838514328,
0.04740401729941368,
0.013811664655804634,
-0.059161365032196045,
0.09809694439172745,
-0.040094006806612015,
-0.1551884561777115,
0.0017892863834276795,
0.015752552077174187,
0.008973302319645882,
-0.03354664519429207,
-0.052260126918554306,
-0.08670564740896225,
-0.00891318079084158,
0.15838342905044556,
-0.09441167861223221,
0.07004296779632568,
-0.0007745604962110519,
0.033766698092222214,
-0.08887085318565369,
0.17045439779758453,
-0.005641345400363207,
-0.022173307836055756,
-0.008819859474897385,
0.07767505198717117,
0.024242892861366272,
-0.03651906177401543,
-0.1008463054895401,
0.015505928546190262,
0.10183527320623398,
0.013532215729355812,
-0.058871980756521225,
0.058584168553352356,
-0.04463495686650276,
-0.028801249340176582,
0.003722079796716571,
-0.10512274503707886,
0.03360525518655777,
0.00046991228009574115,
-0.07601293921470642,
0.005893405992537737,
0.020226383581757545,
0.001245943596586585,
-0.027393624186515808,
0.11483702808618546,
-0.09088370203971863,
0.028041165322065353,
-0.09183994680643082,
-0.13952599465847015,
0.008919195272028446,
-0.10776178538799286,
-0.004726531449705362,
-0.08515720814466476,
-0.16538454592227936,
-0.018863758072257042,
0.04859016463160515,
-0.04007485881447792,
-0.02362024039030075,
-0.05925755202770233,
-0.07198629528284073,
0.04349669814109802,
-0.005864326376467943,
0.09406362473964691,
-0.05822135880589485,
0.0967564806342125,
0.0523277223110199,
0.08290351182222366,
-0.03045186772942543,
0.052985984832048416,
-0.0939209833741188,
0.030807625502347946,
-0.189560204744339,
0.055618081241846085,
-0.04160178825259209,
0.052005816251039505,
-0.09559907764196396,
-0.1049751415848732,
-0.021989313885569572,
-0.005252784583717585,
0.09694705158472061,
0.10337795317173004,
-0.16564467549324036,
-0.09803105890750885,
0.19548283517360687,
-0.09156331419944763,
-0.08803698420524597,
0.13098502159118652,
-0.05640183389186859,
0.03229216858744621,
0.05906382203102112,
0.19082733988761902,
0.053000107407569885,
-0.09028912335634232,
0.0008257119916379452,
-0.036213260143995285,
0.06710461527109146,
-0.01768403686583042,
0.07082512229681015,
-0.007854596711695194,
0.03042711690068245,
0.01279536448419094,
0.011400350369513035,
0.022588388994336128,
-0.1027488186955452,
-0.08268097788095474,
-0.04528224095702171,
-0.07259701937437057,
0.05388937518000603,
0.04641779884696007,
0.06711675971746445,
-0.12081582099199295,
-0.10754842311143875,
0.03710031136870384,
0.07944260537624359,
-0.07615503668785095,
0.04221436008810997,
-0.08097509294748306,
0.07488633692264557,
-0.057539910078048706,
-0.011388523504137993,
-0.18591858446598053,
-0.008659601211547852,
0.027429502457380295,
-0.005654398817569017,
0.02947145700454712,
-0.01144387200474739,
0.08002542704343796,
0.07656674832105637,
-0.040178120136260986,
-0.025620529428124428,
-0.050042133778333664,
-0.0133230434730649,
-0.12913113832473755,
-0.19846084713935852,
-0.03727271035313606,
-0.031159045174717903,
0.10351687669754028,
-0.1881095916032791,
0.049517884850502014,
0.032958611845970154,
0.09978260099887848,
0.03585890308022499,
-0.03247595950961113,
-0.03661888837814331,
0.08518461138010025,
-0.0468377061188221,
-0.04949519410729408,
0.06455367803573608,
-0.002717884723097086,
-0.09404245018959045,
-0.036270011216402054,
-0.1606486737728119,
0.12960852682590485,
0.12691175937652588,
-0.07250460237264633,
-0.0801360234618187,
-0.005777307320386171,
-0.05533416569232941,
-0.027757564559578896,
-0.04621627554297447,
0.008728882297873497,
0.18741783499717712,
0.014111834578216076,
0.1541779339313507,
-0.08541134744882584,
-0.04517417773604393,
0.03729621693491936,
-0.032241497188806534,
0.011563410982489586,
0.12550711631774902,
0.08346886187791824,
-0.07943731546401978,
0.1286662369966507,
0.15001778304576874,
-0.07589004933834076,
0.16245101392269135,
-0.0276210755109787,
-0.07903272658586502,
-0.02905009686946869,
-0.02359064482152462,
0.008488981984555721,
0.08919837325811386,
-0.14964929223060608,
-0.020823676139116287,
0.02298814244568348,
0.0323663093149662,
0.019109414890408516,
-0.20615029335021973,
-0.008192394860088825,
0.05286870524287224,
-0.04676259681582451,
-0.03564172238111496,
-0.006425959523767233,
0.01712467335164547,
0.10805532336235046,
0.014356794767081738,
-0.055772826075553894,
0.01912580616772175,
0.0013025213265791535,
-0.07421378046274185,
0.19140633940696716,
-0.08041026443243027,
-0.15355464816093445,
-0.12810978293418884,
-0.07750896364450455,
-0.05934322997927666,
0.007653649430721998,
0.07414048165082932,
-0.10382970422506332,
-0.0303182415664196,
-0.08576316386461258,
0.04537983611226082,
-0.026873646304011345,
0.035240061581134796,
0.03577860817313194,
-0.012945204973220825,
0.05770459026098251,
-0.11058571189641953,
-0.026673320680856705,
-0.042492855340242386,
-0.047141339629888535,
0.057113975286483765,
0.019417457282543182,
0.11496564000844955,
0.13916468620300293,
-0.025600196793675423,
0.03728015720844269,
-0.029960913583636284,
0.23755085468292236,
-0.06917169690132141,
-0.02159510925412178,
0.1374811828136444,
-0.013543147593736649,
0.07307475060224533,
0.11251846700906754,
0.06206585466861725,
-0.08854836970567703,
0.002491636434569955,
0.03828795999288559,
-0.05313871055841446,
-0.21302050352096558,
-0.025031443685293198,
-0.046295635402202606,
0.006638544145971537,
0.11425517499446869,
0.023960055783391,
0.03596076741814613,
0.06408103555440903,
0.013804965652525425,
0.06680312752723694,
-0.027523597702383995,
0.09577387571334839,
0.10529199987649918,
0.05187077075242996,
0.1472768932580948,
-0.03736725449562073,
-0.04750921204686165,
0.047170549631118774,
-0.014692303724586964,
0.2279309183359146,
0.00916081853210926,
0.16695517301559448,
0.04793957248330116,
0.1477092206478119,
0.028381098061800003,
0.06994063407182693,
-0.01023856457322836,
-0.022295773029327393,
-0.01703610084950924,
-0.04907051846385002,
-0.047272372990846634,
0.024296307936310768,
-0.06058097630739212,
0.057248249650001526,
-0.1365516036748886,
0.022970369085669518,
0.04555764049291611,
0.2618778944015503,
0.03172305226325989,
-0.3472556471824646,
-0.1002122238278389,
0.005614013411104679,
-0.04552006348967552,
-0.01979408599436283,
0.025378236547112465,
0.09719720482826233,
-0.08744892477989197,
0.05507215857505798,
-0.07326015084981918,
0.1096811443567276,
-0.05308644473552704,
0.052249111235141754,
0.047453828155994415,
0.11214492470026016,
-0.014100826345384121,
0.05030013993382454,
-0.288224995136261,
0.2722267210483551,
0.019022956490516663,
0.0682285949587822,
-0.07044826447963715,
0.0049875411204993725,
0.02197965979576111,
0.03519957885146141,
0.05684145539999008,
-0.02115144208073616,
-0.09044095128774643,
-0.19563722610473633,
-0.07211947441101074,
0.02061229944229126,
0.12921065092086792,
-0.03277742862701416,
0.12908430397510529,
-0.028881454840302467,
0.013250868767499924,
0.05412276089191437,
-0.02039189450442791,
-0.07947266101837158,
-0.09611770510673523,
0.014761912636458874,
0.0392737090587616,
-0.03864405304193497,
-0.06719017773866653,
-0.10296200960874557,
-0.05826228857040405,
0.1507227122783661,
-0.023013252764940262,
-0.049378182739019394,
-0.1265472024679184,
0.060623038560152054,
0.08652881532907486,
-0.09682231396436691,
0.019612397998571396,
-0.00677607674151659,
0.10144510120153427,
0.000033204618375748396,
-0.06559253484010696,
0.11147180944681168,
-0.06208471208810806,
-0.1905837208032608,
-0.05349297076463699,
0.12378795444965363,
0.03527543693780899,
0.06882178038358688,
-0.021461280062794685,
0.031822413206100464,
-0.03452833741903305,
-0.08555588126182556,
0.03168082982301712,
0.013114294037222862,
0.042442917823791504,
0.005525163374841213,
-0.032829780131578445,
0.035109858959913254,
-0.06799621134996414,
-0.023313008248806,
0.16705676913261414,
0.253177285194397,
-0.08621221035718918,
0.0457674004137516,
0.05577586591243744,
-0.06555614620447159,
-0.17025873064994812,
0.0317862369120121,
0.06199970468878746,
0.005771013908088207,
0.006295657251030207,
-0.19181957840919495,
0.054887887090444565,
0.10014249384403229,
-0.005545844789594412,
0.09365241229534149,
-0.33491507172584534,
-0.13188865780830383,
0.09214653074741364,
0.13055038452148438,
0.11579543352127075,
-0.15492169559001923,
-0.03848681598901749,
-0.024924738332629204,
-0.1326456367969513,
0.1070517748594284,
-0.09742625802755356,
0.12233110517263412,
-0.034661851823329926,
0.1080123782157898,
0.01697089523077011,
-0.05630628019571304,
0.12142326682806015,
0.005665711592882872,
0.0844845324754715,
-0.06153843179345131,
-0.0003355967055540532,
0.05488954856991768,
-0.06049172207713127,
0.025878962129354477,
-0.08941352367401123,
0.02535502053797245,
-0.0977565199136734,
-0.030583426356315613,
-0.07521884143352509,
0.016741951927542686,
-0.03561945632100105,
-0.055432043969631195,
-0.03732994943857193,
0.003659334499388933,
0.05146348103880882,
-0.015113680623471737,
0.16142582893371582,
-0.008942115120589733,
0.16667746007442474,
0.13008330762386322,
0.09046386927366257,
-0.08709797263145447,
-0.05286401882767677,
0.007389794569462538,
-0.017894109711050987,
0.0669548436999321,
-0.1501956284046173,
0.03140188381075859,
0.14251182973384857,
0.022716309875249863,
0.14328432083129883,
0.08243858814239502,
-0.04297526553273201,
0.02871651202440262,
0.06030705198645592,
-0.16529060900211334,
-0.10059066116809845,
-0.011212041601538658,
-0.008844096213579178,
-0.12138297408819199,
0.05150214955210686,
0.13593029975891113,
-0.06492121517658234,
-0.007893775589764118,
-0.013955570757389069,
0.01040569692850113,
-0.03322751075029373,
0.19329720735549927,
0.03833417966961861,
0.056444793939590454,
-0.10130307823419571,
0.07405608147382736,
0.04188966378569603,
-0.09132052212953568,
0.03690518066287041,
0.11921681463718414,
-0.07762625068426132,
-0.027952129021286964,
0.01797192543745041,
0.19576384127140045,
-0.07224312424659729,
-0.036419034004211426,
-0.15915817022323608,
-0.11084987223148346,
0.09304860234260559,
0.17858274281024933,
0.07654240727424622,
0.001896882662549615,
-0.055322859436273575,
0.023635530844330788,
-0.1283886432647705,
0.08650189638137817,
0.05727207288146019,
0.0689699649810791,
-0.11763856559991837,
0.16589808464050293,
0.0030598528683185577,
0.028483815491199493,
-0.019085491076111794,
0.010289139114320278,
-0.10780223459005356,
0.010127787478268147,
-0.12655365467071533,
-0.016169201582670212,
-0.04053298383951187,
-0.008490345440804958,
-0.007096101995557547,
-0.05092569813132286,
-0.06673354655504227,
0.01096253376454115,
-0.11350123584270477,
-0.03338075056672096,
0.009239702485501766,
0.03322991728782654,
-0.11520674079656601,
-0.009718482382595539,
0.026040710508823395,
-0.0786505863070488,
0.06999355554580688,
0.05358589068055153,
0.0039702593348920345,
0.037376660853624344,
-0.09264218062162399,
0.02270856872200966,
0.04235853627324104,
-0.0039061398711055517,
0.05572621151804924,
-0.07320421189069748,
-0.007380381226539612,
-0.018949760124087334,
0.04972876235842705,
0.025697629898786545,
0.08340973407030106,
-0.12937885522842407,
0.028631607070565224,
-0.0007026239763945341,
-0.07893063873052597,
-0.07002419978380203,
0.05332111567258835,
0.07876299321651459,
0.029989425092935562,
0.17034710943698883,
-0.08417264372110367,
0.05757184326648712,
-0.2092028260231018,
0.001348562422208488,
0.014885411597788334,
-0.10898508131504059,
-0.07022164762020111,
-0.06361772865056992,
0.06974434852600098,
-0.06662110984325409,
0.11821982264518738,
0.0203534048050642,
0.04339184612035751,
0.037538718432188034,
-0.03650867938995361,
-0.02670113369822502,
0.003734608180820942,
0.1751812845468521,
0.0519544743001461,
-0.04838751628994942,
0.05552903935313225,
0.02939252369105816,
0.10127724707126617,
0.12266045063734055,
0.21777822077274323,
0.14708946645259857,
0.03894691914319992,
0.09670494496822357,
0.025432433933019638,
-0.04332306981086731,
-0.1820172220468521,
0.05073332414031029,
-0.02292722649872303,
0.10894192010164261,
-0.014743849635124207,
0.19512467086315155,
0.11514171957969666,
-0.15208296477794647,
0.04726603254675865,
-0.022747209295630455,
-0.07941539585590363,
-0.13579393923282623,
-0.07232115417718887,
-0.0922468900680542,
-0.13861379027366638,
0.004212976433336735,
-0.12077376246452332,
0.05685197934508324,
0.07082255184650421,
0.02051025815308094,
0.0013020924525335431,
0.14730580151081085,
0.014355231076478958,
0.00989349652081728,
0.06532898545265198,
0.003620221745222807,
-0.018465198576450348,
-0.07471563667058945,
-0.08226261287927628,
0.010024570859968662,
-0.016671497374773026,
0.04766174405813217,
-0.02732744812965393,
-0.053787775337696075,
0.02179073914885521,
-0.019136536866426468,
-0.11611424386501312,
0.00983885582536459,
0.021756017580628395,
0.06563956290483475,
0.05766556039452553,
0.005821282975375652,
-0.0004568534786812961,
-0.02220609225332737,
0.2086041420698166,
-0.08150071650743484,
-0.0535026453435421,
-0.10256025940179825,
0.24900439381599426,
0.03225886821746826,
-0.0135867390781641,
0.022436169907450676,
-0.06903573125600815,
-0.009539050050079823,
0.2153550088405609,
0.18664447963237762,
-0.05461517721414566,
0.002710554050281644,
-0.0009269415168091655,
-0.00434150779619813,
-0.007245565298944712,
0.0899820327758789,
0.10326799005270004,
0.05670422315597534,
-0.07672057300806046,
-0.01859072782099247,
-0.05025395378470421,
-0.01381755992770195,
-0.04435155168175697,
0.0676790252327919,
0.03190368413925171,
0.009576192125678062,
-0.04894528165459633,
0.044215984642505646,
-0.05627136304974556,
-0.08281057327985764,
0.03792152926325798,
-0.21512971818447113,
-0.14885850250720978,
-0.0048071835190057755,
0.09268360584974289,
0.004511278122663498,
0.07173781841993332,
-0.013976434245705605,
-0.018985044211149216,
0.08164069056510925,
-0.014499678276479244,
-0.0801888257265091,
-0.0925775021314621,
0.10104835778474808,
-0.12905697524547577,
0.18619291484355927,
-0.04216248542070389,
0.03285389021039009,
0.1279362589120865,
0.04956747218966484,
-0.07652949541807175,
0.0697784274816513,
0.048110898584127426,
-0.059095364063978195,
-0.001592571148648858,
0.15283943712711334,
-0.03610047698020935,
0.09230336546897888,
0.05328521504998207,
-0.15848761796951294,
0.005759235471487045,
-0.05225745216012001,
-0.05274975299835205,
-0.02561980113387108,
-0.044041525572538376,
-0.04756252467632294,
0.12820927798748016,
0.22180987894535065,
-0.031032932922244072,
-0.0030736145563423634,
-0.05981820821762085,
0.019438501447439194,
0.06280802190303802,
0.03888566046953201,
-0.05669305473566055,
-0.25345441699028015,
-0.0005731135024689138,
0.09300336986780167,
-0.011422841809689999,
-0.2805338203907013,
-0.09892770648002625,
0.009529856964945793,
-0.04487624019384384,
-0.09818173944950104,
0.08877147734165192,
0.07851417362689972,
0.057035110890865326,
-0.051057592034339905,
-0.0773637443780899,
-0.057304684072732925,
0.1693039834499359,
-0.14952437579631805,
-0.07335790991783142
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mbart-length-96-learning_rate-2e-05-weight_decay-0.005-finetuned-en-to-ro
This model is a fine-tuned version of [sshleifer/tiny-mbart](https://huggingface.co/sshleifer/tiny-mbart) on the wmt16_en_ro_pre_processed dataset.
It achieves the following results on the evaluation set:
- Loss: 8.5983
- Bleu: 0.0
- Gen Len: 20.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:----:|:-------:|
| 8.3753 | 1.0 | 76290 | 8.5983 | 0.0 | 20.0 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "metrics": ["bleu"], "model-index": [{"name": "tiny-mbart-length-96-learning_rate-2e-05-weight_decay-0.005-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16_en_ro_pre_processed", "type": "wmt16_en_ro_pre_processed", "args": "enro"}, "metrics": [{"type": "bleu", "value": 0.0, "name": "Bleu"}]}]}]}
|
text2text-generation
|
ffsouza/tiny-mbart-length-96-learning_rate-2e-05-weight_decay-0.005-finetuned-en-to-ro
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us
|
tiny-mbart-length-96-learning\_rate-2e-05-weight\_decay-0.005-finetuned-en-to-ro
================================================================================
This model is a fine-tuned version of sshleifer/tiny-mbart on the wmt16\_en\_ro\_pre\_processed dataset.
It achieves the following results on the evaluation set:
* Loss: 8.5983
* Bleu: 0.0
* Gen Len: 20.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu102
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu102\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu102\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
70,
113,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu102\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
-0.09897331148386002,
0.06212788075208664,
-0.003706732066348195,
0.09771396219730377,
0.12977059185504913,
0.016780540347099304,
0.14437679946422577,
0.14073237776756287,
-0.08298103511333466,
0.062452223151922226,
0.1347084641456604,
0.12738986313343048,
0.033955857157707214,
0.1455448418855667,
-0.061345331370830536,
-0.2700382173061371,
0.015838416293263435,
0.04176711663603783,
-0.020830843597650528,
0.13514454662799835,
0.08256837725639343,
-0.1272425651550293,
0.09110759943723679,
0.012491918168962002,
-0.17030619084835052,
0.004715313669294119,
0.007411223370581865,
-0.06403713673353195,
0.11829172819852829,
0.021498162299394608,
0.11776174604892731,
0.02810792811214924,
0.07449494302272797,
-0.17686888575553894,
0.010149477049708366,
0.05981271713972092,
0.012346141040325165,
0.09417496621608734,
0.05924791470170021,
-0.025981128215789795,
0.13035725057125092,
-0.07199026644229889,
0.06958475708961487,
0.026622101664543152,
-0.13616736233234406,
-0.2300657033920288,
-0.07671408355236053,
0.03188447654247284,
0.06817036122083664,
0.09032867103815079,
-0.018115881830453873,
0.13553471863269806,
-0.05925557389855385,
0.10849904268980026,
0.2665161192417145,
-0.2656686007976532,
-0.0680430456995964,
0.0003758948005270213,
0.036514852195978165,
0.08781422674655914,
-0.09227053076028824,
-0.021631114184856415,
0.0399169959127903,
0.04925042763352394,
0.12586063146591187,
-0.014902619644999504,
-0.019858496263623238,
-0.006383994594216347,
-0.14112256467342377,
-0.04904001206159592,
0.1369747668504715,
0.01968200132250786,
-0.03747319057583809,
-0.053346987813711166,
-0.08219780772924423,
-0.19726026058197021,
-0.03892681375145912,
-0.005787925329059362,
0.03019120916724205,
-0.032652173191308975,
-0.0971122458577156,
-0.02164529263973236,
-0.08096976578235626,
-0.07066786289215088,
-0.044155098497867584,
0.1711454838514328,
0.04740401729941368,
0.013811664655804634,
-0.059161365032196045,
0.09809694439172745,
-0.040094006806612015,
-0.1551884561777115,
0.0017892863834276795,
0.015752552077174187,
0.008973302319645882,
-0.03354664519429207,
-0.052260126918554306,
-0.08670564740896225,
-0.00891318079084158,
0.15838342905044556,
-0.09441167861223221,
0.07004296779632568,
-0.0007745604962110519,
0.033766698092222214,
-0.08887085318565369,
0.17045439779758453,
-0.005641345400363207,
-0.022173307836055756,
-0.008819859474897385,
0.07767505198717117,
0.024242892861366272,
-0.03651906177401543,
-0.1008463054895401,
0.015505928546190262,
0.10183527320623398,
0.013532215729355812,
-0.058871980756521225,
0.058584168553352356,
-0.04463495686650276,
-0.028801249340176582,
0.003722079796716571,
-0.10512274503707886,
0.03360525518655777,
0.00046991228009574115,
-0.07601293921470642,
0.005893405992537737,
0.020226383581757545,
0.001245943596586585,
-0.027393624186515808,
0.11483702808618546,
-0.09088370203971863,
0.028041165322065353,
-0.09183994680643082,
-0.13952599465847015,
0.008919195272028446,
-0.10776178538799286,
-0.004726531449705362,
-0.08515720814466476,
-0.16538454592227936,
-0.018863758072257042,
0.04859016463160515,
-0.04007485881447792,
-0.02362024039030075,
-0.05925755202770233,
-0.07198629528284073,
0.04349669814109802,
-0.005864326376467943,
0.09406362473964691,
-0.05822135880589485,
0.0967564806342125,
0.0523277223110199,
0.08290351182222366,
-0.03045186772942543,
0.052985984832048416,
-0.0939209833741188,
0.030807625502347946,
-0.189560204744339,
0.055618081241846085,
-0.04160178825259209,
0.052005816251039505,
-0.09559907764196396,
-0.1049751415848732,
-0.021989313885569572,
-0.005252784583717585,
0.09694705158472061,
0.10337795317173004,
-0.16564467549324036,
-0.09803105890750885,
0.19548283517360687,
-0.09156331419944763,
-0.08803698420524597,
0.13098502159118652,
-0.05640183389186859,
0.03229216858744621,
0.05906382203102112,
0.19082733988761902,
0.053000107407569885,
-0.09028912335634232,
0.0008257119916379452,
-0.036213260143995285,
0.06710461527109146,
-0.01768403686583042,
0.07082512229681015,
-0.007854596711695194,
0.03042711690068245,
0.01279536448419094,
0.011400350369513035,
0.022588388994336128,
-0.1027488186955452,
-0.08268097788095474,
-0.04528224095702171,
-0.07259701937437057,
0.05388937518000603,
0.04641779884696007,
0.06711675971746445,
-0.12081582099199295,
-0.10754842311143875,
0.03710031136870384,
0.07944260537624359,
-0.07615503668785095,
0.04221436008810997,
-0.08097509294748306,
0.07488633692264557,
-0.057539910078048706,
-0.011388523504137993,
-0.18591858446598053,
-0.008659601211547852,
0.027429502457380295,
-0.005654398817569017,
0.02947145700454712,
-0.01144387200474739,
0.08002542704343796,
0.07656674832105637,
-0.040178120136260986,
-0.025620529428124428,
-0.050042133778333664,
-0.0133230434730649,
-0.12913113832473755,
-0.19846084713935852,
-0.03727271035313606,
-0.031159045174717903,
0.10351687669754028,
-0.1881095916032791,
0.049517884850502014,
0.032958611845970154,
0.09978260099887848,
0.03585890308022499,
-0.03247595950961113,
-0.03661888837814331,
0.08518461138010025,
-0.0468377061188221,
-0.04949519410729408,
0.06455367803573608,
-0.002717884723097086,
-0.09404245018959045,
-0.036270011216402054,
-0.1606486737728119,
0.12960852682590485,
0.12691175937652588,
-0.07250460237264633,
-0.0801360234618187,
-0.005777307320386171,
-0.05533416569232941,
-0.027757564559578896,
-0.04621627554297447,
0.008728882297873497,
0.18741783499717712,
0.014111834578216076,
0.1541779339313507,
-0.08541134744882584,
-0.04517417773604393,
0.03729621693491936,
-0.032241497188806534,
0.011563410982489586,
0.12550711631774902,
0.08346886187791824,
-0.07943731546401978,
0.1286662369966507,
0.15001778304576874,
-0.07589004933834076,
0.16245101392269135,
-0.0276210755109787,
-0.07903272658586502,
-0.02905009686946869,
-0.02359064482152462,
0.008488981984555721,
0.08919837325811386,
-0.14964929223060608,
-0.020823676139116287,
0.02298814244568348,
0.0323663093149662,
0.019109414890408516,
-0.20615029335021973,
-0.008192394860088825,
0.05286870524287224,
-0.04676259681582451,
-0.03564172238111496,
-0.006425959523767233,
0.01712467335164547,
0.10805532336235046,
0.014356794767081738,
-0.055772826075553894,
0.01912580616772175,
0.0013025213265791535,
-0.07421378046274185,
0.19140633940696716,
-0.08041026443243027,
-0.15355464816093445,
-0.12810978293418884,
-0.07750896364450455,
-0.05934322997927666,
0.007653649430721998,
0.07414048165082932,
-0.10382970422506332,
-0.0303182415664196,
-0.08576316386461258,
0.04537983611226082,
-0.026873646304011345,
0.035240061581134796,
0.03577860817313194,
-0.012945204973220825,
0.05770459026098251,
-0.11058571189641953,
-0.026673320680856705,
-0.042492855340242386,
-0.047141339629888535,
0.057113975286483765,
0.019417457282543182,
0.11496564000844955,
0.13916468620300293,
-0.025600196793675423,
0.03728015720844269,
-0.029960913583636284,
0.23755085468292236,
-0.06917169690132141,
-0.02159510925412178,
0.1374811828136444,
-0.013543147593736649,
0.07307475060224533,
0.11251846700906754,
0.06206585466861725,
-0.08854836970567703,
0.002491636434569955,
0.03828795999288559,
-0.05313871055841446,
-0.21302050352096558,
-0.025031443685293198,
-0.046295635402202606,
0.006638544145971537,
0.11425517499446869,
0.023960055783391,
0.03596076741814613,
0.06408103555440903,
0.013804965652525425,
0.06680312752723694,
-0.027523597702383995,
0.09577387571334839,
0.10529199987649918,
0.05187077075242996,
0.1472768932580948,
-0.03736725449562073,
-0.04750921204686165,
0.047170549631118774,
-0.014692303724586964,
0.2279309183359146,
0.00916081853210926,
0.16695517301559448,
0.04793957248330116,
0.1477092206478119,
0.028381098061800003,
0.06994063407182693,
-0.01023856457322836,
-0.022295773029327393,
-0.01703610084950924,
-0.04907051846385002,
-0.047272372990846634,
0.024296307936310768,
-0.06058097630739212,
0.057248249650001526,
-0.1365516036748886,
0.022970369085669518,
0.04555764049291611,
0.2618778944015503,
0.03172305226325989,
-0.3472556471824646,
-0.1002122238278389,
0.005614013411104679,
-0.04552006348967552,
-0.01979408599436283,
0.025378236547112465,
0.09719720482826233,
-0.08744892477989197,
0.05507215857505798,
-0.07326015084981918,
0.1096811443567276,
-0.05308644473552704,
0.052249111235141754,
0.047453828155994415,
0.11214492470026016,
-0.014100826345384121,
0.05030013993382454,
-0.288224995136261,
0.2722267210483551,
0.019022956490516663,
0.0682285949587822,
-0.07044826447963715,
0.0049875411204993725,
0.02197965979576111,
0.03519957885146141,
0.05684145539999008,
-0.02115144208073616,
-0.09044095128774643,
-0.19563722610473633,
-0.07211947441101074,
0.02061229944229126,
0.12921065092086792,
-0.03277742862701416,
0.12908430397510529,
-0.028881454840302467,
0.013250868767499924,
0.05412276089191437,
-0.02039189450442791,
-0.07947266101837158,
-0.09611770510673523,
0.014761912636458874,
0.0392737090587616,
-0.03864405304193497,
-0.06719017773866653,
-0.10296200960874557,
-0.05826228857040405,
0.1507227122783661,
-0.023013252764940262,
-0.049378182739019394,
-0.1265472024679184,
0.060623038560152054,
0.08652881532907486,
-0.09682231396436691,
0.019612397998571396,
-0.00677607674151659,
0.10144510120153427,
0.000033204618375748396,
-0.06559253484010696,
0.11147180944681168,
-0.06208471208810806,
-0.1905837208032608,
-0.05349297076463699,
0.12378795444965363,
0.03527543693780899,
0.06882178038358688,
-0.021461280062794685,
0.031822413206100464,
-0.03452833741903305,
-0.08555588126182556,
0.03168082982301712,
0.013114294037222862,
0.042442917823791504,
0.005525163374841213,
-0.032829780131578445,
0.035109858959913254,
-0.06799621134996414,
-0.023313008248806,
0.16705676913261414,
0.253177285194397,
-0.08621221035718918,
0.0457674004137516,
0.05577586591243744,
-0.06555614620447159,
-0.17025873064994812,
0.0317862369120121,
0.06199970468878746,
0.005771013908088207,
0.006295657251030207,
-0.19181957840919495,
0.054887887090444565,
0.10014249384403229,
-0.005545844789594412,
0.09365241229534149,
-0.33491507172584534,
-0.13188865780830383,
0.09214653074741364,
0.13055038452148438,
0.11579543352127075,
-0.15492169559001923,
-0.03848681598901749,
-0.024924738332629204,
-0.1326456367969513,
0.1070517748594284,
-0.09742625802755356,
0.12233110517263412,
-0.034661851823329926,
0.1080123782157898,
0.01697089523077011,
-0.05630628019571304,
0.12142326682806015,
0.005665711592882872,
0.0844845324754715,
-0.06153843179345131,
-0.0003355967055540532,
0.05488954856991768,
-0.06049172207713127,
0.025878962129354477,
-0.08941352367401123,
0.02535502053797245,
-0.0977565199136734,
-0.030583426356315613,
-0.07521884143352509,
0.016741951927542686,
-0.03561945632100105,
-0.055432043969631195,
-0.03732994943857193,
0.003659334499388933,
0.05146348103880882,
-0.015113680623471737,
0.16142582893371582,
-0.008942115120589733,
0.16667746007442474,
0.13008330762386322,
0.09046386927366257,
-0.08709797263145447,
-0.05286401882767677,
0.007389794569462538,
-0.017894109711050987,
0.0669548436999321,
-0.1501956284046173,
0.03140188381075859,
0.14251182973384857,
0.022716309875249863,
0.14328432083129883,
0.08243858814239502,
-0.04297526553273201,
0.02871651202440262,
0.06030705198645592,
-0.16529060900211334,
-0.10059066116809845,
-0.011212041601538658,
-0.008844096213579178,
-0.12138297408819199,
0.05150214955210686,
0.13593029975891113,
-0.06492121517658234,
-0.007893775589764118,
-0.013955570757389069,
0.01040569692850113,
-0.03322751075029373,
0.19329720735549927,
0.03833417966961861,
0.056444793939590454,
-0.10130307823419571,
0.07405608147382736,
0.04188966378569603,
-0.09132052212953568,
0.03690518066287041,
0.11921681463718414,
-0.07762625068426132,
-0.027952129021286964,
0.01797192543745041,
0.19576384127140045,
-0.07224312424659729,
-0.036419034004211426,
-0.15915817022323608,
-0.11084987223148346,
0.09304860234260559,
0.17858274281024933,
0.07654240727424622,
0.001896882662549615,
-0.055322859436273575,
0.023635530844330788,
-0.1283886432647705,
0.08650189638137817,
0.05727207288146019,
0.0689699649810791,
-0.11763856559991837,
0.16589808464050293,
0.0030598528683185577,
0.028483815491199493,
-0.019085491076111794,
0.010289139114320278,
-0.10780223459005356,
0.010127787478268147,
-0.12655365467071533,
-0.016169201582670212,
-0.04053298383951187,
-0.008490345440804958,
-0.007096101995557547,
-0.05092569813132286,
-0.06673354655504227,
0.01096253376454115,
-0.11350123584270477,
-0.03338075056672096,
0.009239702485501766,
0.03322991728782654,
-0.11520674079656601,
-0.009718482382595539,
0.026040710508823395,
-0.0786505863070488,
0.06999355554580688,
0.05358589068055153,
0.0039702593348920345,
0.037376660853624344,
-0.09264218062162399,
0.02270856872200966,
0.04235853627324104,
-0.0039061398711055517,
0.05572621151804924,
-0.07320421189069748,
-0.007380381226539612,
-0.018949760124087334,
0.04972876235842705,
0.025697629898786545,
0.08340973407030106,
-0.12937885522842407,
0.028631607070565224,
-0.0007026239763945341,
-0.07893063873052597,
-0.07002419978380203,
0.05332111567258835,
0.07876299321651459,
0.029989425092935562,
0.17034710943698883,
-0.08417264372110367,
0.05757184326648712,
-0.2092028260231018,
0.001348562422208488,
0.014885411597788334,
-0.10898508131504059,
-0.07022164762020111,
-0.06361772865056992,
0.06974434852600098,
-0.06662110984325409,
0.11821982264518738,
0.0203534048050642,
0.04339184612035751,
0.037538718432188034,
-0.03650867938995361,
-0.02670113369822502,
0.003734608180820942,
0.1751812845468521,
0.0519544743001461,
-0.04838751628994942,
0.05552903935313225,
0.02939252369105816,
0.10127724707126617,
0.12266045063734055,
0.21777822077274323,
0.14708946645259857,
0.03894691914319992,
0.09670494496822357,
0.025432433933019638,
-0.04332306981086731,
-0.1820172220468521,
0.05073332414031029,
-0.02292722649872303,
0.10894192010164261,
-0.014743849635124207,
0.19512467086315155,
0.11514171957969666,
-0.15208296477794647,
0.04726603254675865,
-0.022747209295630455,
-0.07941539585590363,
-0.13579393923282623,
-0.07232115417718887,
-0.0922468900680542,
-0.13861379027366638,
0.004212976433336735,
-0.12077376246452332,
0.05685197934508324,
0.07082255184650421,
0.02051025815308094,
0.0013020924525335431,
0.14730580151081085,
0.014355231076478958,
0.00989349652081728,
0.06532898545265198,
0.003620221745222807,
-0.018465198576450348,
-0.07471563667058945,
-0.08226261287927628,
0.010024570859968662,
-0.016671497374773026,
0.04766174405813217,
-0.02732744812965393,
-0.053787775337696075,
0.02179073914885521,
-0.019136536866426468,
-0.11611424386501312,
0.00983885582536459,
0.021756017580628395,
0.06563956290483475,
0.05766556039452553,
0.005821282975375652,
-0.0004568534786812961,
-0.02220609225332737,
0.2086041420698166,
-0.08150071650743484,
-0.0535026453435421,
-0.10256025940179825,
0.24900439381599426,
0.03225886821746826,
-0.0135867390781641,
0.022436169907450676,
-0.06903573125600815,
-0.009539050050079823,
0.2153550088405609,
0.18664447963237762,
-0.05461517721414566,
0.002710554050281644,
-0.0009269415168091655,
-0.00434150779619813,
-0.007245565298944712,
0.0899820327758789,
0.10326799005270004,
0.05670422315597534,
-0.07672057300806046,
-0.01859072782099247,
-0.05025395378470421,
-0.01381755992770195,
-0.04435155168175697,
0.0676790252327919,
0.03190368413925171,
0.009576192125678062,
-0.04894528165459633,
0.044215984642505646,
-0.05627136304974556,
-0.08281057327985764,
0.03792152926325798,
-0.21512971818447113,
-0.14885850250720978,
-0.0048071835190057755,
0.09268360584974289,
0.004511278122663498,
0.07173781841993332,
-0.013976434245705605,
-0.018985044211149216,
0.08164069056510925,
-0.014499678276479244,
-0.0801888257265091,
-0.0925775021314621,
0.10104835778474808,
-0.12905697524547577,
0.18619291484355927,
-0.04216248542070389,
0.03285389021039009,
0.1279362589120865,
0.04956747218966484,
-0.07652949541807175,
0.0697784274816513,
0.048110898584127426,
-0.059095364063978195,
-0.001592571148648858,
0.15283943712711334,
-0.03610047698020935,
0.09230336546897888,
0.05328521504998207,
-0.15848761796951294,
0.005759235471487045,
-0.05225745216012001,
-0.05274975299835205,
-0.02561980113387108,
-0.044041525572538376,
-0.04756252467632294,
0.12820927798748016,
0.22180987894535065,
-0.031032932922244072,
-0.0030736145563423634,
-0.05981820821762085,
0.019438501447439194,
0.06280802190303802,
0.03888566046953201,
-0.05669305473566055,
-0.25345441699028015,
-0.0005731135024689138,
0.09300336986780167,
-0.011422841809689999,
-0.2805338203907013,
-0.09892770648002625,
0.009529856964945793,
-0.04487624019384384,
-0.09818173944950104,
0.08877147734165192,
0.07851417362689972,
0.057035110890865326,
-0.051057592034339905,
-0.0773637443780899,
-0.057304684072732925,
0.1693039834499359,
-0.14952437579631805,
-0.07335790991783142
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-mbart-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro
This model is a fine-tuned version of [sshleifer/tiny-mbart](https://huggingface.co/sshleifer/tiny-mbart) on the wmt16_en_ro_pre_processed dataset.
It achieves the following results on the evaluation set:
- Loss: 8.5137
- Bleu: 0.0
- Gen Len: 20.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:----:|:-------:|
| 8.2817 | 1.0 | 76290 | 8.5137 | 0.0 | 20.0 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu102
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["wmt16_en_ro_pre_processed"], "metrics": ["bleu"], "model-index": [{"name": "tiny-mbart-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16_en_ro_pre_processed", "type": "wmt16_en_ro_pre_processed", "args": "enro"}, "metrics": [{"type": "bleu", "value": 0.0, "name": "Bleu"}]}]}]}
|
text2text-generation
|
ffsouza/tiny-mbart-length-96-learning_rate-2e-05-weight_decay-0.01-finetuned-en-to-ro
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16_en_ro_pre_processed",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us
|
tiny-mbart-length-96-learning\_rate-2e-05-weight\_decay-0.01-finetuned-en-to-ro
===============================================================================
This model is a fine-tuned version of sshleifer/tiny-mbart on the wmt16\_en\_ro\_pre\_processed dataset.
It achieves the following results on the evaluation set:
* Loss: 8.5137
* Bleu: 0.0
* Gen Len: 20.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu102
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu102\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu102\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
70,
113,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #dataset-wmt16_en_ro_pre_processed #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu102\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
-0.09897331148386002,
0.06212788075208664,
-0.003706732066348195,
0.09771396219730377,
0.12977059185504913,
0.016780540347099304,
0.14437679946422577,
0.14073237776756287,
-0.08298103511333466,
0.062452223151922226,
0.1347084641456604,
0.12738986313343048,
0.033955857157707214,
0.1455448418855667,
-0.061345331370830536,
-0.2700382173061371,
0.015838416293263435,
0.04176711663603783,
-0.020830843597650528,
0.13514454662799835,
0.08256837725639343,
-0.1272425651550293,
0.09110759943723679,
0.012491918168962002,
-0.17030619084835052,
0.004715313669294119,
0.007411223370581865,
-0.06403713673353195,
0.11829172819852829,
0.021498162299394608,
0.11776174604892731,
0.02810792811214924,
0.07449494302272797,
-0.17686888575553894,
0.010149477049708366,
0.05981271713972092,
0.012346141040325165,
0.09417496621608734,
0.05924791470170021,
-0.025981128215789795,
0.13035725057125092,
-0.07199026644229889,
0.06958475708961487,
0.026622101664543152,
-0.13616736233234406,
-0.2300657033920288,
-0.07671408355236053,
0.03188447654247284,
0.06817036122083664,
0.09032867103815079,
-0.018115881830453873,
0.13553471863269806,
-0.05925557389855385,
0.10849904268980026,
0.2665161192417145,
-0.2656686007976532,
-0.0680430456995964,
0.0003758948005270213,
0.036514852195978165,
0.08781422674655914,
-0.09227053076028824,
-0.021631114184856415,
0.0399169959127903,
0.04925042763352394,
0.12586063146591187,
-0.014902619644999504,
-0.019858496263623238,
-0.006383994594216347,
-0.14112256467342377,
-0.04904001206159592,
0.1369747668504715,
0.01968200132250786,
-0.03747319057583809,
-0.053346987813711166,
-0.08219780772924423,
-0.19726026058197021,
-0.03892681375145912,
-0.005787925329059362,
0.03019120916724205,
-0.032652173191308975,
-0.0971122458577156,
-0.02164529263973236,
-0.08096976578235626,
-0.07066786289215088,
-0.044155098497867584,
0.1711454838514328,
0.04740401729941368,
0.013811664655804634,
-0.059161365032196045,
0.09809694439172745,
-0.040094006806612015,
-0.1551884561777115,
0.0017892863834276795,
0.015752552077174187,
0.008973302319645882,
-0.03354664519429207,
-0.052260126918554306,
-0.08670564740896225,
-0.00891318079084158,
0.15838342905044556,
-0.09441167861223221,
0.07004296779632568,
-0.0007745604962110519,
0.033766698092222214,
-0.08887085318565369,
0.17045439779758453,
-0.005641345400363207,
-0.022173307836055756,
-0.008819859474897385,
0.07767505198717117,
0.024242892861366272,
-0.03651906177401543,
-0.1008463054895401,
0.015505928546190262,
0.10183527320623398,
0.013532215729355812,
-0.058871980756521225,
0.058584168553352356,
-0.04463495686650276,
-0.028801249340176582,
0.003722079796716571,
-0.10512274503707886,
0.03360525518655777,
0.00046991228009574115,
-0.07601293921470642,
0.005893405992537737,
0.020226383581757545,
0.001245943596586585,
-0.027393624186515808,
0.11483702808618546,
-0.09088370203971863,
0.028041165322065353,
-0.09183994680643082,
-0.13952599465847015,
0.008919195272028446,
-0.10776178538799286,
-0.004726531449705362,
-0.08515720814466476,
-0.16538454592227936,
-0.018863758072257042,
0.04859016463160515,
-0.04007485881447792,
-0.02362024039030075,
-0.05925755202770233,
-0.07198629528284073,
0.04349669814109802,
-0.005864326376467943,
0.09406362473964691,
-0.05822135880589485,
0.0967564806342125,
0.0523277223110199,
0.08290351182222366,
-0.03045186772942543,
0.052985984832048416,
-0.0939209833741188,
0.030807625502347946,
-0.189560204744339,
0.055618081241846085,
-0.04160178825259209,
0.052005816251039505,
-0.09559907764196396,
-0.1049751415848732,
-0.021989313885569572,
-0.005252784583717585,
0.09694705158472061,
0.10337795317173004,
-0.16564467549324036,
-0.09803105890750885,
0.19548283517360687,
-0.09156331419944763,
-0.08803698420524597,
0.13098502159118652,
-0.05640183389186859,
0.03229216858744621,
0.05906382203102112,
0.19082733988761902,
0.053000107407569885,
-0.09028912335634232,
0.0008257119916379452,
-0.036213260143995285,
0.06710461527109146,
-0.01768403686583042,
0.07082512229681015,
-0.007854596711695194,
0.03042711690068245,
0.01279536448419094,
0.011400350369513035,
0.022588388994336128,
-0.1027488186955452,
-0.08268097788095474,
-0.04528224095702171,
-0.07259701937437057,
0.05388937518000603,
0.04641779884696007,
0.06711675971746445,
-0.12081582099199295,
-0.10754842311143875,
0.03710031136870384,
0.07944260537624359,
-0.07615503668785095,
0.04221436008810997,
-0.08097509294748306,
0.07488633692264557,
-0.057539910078048706,
-0.011388523504137993,
-0.18591858446598053,
-0.008659601211547852,
0.027429502457380295,
-0.005654398817569017,
0.02947145700454712,
-0.01144387200474739,
0.08002542704343796,
0.07656674832105637,
-0.040178120136260986,
-0.025620529428124428,
-0.050042133778333664,
-0.0133230434730649,
-0.12913113832473755,
-0.19846084713935852,
-0.03727271035313606,
-0.031159045174717903,
0.10351687669754028,
-0.1881095916032791,
0.049517884850502014,
0.032958611845970154,
0.09978260099887848,
0.03585890308022499,
-0.03247595950961113,
-0.03661888837814331,
0.08518461138010025,
-0.0468377061188221,
-0.04949519410729408,
0.06455367803573608,
-0.002717884723097086,
-0.09404245018959045,
-0.036270011216402054,
-0.1606486737728119,
0.12960852682590485,
0.12691175937652588,
-0.07250460237264633,
-0.0801360234618187,
-0.005777307320386171,
-0.05533416569232941,
-0.027757564559578896,
-0.04621627554297447,
0.008728882297873497,
0.18741783499717712,
0.014111834578216076,
0.1541779339313507,
-0.08541134744882584,
-0.04517417773604393,
0.03729621693491936,
-0.032241497188806534,
0.011563410982489586,
0.12550711631774902,
0.08346886187791824,
-0.07943731546401978,
0.1286662369966507,
0.15001778304576874,
-0.07589004933834076,
0.16245101392269135,
-0.0276210755109787,
-0.07903272658586502,
-0.02905009686946869,
-0.02359064482152462,
0.008488981984555721,
0.08919837325811386,
-0.14964929223060608,
-0.020823676139116287,
0.02298814244568348,
0.0323663093149662,
0.019109414890408516,
-0.20615029335021973,
-0.008192394860088825,
0.05286870524287224,
-0.04676259681582451,
-0.03564172238111496,
-0.006425959523767233,
0.01712467335164547,
0.10805532336235046,
0.014356794767081738,
-0.055772826075553894,
0.01912580616772175,
0.0013025213265791535,
-0.07421378046274185,
0.19140633940696716,
-0.08041026443243027,
-0.15355464816093445,
-0.12810978293418884,
-0.07750896364450455,
-0.05934322997927666,
0.007653649430721998,
0.07414048165082932,
-0.10382970422506332,
-0.0303182415664196,
-0.08576316386461258,
0.04537983611226082,
-0.026873646304011345,
0.035240061581134796,
0.03577860817313194,
-0.012945204973220825,
0.05770459026098251,
-0.11058571189641953,
-0.026673320680856705,
-0.042492855340242386,
-0.047141339629888535,
0.057113975286483765,
0.019417457282543182,
0.11496564000844955,
0.13916468620300293,
-0.025600196793675423,
0.03728015720844269,
-0.029960913583636284,
0.23755085468292236,
-0.06917169690132141,
-0.02159510925412178,
0.1374811828136444,
-0.013543147593736649,
0.07307475060224533,
0.11251846700906754,
0.06206585466861725,
-0.08854836970567703,
0.002491636434569955,
0.03828795999288559,
-0.05313871055841446,
-0.21302050352096558,
-0.025031443685293198,
-0.046295635402202606,
0.006638544145971537,
0.11425517499446869,
0.023960055783391,
0.03596076741814613,
0.06408103555440903,
0.013804965652525425,
0.06680312752723694,
-0.027523597702383995,
0.09577387571334839,
0.10529199987649918,
0.05187077075242996,
0.1472768932580948,
-0.03736725449562073,
-0.04750921204686165,
0.047170549631118774,
-0.014692303724586964,
0.2279309183359146,
0.00916081853210926,
0.16695517301559448,
0.04793957248330116,
0.1477092206478119,
0.028381098061800003,
0.06994063407182693,
-0.01023856457322836,
-0.022295773029327393,
-0.01703610084950924,
-0.04907051846385002,
-0.047272372990846634,
0.024296307936310768,
-0.06058097630739212,
0.057248249650001526,
-0.1365516036748886,
0.022970369085669518,
0.04555764049291611,
0.2618778944015503,
0.03172305226325989,
-0.3472556471824646,
-0.1002122238278389,
0.005614013411104679,
-0.04552006348967552,
-0.01979408599436283,
0.025378236547112465,
0.09719720482826233,
-0.08744892477989197,
0.05507215857505798,
-0.07326015084981918,
0.1096811443567276,
-0.05308644473552704,
0.052249111235141754,
0.047453828155994415,
0.11214492470026016,
-0.014100826345384121,
0.05030013993382454,
-0.288224995136261,
0.2722267210483551,
0.019022956490516663,
0.0682285949587822,
-0.07044826447963715,
0.0049875411204993725,
0.02197965979576111,
0.03519957885146141,
0.05684145539999008,
-0.02115144208073616,
-0.09044095128774643,
-0.19563722610473633,
-0.07211947441101074,
0.02061229944229126,
0.12921065092086792,
-0.03277742862701416,
0.12908430397510529,
-0.028881454840302467,
0.013250868767499924,
0.05412276089191437,
-0.02039189450442791,
-0.07947266101837158,
-0.09611770510673523,
0.014761912636458874,
0.0392737090587616,
-0.03864405304193497,
-0.06719017773866653,
-0.10296200960874557,
-0.05826228857040405,
0.1507227122783661,
-0.023013252764940262,
-0.049378182739019394,
-0.1265472024679184,
0.060623038560152054,
0.08652881532907486,
-0.09682231396436691,
0.019612397998571396,
-0.00677607674151659,
0.10144510120153427,
0.000033204618375748396,
-0.06559253484010696,
0.11147180944681168,
-0.06208471208810806,
-0.1905837208032608,
-0.05349297076463699,
0.12378795444965363,
0.03527543693780899,
0.06882178038358688,
-0.021461280062794685,
0.031822413206100464,
-0.03452833741903305,
-0.08555588126182556,
0.03168082982301712,
0.013114294037222862,
0.042442917823791504,
0.005525163374841213,
-0.032829780131578445,
0.035109858959913254,
-0.06799621134996414,
-0.023313008248806,
0.16705676913261414,
0.253177285194397,
-0.08621221035718918,
0.0457674004137516,
0.05577586591243744,
-0.06555614620447159,
-0.17025873064994812,
0.0317862369120121,
0.06199970468878746,
0.005771013908088207,
0.006295657251030207,
-0.19181957840919495,
0.054887887090444565,
0.10014249384403229,
-0.005545844789594412,
0.09365241229534149,
-0.33491507172584534,
-0.13188865780830383,
0.09214653074741364,
0.13055038452148438,
0.11579543352127075,
-0.15492169559001923,
-0.03848681598901749,
-0.024924738332629204,
-0.1326456367969513,
0.1070517748594284,
-0.09742625802755356,
0.12233110517263412,
-0.034661851823329926,
0.1080123782157898,
0.01697089523077011,
-0.05630628019571304,
0.12142326682806015,
0.005665711592882872,
0.0844845324754715,
-0.06153843179345131,
-0.0003355967055540532,
0.05488954856991768,
-0.06049172207713127,
0.025878962129354477,
-0.08941352367401123,
0.02535502053797245,
-0.0977565199136734,
-0.030583426356315613,
-0.07521884143352509,
0.016741951927542686,
-0.03561945632100105,
-0.055432043969631195,
-0.03732994943857193,
0.003659334499388933,
0.05146348103880882,
-0.015113680623471737,
0.16142582893371582,
-0.008942115120589733,
0.16667746007442474,
0.13008330762386322,
0.09046386927366257,
-0.08709797263145447,
-0.05286401882767677,
0.007389794569462538,
-0.017894109711050987,
0.0669548436999321,
-0.1501956284046173,
0.03140188381075859,
0.14251182973384857,
0.022716309875249863,
0.14328432083129883,
0.08243858814239502,
-0.04297526553273201,
0.02871651202440262,
0.06030705198645592,
-0.16529060900211334,
-0.10059066116809845,
-0.011212041601538658,
-0.008844096213579178,
-0.12138297408819199,
0.05150214955210686,
0.13593029975891113,
-0.06492121517658234,
-0.007893775589764118,
-0.013955570757389069,
0.01040569692850113,
-0.03322751075029373,
0.19329720735549927,
0.03833417966961861,
0.056444793939590454,
-0.10130307823419571,
0.07405608147382736,
0.04188966378569603,
-0.09132052212953568,
0.03690518066287041,
0.11921681463718414,
-0.07762625068426132,
-0.027952129021286964,
0.01797192543745041,
0.19576384127140045,
-0.07224312424659729,
-0.036419034004211426,
-0.15915817022323608,
-0.11084987223148346,
0.09304860234260559,
0.17858274281024933,
0.07654240727424622,
0.001896882662549615,
-0.055322859436273575,
0.023635530844330788,
-0.1283886432647705,
0.08650189638137817,
0.05727207288146019,
0.0689699649810791,
-0.11763856559991837,
0.16589808464050293,
0.0030598528683185577,
0.028483815491199493,
-0.019085491076111794,
0.010289139114320278,
-0.10780223459005356,
0.010127787478268147,
-0.12655365467071533,
-0.016169201582670212,
-0.04053298383951187,
-0.008490345440804958,
-0.007096101995557547,
-0.05092569813132286,
-0.06673354655504227,
0.01096253376454115,
-0.11350123584270477,
-0.03338075056672096,
0.009239702485501766,
0.03322991728782654,
-0.11520674079656601,
-0.009718482382595539,
0.026040710508823395,
-0.0786505863070488,
0.06999355554580688,
0.05358589068055153,
0.0039702593348920345,
0.037376660853624344,
-0.09264218062162399,
0.02270856872200966,
0.04235853627324104,
-0.0039061398711055517,
0.05572621151804924,
-0.07320421189069748,
-0.007380381226539612,
-0.018949760124087334,
0.04972876235842705,
0.025697629898786545,
0.08340973407030106,
-0.12937885522842407,
0.028631607070565224,
-0.0007026239763945341,
-0.07893063873052597,
-0.07002419978380203,
0.05332111567258835,
0.07876299321651459,
0.029989425092935562,
0.17034710943698883,
-0.08417264372110367,
0.05757184326648712,
-0.2092028260231018,
0.001348562422208488,
0.014885411597788334,
-0.10898508131504059,
-0.07022164762020111,
-0.06361772865056992,
0.06974434852600098,
-0.06662110984325409,
0.11821982264518738,
0.0203534048050642,
0.04339184612035751,
0.037538718432188034,
-0.03650867938995361,
-0.02670113369822502,
0.003734608180820942,
0.1751812845468521,
0.0519544743001461,
-0.04838751628994942,
0.05552903935313225,
0.02939252369105816,
0.10127724707126617,
0.12266045063734055,
0.21777822077274323,
0.14708946645259857,
0.03894691914319992,
0.09670494496822357,
0.025432433933019638,
-0.04332306981086731,
-0.1820172220468521,
0.05073332414031029,
-0.02292722649872303,
0.10894192010164261,
-0.014743849635124207,
0.19512467086315155,
0.11514171957969666,
-0.15208296477794647,
0.04726603254675865,
-0.022747209295630455,
-0.07941539585590363,
-0.13579393923282623,
-0.07232115417718887,
-0.0922468900680542,
-0.13861379027366638,
0.004212976433336735,
-0.12077376246452332,
0.05685197934508324,
0.07082255184650421,
0.02051025815308094,
0.0013020924525335431,
0.14730580151081085,
0.014355231076478958,
0.00989349652081728,
0.06532898545265198,
0.003620221745222807,
-0.018465198576450348,
-0.07471563667058945,
-0.08226261287927628,
0.010024570859968662,
-0.016671497374773026,
0.04766174405813217,
-0.02732744812965393,
-0.053787775337696075,
0.02179073914885521,
-0.019136536866426468,
-0.11611424386501312,
0.00983885582536459,
0.021756017580628395,
0.06563956290483475,
0.05766556039452553,
0.005821282975375652,
-0.0004568534786812961,
-0.02220609225332737,
0.2086041420698166,
-0.08150071650743484,
-0.0535026453435421,
-0.10256025940179825,
0.24900439381599426,
0.03225886821746826,
-0.0135867390781641,
0.022436169907450676,
-0.06903573125600815,
-0.009539050050079823,
0.2153550088405609,
0.18664447963237762,
-0.05461517721414566,
0.002710554050281644,
-0.0009269415168091655,
-0.00434150779619813,
-0.007245565298944712,
0.0899820327758789,
0.10326799005270004,
0.05670422315597534,
-0.07672057300806046,
-0.01859072782099247,
-0.05025395378470421,
-0.01381755992770195,
-0.04435155168175697,
0.0676790252327919,
0.03190368413925171,
0.009576192125678062,
-0.04894528165459633,
0.044215984642505646,
-0.05627136304974556,
-0.08281057327985764,
0.03792152926325798,
-0.21512971818447113,
-0.14885850250720978,
-0.0048071835190057755,
0.09268360584974289,
0.004511278122663498,
0.07173781841993332,
-0.013976434245705605,
-0.018985044211149216,
0.08164069056510925,
-0.014499678276479244,
-0.0801888257265091,
-0.0925775021314621,
0.10104835778474808,
-0.12905697524547577,
0.18619291484355927,
-0.04216248542070389,
0.03285389021039009,
0.1279362589120865,
0.04956747218966484,
-0.07652949541807175,
0.0697784274816513,
0.048110898584127426,
-0.059095364063978195,
-0.001592571148648858,
0.15283943712711334,
-0.03610047698020935,
0.09230336546897888,
0.05328521504998207,
-0.15848761796951294,
0.005759235471487045,
-0.05225745216012001,
-0.05274975299835205,
-0.02561980113387108,
-0.044041525572538376,
-0.04756252467632294,
0.12820927798748016,
0.22180987894535065,
-0.031032932922244072,
-0.0030736145563423634,
-0.05981820821762085,
0.019438501447439194,
0.06280802190303802,
0.03888566046953201,
-0.05669305473566055,
-0.25345441699028015,
-0.0005731135024689138,
0.09300336986780167,
-0.011422841809689999,
-0.2805338203907013,
-0.09892770648002625,
0.009529856964945793,
-0.04487624019384384,
-0.09818173944950104,
0.08877147734165192,
0.07851417362689972,
0.057035110890865326,
-0.051057592034339905,
-0.0773637443780899,
-0.057304684072732925,
0.1693039834499359,
-0.14952437579631805,
-0.07335790991783142
] |
null | null |
transformers
|
T5-small for QA
---
[Google's T5-small](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) pre-trained on the [C4](https://huggingface.co/datasets/c4) dataset, fine-tuned for Question-Answering on [SQuAD v2](https://huggingface.co/datasets/squad_v2) with the following hyperparameters:
```
optimizer=adamw_hf
learning_rate=3e-5
adam_beta1=0.9
adam_beta2=0.999
adam_epsilon=1e-08
num_train_epochs=2
per_device_train_batch_size=12
```
Usage
---
The input [context and question] has to be prepared in a specific way as follows:
```python
from transformers import pipeline
def prep_input(_context, _question):
return " ".join(["question:", _question.strip(), "context:", _context.strip()])
t5qa = pipeline("text2text-generation", "fgaim/t5-small-squad-v2")
context = """
Oxygen is a chemical element with symbol O and atomic number 8. It is a member of the chalcogen group on the periodic table and is a highly reactive nonmetal and oxidizing agent that readily forms compounds (notably oxides) with most elements. By mass, oxygen is the third-most abundant element in the universe, after hydrogen and helium. At standard temperature and pressure, two atoms of the element bind to form dioxygen, a colorless and odorless diatomic gas with the formula O.
"""
t5qa(prep_input(context, "How many atoms combine to form dioxygen?"))
# [{'generated_text': 'two'}]
t5qa(prep_input(context, "What element makes up almost half of the earth's crust by mass?"))
# [{'generated_text': 'oxygen'}]
t5qa(prep_input(context, "What are the most abundent elements of the universe by mass?"))
# [{'generated_text': 'hydrogen and helium'}]
```
|
{"language": ["en"], "license": "apache-2.0", "tags": ["text2text-generation"], "datasets": ["c4", "squad"], "widget": [{"text": "question: What is the atomic number for oxygen? context: Oxygen is a chemical element with symbol O and atomic number 8."}, {"text": "question: What is the chemical symbol of Oxygen? context: Oxygen is a chemical element with symbol O and atomic number 8."}]}
|
text2text-generation
|
fgaim/t5-small-squad-v2
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:c4",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-c4 #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
T5-small for QA
---
Google's T5-small pre-trained on the C4 dataset, fine-tuned for Question-Answering on SQuAD v2 with the following hyperparameters:
Usage
---
The input [context and question] has to be prepared in a specific way as follows:
|
[] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-c4 #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
70
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-c4 #dataset-squad #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
-0.05345983803272247,
0.10568087548017502,
-0.00505443662405014,
0.05532882735133171,
0.13051675260066986,
0.03043091855943203,
0.13740496337413788,
0.14787086844444275,
-0.04366079717874527,
-0.05500632897019386,
0.1338426172733307,
0.2094319462776184,
0.027787676081061363,
0.08085139095783234,
-0.08710924535989761,
-0.20386067032814026,
0.043814536184072495,
0.04766424745321274,
-0.029208501800894737,
0.12170512229204178,
0.12090525776147842,
-0.03662335127592087,
0.08804409950971603,
-0.026242563501000404,
-0.11717851459980011,
0.016621598973870277,
0.057438090443611145,
-0.11736539751291275,
0.11327894032001495,
0.04229316860437393,
0.05775456130504608,
0.06974613666534424,
-0.012776787392795086,
-0.143752321600914,
0.018343038856983185,
0.03215142339468002,
-0.0733560100197792,
0.07049296051263809,
0.07319813966751099,
-0.027612341567873955,
0.10141060501337051,
0.0018257158808410168,
-0.02133934572339058,
0.06891316920518875,
-0.10063778609037399,
-0.08271777629852295,
-0.08639390021562576,
0.040912628173828125,
0.061447594314813614,
0.09826813638210297,
0.01880824938416481,
0.14126518368721008,
-0.07870922982692719,
0.10432017594575882,
0.11981193721294403,
-0.36684465408325195,
0.012469503097236156,
0.046823203563690186,
0.04361055791378021,
0.05921817943453789,
0.00013204281276557595,
0.02002066560089588,
0.03869294747710228,
0.04562465846538544,
0.0640394538640976,
-0.06218167766928673,
-0.23411555588245392,
0.04932740703225136,
-0.07711897790431976,
-0.07842297852039337,
0.3279387652873993,
-0.027857905253767967,
0.041196152567863464,
-0.01708342880010605,
-0.09481198340654373,
-0.022549021989107132,
0.005921103525906801,
0.028200877830386162,
-0.0014769650297239423,
0.06830842792987823,
0.03357566520571709,
-0.05458315461874008,
-0.15991637110710144,
-0.01136728748679161,
-0.19408616423606873,
-0.028278440237045288,
0.0020873816683888435,
0.08098997920751572,
-0.1856805831193924,
0.07821357250213623,
0.05483387038111687,
-0.10947632044553757,
0.022918015718460083,
-0.06489526480436325,
0.04200112074613571,
0.0027785729616880417,
-0.06286313384771347,
-0.0822787806391716,
0.1250874102115631,
0.14868487417697906,
0.0141532514244318,
-0.012895802035927773,
-0.08061850070953369,
0.0790167897939682,
-0.004690379835665226,
0.04087679460644722,
-0.02337532676756382,
-0.03542071208357811,
0.0969942957162857,
-0.11183720827102661,
0.050339460372924805,
-0.03076629713177681,
-0.13389630615711212,
-0.06895110756158829,
0.040472112596035004,
0.10146418958902359,
0.0747864693403244,
0.08990323543548584,
-0.03516437113285065,
-0.019070293754339218,
0.08906194567680359,
-0.08682312071323395,
-0.03079874813556671,
0.00018275700858794153,
0.007220097351819277,
0.08411147445440292,
0.07224323600530624,
0.028041472658514977,
-0.1340782791376114,
0.007627222221344709,
-0.05804084613919258,
-0.04107941314578056,
-0.00943369884043932,
-0.05170980095863342,
0.07913379371166229,
-0.09636251628398895,
0.01051581371575594,
-0.14107263088226318,
-0.2049150913953781,
0.0327996164560318,
0.0457146055996418,
-0.006047205068171024,
-0.11255916208028793,
-0.0005784544046036899,
-0.04905834048986435,
0.041430771350860596,
-0.08398523181676865,
0.052264824509620667,
-0.07082121819257736,
0.07979270070791245,
-0.07769239693880081,
0.05433599278330803,
-0.17641472816467285,
0.06513693928718567,
-0.12656483054161072,
-0.027300922200083733,
-0.012362114153802395,
0.040052734315395355,
-0.011794175021350384,
0.11285450309515,
-0.07463262975215912,
-0.03289294242858887,
0.021740050986409187,
-0.010606211610138416,
-0.01761700212955475,
0.15745854377746582,
-0.15556441247463226,
-0.04250507801771164,
0.2208091914653778,
-0.09836806356906891,
-0.23069247603416443,
0.07515447586774826,
0.022026557475328445,
0.04781533032655716,
0.07671692222356796,
0.1356850117444992,
0.04811748117208481,
-0.041248589754104614,
0.04122230410575867,
0.11752404272556305,
-0.08329389244318008,
-0.1824755221605301,
0.0581192709505558,
-0.03401731327176094,
-0.052367113530635834,
0.05235310643911362,
0.031965821981430054,
0.07292594760656357,
-0.023272978141903877,
-0.07573074102401733,
-0.08212395012378693,
-0.04549214616417885,
0.037960756570100784,
-0.007895436137914658,
0.05386374145746231,
-0.05530682951211929,
-0.012904038652777672,
-0.020291080698370934,
0.016126995906233788,
-0.009283575229346752,
0.06052872911095619,
-0.026730969548225403,
0.08420714735984802,
-0.009389802813529968,
0.03901233524084091,
-0.15336112678050995,
-0.06852497160434723,
-0.009379466064274311,
0.12147453427314758,
-0.004455822519958019,
0.06254756450653076,
0.027484655380249023,
-0.036739908158779144,
-0.010762820020318031,
0.0013359748991206288,
0.12923452258110046,
0.02501612901687622,
-0.04721728712320328,
-0.1275508552789688,
0.025248469784855843,
-0.032482877373695374,
0.07463600486516953,
-0.05067456141114235,
0.030085789039731026,
0.023078424856066704,
0.10295096039772034,
-0.022075733169913292,
0.08177642524242401,
-0.0030819515231996775,
-0.017763236537575722,
-0.08279404789209366,
-0.010013936087489128,
0.10551047325134277,
0.049563221633434296,
-0.07751551270484924,
0.18970520794391632,
-0.0853591188788414,
0.17438526451587677,
0.1874798834323883,
-0.17353211343288422,
0.05381235480308533,
-0.046288877725601196,
-0.04943305253982544,
-0.016834061592817307,
0.02485303208231926,
-0.007831333205103874,
-0.002542866161093116,
0.017258772626519203,
0.16675524413585663,
-0.07141392678022385,
-0.03900257498025894,
0.01684667356312275,
-0.054889511317014694,
0.004674592521041632,
0.04095830023288727,
0.12028985470533371,
-0.17085491120815277,
0.1642731875181198,
0.23755857348442078,
0.01700017973780632,
0.12201204895973206,
-0.06598460674285889,
-0.03964969888329506,
0.04869042709469795,
-0.01194783579558134,
-0.025357335805892944,
-0.027440661564469337,
-0.14297789335250854,
0.03677377477288246,
0.12319047749042511,
0.020973268896341324,
0.056151311844587326,
-0.12005440890789032,
-0.030398089438676834,
-0.016415512189269066,
-0.0454675517976284,
-0.04702261835336685,
0.07405366748571396,
0.04030810296535492,
0.13876307010650635,
-0.0411590039730072,
-0.03790390491485596,
0.1393403857946396,
0.009644870646297932,
-0.12444514036178589,
0.18122676014900208,
-0.15748797357082367,
-0.27451464533805847,
-0.11935658007860184,
-0.11885371059179306,
-0.06624694168567657,
0.0015400908887386322,
0.13776612281799316,
-0.04858090355992317,
-0.033626943826675415,
-0.02380547486245632,
-0.017695076763629913,
-0.004563175607472658,
0.02120908349752426,
-0.05906909331679344,
0.05312487110495567,
-0.00659610191360116,
-0.12359163165092468,
-0.04176473245024681,
0.019371116533875465,
-0.05850677192211151,
0.13417749106884003,
-0.09307930618524551,
0.06988215446472168,
0.15168148279190063,
-0.000999167561531067,
0.030184442177414894,
-0.05170413479208946,
0.1592029482126236,
-0.013987323269248009,
0.043962109833955765,
0.241371750831604,
-0.023736730217933655,
0.06232695281505585,
0.13527053594589233,
-0.006176834460347891,
-0.04448984935879707,
0.026821833103895187,
-0.04862428456544876,
-0.07154827564954758,
-0.32359808683395386,
-0.12125680595636368,
-0.11661204695701599,
0.07604733109474182,
0.05002399533987045,
0.05472854524850845,
0.11331255733966827,
0.07860710471868515,
-0.03138696774840355,
0.035358719527721405,
0.0040317075327038765,
0.07060986757278442,
0.2396107017993927,
-0.008764720521867275,
0.11310236901044846,
-0.10611683130264282,
-0.027510907500982285,
0.12155739963054657,
0.1024225503206253,
0.13264821469783783,
0.034770675003528595,
0.09589331597089767,
0.06741392612457275,
0.1527475267648697,
0.05683586373925209,
0.1557532399892807,
0.0409485325217247,
0.02445300854742527,
-0.022577621042728424,
-0.04610469937324524,
-0.048234544694423676,
0.02197021245956421,
-0.052414294332265854,
-0.11358509957790375,
-0.07277557253837585,
-0.04815904423594475,
0.06861992925405502,
0.20685644447803497,
0.07525049149990082,
-0.22696629166603088,
-0.0063626631163060665,
0.05059933662414551,
0.0019323966698721051,
-0.07810638099908829,
0.07710792869329453,
-0.019919419661164284,
-0.1119743287563324,
0.10573281347751617,
-0.050533998757600784,
0.11736007779836655,
-0.00029188152984716,
0.048921406269073486,
0.008496959693729877,
-0.07744432985782623,
0.04607146233320236,
0.1293509602546692,
-0.3238925337791443,
0.19264645874500275,
-0.00809368398040533,
-0.041886553168296814,
-0.12708261609077454,
0.013299292884767056,
-0.0014316111337393522,
0.14872172474861145,
0.11406237632036209,
0.015334092080593109,
-0.04487079381942749,
0.02440826967358589,
-0.055935055017471313,
0.05373751372098923,
0.026158606633543968,
0.019431471824645996,
0.0002377327182330191,
-0.05353475734591484,
-0.012376819737255573,
0.014685184694826603,
0.05171822011470795,
-0.05115216597914696,
-0.16647377610206604,
0.058212198317050934,
0.09499386698007584,
0.019529182463884354,
-0.026057766750454903,
-0.03287391737103462,
-0.05757967755198479,
0.21608611941337585,
-0.07725855708122253,
-0.12276621162891388,
-0.12048564106225967,
-0.051812659949064255,
0.06365851312875748,
-0.06510531902313232,
0.05838220193982124,
-0.07280261069536209,
0.01743384078145027,
-0.04303588718175888,
-0.2262369692325592,
0.09741457551717758,
-0.11224821209907532,
-0.05822809785604477,
-0.038560736924409866,
0.14906547963619232,
-0.08941047638654709,
0.013484225608408451,
0.04090148210525513,
-0.0040292320773005486,
-0.08392712473869324,
-0.07945062220096588,
-0.01891239546239376,
0.013553031720221043,
0.10027100890874863,
0.0017496306682005525,
-0.10117452591657639,
-0.09395895898342133,
0.002038542414084077,
-0.04409601166844368,
0.29324933886528015,
0.11451919376850128,
-0.07842564582824707,
0.19918027520179749,
0.11886723339557648,
-0.09441231936216354,
-0.27940693497657776,
-0.12979844212532043,
-0.09288650006055832,
-0.05837314948439598,
-0.0068786186166107655,
-0.15482689440250397,
0.10647406429052353,
0.025070538744330406,
-0.038298286497592926,
0.07897288352251053,
-0.2561172842979431,
-0.07992775738239288,
0.15573373436927795,
0.002567727817222476,
0.24263262748718262,
-0.13063541054725647,
-0.0754687488079071,
-0.08548933267593384,
-0.19905658066272736,
0.2248881608247757,
-0.15361393988132477,
0.07315415889024734,
-0.041709158569574356,
0.07267739623785019,
0.006761000957340002,
-0.05380742624402046,
0.05924082547426224,
0.03133460879325867,
-0.0031690674368292093,
-0.11540553718805313,
0.027718733996152878,
0.1453484296798706,
-0.022239159792661667,
0.08441323786973953,
-0.14468522369861603,
0.06477716565132141,
-0.1112036481499672,
-0.00565250962972641,
-0.08980093896389008,
0.0642915666103363,
-0.007449374999850988,
-0.07229913771152496,
-0.01491532102227211,
-0.05890559405088425,
0.07206031680107117,
-0.02107320725917816,
0.15838396549224854,
0.022382279857993126,
0.1039278581738472,
0.18732121586799622,
0.1225573718547821,
-0.11952974647283554,
0.008738203905522823,
-0.09610188752412796,
-0.08177526295185089,
0.032624971121549606,
-0.15780769288539886,
0.04640304297208786,
0.11400435119867325,
-0.03909176215529442,
0.060373228043317795,
0.08425091952085495,
0.0072616469115018845,
-0.038729503750801086,
0.1241917759180069,
-0.2154521942138672,
0.012949822470545769,
-0.040952958166599274,
0.013070622459053993,
-0.015222211368381977,
0.06470012664794922,
0.16700133681297302,
0.008342220447957516,
-0.03602886199951172,
-0.0014335460728034377,
0.0427137091755867,
-0.07269590348005295,
0.08174581080675125,
0.035223692655563354,
0.012787309475243092,
-0.13360339403152466,
0.11619802564382553,
0.04075046628713608,
-0.1230790838599205,
0.0032606569584459066,
0.1449151188135147,
-0.15911535918712616,
-0.1226486936211586,
0.05386613681912422,
0.04773769527673721,
-0.17670471966266632,
-0.07805754244327545,
-0.04449494555592537,
-0.12622147798538208,
0.08678797632455826,
0.13014502823352814,
0.056632936000823975,
0.08693834394216537,
-0.02156137488782406,
-0.09684393554925919,
-0.020669778808951378,
0.040959883481264114,
-0.03549710288643837,
0.025486493483185768,
-0.08870510011911392,
0.03644223511219025,
-0.03207898139953613,
0.12997569143772125,
-0.04326091706752777,
-0.014001665636897087,
-0.09377466142177582,
0.010845685377717018,
-0.1761033535003662,
-0.006529198493808508,
-0.06435691565275192,
-0.02823156863451004,
-0.014087609015405178,
-0.02748287096619606,
-0.0441051684319973,
0.007241980638355017,
-0.09157870709896088,
-0.011179988272488117,
-0.036411527544260025,
0.07944715023040771,
-0.129007950425148,
-0.03713059052824974,
0.035675179213285446,
-0.0330313965678215,
0.14423337578773499,
0.09297679364681244,
-0.11007247120141983,
0.09403670579195023,
-0.14432375133037567,
-0.1046963781118393,
0.08352164924144745,
0.04835091903805733,
0.05018264800310135,
0.01107467245310545,
-0.001578575698658824,
0.12833631038665771,
-0.02638852596282959,
0.03609144315123558,
0.03245405852794647,
-0.10995826125144958,
-0.027494411915540695,
-0.03193431720137596,
-0.08396662771701813,
-0.05935904011130333,
-0.05111448094248772,
0.08051126450300217,
0.02525661699473858,
0.1526716649532318,
-0.044559627771377563,
0.06411942839622498,
-0.11263013631105423,
0.007869790308177471,
-0.014507410116493702,
-0.13343234360218048,
-0.12130085378885269,
-0.0435379296541214,
0.009287938475608826,
-0.029917597770690918,
0.21076619625091553,
0.018970979377627373,
-0.004773318767547607,
0.03932749107480049,
0.10783334076404572,
0.0016655809013172984,
0.005768088158220053,
0.26750215888023376,
0.03725935518741608,
-0.03271772712469101,
-0.0682627409696579,
0.030109528452157974,
-0.0015482478775084019,
0.036430057138204575,
0.17178207635879517,
0.04806717485189438,
0.036938704550266266,
0.07365690916776657,
0.027370240539312363,
-0.006263221614062786,
-0.08320892602205276,
-0.10352130979299545,
0.014207241125404835,
0.11590565741062164,
-0.004788863006979227,
0.09769170731306076,
0.13339783251285553,
-0.060398899018764496,
0.019878823310136795,
-0.04699467495083809,
-0.04783665016293526,
-0.1539652943611145,
-0.1687878519296646,
-0.07154340296983719,
-0.0958346575498581,
-0.015632404014468193,
-0.10932809114456177,
0.057757068425416946,
0.07801304012537003,
0.054095372557640076,
-0.07064035534858704,
0.006166325882077217,
0.03620394691824913,
-0.08068116009235382,
0.035270921885967255,
-0.01729642041027546,
0.03448859602212906,
-0.02615215629339218,
-0.0009646190446801484,
-0.04192608967423439,
0.021625209599733353,
-0.03399499133229256,
0.046694956719875336,
0.02696252427995205,
0.07038819044828415,
-0.12012569606304169,
-0.0721542164683342,
-0.03509721904993057,
0.04429953917860985,
0.004608631134033203,
0.15008197724819183,
0.03642052784562111,
0.0061731799505651,
0.07878587394952774,
0.1835906058549881,
-0.06943366676568985,
-0.1769464910030365,
-0.07352637499570847,
0.16930799186229706,
0.02472231350839138,
0.017771603539586067,
0.04351772740483284,
-0.006194069515913725,
-0.06318094581365585,
0.30510058999061584,
0.3075483441352844,
-0.05524842068552971,
0.008766436949372292,
-0.014931983314454556,
0.01538156159222126,
0.06236054375767708,
0.151315838098526,
0.11411876976490021,
0.20806197822093964,
-0.06281916052103043,
-0.0071946559473872185,
-0.045988161116838455,
0.010091467760503292,
-0.11641829460859299,
0.1124381273984909,
-0.0017180906143039465,
-0.09485233575105667,
0.03728106617927551,
0.10415574163198471,
-0.1364545077085495,
0.09036508947610855,
-0.08596701174974442,
-0.11255965381860733,
-0.06295010447502136,
-0.0242881178855896,
0.11955910921096802,
0.015626395121216774,
0.030333828181028366,
-0.03417526185512543,
-0.03262335807085037,
0.13068833947181702,
-0.0044299159198999405,
-0.22840677201747894,
0.018291855230927467,
0.08574049919843674,
-0.13177353143692017,
0.06897380948066711,
0.003649655729532242,
0.10352266579866409,
0.08574596792459488,
0.07380075007677078,
-0.10944170504808426,
0.053283918648958206,
0.014102498069405556,
0.01469498872756958,
0.054178740829229355,
-0.05528691038489342,
-0.019455472007393837,
-0.07446280121803284,
0.044797252863645554,
-0.07747247070074081,
0.03423687070608139,
0.06569840013980865,
-0.021732768043875694,
-0.036821428686380386,
0.007820367813110352,
-0.0535365492105484,
0.07263214141130447,
0.04994039610028267,
-0.044121675193309784,
-0.0015251453733071685,
-0.09891121089458466,
-0.028961101546883583,
0.015195796266198158,
-0.15489205718040466,
-0.0576443076133728,
-0.058318935334682465,
-0.05673503875732422,
0.06552955508232117,
0.022839907556772232,
-0.17236779630184174,
-0.0010440986370667815,
-0.09831004589796066,
-0.0033817733637988567,
-0.1994839459657669,
0.07548601925373077,
0.08473224192857742,
-0.011865517124533653,
0.003144856309518218,
0.05531148612499237,
0.007834059186279774,
0.07395582646131516,
-0.1082361489534378,
-0.09428982436656952
] |
null | null |
transformers
|
# BERT Base for Tigrinya Language
We pre-train a BERT base-uncased model for Tigrinya on a dataset of 40 million tokens trained for 40 epochs.
This repo contains the original pre-trained Flax model that was trained on a TPU v3.8 and its corresponding PyTorch version.
## Hyperparameters
The hyperparameters corresponding to the model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P | Seq |
|------------|----|----|-----|------|------|------|
| BASE | 12 | 12 | 768 | 3072 | 110M | 512 |
(L = number of layers; AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters; Seq = maximum sequence length.)
## Citation
If you use this model in your product or research, please cite as follows:
```
@article{Fitsum2021TiPLMs,
author={Fitsum Gaim and Wonsuk Yang and Jong C. Park},
title={Monolingual Pre-trained Language Models for Tigrinya},
year=2021,
publisher={WiNLP 2021 at EMNLP 2021}
}
```
|
{"language": "ti", "widget": [{"text": "\u12d3\u1255\u121a \u12f0\u1242\u12a3\u1295\u1235\u1275\u12ee [MASK] \u1265\u130d\u1265\u122a \u1270\u122b\u12a5\u12e9"}]}
|
fill-mask
|
fgaim/tibert-base
|
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"ti",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ti"
] |
TAGS
#transformers #pytorch #jax #bert #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us
|
BERT Base for Tigrinya Language
===============================
We pre-train a BERT base-uncased model for Tigrinya on a dataset of 40 million tokens trained for 40 epochs.
This repo contains the original pre-trained Flax model that was trained on a TPU v3.8 and its corresponding PyTorch version.
Hyperparameters
---------------
The hyperparameters corresponding to the model sizes mentioned above are as follows:
(L = number of layers; AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters; Seq = maximum sequence length.)
If you use this model in your product or research, please cite as follows:
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
45
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
-0.018716592341661453,
0.01956140249967575,
-0.006763755809515715,
0.026492532342672348,
0.0785820409655571,
0.04057798162102699,
0.08437102288007736,
0.10142290592193604,
0.08449754863977432,
0.016109289601445198,
0.16018430888652802,
0.14455920457839966,
-0.03791216015815735,
0.15207906067371368,
-0.03278684988617897,
-0.26467347145080566,
0.05996346101164818,
0.0326806902885437,
-0.09011964499950409,
0.10502596199512482,
0.061749279499053955,
-0.10156816244125366,
0.07450296729803085,
-0.023103389889001846,
-0.1006791740655899,
0.051851291209459305,
0.04248037561774254,
-0.09646754711866379,
0.12945066392421722,
0.004160339944064617,
0.20284701883792877,
0.02951415441930294,
-0.05237147584557533,
-0.05361330136656761,
0.0510825477540493,
0.014506814070045948,
-0.07468386739492416,
0.051914241164922714,
-0.008838496170938015,
-0.0610814094543457,
0.01722273975610733,
0.02521018125116825,
0.026336023584008217,
0.018415464088320732,
-0.17134273052215576,
-0.16421590745449066,
-0.018918069079518318,
0.020931238308548927,
-0.0066371457651257515,
0.05240464583039284,
0.005247147288173437,
0.21583788096904755,
-0.1449425369501114,
0.07656890898942947,
0.19234643876552582,
-0.31402909755706787,
-0.018862802535295486,
0.13257448375225067,
0.14789986610412598,
-0.000621809798758477,
-0.05976932495832443,
0.07593876123428345,
0.024540644139051437,
0.02034100703895092,
0.10721959173679352,
-0.07208669930696487,
-0.06280440837144852,
0.04178937152028084,
-0.09873402118682861,
-0.06086595728993416,
0.20014001429080963,
-0.04482434689998627,
0.0606294609606266,
0.00890424381941557,
-0.11652642488479614,
-0.08941385895013809,
0.001526338397525251,
-0.020502720028162003,
-0.010927369818091393,
0.0344441793859005,
-0.01869789883494377,
-0.026253750547766685,
-0.1377522349357605,
0.025431474670767784,
-0.2012346088886261,
0.1988951414823532,
-0.0019405186176300049,
0.0626368597149849,
-0.17953753471374512,
0.04714170843362808,
-0.0329873152077198,
-0.12370704114437103,
0.0752863958477974,
-0.08689107745885849,
0.018822094425559044,
0.008590287528932095,
-0.07794550061225891,
-0.018680114299058914,
0.06176329776644707,
0.1442805677652359,
0.05944770574569702,
0.020127713680267334,
0.06339091807603836,
0.11196241527795792,
0.04590009152889252,
0.07947126775979996,
0.0005888595478609204,
-0.05232208967208862,
0.010710615664720535,
-0.059299368411302567,
0.019802823662757874,
-0.07317980378866196,
-0.13297979533672333,
-0.038363028317689896,
0.027443185448646545,
0.042699847370386124,
0.07725296914577484,
0.050902288407087326,
-0.07364790141582489,
0.02395290695130825,
0.07047739624977112,
-0.050257910043001175,
0.03840018808841705,
-0.033338628709316254,
0.07773283869028091,
0.06467432528734207,
0.013354365713894367,
-0.007214528042823076,
0.0427621491253376,
0.08338715136051178,
-0.10666663944721222,
-0.02787589840590954,
-0.06823033839464188,
-0.10528109967708588,
0.03726276010274887,
-0.12438034266233444,
0.03198548033833504,
-0.18372991681098938,
-0.049737751483917236,
0.04724344611167908,
0.07081033289432526,
-0.004162527155131102,
-0.04001207649707794,
0.06569570302963257,
-0.04266354814171791,
0.05783088877797127,
-0.03967179358005524,
-0.029812734574079514,
-0.04984335973858833,
0.08934786170721054,
-0.022119585424661636,
0.16938921809196472,
-0.12796461582183838,
0.04153301939368248,
-0.07582353800535202,
0.02518833614885807,
-0.1328711062669754,
-0.06813748925924301,
-0.02829097956418991,
0.1280442476272583,
0.0225665420293808,
-0.035723086446523666,
-0.1540251225233078,
0.05351029708981514,
-0.018545644357800484,
0.1517019271850586,
-0.13946284353733063,
-0.08805006742477417,
0.1928367167711258,
-0.07617892324924469,
-0.1313474029302597,
0.08896268159151077,
0.009487100876867771,
0.012537541799247265,
0.007844962179660797,
0.16637536883354187,
-0.0040877885185182095,
-0.16165992617607117,
0.03784158453345299,
0.12195698916912079,
-0.0925564095377922,
-0.0644633024930954,
0.06226968392729759,
0.022759070619940758,
-0.06220996752381325,
0.013350841589272022,
0.08405653387308121,
0.08586704730987549,
-0.0662229135632515,
-0.053969595581293106,
-0.025041962042450905,
-0.041585635393857956,
0.1396973729133606,
0.027550213038921356,
0.11047530919313431,
-0.09177426993846893,
-0.05153608322143555,
-0.0243193618953228,
0.010790678672492504,
0.09486427158117294,
0.0358414463698864,
-0.0556003674864769,
0.1587260365486145,
-0.06786973029375076,
-0.010929911397397518,
-0.15286991000175476,
-0.10735125094652176,
-0.025299852713942528,
0.07814822345972061,
-0.014499715529382229,
0.21457119286060333,
0.1042620912194252,
-0.05676944553852081,
-0.014598025009036064,
-0.011851778253912926,
0.07580394297838211,
0.048323411494493484,
-0.042516887187957764,
-0.11385691910982132,
-0.018266024067997932,
-0.09519186615943909,
-0.025307541713118553,
-0.022941337898373604,
0.015305832959711552,
0.014649895019829273,
0.14083227515220642,
-0.0006293214973993599,
0.03038640320301056,
-0.044125996530056,
0.03339936211705208,
-0.05667928233742714,
-0.0012783341808244586,
0.05523821711540222,
-0.004058130085468292,
-0.03038300760090351,
0.1830091029405594,
-0.15564535558223724,
0.3778941035270691,
0.20649370551109314,
-0.2701747417449951,
-0.03593064844608307,
0.09442611783742905,
-0.016994141042232513,
0.021418334916234016,
0.03632244095206261,
-0.04158234968781471,
-0.03909726068377495,
-0.04156909137964249,
0.11270143836736679,
-0.011414304375648499,
-0.023103849962353706,
0.02510271593928337,
-0.08186972141265869,
-0.08455482870340347,
0.020703870803117752,
0.07576467096805573,
-0.13830676674842834,
0.20100460946559906,
0.3580632507801056,
-0.03312008082866669,
0.1706019639968872,
0.03998040780425072,
-0.0008925701258704066,
-0.025992805138230324,
-0.0786805972456932,
-0.05078424885869026,
0.09939432144165039,
-0.1918867528438568,
-0.06275881826877594,
0.06785056740045547,
-0.017268722876906395,
0.03533907234668732,
-0.11304644495248795,
-0.058380529284477234,
0.036550212651491165,
0.08725589513778687,
-0.10113472491502762,
0.14896337687969208,
0.03449977561831474,
0.07992418855428696,
0.006679642479866743,
-0.08834362775087357,
0.07000397890806198,
0.011331413872539997,
-0.0035384423099458218,
0.10893746465444565,
-0.12339747697114944,
-0.3126799464225769,
-0.07649705559015274,
-0.1357318013906479,
0.040934719145298004,
0.028706714510917664,
0.07717806100845337,
-0.0674157366156578,
-0.03389695659279823,
0.043433599174022675,
0.0031047312077134848,
-0.09916280955076218,
0.07938291877508163,
-0.09331804513931274,
0.004807931371033192,
-0.03775378316640854,
-0.06034345552325249,
-0.08241048455238342,
-0.017990918830037117,
-0.03606078028678894,
0.14131838083267212,
-0.04471023380756378,
0.08596855401992798,
0.1208510622382164,
-0.009843243286013603,
0.05743534862995148,
-0.0033515451941639185,
0.22435182332992554,
-0.08485199511051178,
0.011270946823060513,
0.14431044459342957,
-0.015009584836661816,
0.07266069948673248,
0.17250145971775055,
0.03214779868721962,
-0.02619399130344391,
-0.012130388990044594,
-0.0367959588766098,
-0.11720903217792511,
-0.09144467860460281,
-0.07773366570472717,
-0.15009059011936188,
-0.012227713130414486,
0.04746619984507561,
0.05668441578745842,
0.13256840407848358,
0.055705390870571136,
0.04067482054233551,
-0.016571909189224243,
-0.10201559215784073,
0.03486325964331627,
0.1251944750547409,
-0.05605435371398926,
0.14170441031455994,
-0.02451098896563053,
-0.13870947062969208,
0.051139384508132935,
0.049826961010694504,
0.05776532366871834,
0.11637112498283386,
-0.02503076195716858,
0.05375967174768448,
0.20673854649066925,
0.15332798659801483,
0.10667400062084198,
0.024898314848542213,
-0.07085411995649338,
-0.026712117716670036,
-0.019017834216356277,
-0.0023149193730205297,
0.06036917492747307,
0.19396241009235382,
-0.09443285316228867,
-0.014395704492926598,
-0.17698344588279724,
0.05036589875817299,
0.06394261866807938,
0.09831920266151428,
-0.2233354151248932,
0.02010962925851345,
0.08707020431756973,
0.0049064611084759235,
-0.056388262659311295,
0.02829798124730587,
0.05529927834868431,
-0.10415264219045639,
0.03149237111210823,
-0.0005025425925850868,
0.08192446827888489,
0.09060977399349213,
0.08652052283287048,
-0.05982251465320587,
-0.10801032930612564,
0.01680404506623745,
0.03838873282074928,
-0.23812198638916016,
0.2550521194934845,
-0.02170681022107601,
-0.09140466898679733,
-0.05605369806289673,
-0.02403327450156212,
0.05895958095788956,
0.12814129889011383,
0.09479700028896332,
0.047181203961372375,
-0.0687108114361763,
-0.12851545214653015,
0.015330538153648376,
0.0014897022629156709,
0.09226493537425995,
-0.051921967417001724,
0.007189408876001835,
-0.023727502673864365,
-0.0318504236638546,
0.016572147607803345,
0.2048088014125824,
-0.0036509118508547544,
-0.11905161291360855,
0.09117826819419861,
0.06924042105674744,
-0.01759910397231579,
-0.01467852108180523,
-0.07560889422893524,
-0.16395166516304016,
0.1534842848777771,
0.039185576140880585,
0.0008931110496632755,
-0.10598065704107285,
-0.11011047661304474,
0.11815934628248215,
-0.06764183193445206,
0.08989080041646957,
-0.07324676960706711,
0.011753262020647526,
-0.09271204471588135,
-0.14852067828178406,
0.1675879806280136,
-0.12130600214004517,
0.0015655728057026863,
-0.09893681108951569,
0.10364512354135513,
-0.09934110194444656,
0.07248358428478241,
-0.004463587887585163,
0.08023601770401001,
-0.1453222781419754,
-0.04110085591673851,
0.04787315055727959,
-0.08205217868089676,
0.03735632449388504,
0.003502310486510396,
-0.02789507992565632,
-0.01973923109471798,
0.06270651519298553,
0.03233236074447632,
0.22265133261680603,
0.24178771674633026,
-0.09735046327114105,
0.14145435392856598,
0.08713201433420181,
-0.015979396179318428,
-0.33901533484458923,
-0.08899673074483871,
-0.15794101357460022,
0.022028397768735886,
0.08518680185079575,
-0.08041436970233917,
0.047041088342666626,
-0.04694472625851631,
-0.07521001249551773,
0.11350074410438538,
-0.17434826493263245,
-0.08199378103017807,
0.18863524496555328,
-0.026513613760471344,
0.44708380103111267,
-0.12922319769859314,
-0.03186441957950592,
-0.010831243358552456,
-0.10295093059539795,
0.07012095302343369,
-0.030793502926826477,
0.09342451393604279,
-0.010647829622030258,
0.05101967230439186,
0.03735759109258652,
-0.06943738460540771,
0.12040378898382187,
-0.08822456002235413,
0.007303926628082991,
-0.10645002126693726,
-0.14312158524990082,
0.11793749034404755,
-0.032803792506456375,
-0.04140540584921837,
-0.03722865507006645,
-0.01396148931235075,
-0.06046976521611214,
0.013600540347397327,
-0.12682703137397766,
0.11328048259019852,
0.006392418872565031,
-0.06134670600295067,
0.006633168552070856,
0.008990205824375153,
-0.001449412084184587,
-0.012109935283660889,
0.22099903225898743,
-0.01807790994644165,
0.2177792340517044,
0.08191961795091629,
-0.014295502565801144,
-0.1304980367422104,
-0.0769704282283783,
0.004632152151316404,
-0.07620365917682648,
0.09807863086462021,
-0.05091368779540062,
0.031178195029497147,
0.0761478915810585,
-0.010782947763800621,
0.04594691842794418,
0.11441923677921295,
0.0002784303214866668,
-0.018138105049729347,
0.1838766187429428,
-0.21150457859039307,
-0.012900900095701218,
-0.027104422450065613,
-0.008278004825115204,
0.06046172231435776,
0.01599052920937538,
0.0852411687374115,
0.007592764217406511,
-0.03372012451291084,
-0.0012880503199994564,
-0.02039349265396595,
-0.07583954930305481,
0.009777488186955452,
0.07037131488323212,
0.0601196251809597,
-0.09630311280488968,
-0.014684705063700676,
0.020370807498693466,
-0.20254921913146973,
-0.009088168852031231,
0.10413571447134018,
-0.0674857571721077,
-0.13599124550819397,
-0.01293158158659935,
0.041345495730638504,
-0.025122227147221565,
0.003983992151916027,
-0.04020093008875847,
-0.09952469915151596,
0.03160359337925911,
0.19966340065002441,
0.10959027707576752,
0.06444192677736282,
-0.01128611620515585,
-0.0017804086674004793,
0.02512287348508835,
0.0008263916242867708,
-0.002812315709888935,
0.04329361394047737,
-0.12077637761831284,
0.03326911851763725,
-0.01183723472058773,
0.1497766226530075,
-0.11036612093448639,
-0.0400470532476902,
-0.16767063736915588,
0.0018725573318079114,
-0.012205228209495544,
-0.12368873506784439,
-0.09534022212028503,
-0.09855550527572632,
0.021209461614489555,
-0.09631325304508209,
-0.06731277704238892,
-0.045469146221876144,
-0.13415434956550598,
-0.00032862366060726345,
0.022215906530618668,
0.030965985730290413,
-0.06225541979074478,
-0.03943479061126709,
0.13731297850608826,
-0.04855744540691376,
0.059784162789583206,
0.11007153987884521,
-0.053365521132946014,
0.0716547816991806,
-0.04946000128984451,
-0.1255914270877838,
0.08785057067871094,
0.015609954483807087,
0.09192873537540436,
0.06405208259820938,
-0.015032433904707432,
0.017506837844848633,
0.0665934830904007,
0.04308469593524933,
0.026281088590621948,
-0.08293873816728592,
0.05159498378634453,
0.028300557285547256,
-0.18251092731952667,
0.013236832804977894,
-0.09109552204608917,
0.10766959935426712,
-0.0215500146150589,
0.08822895586490631,
-0.011787409894168377,
0.06665170192718506,
-0.08033763617277145,
0.03059840016067028,
-0.049959052354097366,
-0.16596882045269012,
-0.027156632393598557,
-0.03319834545254707,
0.026476072147488594,
-0.022881561890244484,
0.21689026057720184,
0.036236584186553955,
-0.04384949430823326,
0.03971150517463684,
0.07271911948919296,
-0.0054847244173288345,
0.0075235129334032536,
0.15344920754432678,
0.08896595239639282,
-0.07860264927148819,
-0.08185818791389465,
0.08368752151727676,
0.02314853109419346,
-0.004391250666230917,
0.08760112524032593,
0.09972148388624191,
0.10405497997999191,
0.09535703808069229,
0.016476692631840706,
0.0059495228342711926,
-0.14433994889259338,
-0.19991211593151093,
-0.03208185359835625,
0.07524742931127548,
-0.027213726192712784,
-0.006378382910043001,
0.1622462421655655,
-0.006616086233407259,
0.05082191154360771,
-0.08250853419303894,
0.0054335203021764755,
-0.16892026364803314,
-0.11435629427433014,
-0.06678326427936554,
-0.07684695720672607,
-0.01552826538681984,
-0.02601679041981697,
0.030689217150211334,
0.12454147636890411,
0.021338360384106636,
-0.005289506632834673,
0.13845562934875488,
0.025136087089776993,
-0.024829983711242676,
-0.002981876954436302,
0.005757165141403675,
0.024622352793812752,
-0.033317647874355316,
0.005135134793817997,
-0.15391136705875397,
-0.012859896756708622,
-0.061864253133535385,
-0.013076270930469036,
-0.10050825029611588,
-0.006403560750186443,
-0.09892002493143082,
-0.13717244565486908,
-0.07084160298109055,
0.004031373653560877,
-0.0410948283970356,
0.0783693715929985,
-0.007653805892914534,
0.046144116669893265,
-0.011215322650969028,
0.18185047805309296,
-0.10490322858095169,
-0.058193139731884,
-0.03452982380986214,
0.16888810694217682,
0.008640413172543049,
0.08472440391778946,
-0.04152536019682884,
0.027449890971183777,
-0.12871034443378448,
0.25930070877075195,
0.3661806285381317,
-0.07336948812007904,
0.09285399317741394,
0.06491922587156296,
0.0193422120064497,
0.030152160674333572,
0.07597483694553375,
0.072634257376194,
0.23791664838790894,
-0.11797590553760529,
-0.010488714091479778,
-0.057547636330127716,
-0.02148490771651268,
-0.08191554248332977,
0.0053735957480967045,
0.04902149364352226,
-0.03245244547724724,
-0.06161678954958916,
0.0399104468524456,
-0.14287924766540527,
0.09575983136892319,
0.06072020158171654,
-0.2579907476902008,
-0.056728679686784744,
-0.0000043243016989436,
0.18387234210968018,
-0.0001959197106771171,
0.1151711568236351,
-0.04594285786151886,
-0.08790366351604462,
0.015928110107779503,
0.0007014860166236758,
-0.17261230945587158,
-0.06557457149028778,
0.14371319115161896,
-0.017475953325629234,
0.07612664252519608,
-0.05169067531824112,
-0.008168055675923824,
0.09831497818231583,
0.06369658559560776,
-0.03108178824186325,
-0.0010998303769156337,
0.03740651533007622,
-0.09196978062391281,
-0.10842049866914749,
0.02136641927063465,
0.003911149222403765,
-0.0861721932888031,
0.060279786586761475,
-0.16354860365390778,
0.045141272246837616,
-0.08985745161771774,
-0.010472030378878117,
0.015027366578578949,
0.034435566514730453,
-0.02491009049117565,
0.0748371034860611,
0.06510620564222336,
0.008458446711301804,
-0.0497715100646019,
-0.02371944673359394,
-0.04079500213265419,
0.08074639737606049,
-0.07892812788486481,
-0.18614140152931213,
-0.06953465193510056,
-0.06266763806343079,
0.02530635893344879,
-0.011055922135710716,
-0.12815025448799133,
-0.06836600601673126,
-0.06444620341062546,
0.032778434455394745,
-0.11356262117624283,
0.027883388102054596,
0.08889710158109665,
0.033887699246406555,
0.010468434542417526,
-0.05898585915565491,
0.043185360729694366,
0.055784132331609726,
-0.1632528156042099,
-0.072266586124897
] |
null | null |
transformers
|
# Tigrinya POS tagging with TiELECTRA
This model is a fine-tuned version of [TiELECTRA](https://huggingface.co/fgaim/tielectra-small) on the NTC-v1 dataset (Tedla et al. 2016).
## Basic usage
```python
from transformers import pipeline
ti_pos = pipeline("token-classification", model="fgaim/tielectra-small-pos")
ti_pos("ድምጻዊ ኣብርሃም ኣፈወርቂ ንዘልኣለም ህያው ኮይኑ ኣብ ልብና ይነብር")
```
## Training
### Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Results
The model achieves the following results on the test set:
- Loss: 0.2236
- Adj Precision: 0.9148
- Adj Recall: 0.9192
- Adj F1: 0.9170
- Adj Number: 1670
- Adv Precision: 0.8228
- Adv Recall: 0.8058
- Adv F1: 0.8142
- Adv Number: 484
- Con Precision: 0.9793
- Con Recall: 0.9743
- Con F1: 0.9768
- Con Number: 972
- Fw Precision: 0.5
- Fw Recall: 0.3214
- Fw F1: 0.3913
- Fw Number: 28
- Int Precision: 0.64
- Int Recall: 0.6154
- Int F1: 0.6275
- Int Number: 26
- N Precision: 0.9525
- N Recall: 0.9587
- N F1: 0.9556
- N Number: 3992
- Num Precision: 0.9825
- Num Recall: 0.9372
- Num F1: 0.9593
- Num Number: 239
- N Prp Precision: 0.9132
- N Prp Recall: 0.9404
- N Prp F1: 0.9266
- N Prp Number: 470
- N V Precision: 0.9667
- N V Recall: 0.9760
- N V F1: 0.9713
- N V Number: 416
- Pre Precision: 0.9645
- Pre Recall: 0.9592
- Pre F1: 0.9619
- Pre Number: 907
- Pro Precision: 0.9395
- Pro Recall: 0.9079
- Pro F1: 0.9234
- Pro Number: 445
- Pun Precision: 1.0
- Pun Recall: 0.9994
- Pun F1: 0.9997
- Pun Number: 1607
- Unc Precision: 0.9286
- Unc Recall: 0.8125
- Unc F1: 0.8667
- Unc Number: 16
- V Precision: 0.7609
- V Recall: 0.8974
- V F1: 0.8235
- V Number: 78
- V Aux Precision: 0.9581
- V Aux Recall: 0.9786
- V Aux F1: 0.9682
- V Aux Number: 654
- V Ger Precision: 0.9183
- V Ger Recall: 0.9415
- V Ger F1: 0.9297
- V Ger Number: 513
- V Imf Precision: 0.9473
- V Imf Recall: 0.9442
- V Imf F1: 0.9458
- V Imf Number: 914
- V Imv Precision: 0.8163
- V Imv Recall: 0.5714
- V Imv F1: 0.6723
- V Imv Number: 70
- V Prf Precision: 0.8927
- V Prf Recall: 0.8776
- V Prf F1: 0.8851
- V Prf Number: 294
- V Rel Precision: 0.9535
- V Rel Recall: 0.9485
- V Rel F1: 0.9510
- V Rel Number: 757
- Overall Precision: 0.9456
- Overall Recall: 0.9456
- Overall F1: 0.9456
- Overall Accuracy: 0.9456
### Framework versions
- Transformers 4.10.3
- Pytorch 1.9.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.1
## Citation
If you use this model in your product or research, please cite as follows:
```
@article{Fitsum2021TiPLMs,
author= {Fitsum Gaim and Wonsuk Yang and Jong C. Park},
title= {Monolingual Pre-trained Language Models for Tigrinya},
year= 2021,
publisher= {WiNLP 2021/EMNLP 2021}
}
```
## References
```
Tedla, Y., Yamamoto, K. & Marasinghe, A. 2016.
Tigrinya Part-of-Speech Tagging with Morphological Patterns and the New Nagaoka Tigrinya Corpus.
International Journal Of Computer Applications 146 pp. 33-41 (2016).
```
|
{"language": "ti", "datasets": ["TLMD", "NTC"], "metrics": ["f1", "precision", "recall", "accuracy"], "widget": [{"text": "\u12f5\u121d\u133b\u12ca \u12a3\u1265\u122d\u1203\u121d \u12a3\u1348\u12c8\u122d\u1242 \u1295\u12d8\u120d\u12a3\u1208\u121d \u1205\u12eb\u12cd \u12ae\u12ed\u1291 \u12a3\u1265 \u120d\u1265\u1293 \u12ed\u1290\u1265\u122d"}]}
|
token-classification
|
fgaim/tielectra-small-pos
|
[
"transformers",
"pytorch",
"electra",
"token-classification",
"ti",
"dataset:TLMD",
"dataset:NTC",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ti"
] |
TAGS
#transformers #pytorch #electra #token-classification #ti #dataset-TLMD #dataset-NTC #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Tigrinya POS tagging with TiELECTRA
This model is a fine-tuned version of TiELECTRA on the NTC-v1 dataset (Tedla et al. 2016).
## Basic usage
## Training
### Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Results
The model achieves the following results on the test set:
- Loss: 0.2236
- Adj Precision: 0.9148
- Adj Recall: 0.9192
- Adj F1: 0.9170
- Adj Number: 1670
- Adv Precision: 0.8228
- Adv Recall: 0.8058
- Adv F1: 0.8142
- Adv Number: 484
- Con Precision: 0.9793
- Con Recall: 0.9743
- Con F1: 0.9768
- Con Number: 972
- Fw Precision: 0.5
- Fw Recall: 0.3214
- Fw F1: 0.3913
- Fw Number: 28
- Int Precision: 0.64
- Int Recall: 0.6154
- Int F1: 0.6275
- Int Number: 26
- N Precision: 0.9525
- N Recall: 0.9587
- N F1: 0.9556
- N Number: 3992
- Num Precision: 0.9825
- Num Recall: 0.9372
- Num F1: 0.9593
- Num Number: 239
- N Prp Precision: 0.9132
- N Prp Recall: 0.9404
- N Prp F1: 0.9266
- N Prp Number: 470
- N V Precision: 0.9667
- N V Recall: 0.9760
- N V F1: 0.9713
- N V Number: 416
- Pre Precision: 0.9645
- Pre Recall: 0.9592
- Pre F1: 0.9619
- Pre Number: 907
- Pro Precision: 0.9395
- Pro Recall: 0.9079
- Pro F1: 0.9234
- Pro Number: 445
- Pun Precision: 1.0
- Pun Recall: 0.9994
- Pun F1: 0.9997
- Pun Number: 1607
- Unc Precision: 0.9286
- Unc Recall: 0.8125
- Unc F1: 0.8667
- Unc Number: 16
- V Precision: 0.7609
- V Recall: 0.8974
- V F1: 0.8235
- V Number: 78
- V Aux Precision: 0.9581
- V Aux Recall: 0.9786
- V Aux F1: 0.9682
- V Aux Number: 654
- V Ger Precision: 0.9183
- V Ger Recall: 0.9415
- V Ger F1: 0.9297
- V Ger Number: 513
- V Imf Precision: 0.9473
- V Imf Recall: 0.9442
- V Imf F1: 0.9458
- V Imf Number: 914
- V Imv Precision: 0.8163
- V Imv Recall: 0.5714
- V Imv F1: 0.6723
- V Imv Number: 70
- V Prf Precision: 0.8927
- V Prf Recall: 0.8776
- V Prf F1: 0.8851
- V Prf Number: 294
- V Rel Precision: 0.9535
- V Rel Recall: 0.9485
- V Rel F1: 0.9510
- V Rel Number: 757
- Overall Precision: 0.9456
- Overall Recall: 0.9456
- Overall F1: 0.9456
- Overall Accuracy: 0.9456
### Framework versions
- Transformers 4.10.3
- Pytorch 1.9.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.1
If you use this model in your product or research, please cite as follows:
## References
|
[
"# Tigrinya POS tagging with TiELECTRA\n\nThis model is a fine-tuned version of TiELECTRA on the NTC-v1 dataset (Tedla et al. 2016).",
"## Basic usage",
"## Training",
"### Hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10.0",
"### Results\n\nThe model achieves the following results on the test set:\n- Loss: 0.2236\n- Adj Precision: 0.9148\n- Adj Recall: 0.9192\n- Adj F1: 0.9170\n- Adj Number: 1670\n- Adv Precision: 0.8228\n- Adv Recall: 0.8058\n- Adv F1: 0.8142\n- Adv Number: 484\n- Con Precision: 0.9793\n- Con Recall: 0.9743\n- Con F1: 0.9768\n- Con Number: 972\n- Fw Precision: 0.5\n- Fw Recall: 0.3214\n- Fw F1: 0.3913\n- Fw Number: 28\n- Int Precision: 0.64\n- Int Recall: 0.6154\n- Int F1: 0.6275\n- Int Number: 26\n- N Precision: 0.9525\n- N Recall: 0.9587\n- N F1: 0.9556\n- N Number: 3992\n- Num Precision: 0.9825\n- Num Recall: 0.9372\n- Num F1: 0.9593\n- Num Number: 239\n- N Prp Precision: 0.9132\n- N Prp Recall: 0.9404\n- N Prp F1: 0.9266\n- N Prp Number: 470\n- N V Precision: 0.9667\n- N V Recall: 0.9760\n- N V F1: 0.9713\n- N V Number: 416\n- Pre Precision: 0.9645\n- Pre Recall: 0.9592\n- Pre F1: 0.9619\n- Pre Number: 907\n- Pro Precision: 0.9395\n- Pro Recall: 0.9079\n- Pro F1: 0.9234\n- Pro Number: 445\n- Pun Precision: 1.0\n- Pun Recall: 0.9994\n- Pun F1: 0.9997\n- Pun Number: 1607\n- Unc Precision: 0.9286\n- Unc Recall: 0.8125\n- Unc F1: 0.8667\n- Unc Number: 16\n- V Precision: 0.7609\n- V Recall: 0.8974\n- V F1: 0.8235\n- V Number: 78\n- V Aux Precision: 0.9581\n- V Aux Recall: 0.9786\n- V Aux F1: 0.9682\n- V Aux Number: 654\n- V Ger Precision: 0.9183\n- V Ger Recall: 0.9415\n- V Ger F1: 0.9297\n- V Ger Number: 513\n- V Imf Precision: 0.9473\n- V Imf Recall: 0.9442\n- V Imf F1: 0.9458\n- V Imf Number: 914\n- V Imv Precision: 0.8163\n- V Imv Recall: 0.5714\n- V Imv F1: 0.6723\n- V Imv Number: 70\n- V Prf Precision: 0.8927\n- V Prf Recall: 0.8776\n- V Prf F1: 0.8851\n- V Prf Number: 294\n- V Rel Precision: 0.9535\n- V Rel Recall: 0.9485\n- V Rel F1: 0.9510\n- V Rel Number: 757\n- Overall Precision: 0.9456\n- Overall Recall: 0.9456\n- Overall F1: 0.9456\n- Overall Accuracy: 0.9456",
"### Framework versions\n\n- Transformers 4.10.3\n- Pytorch 1.9.0+cu111\n- Datasets 1.10.2\n- Tokenizers 0.10.1\n\n\nIf you use this model in your product or research, please cite as follows:",
"## References"
] |
[
"TAGS\n#transformers #pytorch #electra #token-classification #ti #dataset-TLMD #dataset-NTC #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Tigrinya POS tagging with TiELECTRA\n\nThis model is a fine-tuned version of TiELECTRA on the NTC-v1 dataset (Tedla et al. 2016).",
"## Basic usage",
"## Training",
"### Hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10.0",
"### Results\n\nThe model achieves the following results on the test set:\n- Loss: 0.2236\n- Adj Precision: 0.9148\n- Adj Recall: 0.9192\n- Adj F1: 0.9170\n- Adj Number: 1670\n- Adv Precision: 0.8228\n- Adv Recall: 0.8058\n- Adv F1: 0.8142\n- Adv Number: 484\n- Con Precision: 0.9793\n- Con Recall: 0.9743\n- Con F1: 0.9768\n- Con Number: 972\n- Fw Precision: 0.5\n- Fw Recall: 0.3214\n- Fw F1: 0.3913\n- Fw Number: 28\n- Int Precision: 0.64\n- Int Recall: 0.6154\n- Int F1: 0.6275\n- Int Number: 26\n- N Precision: 0.9525\n- N Recall: 0.9587\n- N F1: 0.9556\n- N Number: 3992\n- Num Precision: 0.9825\n- Num Recall: 0.9372\n- Num F1: 0.9593\n- Num Number: 239\n- N Prp Precision: 0.9132\n- N Prp Recall: 0.9404\n- N Prp F1: 0.9266\n- N Prp Number: 470\n- N V Precision: 0.9667\n- N V Recall: 0.9760\n- N V F1: 0.9713\n- N V Number: 416\n- Pre Precision: 0.9645\n- Pre Recall: 0.9592\n- Pre F1: 0.9619\n- Pre Number: 907\n- Pro Precision: 0.9395\n- Pro Recall: 0.9079\n- Pro F1: 0.9234\n- Pro Number: 445\n- Pun Precision: 1.0\n- Pun Recall: 0.9994\n- Pun F1: 0.9997\n- Pun Number: 1607\n- Unc Precision: 0.9286\n- Unc Recall: 0.8125\n- Unc F1: 0.8667\n- Unc Number: 16\n- V Precision: 0.7609\n- V Recall: 0.8974\n- V F1: 0.8235\n- V Number: 78\n- V Aux Precision: 0.9581\n- V Aux Recall: 0.9786\n- V Aux F1: 0.9682\n- V Aux Number: 654\n- V Ger Precision: 0.9183\n- V Ger Recall: 0.9415\n- V Ger F1: 0.9297\n- V Ger Number: 513\n- V Imf Precision: 0.9473\n- V Imf Recall: 0.9442\n- V Imf F1: 0.9458\n- V Imf Number: 914\n- V Imv Precision: 0.8163\n- V Imv Recall: 0.5714\n- V Imv F1: 0.6723\n- V Imv Number: 70\n- V Prf Precision: 0.8927\n- V Prf Recall: 0.8776\n- V Prf F1: 0.8851\n- V Prf Number: 294\n- V Rel Precision: 0.9535\n- V Rel Recall: 0.9485\n- V Rel F1: 0.9510\n- V Rel Number: 757\n- Overall Precision: 0.9456\n- Overall Recall: 0.9456\n- Overall F1: 0.9456\n- Overall Accuracy: 0.9456",
"### Framework versions\n\n- Transformers 4.10.3\n- Pytorch 1.9.0+cu111\n- Datasets 1.10.2\n- Tokenizers 0.10.1\n\n\nIf you use this model in your product or research, please cite as follows:",
"## References"
] |
[
60,
43,
3,
2,
90,
731,
51,
3
] |
[
"passage: TAGS\n#transformers #pytorch #electra #token-classification #ti #dataset-TLMD #dataset-NTC #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Tigrinya POS tagging with TiELECTRA\n\nThis model is a fine-tuned version of TiELECTRA on the NTC-v1 dataset (Tedla et al. 2016).## Basic usage## Training### Hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10.0"
] |
[
-0.10564864426851273,
0.015356437303125858,
-0.0004011961573269218,
0.11663652956485748,
0.1792474389076233,
0.057295866310596466,
0.05913279205560684,
0.08217132836580276,
-0.026685185730457306,
0.016018638387322426,
0.10245402902364731,
0.11692693829536438,
-0.010284905321896076,
0.19538044929504395,
-0.04859508201479912,
-0.22994615137577057,
0.0035474065225571394,
0.06750630587339401,
-0.0635034367442131,
0.12368544936180115,
0.11115852743387222,
-0.11334268003702164,
0.10837919265031815,
0.00903906300663948,
-0.1754622459411621,
0.03891390934586525,
0.011269880458712578,
-0.1005680114030838,
0.1306394636631012,
0.023270202800631523,
0.16969476640224457,
-0.024137046188116074,
0.09764339029788971,
-0.041867274791002274,
0.012228403240442276,
0.061963096261024475,
0.0061555178835988045,
0.06714555621147156,
0.017891554161906242,
0.005476081278175116,
0.17343029379844666,
-0.12239862978458405,
0.059323396533727646,
-0.010394909419119358,
-0.1295190304517746,
-0.13183599710464478,
-0.02356904372572899,
0.028494959697127342,
0.03271860629320145,
0.07196434587240219,
0.0014105625450611115,
0.23554891347885132,
-0.08733173459768295,
0.09418738633394241,
0.17147791385650635,
-0.2813428044319153,
-0.06757209450006485,
0.132989764213562,
0.04468539357185364,
0.060303639620542526,
-0.05485045909881592,
0.006994559895247221,
0.0920393168926239,
0.010493570007383823,
0.15263527631759644,
-0.03909379243850708,
-0.13398467004299164,
0.028185922652482986,
-0.16272231936454773,
-0.013459701091051102,
0.300716370344162,
0.04444478824734688,
-0.024376235902309418,
0.007011792156845331,
-0.11322825402021408,
-0.14004701375961304,
0.0022607180289924145,
-0.04491320624947548,
0.01573435589671135,
-0.021738693118095398,
-0.02135583572089672,
0.015924122184515,
-0.09359975904226303,
-0.018905486911535263,
-0.09440034627914429,
0.11709930002689362,
0.04735533148050308,
0.01982022076845169,
-0.048444777727127075,
0.07519832253456116,
-0.04899362102150917,
-0.0540042519569397,
0.028643516823649406,
-0.01541185099631548,
-0.07525768876075745,
-0.09078475832939148,
-0.05433330312371254,
-0.0327056460082531,
-0.016140535473823547,
0.09706852585077286,
0.06761207431554794,
0.021805142983794212,
0.0915430411696434,
0.008268855512142181,
0.005295008886605501,
0.19687673449516296,
-0.08587484061717987,
-0.05533194541931152,
0.002112029120326042,
-0.021684451028704643,
-0.03190666809678078,
-0.010458716191351414,
-0.09452372789382935,
-0.08879631757736206,
0.09522498399019241,
-0.013812138698995113,
-0.07948017865419388,
0.10080642253160477,
-0.02364393137395382,
-0.0625920444726944,
-0.026938147842884064,
-0.07986237853765488,
0.048513468354940414,
-0.04456630349159241,
-0.03946012258529663,
0.12155809998512268,
0.01819857396185398,
0.017816169187426567,
-0.012915274128317833,
0.054361987859010696,
-0.1290128082036972,
-0.011250054463744164,
-0.08065365254878998,
-0.1422027349472046,
-0.029124097898602486,
-0.1634547859430313,
0.03156309947371483,
-0.1806182861328125,
-0.15106317400932312,
-0.003466410329565406,
0.05543399229645729,
-0.02092849835753441,
-0.06214951351284981,
-0.032454248517751694,
-0.03829953074455261,
0.012867125682532787,
-0.0022944125812500715,
0.03818974643945694,
-0.06675112992525101,
0.053039420396089554,
0.005661537870764732,
0.08600465208292007,
-0.06855936348438263,
0.02237348072230816,
-0.08351297676563263,
0.009419195353984833,
-0.07545434683561325,
0.021394386887550354,
0.01848301663994789,
0.07701832801103592,
-0.09609884023666382,
-0.10405717045068741,
-0.0309629924595356,
0.0285263080149889,
0.03294902667403221,
0.07854529470205307,
-0.17460526525974274,
-0.022613324224948883,
0.15824544429779053,
-0.06581851094961166,
-0.08319756388664246,
0.10906597971916199,
-0.04295751079916954,
0.10297120362520218,
0.11484041064977646,
0.10171961784362793,
0.12670622766017914,
-0.10750134289264679,
0.012192382477223873,
0.0040034581907093525,
-0.04326033964753151,
-0.11610894650220871,
0.0632425919175148,
0.09213116019964218,
-0.102088563144207,
-0.0001106776762753725,
0.00029195897514000535,
0.04112116992473602,
-0.09441552311182022,
-0.05621393024921417,
-0.06415536999702454,
-0.09334223717451096,
0.07263711094856262,
0.017478935420513153,
0.06116645038127899,
-0.09135223925113678,
-0.04351509362459183,
0.12444533407688141,
0.12111728638410568,
-0.04526340961456299,
0.019897017627954483,
-0.10764987021684647,
0.04849905148148537,
-0.07246831059455872,
-0.036383721977472305,
-0.1313151717185974,
-0.061731502413749695,
-0.010850869119167328,
0.101552315056324,
0.03346272557973862,
0.17348042130470276,
0.04404723644256592,
0.001826408552005887,
-0.038592830300331116,
0.012237143702805042,
-0.0027758430223912,
0.008682587184011936,
-0.11985861510038376,
-0.10709090530872345,
-0.023277711123228073,
-0.017489118501544,
0.054828282445669174,
-0.21837617456912994,
-0.006255352403968573,
-0.06947474181652069,
0.07537878304719925,
0.03138409182429314,
0.022035526111721992,
-0.046310797333717346,
0.0603870153427124,
-0.05673326924443245,
-0.08123552054166794,
0.04207149147987366,
-0.004872601479291916,
-0.05257822945713997,
-0.06215740367770195,
-0.11158068478107452,
0.16955092549324036,
0.10915301740169525,
-0.04309046268463135,
-0.09943999350070953,
0.036151181906461716,
-0.030306270346045494,
-0.004931436851620674,
-0.06450565904378891,
0.00047545143752358854,
0.05849089473485947,
-0.07688970118761063,
0.11984508484601974,
-0.06851132959127426,
-0.01553155854344368,
0.024052100256085396,
-0.04001126065850258,
-0.016398770734667778,
0.0949435904622078,
0.1593591868877411,
-0.21911251544952393,
0.07838842272758484,
0.11561231315135956,
-0.10909602791070938,
0.12270314246416092,
0.013922661542892456,
-0.0788157507777214,
-0.018467796966433525,
-0.028561828657984734,
0.002571111312136054,
0.05931570380926132,
-0.14983732998371124,
-0.0108836954459548,
0.04300820454955101,
0.05307077243924141,
0.00441058911383152,
-0.1311655342578888,
-0.03041951358318329,
0.06051621586084366,
0.013844980858266354,
-0.0291423462331295,
0.05996972322463989,
0.0029287789948284626,
0.10314112901687622,
-0.010714379139244556,
-0.11021474003791809,
0.018779423087835312,
0.04970523715019226,
-0.05464276671409607,
0.16750690340995789,
-0.05118902400135994,
-0.187277689576149,
-0.08215004950761795,
-0.05826828256249428,
0.0017553040524944663,
0.031405020505189896,
0.04066864773631096,
-0.06335485726594925,
-0.07787715643644333,
-0.07247957587242126,
-0.010187441483139992,
-0.04729755222797394,
0.08229620009660721,
0.024625418707728386,
0.0010993822943419218,
0.04830130562186241,
-0.10181073099374771,
-0.0024111398961395025,
-0.03932438790798187,
-0.05319605767726898,
0.07616139203310013,
-0.04018206521868706,
0.0924210175871849,
0.17915163934230804,
-0.07996799051761627,
0.033055175095796585,
-0.025114066898822784,
0.24533569812774658,
-0.05298792943358421,
0.02723889797925949,
0.15668508410453796,
0.04193625971674919,
0.010375034995377064,
0.11175045371055603,
0.010768093168735504,
-0.09958770871162415,
0.04517538473010063,
0.02702653594315052,
-0.09442413598299026,
-0.1966802328824997,
-0.082016721367836,
-0.10761184990406036,
-0.012935440056025982,
0.13829076290130615,
0.02696348913013935,
0.029490504413843155,
0.09856899827718735,
0.04621355980634689,
0.1547171175479889,
-0.09900599718093872,
0.06579013913869858,
0.14584670960903168,
0.05008542910218239,
0.11624620854854584,
-0.06410954147577286,
-0.08718980848789215,
0.07461269944906235,
-0.00914684683084488,
0.20298589766025543,
0.015674540773034096,
0.017979852855205536,
0.03433099016547203,
0.15411460399627686,
0.015978023409843445,
0.09640379250049591,
-0.017473656684160233,
-0.05348408967256546,
-0.049133408814668655,
-0.004550014156848192,
0.02833355776965618,
0.02912229858338833,
0.029172662645578384,
-0.0037719139363616705,
-0.046657316386699677,
0.046375200152397156,
0.02797466143965721,
0.08840625733137131,
0.12174535542726517,
-0.3870524764060974,
-0.037195708602666855,
-0.013505907729268074,
-0.006761682219803333,
-0.08639764785766602,
0.02357769012451172,
0.07979435473680496,
-0.0798148438334465,
0.036044325679540634,
-0.032057687640190125,
0.08837376534938812,
-0.014184791594743729,
0.011984712444245815,
0.06588543951511383,
0.10040165483951569,
-0.024830477312207222,
0.07075166702270508,
-0.2084178477525711,
0.20123615860939026,
-0.01294257864356041,
0.0363757498562336,
-0.057012900710105896,
-0.061774466186761856,
-0.01743438094854355,
0.11994269490242004,
0.021302450448274612,
0.02094194106757641,
-0.0016479409532621503,
-0.16173486411571503,
-0.04477239400148392,
0.043832939118146896,
0.04502807557582855,
-0.0130685493350029,
0.09185021370649338,
-0.07345467805862427,
0.013128448277711868,
0.07586836814880371,
0.07442250847816467,
-0.08792188763618469,
-0.06496305018663406,
-0.03125178813934326,
0.14750245213508606,
-0.05406102538108826,
-0.002700609154999256,
-0.10746210813522339,
-0.1625501960515976,
0.14405234158039093,
0.10060227662324905,
-0.008197363466024399,
-0.1361376792192459,
0.09789855778217316,
0.11957041919231415,
-0.07477302849292755,
0.0864437147974968,
0.0348857082426548,
0.0402369424700737,
0.06690357625484467,
-0.0772775262594223,
0.1220482885837555,
-0.051677070558071136,
-0.06439150869846344,
-0.03092355653643608,
0.05730312317609787,
0.018770920112729073,
0.022261878475546837,
0.00736966822296381,
0.01036002952605486,
-0.012051685713231564,
-0.0749494731426239,
-0.014677327126264572,
0.03492328152060509,
0.0337129607796669,
0.07336412370204926,
-0.010330434888601303,
-0.03412104770541191,
-0.0034052233677357435,
-0.03397942706942558,
0.15449556708335876,
0.15610520541667938,
-0.08279628306627274,
0.007681333925575018,
-0.04757077991962433,
-0.04309002682566643,
-0.16032665967941284,
0.04589322581887245,
0.023029284551739693,
0.04475569725036621,
0.07836240530014038,
-0.09125885367393494,
0.13026447594165802,
0.05795394629240036,
0.01696685329079628,
0.05787166953086853,
-0.30356308817863464,
-0.11719774454832077,
0.12128701061010361,
0.10379759967327118,
0.24082398414611816,
-0.06087835878133774,
0.024514051154255867,
-0.02938440628349781,
-0.10329553484916687,
0.12785430252552032,
-0.10782194882631302,
0.13073761761188507,
-0.018490897491574287,
0.06529272347688675,
0.02379417233169079,
-0.010510723106563091,
0.0976688414812088,
0.04104378819465637,
0.07745825499296188,
-0.05810248479247093,
-0.023012686520814896,
0.06106759235262871,
-0.047558970749378204,
0.04561605677008629,
-0.0026241519954055548,
0.054980386048555374,
-0.10457094013690948,
-0.05642722174525261,
-0.033666983246803284,
0.05946647375822067,
0.02663290873169899,
-0.07913694530725479,
-0.0955902710556984,
0.012015477754175663,
0.04973294213414192,
-0.04562703147530556,
0.07606043666601181,
0.04283195361495018,
0.049273863434791565,
-0.00720252888277173,
0.053990740329027176,
-0.06560888886451721,
-0.05615836754441261,
-0.015279720537364483,
0.007349705323576927,
0.08282217383384705,
-0.12653705477714539,
0.04365607723593712,
0.10735232383012772,
0.007573910057544708,
0.10099438577890396,
0.07638026773929596,
0.015214071609079838,
0.006449292413890362,
0.07617770880460739,
-0.09828551858663559,
-0.03236531838774681,
-0.001128469011746347,
-0.10289914160966873,
-0.07878734171390533,
0.08104460686445236,
0.13191884756088257,
-0.09936414659023285,
-0.0034211387392133474,
-0.022683575749397278,
-0.056602731347084045,
-0.04301319271326065,
0.13626810908317566,
0.060667142271995544,
0.01389866042882204,
-0.08202988654375076,
0.02632877230644226,
0.068668432533741,
-0.032434798777103424,
0.008501490578055382,
0.012384455651044846,
-0.108665831387043,
-0.043984945863485336,
0.12420272827148438,
0.09915103763341904,
-0.15560045838356018,
-0.03534155711531639,
-0.07412026077508926,
-0.07704351842403412,
0.09453443437814713,
0.11350567638874054,
0.10442527383565903,
0.0125562259927392,
-0.08209944516420364,
-0.00638459250330925,
-0.1199093610048294,
0.04396730288863182,
0.09846846759319305,
0.08511567115783691,
-0.2600165605545044,
0.14779455959796906,
-0.025162259116768837,
0.0385311096906662,
-0.03613344207406044,
-0.007882561534643173,
-0.10459736734628677,
0.01518564485013485,
-0.015202485956251621,
0.01605992391705513,
-0.01021141093224287,
0.02223185822367668,
0.005658416543155909,
-0.07733040302991867,
-0.09192918986082077,
0.015712160617113113,
-0.07094769179821014,
0.002559643005952239,
0.041986558586359024,
0.07036779075860977,
-0.05493396520614624,
-0.03515937551856041,
0.014887954108417034,
-0.045886024832725525,
0.05595262348651886,
0.001543704536743462,
-0.02633456140756607,
0.03784526512026787,
-0.12554703652858734,
0.018133558332920074,
0.10737285763025284,
0.03626559302210808,
0.10179020464420319,
-0.04421430453658104,
0.030812496319413185,
0.010452301241457462,
0.09224295616149902,
0.017701728269457817,
-0.007480122148990631,
-0.08980804681777954,
-0.018497537821531296,
-0.11993696540594101,
-0.09688372910022736,
-0.03508208319544792,
-0.00905739888548851,
0.07170204073190689,
0.03011350892484188,
0.1776333451271057,
-0.03693242743611336,
-0.020680518820881844,
-0.1680319458246231,
-0.011213704012334347,
-0.024228233844041824,
-0.05125211551785469,
-0.09446226060390472,
-0.05217018350958824,
0.061070363968610764,
-0.033624328672885895,
0.11349374800920486,
0.07866424322128296,
0.0008831836166791618,
-0.005675619933754206,
0.022463828325271606,
-0.029839083552360535,
-0.013772045262157917,
0.26769495010375977,
0.03577563166618347,
-0.010076737031340599,
0.020178910344839096,
0.03728784993290901,
0.13757570087909698,
0.14890943467617035,
0.14810863137245178,
0.10292534530162811,
-0.10046312212944031,
0.08907714486122131,
-0.06813549995422363,
-0.07056278735399246,
-0.20450866222381592,
-0.013891467824578285,
-0.0898154005408287,
0.07752205431461334,
0.0065755886025726795,
0.10628372430801392,
0.11048738658428192,
-0.12070634216070175,
-0.008842604234814644,
-0.06759286671876907,
-0.08482768386602402,
-0.10936370491981506,
-0.16926024854183197,
-0.12164619565010071,
-0.1598288118839264,
-0.0023222737945616245,
-0.10494288802146912,
0.019778305664658546,
0.1174759715795517,
0.019461603835225105,
-0.003837460419163108,
0.12039714306592941,
-0.02924971468746662,
-0.0229764673858881,
0.019124096259474754,
0.0027169857639819384,
-0.00971639808267355,
-0.04873695224523544,
-0.03863257169723511,
-0.0506477877497673,
0.003504885593429208,
0.02662101574242115,
-0.07623009383678436,
0.0024621973279863596,
0.0368683896958828,
-0.03047575056552887,
-0.050807174295186996,
-0.026286009699106216,
0.018489163368940353,
0.012329868040978909,
-0.05065234750509262,
0.032128024846315384,
-0.03557995706796646,
-0.024099620059132576,
0.2449951022863388,
-0.04188012331724167,
-0.09582728147506714,
-0.12462806701660156,
0.15398333966732025,
0.06369707733392715,
0.015348581597208977,
0.03865077719092369,
-0.06189220771193504,
-0.0006100011523813009,
0.24367113411426544,
0.15278448164463043,
-0.05725271999835968,
-0.006641837302595377,
-0.04212743043899536,
-0.010492322035133839,
-0.011074353009462357,
0.15242567658424377,
0.07132366299629211,
0.038943931460380554,
-0.04361448436975479,
-0.03607388585805893,
-0.07959405332803726,
0.027783842757344246,
-0.03949186950922012,
0.00711788609623909,
0.039109133183956146,
-0.02443717233836651,
-0.04590051248669624,
0.06768231093883514,
-0.035204675048589706,
-0.04594160616397858,
0.08898372203111649,
-0.09795462340116501,
-0.12227649986743927,
-0.03489257022738457,
0.060548920184373856,
0.00549525348469615,
0.07241286337375641,
-0.10639115422964096,
-0.02381906844675541,
-0.016757404431700706,
0.0030846407171338797,
-0.13199801743030548,
-0.06537792831659317,
0.09576794505119324,
0.004167717881500721,
0.13029909133911133,
-0.04628003388643265,
0.0829506665468216,
0.08404473215341568,
0.04866504669189453,
-0.09427397698163986,
0.029384002089500427,
-0.001049693557433784,
0.031460028141736984,
0.0677952840924263,
0.013470172882080078,
-0.04574517905712128,
0.03827216476202011,
0.048195384442806244,
-0.15933442115783691,
-0.03250177577137947,
0.018198681995272636,
0.06787312030792236,
-0.07681383192539215,
-0.023507501929998398,
-0.08570787310600281,
0.15263678133487701,
0.15714292228221893,
-0.04338042810559273,
-0.018299488350749016,
-0.06498747318983078,
0.03702281787991524,
0.057754036039114,
-0.012578348629176617,
-0.04329245537519455,
-0.1787882149219513,
-0.04408898949623108,
-0.02752097137272358,
-0.00235154596157372,
-0.12168143689632416,
-0.07040626555681229,
-0.09102876484394073,
-0.06909023970365524,
-0.09123528003692627,
0.10758274048566818,
0.07006531953811646,
0.02112307772040367,
-0.04887879639863968,
-0.02794520929455757,
-0.060051850974559784,
0.12884224951267242,
-0.15432916581630707,
-0.08973143249750137
] |
null | null |
transformers
|
# Sentiment Analysis for Tigrinya with TiELECTRA small
This model is a fine-tuned version of [TiELECTRA small](https://huggingface.co/fgaim/tielectra-small) on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).
## Basic usage
```python
from transformers import pipeline
ti_sent = pipeline("sentiment-analysis", model="fgaim/tielectra-small-sentiment")
ti_sent("ድምጻዊ ኣብርሃም ኣፈወርቂ ንዘልኣለም ህያው ኮይኑ ኣብ ልብና ይነብር")
```
## Training
### Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Results
The model achieves the following results on the evaluation set:
- F1: 0.8229
- Precision: 0.8056
- Recall: 0.841
- Accuracy: 0.819
- Loss: 0.4299
### Framework versions
- Transformers 4.10.3
- Pytorch 1.9.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.1
## Citation
If you use this model in your product or research, please cite as follows:
```
@article{Fitsum2021TiPLMs,
author={Fitsum Gaim and Wonsuk Yang and Jong C. Park},
title={Monolingual Pre-trained Language Models for Tigrinya},
year=2021,
publisher= {WiNLP 2021/EMNLP 2021}
}
```
## References
```
Tela, A., Woubie, A. and Hautamäki, V. 2020.
Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya.
ArXiv, abs/2006.07698.
```
|
{"language": "ti", "metrics": ["f1", "precision", "recall", "accuracy"], "widget": [{"text": "\u12f5\u121d\u133b\u12ca \u12a3\u1265\u122d\u1203\u121d \u12a3\u1348\u12c8\u122d\u1242 \u1295\u12d8\u120d\u12a3\u1208\u121d \u1205\u12eb\u12cd \u12ae\u12ed\u1291 \u12a3\u1265 \u120d\u1265\u1293 \u12ed\u1290\u1265\u122d"}]}
|
text-classification
|
fgaim/tielectra-small-sentiment
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"ti",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ti"
] |
TAGS
#transformers #pytorch #electra #text-classification #ti #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Sentiment Analysis for Tigrinya with TiELECTRA small
This model is a fine-tuned version of TiELECTRA small on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).
## Basic usage
## Training
### Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Results
The model achieves the following results on the evaluation set:
- F1: 0.8229
- Precision: 0.8056
- Recall: 0.841
- Accuracy: 0.819
- Loss: 0.4299
### Framework versions
- Transformers 4.10.3
- Pytorch 1.9.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.1
If you use this model in your product or research, please cite as follows:
## References
|
[
"# Sentiment Analysis for Tigrinya with TiELECTRA small\n\nThis model is a fine-tuned version of TiELECTRA small on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).",
"## Basic usage",
"## Training",
"### Hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Results\n\nThe model achieves the following results on the evaluation set:\n- F1: 0.8229\n- Precision: 0.8056\n- Recall: 0.841\n- Accuracy: 0.819\n- Loss: 0.4299",
"### Framework versions\n\n- Transformers 4.10.3\n- Pytorch 1.9.0+cu111\n- Datasets 1.10.2\n- Tokenizers 0.10.1\n\n\nIf you use this model in your product or research, please cite as follows:",
"## References"
] |
[
"TAGS\n#transformers #pytorch #electra #text-classification #ti #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Sentiment Analysis for Tigrinya with TiELECTRA small\n\nThis model is a fine-tuned version of TiELECTRA small on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).",
"## Basic usage",
"## Training",
"### Hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Results\n\nThe model achieves the following results on the evaluation set:\n- F1: 0.8229\n- Precision: 0.8056\n- Recall: 0.841\n- Accuracy: 0.819\n- Loss: 0.4299",
"### Framework versions\n\n- Transformers 4.10.3\n- Pytorch 1.9.0+cu111\n- Datasets 1.10.2\n- Tokenizers 0.10.1\n\n\nIf you use this model in your product or research, please cite as follows:",
"## References"
] |
[
47,
52,
3,
2,
89,
52,
51,
3
] |
[
"passage: TAGS\n#transformers #pytorch #electra #text-classification #ti #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Sentiment Analysis for Tigrinya with TiELECTRA small\n\nThis model is a fine-tuned version of TiELECTRA small on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).## Basic usage## Training### Hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0### Results\n\nThe model achieves the following results on the evaluation set:\n- F1: 0.8229\n- Precision: 0.8056\n- Recall: 0.841\n- Accuracy: 0.819\n- Loss: 0.4299### Framework versions\n\n- Transformers 4.10.3\n- Pytorch 1.9.0+cu111\n- Datasets 1.10.2\n- Tokenizers 0.10.1\n\n\nIf you use this model in your product or research, please cite as follows:## References"
] |
[
-0.06383460760116577,
0.0928911417722702,
-0.0019137859344482422,
0.09667100012302399,
0.12535157799720764,
0.0012252844171598554,
0.07934927940368652,
0.14776216447353363,
0.0014958546962589025,
0.10109357535839081,
0.06986942887306213,
0.08731227368116379,
0.06797723472118378,
0.1355658918619156,
-0.07011596858501434,
-0.23238395154476166,
0.0001817943702917546,
0.015060137957334518,
0.042795442044734955,
0.12332241237163544,
0.13079918920993805,
-0.13597065210342407,
0.1244131550192833,
0.022658515721559525,
-0.1409231424331665,
0.011466828174889088,
0.05652010440826416,
-0.09089358150959015,
0.09461068361997604,
0.017417222261428833,
0.057789333164691925,
0.0078046564012765884,
0.07523693889379501,
-0.12182371318340302,
-0.0008405557018704712,
0.018873538821935654,
0.01877090148627758,
0.062255971133708954,
0.10336823761463165,
-0.003073109546676278,
0.08471599221229553,
-0.14245638251304626,
0.04654013738036156,
0.06145287677645683,
-0.11627107858657837,
-0.10979519784450531,
-0.07662250846624374,
0.06950752437114716,
0.0744619145989418,
0.07849098742008209,
-0.049804333597421646,
0.18435271084308624,
-0.07476484030485153,
0.10358206927776337,
0.2144092321395874,
-0.2282593995332718,
-0.058507636189460754,
0.0390971414744854,
0.024665260687470436,
0.06825800985097885,
-0.07925880700349808,
0.03256372734904289,
0.04996200278401375,
0.001696286373771727,
0.08144950866699219,
-0.04265255481004715,
-0.048945434391498566,
-0.020712755620479584,
-0.11772548407316208,
-0.06553614884614944,
0.24044883251190186,
0.0744355320930481,
-0.03720000013709068,
-0.10078435391187668,
-0.07850156724452972,
-0.07531292736530304,
-0.07585158944129944,
-0.060446470975875854,
0.07249277830123901,
-0.0483170785009861,
-0.05820108950138092,
-0.004622588399797678,
-0.09808753430843353,
-0.015010247007012367,
-0.04757307842373848,
0.03370629996061325,
0.004240547772496939,
0.04587569460272789,
-0.07791969925165176,
0.00993468426167965,
-0.10873906314373016,
-0.12317489087581635,
-0.04862074553966522,
-0.018485430628061295,
-0.0057948981411755085,
-0.10037273168563843,
-0.031695619225502014,
0.005087585654109716,
0.04330561310052872,
0.10557607561349869,
0.008915849030017853,
0.059699319303035736,
0.007693388499319553,
-0.018404439091682434,
0.009094676934182644,
0.19895485043525696,
-0.08399388939142227,
-0.06653528660535812,
0.03684747964143753,
0.030812116339802742,
0.02416226826608181,
0.018383555114269257,
-0.05498029291629791,
-0.03762077912688255,
0.09964656829833984,
0.039975106716156006,
-0.010346919298171997,
0.042919229716062546,
-0.06608464568853378,
-0.03191908821463585,
-0.019776320084929466,
-0.11410093307495117,
0.04206499084830284,
0.009964140132069588,
-0.08634167164564133,
0.11106742918491364,
-0.03708124905824661,
0.016704361885786057,
-0.04768228903412819,
0.01132954005151987,
-0.11364565789699554,
0.027595950290560722,
-0.04524684697389603,
-0.0941578596830368,
0.037071213126182556,
-0.06970367580652237,
0.009028295986354351,
-0.12754018604755402,
-0.12820963561534882,
-0.02487774007022381,
-0.002807444194331765,
-0.07950092107057571,
-0.03663770481944084,
-0.05695304647088051,
-0.06973373889923096,
0.040098804980516434,
0.01596679911017418,
-0.03152772784233093,
-0.05613362416625023,
0.07505658268928528,
0.05062253028154373,
0.081511951982975,
0.041207198053598404,
0.03792217746376991,
-0.12127771228551865,
0.008955796249210835,
-0.12221675366163254,
0.08479301631450653,
-0.031458523124456406,
0.04563206061720848,
-0.1566344052553177,
-0.10076717287302017,
0.015293744392693043,
-0.01356901042163372,
0.024212541058659554,
0.1675681620836258,
-0.20879630744457245,
-0.036924611777067184,
0.15687228739261627,
-0.07392770051956177,
-0.08987295627593994,
0.14155696332454681,
-0.012828842736780643,
0.0609457865357399,
0.10420229285955429,
0.1604820042848587,
0.03727438673377037,
-0.08371590077877045,
-0.06474092602729797,
-0.028104722499847412,
-0.0227205753326416,
0.015427954494953156,
0.0810919776558876,
0.016903162002563477,
0.0700896829366684,
-0.011189223267138004,
-0.023044049739837646,
-0.05563966929912567,
-0.07538092136383057,
-0.05638383328914642,
-0.053717076778411865,
-0.03218134120106697,
0.01704772189259529,
0.0032712945248931646,
0.036287177354097366,
-0.06562274694442749,
-0.051125604659318924,
0.10338833928108215,
0.10502859950065613,
-0.035601139068603516,
0.004466200713068247,
-0.1251242309808731,
0.15391068160533905,
-0.1103983074426651,
-0.050869639962911606,
-0.1519518792629242,
-0.014645576477050781,
0.03734802082180977,
0.003090043319389224,
0.002817876171320677,
0.03915414586663246,
0.06099751964211464,
-0.03239891305565834,
-0.019796859472990036,
0.022358346730470657,
-0.04773793742060661,
-0.00544936116784811,
-0.05002386495471001,
-0.10720222443342209,
-0.00510522723197937,
0.0032485576812177896,
0.13436470925807953,
-0.19473284482955933,
-0.03150269389152527,
0.12411787360906601,
0.10621577501296997,
0.037054408341646194,
-0.04065508395433426,
-0.0018685824470594525,
-0.007490501739084721,
-0.011064639315009117,
-0.07737311720848083,
0.0533643402159214,
-0.02679140493273735,
-0.05214786157011986,
-0.00634961249306798,
-0.12019890546798706,
0.03148927539587021,
0.1101728156208992,
-0.028683114796876907,
-0.08551699668169022,
0.021866828203201294,
-0.022517427802085876,
0.0097506083548069,
-0.06086507439613342,
-0.02477961964905262,
0.10258281230926514,
0.013012279756367207,
0.08215538412332535,
-0.1144850105047226,
-0.0543743371963501,
0.03756861016154289,
-0.054388806223869324,
-0.0458727702498436,
0.10909666866064072,
0.06249840557575226,
-0.2112044245004654,
0.08327027410268784,
0.06292148679494858,
-0.03281527757644653,
0.11224732547998428,
-0.03068220242857933,
-0.08631378412246704,
-0.03356827795505524,
-0.017908375710248947,
-0.000046298398956423625,
0.046677857637405396,
-0.03619740903377533,
0.006261954549700022,
0.05980474501848221,
0.006441148463636637,
-0.03333352133631706,
-0.10846293717622757,
0.030218880623579025,
0.052128177136182785,
-0.048736196011304855,
-0.04857207462191582,
0.04843958839774132,
0.038021270185709,
0.1180228590965271,
0.04039890319108963,
0.010083917528390884,
-0.003235762706026435,
-0.026276182383298874,
-0.08583185821771622,
0.14582017064094543,
-0.04467674717307091,
-0.18786470592021942,
-0.11852286756038666,
0.00971270352602005,
-0.05581018701195717,
0.0023419978097081184,
0.022494709119200706,
-0.07688761502504349,
-0.077372707426548,
-0.08272769302129745,
0.0035373885184526443,
-0.02424248307943344,
-0.014320154674351215,
0.05038551241159439,
0.015033457428216934,
0.03989925608038902,
-0.12246078252792358,
-0.052267130464315414,
-0.03521263226866722,
-0.07559100538492203,
0.02794361673295498,
0.008708717301487923,
0.06138654798269272,
0.0758979320526123,
-0.05263160914182663,
0.027215031906962395,
-0.039840515702962875,
0.27551135420799255,
-0.08639633655548096,
0.05926800146698952,
0.20915760099887848,
0.05181160941720009,
0.06363127380609512,
0.17962847650051117,
0.029485464096069336,
-0.10368850827217102,
0.019553426653146744,
0.04515325650572777,
-0.00005610095468000509,
-0.17393508553504944,
-0.03392885625362396,
-0.05594421550631523,
-0.023160863667726517,
0.1245374009013176,
0.005611096974462271,
-0.011833211407065392,
0.08754482120275497,
-0.002659821417182684,
0.030616745352745056,
-0.016415076330304146,
0.09716210514307022,
0.22859998047351837,
0.0839601382613182,
0.1016140952706337,
-0.035840220749378204,
0.00009662965021561831,
0.09521287679672241,
-0.058629486709833145,
0.15357837080955505,
0.0013788824435323477,
0.18375428020954132,
0.033991988748311996,
0.0737561285495758,
0.02522166632115841,
0.04753529652953148,
-0.014311375096440315,
0.002451844047755003,
-0.014194954186677933,
-0.06626296043395996,
-0.06099925562739372,
0.02838563732802868,
0.05644230544567108,
0.038862746208906174,
-0.06460844725370407,
0.034232232719659805,
0.08432875573635101,
0.22834362089633942,
0.07684138417243958,
-0.39001747965812683,
-0.10423798859119415,
0.024334492161870003,
-0.0663800984621048,
-0.06040431559085846,
0.0018780296668410301,
0.0537225715816021,
-0.12623709440231323,
0.06001829355955124,
-0.03230610489845276,
0.07847816497087479,
-0.08126670867204666,
0.007107404991984367,
0.014032132923603058,
0.07408837229013443,
-0.0001645758020458743,
0.06630611419677734,
-0.1809099167585373,
0.20580251514911652,
0.018763409927487373,
0.0514383539557457,
-0.04831627011299133,
-0.015054041519761086,
0.018786942586302757,
0.0041523524560034275,
0.1323106586933136,
0.014078612439334393,
-0.017804693430662155,
-0.1735030561685562,
-0.08946090191602707,
0.02317240834236145,
0.07211503386497498,
-0.042875226587057114,
0.12548597157001495,
-0.1025342121720314,
-0.01580585166811943,
0.003859142307192087,
-0.02350822277367115,
-0.08823180943727493,
-0.07186652719974518,
-0.00513351708650589,
0.06881159543991089,
0.012637761421501637,
-0.015661880373954773,
-0.08262446522712708,
-0.05690179392695427,
0.09976799041032791,
-0.03243212774395943,
-0.07340358942747116,
-0.13235485553741455,
0.07081998139619827,
0.14228466153144836,
-0.0786963403224945,
0.10024333000183105,
-0.001377756823785603,
0.1302599459886551,
0.05302271991968155,
-0.05799911171197891,
0.06929836422204971,
-0.056420642882585526,
-0.15849393606185913,
0.015454933978617191,
0.1380467265844345,
-0.017208004370331764,
0.03892499953508377,
0.03584953397512436,
0.07865282893180847,
-0.0029947496950626373,
-0.08086495846509933,
-0.04440559446811676,
0.07339958101511002,
0.05333365872502327,
0.012035166844725609,
-0.005892376881092787,
-0.07359190285205841,
-0.09077135473489761,
-0.004636866971850395,
0.15869039297103882,
0.2812035083770752,
-0.08112264424562454,
0.044718507677316666,
0.02204861491918564,
-0.020374100655317307,
-0.13732224702835083,
0.027680182829499245,
0.06629276275634766,
0.006125879008322954,
0.07732648402452469,
-0.042798303067684174,
0.0960654690861702,
0.05395261570811272,
-0.004512609448283911,
-0.03795326128602028,
-0.28181859850883484,
-0.12171253561973572,
0.04698622226715088,
0.10582820326089859,
0.11426770687103271,
-0.07223806530237198,
-0.03948740288615227,
-0.03708532080054283,
-0.1343192309141159,
0.11046651750802994,
0.03417238965630531,
0.11044485121965408,
-0.012703662738204002,
0.12690386176109314,
0.04045422375202179,
-0.016805551946163177,
0.11885344237089157,
0.00407234113663435,
0.046874430030584335,
-0.06496145576238632,
-0.0765048936009407,
0.03439585119485855,
-0.06253141909837723,
0.09524054825305939,
0.044562928378582,
0.05113404616713524,
-0.11875128746032715,
-0.03973117470741272,
-0.06570465117692947,
0.06006282567977905,
-0.022330034524202347,
-0.03735775500535965,
-0.058049872517585754,
0.0332191027700901,
0.06764418631792068,
-0.054141320288181305,
-0.004139889031648636,
-0.04883505776524544,
-0.032033637166023254,
0.06331076472997665,
0.12199220806360245,
0.011122656054794788,
-0.07972084730863571,
0.02284851297736168,
-0.008899656124413013,
0.03290656581521034,
-0.11747891455888748,
0.07009252160787582,
0.11436827480792999,
0.038869552314281464,
0.12344872951507568,
0.0355721153318882,
-0.021006690338253975,
0.011781562119722366,
0.041350990533828735,
-0.07947452366352081,
-0.13444045186042786,
-0.000684996834024787,
-0.0807669386267662,
-0.13460594415664673,
-0.060498856008052826,
0.13708767294883728,
-0.06453366577625275,
-0.03936750814318657,
-0.005176232662051916,
0.04194018617272377,
-0.004149105399847031,
0.09913551062345505,
0.0011940698605030775,
0.042557645589113235,
-0.0900353193283081,
0.1006021499633789,
0.07020595669746399,
-0.12043330073356628,
0.053751230239868164,
0.059887442737817764,
-0.08609935641288757,
-0.03563547134399414,
0.06089680269360542,
0.043786048889160156,
-0.08784293383359909,
-0.03413521870970726,
-0.06979375332593918,
-0.0874839797616005,
0.04947685822844505,
0.14351390302181244,
0.07599800825119019,
0.021266886964440346,
-0.03177526220679283,
-0.011768405325710773,
-0.1160915344953537,
0.10260053724050522,
0.11554914712905884,
0.016894491389393806,
-0.1782374083995819,
0.05653677135705948,
-0.032607369124889374,
-0.005214089062064886,
-0.026162613183259964,
0.01905754581093788,
-0.11048000305891037,
-0.028274020180106163,
-0.0041440813802182674,
0.09142281115055084,
-0.060901105403900146,
0.02471139281988144,
-0.018512437120079994,
-0.030945299193263054,
-0.09867861866950989,
0.01442805863916874,
-0.07936976104974747,
-0.03754014894366264,
0.021027052775025368,
0.11317335814237595,
-0.1336042881011963,
0.00681692361831665,
0.059504542499780655,
-0.05373947694897652,
0.10654683411121368,
0.06214834749698639,
-0.010685360990464687,
0.012227142229676247,
-0.08874756842851639,
-0.015391003340482712,
0.0499160997569561,
0.017020870000123978,
0.05659142881631851,
-0.15015298128128052,
0.04111368581652641,
-0.0032935957424342632,
0.02988189272582531,
-0.005315860733389854,
0.06100162863731384,
-0.11063792556524277,
-0.023224974051117897,
-0.06265497952699661,
-0.06049497425556183,
-0.08998702466487885,
0.022959595546126366,
0.09483035653829575,
-0.0014232174726203084,
0.1875704973936081,
-0.07884011417627335,
0.0056244744919240475,
-0.17588074505329132,
0.003879944561049342,
-0.03443333879113197,
-0.04773417487740517,
-0.13308581709861755,
-0.059704363346099854,
0.07902368158102036,
-0.04000881686806679,
0.06874614208936691,
0.01886005885899067,
0.07440219819545746,
0.05666545033454895,
0.025574885308742523,
-0.06819619238376617,
0.00618134718388319,
0.1880047768354416,
0.037666205316782,
-0.039529573172330856,
0.03053794428706169,
0.017221076413989067,
0.06375745683908463,
0.03993719816207886,
0.12843552231788635,
0.20172184705734253,
-0.059899333864450455,
0.06250477582216263,
0.02401382103562355,
-0.037344031035900116,
-0.11958333104848862,
0.057965755462646484,
-0.08744840323925018,
0.0705515667796135,
-0.04616643115878105,
0.1213449165225029,
0.15678514540195465,
-0.1422412097454071,
0.06003352627158165,
-0.09563051164150238,
-0.05717860907316208,
-0.10458377748727798,
-0.1521795094013214,
-0.13016226887702942,
-0.11619536578655243,
0.016113266348838806,
-0.13168005645275116,
0.07402453571557999,
0.07579036802053452,
0.04042484238743782,
-0.041829854249954224,
0.13586096465587616,
-0.13387218117713928,
-0.06851032376289368,
0.08587094396352768,
0.010358575731515884,
-0.0249632615596056,
0.004730135202407837,
-0.030680149793624878,
0.004193579778075218,
0.029669178649783134,
0.07313462346792221,
0.011072997003793716,
0.006719294935464859,
0.003488403744995594,
-0.0731649398803711,
-0.10434436798095703,
-0.002839714754372835,
0.03447319567203522,
0.008216285146772861,
-0.04884563758969307,
0.0346156544983387,
-0.011600502766668797,
-0.00661851791664958,
0.2668314576148987,
-0.07559937983751297,
-0.0654003694653511,
-0.13001404702663422,
0.17918524146080017,
0.018669012933969498,
0.011549735441803932,
0.015556791797280312,
-0.1316276639699936,
-0.004215753637254238,
0.22017888724803925,
0.10051340609788895,
-0.024897314608097076,
-0.016975147649645805,
-0.04003095254302025,
0.0067824553698301315,
-0.03305298462510109,
0.04658098518848419,
0.01415134035050869,
0.03619464859366417,
-0.07051055878400803,
0.04888388514518738,
-0.017238350585103035,
-0.007446542382240295,
-0.03577427938580513,
0.08265151083469391,
0.02142319269478321,
-0.00620512617751956,
-0.06723778694868088,
0.09065017104148865,
-0.027135631069540977,
-0.12653730809688568,
0.035533078014850616,
-0.147543802857399,
-0.10487447679042816,
-0.00674851331859827,
-0.030713757500052452,
0.02895895019173622,
0.07991902530193329,
-0.015677843242883682,
-0.03956498205661774,
0.06318812072277069,
0.039664845913648605,
-0.12709541618824005,
-0.02768951840698719,
0.10811308026313782,
-0.03666193410754204,
0.1753734052181244,
-0.023284267634153366,
0.06349699199199677,
0.12418323755264282,
0.00391775369644165,
-0.1092086136341095,
0.036454398185014725,
0.06519608199596405,
-0.04785097762942314,
0.0583859421312809,
0.13394421339035034,
-0.009660596959292889,
0.05131363868713379,
0.06590025126934052,
-0.17374132573604584,
-0.01610066182911396,
-0.020362939685583115,
0.05743058770895004,
-0.09665093570947647,
0.009951407089829445,
-0.08958741277456284,
0.1444185972213745,
0.15833279490470886,
-0.050065673887729645,
-0.006510119419544935,
-0.07947884500026703,
0.06888751685619354,
0.06901955604553223,
0.03663790225982666,
-0.03061581216752529,
-0.19617117941379547,
0.019979756325483322,
-0.004520630929619074,
0.020642932504415512,
-0.16880455613136292,
-0.07290355861186981,
-0.016968244686722755,
-0.02338205836713314,
-0.08246056735515594,
0.08540963381528854,
0.059365853667259216,
0.01171308383345604,
-0.017379989847540855,
-0.09905074536800385,
-0.009777596220374107,
0.1739354133605957,
-0.13513125479221344,
-0.05186808854341507
] |
null | null |
transformers
|
# Pre-trained ELECTRA small for Tigrinya Language
We pre-train ELECTRA small on the [TLMD](https://zenodo.org/record/5139094) dataset, with over 40 million tokens.
Contained are trained Flax and PyTorch models.
## Hyperparameters
The hyperparameters corresponding to model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P | Seq |
|------------|----|----|-----|------|------|------|
| SMALL | 12 | 4 | 256 | 1024 | 14M | 512 |
(L = number of layers; AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters; Seq = maximum sequence length.)
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
## Citation
If you use this model in your product or research, please cite as follows:
```
@article{Fitsum2021TiPLMs,
author={Fitsum Gaim and Wonsuk Yang and Jong C. Park},
title={Monolingual Pre-trained Language Models for Tigrinya},
year=2021,
publisher={WiNLP 2021 at EMNLP 2021}
}
```
|
{"language": "ti", "widget": [{"text": "\u12d3\u1255\u121a \u1218\u1295\u12a5\u1230\u12ed \u12a4\u122d\u1275\u122b [MASK] \u1270\u122b\u12a5\u12e9"}]}
|
fill-mask
|
fgaim/tielectra-small
|
[
"transformers",
"pytorch",
"jax",
"electra",
"fill-mask",
"ti",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ti"
] |
TAGS
#transformers #pytorch #jax #electra #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Pre-trained ELECTRA small for Tigrinya Language
===============================================
We pre-train ELECTRA small on the TLMD dataset, with over 40 million tokens.
Contained are trained Flax and PyTorch models.
Hyperparameters
---------------
The hyperparameters corresponding to model sizes mentioned above are as follows:
(L = number of layers; AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters; Seq = maximum sequence length.)
### Framework versions
* Transformers 4.12.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3
If you use this model in your product or research, please cite as follows:
|
[
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3\n\n\nIf you use this model in your product or research, please cite as follows:"
] |
[
"TAGS\n#transformers #pytorch #jax #electra #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3\n\n\nIf you use this model in your product or research, please cite as follows:"
] |
[
46,
54
] |
[
"passage: TAGS\n#transformers #pytorch #jax #electra #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3\n\n\nIf you use this model in your product or research, please cite as follows:"
] |
[
-0.11817972362041473,
0.07896044850349426,
-0.0035891460720449686,
0.043459054082632065,
0.09188172221183777,
0.017020639032125473,
0.14598193764686584,
0.03180411458015442,
-0.075920470058918,
0.029922131448984146,
0.1609889715909958,
0.1321931779384613,
-0.02247013710439205,
0.20639458298683167,
-0.05246930569410324,
-0.3010999858379364,
0.08319848030805588,
0.1496291309595108,
-0.07488887012004852,
0.1394515335559845,
0.10470247268676758,
-0.1409856677055359,
0.10093541443347931,
0.09954839199781418,
-0.1936422884464264,
-0.0277872197329998,
0.037602655589580536,
-0.15080100297927856,
0.07669425755739212,
-0.001517808181233704,
0.16652466356754303,
0.08307117968797684,
0.04490837827324867,
0.029711870476603508,
0.031797342002391815,
-0.00010031375859398395,
-0.03512733802199364,
0.09391434490680695,
-0.006008635275065899,
-0.031977102160453796,
0.099881611764431,
0.02691035345196724,
0.05775006487965584,
-0.02159186452627182,
-0.10281794518232346,
-0.23995058238506317,
-0.06964430958032608,
0.11864262074232101,
0.04480643942952156,
0.051344651728868484,
0.013791716657578945,
0.2612631618976593,
-0.034891143441200256,
0.08396363258361816,
0.19507481157779694,
-0.2143344283103943,
-0.05766924470663071,
-0.037056080996990204,
0.09198078513145447,
-0.01723925769329071,
0.028257910162210464,
0.007581162732094526,
0.05522996559739113,
0.06833076477050781,
0.1348930448293686,
-0.03183720260858536,
-0.04269268363714218,
-0.016188569366931915,
-0.1441771686077118,
-0.10109444707632065,
0.2350151687860489,
-0.05586175620555878,
-0.03565343841910362,
-0.023639865219593048,
-0.13681179285049438,
-0.09880530089139938,
-0.05995641276240349,
-0.036668017506599426,
0.0017250864766538143,
-0.07639659196138382,
-0.10398063063621521,
-0.027847638353705406,
-0.1084630936384201,
-0.07132939994335175,
-0.1766003668308258,
0.33673006296157837,
0.039420511573553085,
0.09796237200498581,
-0.14480911195278168,
0.04258398711681366,
-0.04192217439413071,
-0.19425800442695618,
0.01999826915562153,
-0.1175861656665802,
0.10777977108955383,
-0.02260732650756836,
-0.10045389831066132,
-0.043252840638160706,
0.040380630642175674,
0.2029830515384674,
-0.06470903009176254,
-0.012120415456593037,
0.04135946184396744,
0.02679854817688465,
0.027420295402407646,
0.1458515077829361,
-0.07987959682941437,
0.12759993970394135,
0.04113294184207916,
0.016878843307495117,
0.09702946245670319,
-0.04462302476167679,
-0.14764806628227234,
-0.043142445385456085,
0.08107027411460876,
0.02384759671986103,
0.02252929098904133,
0.05138289928436279,
-0.006402408238500357,
-0.037462953478097916,
0.18619957566261292,
-0.05835997313261032,
-0.0014268172672018409,
-0.02558443881571293,
-0.010492579080164433,
0.046288881450891495,
0.06222052127122879,
-0.011218621395528316,
-0.007067039608955383,
0.05880989879369736,
-0.09325084090232849,
-0.033020880073308945,
-0.08580290526151657,
-0.12100397795438766,
0.029580729082226753,
-0.1224968284368515,
0.05672208592295647,
-0.19989722967147827,
-0.07738188654184341,
0.0896795243024826,
0.061837196350097656,
-0.0061808074824512005,
-0.07391378283500671,
0.04896914213895798,
-0.09266260266304016,
0.036863505840301514,
-0.02591772750020027,
-0.0009973992127925158,
-0.03562479838728905,
0.08714444190263748,
0.11555507779121399,
0.11593544483184814,
-0.12928634881973267,
0.034592606127262115,
-0.07239828258752823,
0.03513278812170029,
-0.22999070584774017,
0.007192840799689293,
-0.059592194855213165,
-0.0030517452396452427,
-0.030504586175084114,
-0.15197166800498962,
0.03648838773369789,
0.014930864796042442,
0.07780110836029053,
0.1393892914056778,
-0.08825921267271042,
-0.09792609512805939,
0.11762465536594391,
-0.18392685055732727,
-0.15754254162311554,
0.07538945227861404,
-0.009403057396411896,
-0.05420719087123871,
0.0013303676387295127,
0.0482732318341732,
0.08593451231718063,
-0.1744139939546585,
-0.02477436512708664,
-0.024942027404904366,
-0.04583623632788658,
-0.11464491486549377,
0.055483683943748474,
0.06477395445108414,
-0.010101442225277424,
0.010419049300253391,
-0.04491792246699333,
0.038907524198293686,
-0.10178868472576141,
-0.0859135091304779,
-0.07155493646860123,
-0.054780200123786926,
0.16818374395370483,
0.038637325167655945,
0.06091378629207611,
-0.03535066545009613,
-0.0618622824549675,
0.09035972505807877,
0.0744694173336029,
-0.01860126480460167,
0.011513680219650269,
-0.1428123414516449,
0.20618775486946106,
-0.1724623590707779,
-0.021850503981113434,
-0.2039891928434372,
-0.061643507331609726,
-0.004636741708964109,
0.045750293880701065,
0.0034265678841620684,
-0.028060197830200195,
0.08909968286752701,
0.07170421630144119,
-0.0037126170936971903,
-0.030892889946699142,
-0.03530729562044144,
0.03312627226114273,
-0.0230085626244545,
-0.10111350566148758,
-0.08421146124601364,
-0.05276516452431679,
0.017558284103870392,
0.04852289333939552,
0.02495432272553444,
0.0653611347079277,
0.1559465080499649,
-0.005449381656944752,
0.04847097024321556,
-0.0033083283342421055,
0.0807366892695427,
-0.01054861955344677,
-0.030129175633192062,
0.0023317099548876286,
0.020574424415826797,
-0.03727094456553459,
0.08441551774740219,
-0.11355452239513397,
0.26057136058807373,
0.20471635460853577,
-0.04013819247484207,
-0.0014325164956972003,
0.03006821684539318,
-0.03976454213261604,
0.021464545279741287,
-0.01414551492780447,
0.05802997201681137,
0.07866394519805908,
-0.010459725745022297,
0.09360815584659576,
-0.08640821278095245,
-0.019110623747110367,
0.11694962531328201,
-0.040940072387456894,
-0.07573629170656204,
-0.008694172836840153,
0.11801976710557938,
-0.07016727328300476,
0.10935452580451965,
0.1331099569797516,
-0.15729859471321106,
0.06604652851819992,
-0.06446149945259094,
-0.052255336195230484,
-0.03550686687231064,
-0.13255509734153748,
0.021349456161260605,
0.1782415807247162,
-0.08210688084363937,
-0.03399652615189552,
0.10545916855335236,
-0.07260718941688538,
0.02866634540259838,
-0.13950632512569427,
0.006348897702991962,
0.0242471881210804,
0.015450077131390572,
-0.15120136737823486,
0.13739845156669617,
0.04857033118605614,
0.07368262112140656,
0.0016260402044281363,
-0.06791374087333679,
0.03927617520093918,
0.059460170567035675,
-0.02100636623799801,
0.15430423617362976,
-0.12565234303474426,
-0.22069086134433746,
-0.15609565377235413,
-0.07068300247192383,
-0.07213153690099716,
0.0284121073782444,
0.04118157923221588,
-0.054828424006700516,
-0.07616657018661499,
0.03688318282365799,
0.014250069856643677,
0.00672171451151371,
0.06911274045705795,
-0.017078420147299767,
-0.028143255040049553,
0.015728963539004326,
-0.11638808250427246,
-0.04469476640224457,
-0.01399983000010252,
0.0667647197842598,
0.11435385048389435,
-0.01536325179040432,
0.14546899497509003,
0.10195502638816833,
-0.0600908137857914,
0.04563560336828232,
0.009129498153924942,
0.2254185676574707,
-0.09765692800283432,
-0.045213766396045685,
0.237984761595726,
0.004021478351205587,
0.07395138591527939,
0.11018045246601105,
0.04579604044556618,
-0.02339272014796734,
-0.025635745376348495,
-0.1002747192978859,
-0.08048975467681885,
-0.11077980697154999,
-0.08133996278047562,
-0.05543586239218712,
-0.09871608763933182,
0.09571738541126251,
0.00828767940402031,
0.07793723046779633,
0.15032705664634705,
0.012597658671438694,
0.029708372429013252,
-0.100921630859375,
0.11938832700252533,
0.23155294358730316,
0.05879531428217888,
0.14347350597381592,
-0.03940996900200844,
-0.09282343089580536,
0.028418682515621185,
-0.011735072359442711,
0.19600173830986023,
-0.012082933448255062,
0.029498113319277763,
0.0781882107257843,
0.13958415389060974,
0.11583138257265091,
0.16367127001285553,
0.04908952862024307,
-0.014281811192631721,
0.010779710486531258,
-0.060264311730861664,
-0.07365992665290833,
-0.02388332225382328,
0.012038043700158596,
0.04101673141121864,
-0.11096414923667908,
0.008555705659091473,
0.03160323575139046,
0.20588286221027374,
-0.017586620524525642,
-0.42300307750701904,
-0.09944579005241394,
-0.0017899131635203958,
-0.009040819481015205,
-0.09261132031679153,
-0.010342470370233059,
-0.02269546501338482,
-0.05215355008840561,
0.0869852676987648,
-0.07180260866880417,
0.07520359754562378,
0.02663685567677021,
0.00964188203215599,
-0.021486621350049973,
0.10576725751161575,
0.0176166333258152,
0.024141963571310043,
-0.23580941557884216,
0.28155016899108887,
0.0027005635201931,
0.024691054597496986,
-0.07279513776302338,
0.005166925024241209,
0.034111492335796356,
0.15114426612854004,
0.03708881512284279,
-0.01662699319422245,
-0.03415653482079506,
-0.04769575223326683,
-0.07082926481962204,
0.050112348049879074,
0.032272279262542725,
0.028148602694272995,
0.09292246401309967,
0.010333810932934284,
-0.038125164806842804,
0.0036026276648044586,
-0.03271101415157318,
-0.04769658297300339,
-0.08792996406555176,
0.06390989571809769,
-0.0036116254050284624,
0.01677662320435047,
-0.06472189724445343,
-0.14208856225013733,
-0.029837897047400475,
0.03982938081026077,
0.04912560433149338,
-0.12045824527740479,
-0.10589046031236649,
-0.07661709189414978,
0.18651103973388672,
-0.07947996258735657,
0.1439165472984314,
-0.07157450169324875,
0.158982053399086,
-0.059790149331092834,
-0.11560383439064026,
0.042011115700006485,
-0.1067081168293953,
-0.11350667476654053,
0.01995282992720604,
0.07229191809892654,
-0.0227496400475502,
0.0498078279197216,
-0.00024639262119308114,
0.078359454870224,
-0.11523094773292542,
-0.07306725531816483,
-0.03517835587263107,
-0.04678626358509064,
0.1572946459054947,
-0.08098754286766052,
0.0880492553114891,
-0.016802962869405746,
-0.01998201757669449,
0.07867510616779327,
0.08581630140542984,
0.11583687365055084,
-0.09109541773796082,
0.07350488007068634,
0.11104372143745422,
0.039238814264535904,
-0.18170307576656342,
-0.11231103539466858,
0.005625505931675434,
0.016288455575704575,
-0.08477400988340378,
-0.08651428669691086,
0.011483454145491123,
-0.0013382148463279009,
-0.04230857640504837,
0.01739339344203472,
-0.1756453514099121,
-0.10155704617500305,
0.17173954844474792,
0.08818725496530533,
0.08275998383760452,
-0.1191234216094017,
-0.011929798871278763,
-0.010617353953421116,
-0.21218164265155792,
0.13558067381381989,
-0.03879170119762421,
0.021245786920189857,
-0.053342290222644806,
0.15174055099487305,
0.013313734903931618,
-0.0685095265507698,
0.15295900404453278,
-0.09652476012706757,
0.0033641806803643703,
-0.04203465208411217,
-0.0914343073964119,
0.19717130064964294,
-0.04065067321062088,
0.06949620693922043,
0.007964005693793297,
0.05314524471759796,
-0.006962752901017666,
-0.04174325242638588,
-0.097224161028862,
0.05148540437221527,
-0.04011576995253563,
-0.03813749924302101,
0.009181558154523373,
-0.03227170929312706,
-0.04163302853703499,
-0.0696345642209053,
0.1569279581308365,
0.042307619005441666,
0.12528440356254578,
0.12670248746871948,
0.14728012681007385,
-0.14204683899879456,
-0.04409923404455185,
0.03308415412902832,
-0.09120725840330124,
0.11732232570648193,
-0.11536479741334915,
0.01910223253071308,
0.08048570156097412,
0.020007841289043427,
-0.0033589638769626617,
0.09193853288888931,
-0.02886517159640789,
0.027303483337163925,
0.12958964705467224,
-0.20190906524658203,
-0.03502136841416359,
-0.03054138831794262,
0.058246590197086334,
-0.08510945737361908,
0.10591208934783936,
0.09336697310209274,
-0.07862028479576111,
-0.06632056087255478,
0.005966921336948872,
-0.04737740755081177,
-0.0226662028580904,
0.08635763823986053,
0.09068591892719269,
0.08149712532758713,
-0.1385958194732666,
0.011033037677407265,
0.007229007314890623,
-0.14962147176265717,
-0.0092058926820755,
0.014815468341112137,
-0.09176457673311234,
-0.07137549668550491,
0.019987983629107475,
0.019922303035855293,
-0.14665594696998596,
-0.08265849202871323,
-0.12590326368808746,
-0.11950116604566574,
0.05500954017043114,
0.2551548182964325,
0.11339975893497467,
0.002938223537057638,
-0.027280066162347794,
0.02790897712111473,
-0.11828544735908508,
0.0589485764503479,
0.061793599277734756,
0.07883945107460022,
-0.1849769949913025,
0.09789590537548065,
-0.01733461767435074,
0.12561742961406708,
-0.08212066441774368,
-0.001878903480246663,
-0.10156763345003128,
0.0260695219039917,
-0.1187949851155281,
0.009793291799724102,
-0.12144874781370163,
-0.005415944382548332,
-0.03682812303304672,
-0.05422361195087433,
-0.11247406899929047,
0.03898582607507706,
-0.08944054692983627,
0.03520909324288368,
0.049719035625457764,
-0.04121742025017738,
-0.10541374236345291,
0.0038055703043937683,
0.04783915728330612,
-0.07550747692584991,
0.04540540650486946,
0.1449749916791916,
-0.05128374323248863,
0.05939045920968056,
0.10648652911186218,
-0.06851351261138916,
0.07893511652946472,
0.03287733346223831,
0.10072197020053864,
-0.12298865616321564,
0.01913103088736534,
0.03350336104631424,
0.006149528548121452,
0.0029658572748303413,
0.10795964300632477,
-0.07155822217464447,
0.05096691474318504,
0.05600159615278244,
-0.03589281067252159,
-0.08172785490751266,
0.014320665039122105,
0.1681211292743683,
0.02646157331764698,
0.12143724411725998,
-0.024640845134854317,
0.005651361308991909,
-0.05871810391545296,
0.030072974041104317,
-0.08116329461336136,
-0.13971497118473053,
-0.06278342753648758,
-0.012981380335986614,
0.033760927617549896,
-0.022489121183753014,
0.18834391236305237,
-0.025094274431467056,
0.11221324652433395,
0.00502358702942729,
0.10813300311565399,
-0.048901498317718506,
0.023568304255604744,
0.16721254587173462,
0.05079744756221771,
-0.006831996608525515,
0.0019999544601887465,
0.0869377851486206,
0.024603236466646194,
-0.02695930376648903,
0.11746634542942047,
0.023464420810341835,
0.09584427624940872,
0.10041812062263489,
0.07817435264587402,
0.05175434798002243,
-0.18053118884563446,
-0.1759890615940094,
0.008235401473939419,
0.04670892283320427,
0.030369814485311508,
0.04299381002783775,
0.15540200471878052,
-0.04178537800908089,
0.039059124886989594,
0.015349416062235832,
-0.031571000814437866,
-0.10603746771812439,
-0.06439363211393356,
-0.07542069256305695,
-0.15504151582717896,
0.012844965793192387,
-0.10218540579080582,
-0.04879271611571312,
0.01877162605524063,
-0.011367757804691792,
-0.004292919300496578,
0.12510260939598083,
0.05661267414689064,
-0.002127672079950571,
0.007982882671058178,
0.009788199327886105,
-0.011700721457600594,
0.0746038407087326,
-0.030245263129472733,
-0.04928630590438843,
-0.014146373607218266,
0.0026873683091253042,
-0.0061916569247841835,
-0.05421999841928482,
0.04217592999339104,
-0.05296028032898903,
-0.10066584497690201,
0.0005197424907237291,
0.030780795961618423,
0.05684232339262962,
0.016922567039728165,
-0.024684401229023933,
0.024764617905020714,
0.003013341687619686,
0.08547721803188324,
-0.07894740998744965,
-0.1436230093240738,
-0.16464050114154816,
0.2519151568412781,
-0.03337603062391281,
-0.012485489249229431,
0.08981548249721527,
-0.026743363589048386,
-0.1167679950594902,
0.16954919695854187,
0.19803203642368317,
0.059753116220235825,
0.04514457285404205,
0.06326939910650253,
-0.006410764995962381,
-0.06164608523249626,
0.05898405611515045,
0.09101246297359467,
0.19977323710918427,
-0.075473852455616,
-0.07910069078207016,
-0.1132986918091774,
0.03795894607901573,
-0.03893238306045532,
-0.02676706574857235,
-0.0015836111269891262,
-0.09060394018888474,
-0.03960048407316208,
0.05542741343379021,
-0.1316034495830536,
0.06526569277048111,
0.011977549642324448,
-0.12785638868808746,
-0.0736299678683281,
0.0009364242432639003,
0.05121323838829994,
-0.011954043060541153,
0.07494424283504486,
-0.021562065929174423,
-0.05048457533121109,
0.12463998049497604,
0.025173760950565338,
-0.11672215908765793,
-0.008300254121422768,
0.17219501733779907,
-0.02067241072654724,
0.06356051564216614,
-0.025552213191986084,
0.13943663239479065,
0.12275367230176926,
0.05850521847605705,
-0.08300705254077911,
0.02049902267754078,
0.057498835027217865,
-0.13601474463939667,
0.012999322265386581,
0.07973872125148773,
0.0011970876948907971,
-0.11683762818574905,
-0.005177028477191925,
-0.13994057476520538,
-0.005325672682374716,
-0.09518356621265411,
0.04551776498556137,
0.0073951357044279575,
0.042753275483846664,
-0.0547565296292305,
0.0679381862282753,
0.0835629552602768,
-0.022099534049630165,
-0.028293831273913383,
-0.043593261390924454,
0.09967760741710663,
0.1159355565905571,
-0.04623585194349289,
-0.08240055292844772,
-0.17366880178451538,
-0.05106625705957413,
0.05351738631725311,
-0.04266666620969772,
-0.16118845343589783,
-0.030575213953852654,
-0.025600435212254524,
-0.04685351625084877,
-0.04979362711310387,
0.014306437224149704,
0.09000232815742493,
0.014579013921320438,
-0.022584956139326096,
0.16309963166713715,
-0.09404218941926956,
0.07992732524871826,
-0.22304797172546387,
-0.04903891310095787
] |
null | null |
transformers
|
# TiRoBERTa: RoBERTa Pretrained for the Tigrinya Language
We pretrain a RoBERTa base model for Tigrinya on a dataset of 40 million tokens trained for 40 epochs.
Contained in this repo is the original pretrained Flax model that was trained on a TPU v3.8 and it's corresponding PyTorch version.
## Hyperparameters
The hyperparameters corresponding to model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P | Seq |
|------------|----|----|-----|------|------|------|
| BASE | 12 | 12 | 768 | 3072 | 125M | 512 |
(L = number of layers; AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters; Seq = maximum sequence length.)
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
## Citation
If you use this model in your product or research, please cite as follows:
```
@article{Fitsum2021TiPLMs,
author={Fitsum Gaim and Wonsuk Yang and Jong C. Park},
title={Monolingual Pre-trained Language Models for Tigrinya},
year=2021,
publisher={WiNLP 2021 at EMNLP 2021}
}
```
|
{"language": "ti", "widget": [{"text": "\u12d3\u1255\u121a \u1218\u1295\u12a5\u1230\u12ed \u12a4\u122d\u1275\u122b <mask> \u1270\u122b\u12a5\u12e9"}]}
|
fill-mask
|
fgaim/tiroberta-base
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"ti",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ti"
] |
TAGS
#transformers #pytorch #jax #safetensors #roberta #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us
|
TiRoBERTa: RoBERTa Pretrained for the Tigrinya Language
=======================================================
We pretrain a RoBERTa base model for Tigrinya on a dataset of 40 million tokens trained for 40 epochs.
Contained in this repo is the original pretrained Flax model that was trained on a TPU v3.8 and it's corresponding PyTorch version.
Hyperparameters
---------------
The hyperparameters corresponding to model sizes mentioned above are as follows:
(L = number of layers; AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters; Seq = maximum sequence length.)
### Framework versions
* Transformers 4.12.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3
If you use this model in your product or research, please cite as follows:
|
[
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3\n\n\nIf you use this model in your product or research, please cite as follows:"
] |
[
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3\n\n\nIf you use this model in your product or research, please cite as follows:"
] |
[
51,
54
] |
[
"passage: TAGS\n#transformers #pytorch #jax #safetensors #roberta #fill-mask #ti #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Framework versions\n\n\n* Transformers 4.12.0.dev0\n* Pytorch 1.9.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3\n\n\nIf you use this model in your product or research, please cite as follows:"
] |
[
-0.11556901037693024,
0.05383968725800514,
-0.0028003768529742956,
0.04625615105032921,
0.09170034527778625,
0.004894590005278587,
0.14731818437576294,
0.038577139377593994,
-0.10158193111419678,
0.01579907163977623,
0.1585691124200821,
0.1141531690955162,
-0.03272339701652527,
0.19731973111629486,
-0.062156956642866135,
-0.2731761336326599,
0.0906236469745636,
0.13206012547016144,
-0.0568728893995285,
0.1477605253458023,
0.10863564908504486,
-0.1384282261133194,
0.09607633203268051,
0.08520586788654327,
-0.19767379760742188,
-0.021287957206368446,
0.06604614853858948,
-0.1545289009809494,
0.07650258392095566,
-0.014353047125041485,
0.18002529442310333,
0.08547866344451904,
0.043494392186403275,
0.01926112361252308,
0.03378928825259209,
0.023003431037068367,
-0.02933534048497677,
0.09132792055606842,
0.018414899706840515,
-0.04585666209459305,
0.048997871577739716,
0.005183830391615629,
0.055549900978803635,
-0.01375456154346466,
-0.10248936712741852,
-0.261606365442276,
-0.08815409243106842,
0.1260666698217392,
0.04469815641641617,
0.054010942578315735,
0.01701008528470993,
0.27975761890411377,
-0.04079141467809677,
0.08974221348762512,
0.2218734323978424,
-0.2468022108078003,
-0.06490611284971237,
-0.016877954825758934,
0.09138639271259308,
-0.014629479497671127,
-0.0007673605578020215,
0.004392549395561218,
0.05270276963710785,
0.06032035872340202,
0.13702046871185303,
-0.04990489408373833,
-0.09270934015512466,
-0.018043534830212593,
-0.13469843566417694,
-0.0788736492395401,
0.2210351824760437,
-0.058523353189229965,
-0.046863581985235214,
-0.026886427775025368,
-0.14010652899742126,
-0.06957409530878067,
-0.06283391267061234,
-0.03673792630434036,
-0.0027635816950351,
-0.06665275245904922,
-0.1271129995584488,
-0.018634196370840073,
-0.11277447640895844,
-0.08238998055458069,
-0.16877615451812744,
0.3253819942474365,
0.03148927167057991,
0.08990732580423355,
-0.13832397758960724,
0.04667337238788605,
-0.03205810487270355,
-0.19638855755329132,
0.02680320106446743,
-0.1258939653635025,
0.10290387272834778,
-0.02436230517923832,
-0.08078370243310928,
-0.05188322067260742,
0.04444802179932594,
0.2012462615966797,
-0.11327210813760757,
-0.018133092671632767,
0.012817827053368092,
0.023288363590836525,
-0.001574518159031868,
0.1395324021577835,
-0.08595194667577744,
0.10682147741317749,
0.046473585069179535,
0.01867091841995716,
0.09363123774528503,
-0.029469242319464684,
-0.12262695282697678,
-0.046387579292058945,
0.1067967638373375,
0.03901825472712517,
0.020518533885478973,
0.06556670367717743,
0.008828353136777878,
-0.021275455132126808,
0.18513938784599304,
-0.06369058787822723,
0.002543436363339424,
-0.022556912153959274,
0.019177652895450592,
0.015695888549089432,
0.056692153215408325,
-0.011675328947603703,
0.0033007818274199963,
0.05333011969923973,
-0.10015904903411865,
-0.032550886273384094,
-0.08183887600898743,
-0.12333136051893234,
0.02614409476518631,
-0.08814689517021179,
0.0527324452996254,
-0.2069179117679596,
-0.09996359795331955,
0.08492501080036163,
0.05533963814377785,
-0.006969391368329525,
-0.03726013004779816,
0.059582870453596115,
-0.11205390095710754,
0.03386085852980614,
-0.03017215058207512,
0.022232798859477043,
-0.04016036167740822,
0.09179147332906723,
0.10934634506702423,
0.10272929072380066,
-0.11550165712833405,
0.021193794906139374,
-0.0958930179476738,
0.04385717958211899,
-0.22829605638980865,
-0.007211590185761452,
-0.062168415635824203,
0.013330690562725067,
-0.022345036268234253,
-0.13702912628650665,
0.02436760812997818,
0.01762716844677925,
0.07418242841959,
0.1376468539237976,
-0.07767947018146515,
-0.07530424743890762,
0.15299274027347565,
-0.18635600805282593,
-0.16417111456394196,
0.07990675419569016,
-0.013718944042921066,
-0.026326367631554604,
0.002742406912147999,
0.06869509816169739,
0.10802365839481354,
-0.15952670574188232,
-0.03157240152359009,
-0.020917631685733795,
-0.05109461769461632,
-0.12436815351247787,
0.05022577568888664,
0.06147358566522598,
-0.04449113830924034,
0.016267601400613785,
-0.044840529561042786,
0.03756261244416237,
-0.09799510985612869,
-0.08480299264192581,
-0.08612408488988876,
-0.06179485097527504,
0.15144622325897217,
0.04119199886918068,
0.06322761625051498,
-0.052437037229537964,
-0.06349707394838333,
0.08826551586389542,
0.06852946430444717,
-0.029220450669527054,
0.012319549918174744,
-0.12360058724880219,
0.20141273736953735,
-0.17472133040428162,
-0.023216689005494118,
-0.19831582903862,
-0.0865713506937027,
-0.009887708351016045,
0.06535511463880539,
-0.03412410244345665,
-0.05078636109828949,
0.10421684384346008,
0.07417028397321701,
-0.03098604828119278,
-0.03796171769499779,
0.001761124236509204,
0.046432290226221085,
-0.015240049920976162,
-0.10235834866762161,
-0.0669887363910675,
-0.06110525503754616,
0.018350498750805855,
0.04016062617301941,
0.020702138543128967,
0.06621335446834564,
0.16034674644470215,
0.008746766485273838,
0.0517408549785614,
-0.004270270466804504,
0.07734372466802597,
-0.002423512749373913,
-0.04444184899330139,
0.009115781635046005,
0.026043230667710304,
-0.00984301045536995,
0.10589129477739334,
-0.12059366703033447,
0.27657902240753174,
0.20254197716712952,
-0.01794767752289772,
-0.016172153875231743,
0.033208224922418594,
-0.04745717719197273,
0.025560878217220306,
-0.02095235139131546,
0.05042912811040878,
0.037032533437013626,
-0.013507087714970112,
0.09255659580230713,
-0.0988885834813118,
-0.03465805947780609,
0.10484561324119568,
-0.050173256546258926,
-0.0750066265463829,
-0.009508154354989529,
0.058097586035728455,
-0.10231396555900574,
0.1291503608226776,
0.15753649175167084,
-0.14114657044410706,
0.07836122810840607,
-0.07145195454359055,
-0.05163552612066269,
-0.03402125835418701,
-0.10760968178510666,
0.023999042809009552,
0.1898985654115677,
-0.04447709023952484,
-0.03752630203962326,
0.09815137088298798,
-0.06661144644021988,
0.026459291577339172,
-0.13544782996177673,
-0.003114189486950636,
0.004627287853509188,
0.016190262511372566,
-0.12645718455314636,
0.1281357705593109,
0.05480888858437538,
0.08088213205337524,
-0.005222534295171499,
-0.07190168648958206,
0.04548727348446846,
0.05178999528288841,
-0.026211131364107132,
0.16508826613426208,
-0.11962990462779999,
-0.22899958491325378,
-0.1509067267179489,
-0.06788744032382965,
-0.07424448430538177,
0.02041459269821644,
0.03741110488772392,
-0.04820301756262779,
-0.0832827240228653,
0.009924824349582195,
-0.010178948752582073,
0.03291163221001625,
0.07449864596128464,
-0.031852997839450836,
-0.014259487390518188,
0.03401648625731468,
-0.11224910616874695,
-0.042487338185310364,
-0.010162608698010445,
0.04103986173868179,
0.11395911872386932,
-0.01158719789236784,
0.13755720853805542,
0.09211020171642303,
-0.05896037071943283,
0.02970808744430542,
-0.00384749798104167,
0.19514232873916626,
-0.09413348138332367,
-0.04064098000526428,
0.21620140969753265,
0.005230044946074486,
0.06710107624530792,
0.13732777535915375,
0.04541875794529915,
-0.017054090276360512,
-0.012177739292383194,
-0.09257727861404419,
-0.07254165410995483,
-0.11266501247882843,
-0.07269406318664551,
-0.04977649450302124,
-0.08331160247325897,
0.08228420466184616,
0.014221280813217163,
0.07776488363742828,
0.14391309022903442,
0.014212545938789845,
0.0005316338501870632,
-0.08030786365270615,
0.12492893636226654,
0.19539128243923187,
0.05316267907619476,
0.14941835403442383,
-0.04142438992857933,
-0.09056169539690018,
0.02677321620285511,
-0.0063091604970395565,
0.17709806561470032,
-0.031306784600019455,
0.0004937085323035717,
0.07326173037290573,
0.15702269971370697,
0.11132826656103134,
0.17957603931427002,
0.03580549731850624,
-0.02531787008047104,
0.01258750818669796,
-0.06706596165895462,
-0.058919183909893036,
-0.032490383833646774,
-0.029016833752393723,
0.05766952037811279,
-0.10045807808637619,
-0.001770899980328977,
0.06270939856767654,
0.20315450429916382,
-0.005924816709011793,
-0.40722328424453735,
-0.09924069046974182,
0.008199452422559261,
-0.007953259162604809,
-0.09363298863172531,
0.004022387787699699,
0.005331173073500395,
-0.042882487177848816,
0.07214274257421494,
-0.08178740739822388,
0.06227850168943405,
0.054282110184431076,
0.011335615999996662,
-0.022764919325709343,
0.09807190299034119,
0.0052010477520525455,
0.02799282595515251,
-0.2275221347808838,
0.2836167514324188,
0.016433652490377426,
0.032857537269592285,
-0.05956784263253212,
0.0019074055599048734,
0.037140995264053345,
0.14460237324237823,
0.06425754725933075,
-0.01049752440303564,
-0.0502716563642025,
-0.04369215667247772,
-0.07443918287754059,
0.045457493513822556,
0.03245478868484497,
0.055416643619537354,
0.0993700698018074,
-0.013592837378382683,
-0.049315713346004486,
0.02012079395353794,
-0.04413741081953049,
-0.052483461797237396,
-0.07538916915655136,
0.05065637081861496,
-0.007065236568450928,
0.002496478846296668,
-0.09174010902643204,
-0.1351950466632843,
-0.032416149973869324,
0.06971488147974014,
0.08045341819524765,
-0.10482804477214813,
-0.10340479761362076,
-0.08777965605258942,
0.18084090948104858,
-0.07229800522327423,
0.13223488628864288,
-0.07569881528615952,
0.14743146300315857,
-0.06600215286016464,
-0.12410680204629898,
0.04698595032095909,
-0.11452217400074005,
-0.12108635157346725,
0.017445599660277367,
0.09797614812850952,
-0.04572603851556778,
0.035024289041757584,
0.006360214669257402,
0.08170997351408005,
-0.10007207095623016,
-0.0776597186923027,
-0.04696493595838547,
-0.06131014600396156,
0.1640554666519165,
-0.09346581250429153,
0.06509353220462799,
-0.04129553586244583,
-0.014161626808345318,
0.07786659896373749,
0.0972876325249672,
0.12457072734832764,
-0.09714889526367188,
0.0760984867811203,
0.12341716885566711,
0.050760261714458466,
-0.21585573256015778,
-0.09294545650482178,
-0.01677713543176651,
-0.003780689090490341,
-0.08197829127311707,
-0.03227434307336807,
0.03334224224090576,
-0.003850124077871442,
-0.049191150814294815,
-0.0035318683367222548,
-0.15034011006355286,
-0.0997031033039093,
0.19154058396816254,
0.10643944144248962,
0.1085161417722702,
-0.13145877420902252,
-0.02007347345352173,
-0.013878202997148037,
-0.19029362499713898,
0.14367718994617462,
-0.10108832269906998,
0.02321631833910942,
-0.04762449115514755,
0.11343415826559067,
0.009874420240521431,
-0.08681152760982513,
0.1394273191690445,
-0.1097746342420578,
0.026972422376275063,
-0.04706437513232231,
-0.09461896866559982,
0.22521072626113892,
-0.046593740582466125,
0.07222114503383636,
0.023285597562789917,
0.06409461796283722,
0.030562279745936394,
-0.038908299058675766,
-0.10801934450864792,
0.0564553365111351,
-0.03995348885655403,
-0.05001838132739067,
0.010985320433974266,
-0.0006827572360634804,
-0.04840778931975365,
-0.08731625229120255,
0.15283802151679993,
0.04172797501087189,
0.14658786356449127,
0.15196597576141357,
0.1381022185087204,
-0.14737343788146973,
0.0006200627540238202,
0.051522690802812576,
-0.1069231927394867,
0.11231662333011627,
-0.1015903577208519,
0.03024352341890335,
0.06205253303050995,
0.01813800446689129,
-0.0027095226105302572,
0.09403561055660248,
-0.030156690627336502,
0.007809498813003302,
0.14042575657367706,
-0.22905421257019043,
-0.04029879346489906,
-0.03459114953875542,
0.03263836354017258,
-0.09668715298175812,
0.1064101904630661,
0.0975060909986496,
-0.0764058455824852,
-0.062276992946863174,
0.010608245618641376,
-0.03995562344789505,
-0.02803564816713333,
0.08701561391353607,
0.09769830852746964,
0.07975506037473679,
-0.12686583399772644,
0.020057354122400284,
-0.0005713971913792193,
-0.11777075380086899,
-0.02267325669527054,
0.01419903989881277,
-0.10567298531532288,
-0.07534736394882202,
0.035066209733486176,
0.02469012513756752,
-0.1024777740240097,
-0.0885411873459816,
-0.12488245964050293,
-0.12272483110427856,
0.03681935742497444,
0.2667813003063202,
0.10887777805328369,
0.02208247408270836,
-0.001306352554820478,
0.021642619743943214,
-0.12924650311470032,
0.0841531902551651,
0.05303747579455376,
0.0857071727514267,
-0.18807850778102875,
0.10131530463695526,
-0.02030654437839985,
0.11257147043943405,
-0.08263865858316422,
0.01654968410730362,
-0.13101771473884583,
0.014909288845956326,
-0.12067069113254547,
-0.0019831093959510326,
-0.1302405744791031,
-0.0004771715321112424,
-0.03357759490609169,
-0.05652700737118721,
-0.11551963537931442,
0.036942411214113235,
-0.0770929679274559,
0.03633324056863785,
0.05527409166097641,
-0.04464099556207657,
-0.12516607344150543,
-0.008139344863593578,
0.04866524413228035,
-0.0694715678691864,
0.040493857115507126,
0.13863272964954376,
-0.04112961143255234,
0.07351063936948776,
0.056663867086172104,
-0.08603476732969284,
0.07136581093072891,
0.01720704697072506,
0.09652931988239288,
-0.14219127595424652,
0.014756236225366592,
0.05272010341286659,
0.016972914338111877,
0.01934782601892948,
0.12153048813343048,
-0.07517943531274796,
0.062232159078121185,
0.04642380774021149,
-0.03505606949329376,
-0.07641706615686417,
0.00527836661785841,
0.17792220413684845,
0.010936504229903221,
0.130209282040596,
-0.05050788074731827,
-0.00022189524315763265,
-0.07481342554092407,
0.02957705594599247,
-0.08647476881742477,
-0.16244754195213318,
-0.06280135363340378,
-0.0012697380734607577,
0.03356672078371048,
-0.015111071057617664,
0.19185160100460052,
-0.023128220811486244,
0.10288859903812408,
0.013058178126811981,
0.08362860232591629,
-0.024860795587301254,
0.027373041957616806,
0.1765250265598297,
0.041904155164957047,
-0.01877100020647049,
0.00448161456733942,
0.08486124128103256,
0.024805951863527298,
-0.0422406941652298,
0.1210712194442749,
0.05288802087306976,
0.07803956419229507,
0.09343860298395157,
0.07274755090475082,
0.03645018860697746,
-0.14340505003929138,
-0.1853034645318985,
-0.009652444161474705,
0.022605065256357193,
0.012056211940944195,
0.02912389300763607,
0.1894952952861786,
-0.056097276508808136,
0.037287477403879166,
0.003870248794555664,
-0.035899657756090164,
-0.1133989691734314,
-0.04559033364057541,
-0.0867658331990242,
-0.1440705955028534,
0.010528653860092163,
-0.10896517336368561,
-0.05702156946063042,
0.01568135991692543,
-0.0033209570683538914,
0.00257220515049994,
0.17709451913833618,
0.055533330887556076,
0.006997307296842337,
0.015622375532984734,
0.019237129017710686,
-0.009255338460206985,
0.07446152716875076,
-0.02448548749089241,
-0.05344868078827858,
-0.017040329053997993,
-0.005865344312041998,
-0.003002087352797389,
-0.040878813713788986,
0.035339850932359695,
-0.05419323593378067,
-0.10169379413127899,
-0.004231914412230253,
0.04710191488265991,
0.05048241466283798,
0.02089109644293785,
-0.012428228743374348,
0.02738162875175476,
0.013945309445261955,
0.11565878987312317,
-0.08311554789543152,
-0.1656409204006195,
-0.16108635067939758,
0.26452359557151794,
-0.03201751038432121,
-0.016885321587324142,
0.08135026693344116,
-0.037754785269498825,
-0.08641385287046432,
0.18050287663936615,
0.2336496263742447,
0.06269769370555878,
0.056920379400253296,
0.05234807729721069,
-0.006979047320783138,
-0.06859466433525085,
0.0673794224858284,
0.09702181816101074,
0.19893968105316162,
-0.07617712765932083,
-0.07753504812717438,
-0.09067676961421967,
0.019042866304516792,
-0.03849735110998154,
0.006259495858103037,
-0.0007733270758762956,
-0.07559752464294434,
-0.050551995635032654,
0.052301548421382904,
-0.1076052263379097,
0.04889563471078873,
0.004414684139192104,
-0.13145111501216888,
-0.07430151104927063,
0.009046788327395916,
0.056685127317905426,
-0.02899068035185337,
0.06512214243412018,
-0.015837058424949646,
-0.025036383420228958,
0.10026859492063522,
0.03387031331658363,
-0.09926413744688034,
-0.01120822038501501,
0.16108590364456177,
-0.019756367430090904,
0.098439060151577,
-0.02455529384315014,
0.11235825717449188,
0.1328444927930832,
0.0339743010699749,
-0.10664061456918716,
0.034626904875040054,
0.06323960423469543,
-0.15111540257930756,
0.0130056357011199,
0.07004105299711227,
-0.0038258882705122232,
-0.09500598907470703,
-0.007789712864905596,
-0.13980041444301605,
-0.0005265315994620323,
-0.09037534892559052,
0.03442778438329697,
-0.013875220902264118,
0.03144409507513046,
-0.04874246567487717,
0.07698572427034378,
0.08854758739471436,
-0.023254912346601486,
-0.008755465969443321,
-0.05458943918347359,
0.09587042778730392,
0.10330329090356827,
-0.049007490277290344,
-0.06316006183624268,
-0.1791381984949112,
-0.029776785522699356,
0.07624921202659607,
-0.03714624419808388,
-0.18503040075302124,
-0.03066668100655079,
-0.01356207299977541,
-0.05231337994337082,
-0.06768529117107391,
0.021564852446317673,
0.09156257659196854,
0.027148939669132233,
-0.019244756549596786,
0.1413450986146927,
-0.09552868455648422,
0.08388935774564743,
-0.2122555375099182,
-0.04910914972424507
] |
null | null |
transformers
|
# Tigrinya POS tagging with TiRoBERTa
This model is a fine-tuned version of [TiRoBERTa](https://huggingface.co/fgaim/tiroberta) on the NTC-v1 dataset (Tedla et al. 2016).
## Training
### Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Results
The model achieves the following results on the test set:
- Loss: 0.3194
- Adj Precision: 0.9219
- Adj Recall: 0.9335
- Adj F1: 0.9277
- Adj Number: 1670
- Adv Precision: 0.8297
- Adv Recall: 0.8554
- Adv F1: 0.8423
- Adv Number: 484
- Con Precision: 0.9844
- Con Recall: 0.9763
- Con F1: 0.9804
- Con Number: 972
- Fw Precision: 0.7895
- Fw Recall: 0.5357
- Fw F1: 0.6383
- Fw Number: 28
- Int Precision: 0.6552
- Int Recall: 0.7308
- Int F1: 0.6909
- Int Number: 26
- N Precision: 0.9650
- N Recall: 0.9662
- N F1: 0.9656
- N Number: 3992
- Num Precision: 0.9747
- Num Recall: 0.9665
- Num F1: 0.9706
- Num Number: 239
- N Prp Precision: 0.9308
- N Prp Recall: 0.9447
- N Prp F1: 0.9377
- N Prp Number: 470
- N V Precision: 0.9854
- N V Recall: 0.9736
- N V F1: 0.9794
- N V Number: 416
- Pre Precision: 0.9722
- Pre Recall: 0.9625
- Pre F1: 0.9673
- Pre Number: 907
- Pro Precision: 0.9448
- Pro Recall: 0.9236
- Pro F1: 0.9341
- Pro Number: 445
- Pun Precision: 1.0
- Pun Recall: 0.9994
- Pun F1: 0.9997
- Pun Number: 1607
- Unc Precision: 1.0
- Unc Recall: 0.875
- Unc F1: 0.9333
- Unc Number: 16
- V Precision: 0.8780
- V Recall: 0.9231
- V F1: 0.9
- V Number: 78
- V Aux Precision: 0.9685
- V Aux Recall: 0.9878
- V Aux F1: 0.9780
- V Aux Number: 654
- V Ger Precision: 0.9388
- V Ger Recall: 0.9571
- V Ger F1: 0.9479
- V Ger Number: 513
- V Imf Precision: 0.9634
- V Imf Recall: 0.9497
- V Imf F1: 0.9565
- V Imf Number: 914
- V Imv Precision: 0.8793
- V Imv Recall: 0.7286
- V Imv F1: 0.7969
- V Imv Number: 70
- V Prf Precision: 0.8960
- V Prf Recall: 0.9082
- V Prf F1: 0.9020
- V Prf Number: 294
- V Rel Precision: 0.9678
- V Rel Recall: 0.9538
- V Rel F1: 0.9607
- V Rel Number: 757
- Overall Precision: 0.9562
- Overall Recall: 0.9562
- Overall F1: 0.9562
- Overall Accuracy: 0.9562
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
## Citation
If you use this model in your product or research, please cite as follows:
```
@article{Fitsum2021TiPLMs,
author={Fitsum Gaim and Wonsuk Yang and Jong C. Park},
title={Monolingual Pre-trained Language Models for Tigrinya},
year=2021,
publisher={WiNLP 2021/EMNLP 2021}
}
```
## References
```
Tedla, Y., Yamamoto, K. & Marasinghe, A. 2016.
Tigrinya Part-of-Speech Tagging with Morphological Patterns and the New Nagaoka Tigrinya Corpus.
International Journal Of Computer Applications 146 pp. 33-41 (2016).
```
|
{"language": "ti", "datasets": ["TLMD", "NTC"], "metrics": ["f1", "precision", "recall", "accuracy"], "widget": [{"text": "\u12f5\u121d\u133b\u12ca \u12a3\u1265\u122d\u1203\u121d \u12a3\u1348\u12c8\u122d\u1242 \u1295\u12d8\u120d\u12a3\u1208\u121d \u1205\u12eb\u12cd \u12ae\u12ed\u1291 \u12a3\u1265 \u120d\u1265\u1293 \u12ed\u1290\u1265\u122d"}]}
|
token-classification
|
fgaim/tiroberta-pos
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"token-classification",
"ti",
"dataset:TLMD",
"dataset:NTC",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ti"
] |
TAGS
#transformers #pytorch #safetensors #roberta #token-classification #ti #dataset-TLMD #dataset-NTC #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Tigrinya POS tagging with TiRoBERTa
This model is a fine-tuned version of TiRoBERTa on the NTC-v1 dataset (Tedla et al. 2016).
## Training
### Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Results
The model achieves the following results on the test set:
- Loss: 0.3194
- Adj Precision: 0.9219
- Adj Recall: 0.9335
- Adj F1: 0.9277
- Adj Number: 1670
- Adv Precision: 0.8297
- Adv Recall: 0.8554
- Adv F1: 0.8423
- Adv Number: 484
- Con Precision: 0.9844
- Con Recall: 0.9763
- Con F1: 0.9804
- Con Number: 972
- Fw Precision: 0.7895
- Fw Recall: 0.5357
- Fw F1: 0.6383
- Fw Number: 28
- Int Precision: 0.6552
- Int Recall: 0.7308
- Int F1: 0.6909
- Int Number: 26
- N Precision: 0.9650
- N Recall: 0.9662
- N F1: 0.9656
- N Number: 3992
- Num Precision: 0.9747
- Num Recall: 0.9665
- Num F1: 0.9706
- Num Number: 239
- N Prp Precision: 0.9308
- N Prp Recall: 0.9447
- N Prp F1: 0.9377
- N Prp Number: 470
- N V Precision: 0.9854
- N V Recall: 0.9736
- N V F1: 0.9794
- N V Number: 416
- Pre Precision: 0.9722
- Pre Recall: 0.9625
- Pre F1: 0.9673
- Pre Number: 907
- Pro Precision: 0.9448
- Pro Recall: 0.9236
- Pro F1: 0.9341
- Pro Number: 445
- Pun Precision: 1.0
- Pun Recall: 0.9994
- Pun F1: 0.9997
- Pun Number: 1607
- Unc Precision: 1.0
- Unc Recall: 0.875
- Unc F1: 0.9333
- Unc Number: 16
- V Precision: 0.8780
- V Recall: 0.9231
- V F1: 0.9
- V Number: 78
- V Aux Precision: 0.9685
- V Aux Recall: 0.9878
- V Aux F1: 0.9780
- V Aux Number: 654
- V Ger Precision: 0.9388
- V Ger Recall: 0.9571
- V Ger F1: 0.9479
- V Ger Number: 513
- V Imf Precision: 0.9634
- V Imf Recall: 0.9497
- V Imf F1: 0.9565
- V Imf Number: 914
- V Imv Precision: 0.8793
- V Imv Recall: 0.7286
- V Imv F1: 0.7969
- V Imv Number: 70
- V Prf Precision: 0.8960
- V Prf Recall: 0.9082
- V Prf F1: 0.9020
- V Prf Number: 294
- V Rel Precision: 0.9678
- V Rel Recall: 0.9538
- V Rel F1: 0.9607
- V Rel Number: 757
- Overall Precision: 0.9562
- Overall Recall: 0.9562
- Overall F1: 0.9562
- Overall Accuracy: 0.9562
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
If you use this model in your product or research, please cite as follows:
## References
|
[
"# Tigrinya POS tagging with TiRoBERTa\n\nThis model is a fine-tuned version of TiRoBERTa on the NTC-v1 dataset (Tedla et al. 2016).",
"## Training",
"### Hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10.0",
"### Results\n\nThe model achieves the following results on the test set:\n- Loss: 0.3194\n- Adj Precision: 0.9219\n- Adj Recall: 0.9335\n- Adj F1: 0.9277\n- Adj Number: 1670\n- Adv Precision: 0.8297\n- Adv Recall: 0.8554\n- Adv F1: 0.8423\n- Adv Number: 484\n- Con Precision: 0.9844\n- Con Recall: 0.9763\n- Con F1: 0.9804\n- Con Number: 972\n- Fw Precision: 0.7895\n- Fw Recall: 0.5357\n- Fw F1: 0.6383\n- Fw Number: 28\n- Int Precision: 0.6552\n- Int Recall: 0.7308\n- Int F1: 0.6909\n- Int Number: 26\n- N Precision: 0.9650\n- N Recall: 0.9662\n- N F1: 0.9656\n- N Number: 3992\n- Num Precision: 0.9747\n- Num Recall: 0.9665\n- Num F1: 0.9706\n- Num Number: 239\n- N Prp Precision: 0.9308\n- N Prp Recall: 0.9447\n- N Prp F1: 0.9377\n- N Prp Number: 470\n- N V Precision: 0.9854\n- N V Recall: 0.9736\n- N V F1: 0.9794\n- N V Number: 416\n- Pre Precision: 0.9722\n- Pre Recall: 0.9625\n- Pre F1: 0.9673\n- Pre Number: 907\n- Pro Precision: 0.9448\n- Pro Recall: 0.9236\n- Pro F1: 0.9341\n- Pro Number: 445\n- Pun Precision: 1.0\n- Pun Recall: 0.9994\n- Pun F1: 0.9997\n- Pun Number: 1607\n- Unc Precision: 1.0\n- Unc Recall: 0.875\n- Unc F1: 0.9333\n- Unc Number: 16\n- V Precision: 0.8780\n- V Recall: 0.9231\n- V F1: 0.9\n- V Number: 78\n- V Aux Precision: 0.9685\n- V Aux Recall: 0.9878\n- V Aux F1: 0.9780\n- V Aux Number: 654\n- V Ger Precision: 0.9388\n- V Ger Recall: 0.9571\n- V Ger F1: 0.9479\n- V Ger Number: 513\n- V Imf Precision: 0.9634\n- V Imf Recall: 0.9497\n- V Imf F1: 0.9565\n- V Imf Number: 914\n- V Imv Precision: 0.8793\n- V Imv Recall: 0.7286\n- V Imv F1: 0.7969\n- V Imv Number: 70\n- V Prf Precision: 0.8960\n- V Prf Recall: 0.9082\n- V Prf F1: 0.9020\n- V Prf Number: 294\n- V Rel Precision: 0.9678\n- V Rel Recall: 0.9538\n- V Rel F1: 0.9607\n- V Rel Number: 757\n- Overall Precision: 0.9562\n- Overall Recall: 0.9562\n- Overall F1: 0.9562\n- Overall Accuracy: 0.9562",
"### Framework versions\n\n- Transformers 4.12.0.dev0\n- Pytorch 1.9.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3\n\n\nIf you use this model in your product or research, please cite as follows:",
"## References"
] |
[
"TAGS\n#transformers #pytorch #safetensors #roberta #token-classification #ti #dataset-TLMD #dataset-NTC #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Tigrinya POS tagging with TiRoBERTa\n\nThis model is a fine-tuned version of TiRoBERTa on the NTC-v1 dataset (Tedla et al. 2016).",
"## Training",
"### Hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10.0",
"### Results\n\nThe model achieves the following results on the test set:\n- Loss: 0.3194\n- Adj Precision: 0.9219\n- Adj Recall: 0.9335\n- Adj F1: 0.9277\n- Adj Number: 1670\n- Adv Precision: 0.8297\n- Adv Recall: 0.8554\n- Adv F1: 0.8423\n- Adv Number: 484\n- Con Precision: 0.9844\n- Con Recall: 0.9763\n- Con F1: 0.9804\n- Con Number: 972\n- Fw Precision: 0.7895\n- Fw Recall: 0.5357\n- Fw F1: 0.6383\n- Fw Number: 28\n- Int Precision: 0.6552\n- Int Recall: 0.7308\n- Int F1: 0.6909\n- Int Number: 26\n- N Precision: 0.9650\n- N Recall: 0.9662\n- N F1: 0.9656\n- N Number: 3992\n- Num Precision: 0.9747\n- Num Recall: 0.9665\n- Num F1: 0.9706\n- Num Number: 239\n- N Prp Precision: 0.9308\n- N Prp Recall: 0.9447\n- N Prp F1: 0.9377\n- N Prp Number: 470\n- N V Precision: 0.9854\n- N V Recall: 0.9736\n- N V F1: 0.9794\n- N V Number: 416\n- Pre Precision: 0.9722\n- Pre Recall: 0.9625\n- Pre F1: 0.9673\n- Pre Number: 907\n- Pro Precision: 0.9448\n- Pro Recall: 0.9236\n- Pro F1: 0.9341\n- Pro Number: 445\n- Pun Precision: 1.0\n- Pun Recall: 0.9994\n- Pun F1: 0.9997\n- Pun Number: 1607\n- Unc Precision: 1.0\n- Unc Recall: 0.875\n- Unc F1: 0.9333\n- Unc Number: 16\n- V Precision: 0.8780\n- V Recall: 0.9231\n- V F1: 0.9\n- V Number: 78\n- V Aux Precision: 0.9685\n- V Aux Recall: 0.9878\n- V Aux F1: 0.9780\n- V Aux Number: 654\n- V Ger Precision: 0.9388\n- V Ger Recall: 0.9571\n- V Ger F1: 0.9479\n- V Ger Number: 513\n- V Imf Precision: 0.9634\n- V Imf Recall: 0.9497\n- V Imf F1: 0.9565\n- V Imf Number: 914\n- V Imv Precision: 0.8793\n- V Imv Recall: 0.7286\n- V Imv F1: 0.7969\n- V Imv Number: 70\n- V Prf Precision: 0.8960\n- V Prf Recall: 0.9082\n- V Prf F1: 0.9020\n- V Prf Number: 294\n- V Rel Precision: 0.9678\n- V Rel Recall: 0.9538\n- V Rel F1: 0.9607\n- V Rel Number: 757\n- Overall Precision: 0.9562\n- Overall Recall: 0.9562\n- Overall F1: 0.9562\n- Overall Accuracy: 0.9562",
"### Framework versions\n\n- Transformers 4.12.0.dev0\n- Pytorch 1.9.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3\n\n\nIf you use this model in your product or research, please cite as follows:",
"## References"
] |
[
65,
43,
2,
90,
725,
54,
3
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #roberta #token-classification #ti #dataset-TLMD #dataset-NTC #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Tigrinya POS tagging with TiRoBERTa\n\nThis model is a fine-tuned version of TiRoBERTa on the NTC-v1 dataset (Tedla et al. 2016).## Training### Hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 10.0"
] |
[
-0.12409871071577072,
0.025563187897205353,
-0.000051309569244040176,
0.1230221763253212,
0.1749490648508072,
0.03733820840716362,
0.0786648616194725,
0.07043658196926117,
-0.07431109994649887,
0.021996693685650826,
0.0915224701166153,
0.11086460947990417,
-0.024498268961906433,
0.20603041350841522,
-0.05072828009724617,
-0.22524641454219818,
0.0081176171079278,
0.045402172952890396,
-0.03804250434041023,
0.11947587877511978,
0.10304208844900131,
-0.1127215251326561,
0.09757480770349503,
0.00490187481045723,
-0.16408173739910126,
0.04013500362634659,
0.03802439197897911,
-0.10207659751176834,
0.11964204162359238,
0.007999865338206291,
0.15933191776275635,
-0.01438944786787033,
0.08711180090904236,
0.005913920234888792,
0.01850154995918274,
0.07567059248685837,
0.008025155402719975,
0.07066768407821655,
0.06409101188182831,
-0.027258196845650673,
0.1776302307844162,
-0.1189080998301506,
0.05043915659189224,
-0.0024873833172023296,
-0.12754741311073303,
-0.133747860789299,
-0.026725785806775093,
0.0377504862844944,
0.026461608707904816,
0.09230349957942963,
-0.019066935405135155,
0.24005991220474243,
-0.0860525518655777,
0.10778864473104477,
0.17322827875614166,
-0.31774887442588806,
-0.08258946985006332,
0.1451914757490158,
0.03353410214185715,
0.0750516876578331,
-0.07816511392593384,
0.0025335459504276514,
0.09714434295892715,
-0.005795838311314583,
0.15137916803359985,
-0.037336040288209915,
-0.12266255170106888,
0.010841448791325092,
-0.18725290894508362,
0.0006740741664543748,
0.26081907749176025,
0.03287087008357048,
-0.05311916023492813,
0.008287091739475727,
-0.1134207472205162,
-0.13833685219287872,
-0.01356012187898159,
-0.05567420274019241,
0.009004342369735241,
-0.022154495120048523,
-0.06347117573022842,
0.010462186299264431,
-0.09519334882497787,
-0.042850568890571594,
-0.06996095180511475,
0.12277326732873917,
0.054236043244600296,
0.009375163353979588,
-0.053565844893455505,
0.08396723121404648,
-0.0392770953476429,
-0.044413287192583084,
0.04015730693936348,
-0.034554433077573776,
-0.0809929147362709,
-0.09636791050434113,
-0.07123833149671555,
-0.08421096950769424,
-0.017558304592967033,
0.1227036863565445,
0.04491107910871506,
0.01728123612701893,
0.04198678955435753,
0.0063833980821073055,
-0.027424806728959084,
0.1984679102897644,
-0.1019904837012291,
-0.058051835745573044,
0.014416228048503399,
0.0008676947909407318,
-0.021142465993762016,
0.009071014821529388,
-0.07866106182336807,
-0.08710584789514542,
0.1443599909543991,
-0.005145407747477293,
-0.08097332715988159,
0.1155770868062973,
-0.030041517689824104,
-0.047732166945934296,
-0.024222182109951973,
-0.07084602117538452,
0.02926771529018879,
-0.03722333908081055,
-0.03565117344260216,
0.06643308699131012,
0.0290415957570076,
-0.004943527281284332,
-0.006902292836457491,
0.07940709590911865,
-0.1358005255460739,
-0.018258169293403625,
-0.08164539188146591,
-0.12089914083480835,
-0.03794049099087715,
-0.1711137592792511,
0.026722250506281853,
-0.1923525184392929,
-0.1343434602022171,
0.007555223070085049,
0.037109050899744034,
-0.026520563289523125,
-0.018587198108434677,
-0.03780773654580116,
-0.045993249863386154,
-0.0008797269547358155,
0.0023080676328390837,
0.09104225784540176,
-0.057656802237033844,
0.057417094707489014,
0.006779118906706572,
0.09236875176429749,
-0.0516463927924633,
0.014857692644000053,
-0.11217834800481796,
0.02120545506477356,
-0.0655243769288063,
0.00714187603443861,
-0.012638532556593418,
0.05930878967046738,
-0.0826389491558075,
-0.09210838377475739,
-0.05378228425979614,
0.017007064074277878,
0.04982054606080055,
0.09418115764856339,
-0.17130538821220398,
-0.015211689285933971,
0.22221989929676056,
-0.08147183805704117,
-0.06723665446043015,
0.12181840091943741,
-0.027839522808790207,
0.0848938375711441,
0.11566883325576782,
0.130886510014534,
0.10089311748743057,
-0.10653521865606308,
0.00738589558750391,
0.00885127205401659,
-0.05749249458312988,
-0.09692265838384628,
0.07365390658378601,
0.10082319378852844,
-0.15067178010940552,
0.010019557550549507,
0.02175598032772541,
0.03485240042209625,
-0.10075414925813675,
-0.05734535679221153,
-0.06730697304010391,
-0.11747439950704575,
0.0885622575879097,
0.012545829638838768,
0.07287999242544174,
-0.10514355450868607,
-0.04229963570833206,
0.06951882690191269,
0.11618707329034805,
-0.028118951246142387,
0.00027183169731870294,
-0.10453838109970093,
0.06407652795314789,
-0.06796714663505554,
-0.03218531981110573,
-0.14270296692848206,
-0.049613360315561295,
-0.003628409467637539,
0.1149538904428482,
0.03651804476976395,
0.1899998039007187,
0.060688406229019165,
0.03394915163516998,
-0.05142921954393387,
-0.003565139602869749,
0.016039088368415833,
0.0145157715305686,
-0.10348023474216461,
-0.10961303859949112,
0.001568661187775433,
-0.031639955937862396,
0.09876246750354767,
-0.22247160971164703,
0.0010689180344343185,
-0.06264270842075348,
0.0795895978808403,
0.054381389170885086,
0.010633467696607113,
-0.020534204319119453,
0.0621761679649353,
-0.045222409069538116,
-0.08896631002426147,
0.04488157853484154,
0.0036816056817770004,
-0.025701217353343964,
-0.05042846128344536,
-0.10986607521772385,
0.18601910769939423,
0.1185280978679657,
-0.006411889102309942,
-0.12524515390396118,
0.03563132882118225,
-0.04824845865368843,
0.000352012604707852,
-0.05108967050909996,
0.014270921237766743,
0.02972351759672165,
-0.08637766540050507,
0.13248172402381897,
-0.07714729011058807,
-0.004168270621448755,
0.02769659459590912,
-0.05093207210302353,
-0.019150299951434135,
0.10357529670000076,
0.10935717821121216,
-0.24568089842796326,
0.08457940071821213,
0.12683384120464325,
-0.11521085351705551,
0.1385909765958786,
0.00880363117903471,
-0.09698288142681122,
-0.015104283578693867,
-0.010105808265507221,
0.01824403926730156,
0.0645039901137352,
-0.14914797246456146,
-0.0058960095047950745,
0.03821907937526703,
0.04511353373527527,
0.020633630454540253,
-0.1339184045791626,
-0.0384904183447361,
0.031523313373327255,
0.027293261140584946,
-0.007432241458445787,
0.07318899780511856,
0.0018047173507511616,
0.11790378391742706,
-0.017430642619729042,
-0.10607994347810745,
0.015888046473264694,
0.05574523285031319,
-0.05601042881608009,
0.16409698128700256,
-0.047050051391124725,
-0.20660746097564697,
-0.08733684569597244,
-0.05848035588860512,
-0.014511329121887684,
0.046137623488903046,
0.036393024027347565,
-0.05840783193707466,
-0.06365157663822174,
-0.07684168964624405,
0.004909544717520475,
-0.031766872853040695,
0.09688306599855423,
-0.00459440378472209,
0.024969972670078278,
0.07763355225324631,
-0.09581371396780014,
-0.00035360801848582923,
-0.041997380554676056,
-0.08007672429084778,
0.09070364385843277,
-0.034188538789749146,
0.09559879451990128,
0.13999123871326447,
-0.08000069856643677,
0.02023552916944027,
-0.03848007693886757,
0.2488938570022583,
-0.03598750755190849,
0.015124709345400333,
0.14047695696353912,
0.03403286263346672,
0.006762009114027023,
0.07631975412368774,
-0.009366503916680813,
-0.11001253873109818,
0.05783945322036743,
0.022969603538513184,
-0.07532880455255508,
-0.19476385414600372,
-0.07130461931228638,
-0.07902578264474869,
0.02813754789531231,
0.14371196925640106,
0.027936916798353195,
0.028803592547774315,
0.11037957668304443,
0.04848668724298477,
0.13471531867980957,
-0.08444071561098099,
0.07083962857723236,
0.05099122226238251,
0.02906123176217079,
0.12620332837104797,
-0.06662458181381226,
-0.0904974490404129,
0.08637098968029022,
-0.03243191912770271,
0.19422803819179535,
-0.004388182889670134,
-0.022131476551294327,
0.044428642839193344,
0.1833885759115219,
0.021885640919208527,
0.10838212817907333,
-0.013498320244252682,
-0.06517603248357773,
-0.05386453866958618,
-0.02049577794969082,
0.003679979592561722,
0.021720724180340767,
-0.0008890990866348147,
0.009356385096907616,
-0.05429833382368088,
0.043803054839372635,
0.03757380321621895,
0.09053131192922592,
0.11753468215465546,
-0.388651043176651,
-0.04864314943552017,
-0.032973166555166245,
0.014977662824094296,
-0.07949692755937576,
0.03369584307074547,
0.10170508176088333,
-0.06804068386554718,
0.016733525320887566,
-0.04663481563329697,
0.07176197320222855,
0.008978519588708878,
0.005163185764104128,
0.02007625810801983,
0.11631114035844803,
-0.026924902573227882,
0.07885196805000305,
-0.22786018252372742,
0.18853896856307983,
-0.01502261683344841,
0.052031632512807846,
-0.048896949738264084,
-0.07420048862695694,
-0.006727794650942087,
0.1338323950767517,
0.051419876515865326,
0.01377920527011156,
0.02761402539908886,
-0.16356052458286285,
-0.06482604146003723,
0.021897507831454277,
0.07903879135847092,
0.0023832356091588736,
0.09046890586614609,
-0.08115150779485703,
-0.017761442810297012,
0.06781332939863205,
0.05029425770044327,
-0.09617926925420761,
-0.05791948735713959,
-0.037599481642246246,
0.11925777047872543,
-0.02621839940547943,
-0.032728925347328186,
-0.10742668807506561,
-0.16699571907520294,
0.14743675291538239,
0.14362284541130066,
0.023890428245067596,
-0.1191646158695221,
0.09849902987480164,
0.10836420208215714,
-0.06299713253974915,
0.08415969461202621,
0.025665272027254105,
0.04781046137213707,
0.05085596442222595,
-0.08638884872198105,
0.11420919746160507,
-0.07135405391454697,
-0.08126311004161835,
-0.03923653066158295,
0.07278524339199066,
0.00664296792820096,
0.019018113613128662,
0.004243206232786179,
0.0024936283007264137,
-0.0035081873647868633,
-0.057317234575748444,
-0.014207368716597557,
0.02564460225403309,
0.046935226768255234,
0.06013793870806694,
-0.027978239580988884,
-0.10734280198812485,
-0.02563820779323578,
-0.022044803947210312,
0.13918305933475494,
0.17632992565631866,
-0.07998225092887878,
-0.0016031315317377448,
-0.027635015547275543,
-0.01619790866971016,
-0.1898507922887802,
0.07815723121166229,
0.004236358217895031,
0.02434162236750126,
0.06013710796833038,
-0.06367219984531403,
0.1194230243563652,
0.057958196848630905,
0.015824930742383003,
0.06548697501420975,
-0.28012821078300476,
-0.12269914150238037,
0.1283658742904663,
0.1446622610092163,
0.22048255801200867,
-0.06175341457128525,
0.021998926997184753,
-0.013524173758924007,
-0.009860263206064701,
0.1290530562400818,
-0.15655383467674255,
0.13368041813373566,
-0.004586298018693924,
0.010364589281380177,
0.027730563655495644,
-0.031796809285879135,
0.06983521580696106,
-0.006593977566808462,
0.07905349880456924,
-0.05820845067501068,
0.0026333436835557222,
0.1125994473695755,
-0.039813313633203506,
0.04241493344306946,
0.015712909400463104,
0.0832236111164093,
-0.024120623245835304,
-0.06633812934160233,
-0.040258534252643585,
0.06263468414545059,
0.017295263707637787,
-0.0893145427107811,
-0.07993268966674805,
0.050356097519397736,
0.01790592260658741,
-0.06648900359869003,
0.01253530103713274,
0.03741009160876274,
0.03240525722503662,
0.011810842901468277,
0.03356053680181503,
-0.05462181195616722,
-0.005084317643195391,
-0.01706550642848015,
-0.0120617114007473,
0.07605359703302383,
-0.08708222955465317,
0.02149340510368347,
0.07963504642248154,
0.0005730041884817183,
0.10115818679332733,
0.07087281346321106,
0.004337586462497711,
-0.004110208712518215,
0.09458626806735992,
-0.13383615016937256,
-0.05562439560890198,
-0.02920309081673622,
-0.10836519300937653,
-0.06101895496249199,
0.10012373328208923,
0.12818029522895813,
-0.10438721626996994,
0.0009056894923560321,
-0.021716417744755745,
-0.06642337888479233,
-0.039479970932006836,
0.1340845674276352,
0.07115909457206726,
0.0036026891320943832,
-0.07294297963380814,
0.047158703207969666,
0.06365358829498291,
-0.05166582018136978,
-0.0028131308499723673,
0.0022497130557894707,
-0.11915483325719833,
-0.04906953498721123,
0.09465914964675903,
0.13958382606506348,
-0.08122279495000839,
-0.03212791308760643,
-0.07432624697685242,
-0.05021214485168457,
0.08632594347000122,
0.11290763318538666,
0.10448621213436127,
0.012069519609212875,
-0.07267484813928604,
-0.0024454561062157154,
-0.12743760645389557,
0.06242561340332031,
0.09388834983110428,
0.10376328974962234,
-0.2618028521537781,
0.18427550792694092,
-0.026953143998980522,
0.018780430778861046,
-0.04600771144032478,
-0.0013557558413594961,
-0.11869027465581894,
0.004309395793825388,
-0.01621708832681179,
0.020565610378980637,
-0.02414427138864994,
0.015387710183858871,
-0.008233500644564629,
-0.06956731528043747,
-0.07728211581707001,
0.019035492092370987,
-0.05906073749065399,
0.017491154372692108,
0.041730307042598724,
0.05281468853354454,
-0.07291148602962494,
-0.02729465439915657,
0.002821320667862892,
-0.04623743146657944,
0.05020211637020111,
0.022181233391165733,
-0.024448882788419724,
0.052097368985414505,
-0.1459244340658188,
0.02634435147047043,
0.09873022139072418,
0.01352084893733263,
0.09374511241912842,
-0.026028161868453026,
0.029112255200743675,
0.008272560313344002,
0.0958952084183693,
0.023586612194776535,
0.00830597709864378,
-0.08493578433990479,
-0.009026956744492054,
-0.079123854637146,
-0.08080379664897919,
-0.036941222846508026,
-0.011099284514784813,
0.06787099689245224,
0.003590409643948078,
0.18695518374443054,
-0.06165442615747452,
-0.01848006434738636,
-0.15327903628349304,
-0.014122514985501766,
-0.03404133766889572,
-0.07733891159296036,
-0.10654492676258087,
-0.04928240925073624,
0.0600956454873085,
-0.03616707772016525,
0.09435272216796875,
0.09243892133235931,
-0.009597866795957088,
-0.015315520577132702,
-0.0015142658958211541,
-0.008308466523885727,
0.0062086209654808044,
0.28376904129981995,
0.03798552230000496,
-0.017770592123270035,
-0.010869600810110569,
0.052638113498687744,
0.13725899159908295,
0.17417193949222565,
0.1381436437368393,
0.16213813424110413,
-0.1351584941148758,
0.08238725364208221,
-0.07443434000015259,
-0.047803845256567,
-0.2206965833902359,
-0.007518403232097626,
-0.11466720700263977,
0.06324532628059387,
-0.00858541950583458,
0.12928420305252075,
0.10695956647396088,
-0.11054850369691849,
-0.011412634514272213,
-0.06046104431152344,
-0.08336455374956131,
-0.0841403380036354,
-0.1727423220872879,
-0.12713295221328735,
-0.1326122134923935,
0.0038316501304507256,
-0.09681511670351028,
0.0025348158087581396,
0.07540681213140488,
0.02630893886089325,
-0.02204151451587677,
0.19741463661193848,
-0.04460563510656357,
-0.02008947916328907,
0.03287799283862114,
0.0008040191605687141,
-0.03013908863067627,
-0.03944336622953415,
-0.06069924309849739,
-0.03612389788031578,
-0.006386464927345514,
0.028147118166089058,
-0.08168656378984451,
-0.011095852591097355,
0.027177846059203148,
-0.027646504342556,
-0.04398765787482262,
-0.0139847407117486,
0.022653227671980858,
0.009050865657627583,
-0.042730458080768585,
0.03234931454062462,
-0.04325919598340988,
-0.014269818551838398,
0.27835091948509216,
-0.039274804294109344,
-0.11537719517946243,
-0.12151350826025009,
0.18589790165424347,
0.0415017269551754,
0.027914084494113922,
0.042300883680582047,
-0.07336409389972687,
0.03270836919546127,
0.2717874348163605,
0.1682649850845337,
-0.05088202282786369,
0.0031440029852092266,
-0.026088323444128036,
-0.013763834722340107,
-0.04160050302743912,
0.17048931121826172,
0.07321953773498535,
0.028890639543533325,
-0.034422826021909714,
-0.02429909259080887,
-0.07555076479911804,
0.012562516145408154,
-0.02642456442117691,
0.03216039389371872,
0.0403066985309124,
-0.02158365584909916,
-0.05928754433989525,
0.055516187101602554,
-0.032696906477212906,
-0.06554176658391953,
0.05247638747096062,
-0.09451033174991608,
-0.1369868367910385,
-0.04189891740679741,
0.059495631605386734,
-0.0009065933409146965,
0.08037369698286057,
-0.09185802191495895,
-0.0037144089583307505,
-0.05454307422041893,
-0.0018190128030255437,
-0.1027722954750061,
-0.0653250515460968,
0.07017847895622253,
0.027619941160082817,
0.15123118460178375,
-0.04513368383049965,
0.06006600335240364,
0.08761314302682877,
0.04005341976881027,
-0.1129438579082489,
0.04714164510369301,
-0.009849789552390575,
0.000006485730409622192,
0.07177404314279556,
0.042434435337781906,
-0.050774045288562775,
0.0473049059510231,
0.03273305669426918,
-0.16689813137054443,
-0.023290276527404785,
0.01738579571247101,
0.04874102771282196,
-0.08234188705682755,
0.001144343288615346,
-0.08542686700820923,
0.158009871840477,
0.16489563882350922,
-0.03635893017053604,
-0.0010884690564125776,
-0.07570429891347885,
0.052398741245269775,
0.05229923129081726,
-0.01715863309800625,
-0.018084319308400154,
-0.1998501420021057,
-0.055367741733789444,
0.014972447417676449,
0.0030322170350700617,
-0.20188242197036743,
-0.04712533950805664,
-0.0894760861992836,
-0.05801756680011749,
-0.10960748791694641,
0.10618136078119278,
0.059757065027952194,
0.02660355344414711,
-0.030146680772304535,
-0.04648569971323013,
-0.06043302267789841,
0.12071839720010757,
-0.13980214297771454,
-0.08986424654722214
] |
null | null |
transformers
|
# Sentiment Analysis for Tigrinya with TiRoBERTa
This model is a fine-tuned version of [TiRoBERTa](https://huggingface.co/fgaim/roberta-base-tigrinya) on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).
## Basic usage
```python
from transformers import pipeline
ti_sent = pipeline("sentiment-analysis", model="fgaim/tiroberta-sentiment")
ti_sent("ድምጻዊ ኣብርሃም ኣፈወርቂ ንዘልኣለም ህያው ኮይኑ ኣብ ልብና ይነብር")
```
## Training
### Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Results
It achieves the following results on the evaluation set:
- F1: 0.8477
- Precision: 0.7607
- Recall: 0.957
- Accuracy: 0.828
- Loss: 0.6796
### Framework versions
- Transformers 4.10.3
- Pytorch 1.9.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.1
## Citation
If you use this model in your product or research, please cite as follows:
```
@article{Fitsum2021TiPLMs,
author={Fitsum Gaim and Wonsuk Yang and Jong C. Park},
title={Monolingual Pre-trained Language Models for Tigrinya},
year=2021,
publisher={WiNLP 2021/EMNLP 2021}
}
```
## References
```
Tela, A., Woubie, A. and Hautamäki, V. 2020.
Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya.
ArXiv, abs/2006.07698.
```
|
{"language": "ti", "datasets": ["TLMD"], "metrics": ["accuracy", "f1", "precision", "recall"], "widget": [{"text": "\u12f5\u121d\u133b\u12ca \u12a3\u1265\u122d\u1203\u121d \u12a3\u1348\u12c8\u122d\u1242 \u1295\u12d8\u120d\u12a3\u1208\u121d \u1205\u12eb\u12cd \u12ae\u12ed\u1291 \u12a3\u1265 \u120d\u1265\u1293 \u12ed\u1290\u1265\u122d"}]}
|
text-classification
|
fgaim/tiroberta-sentiment
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"ti",
"dataset:TLMD",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ti"
] |
TAGS
#transformers #pytorch #roberta #text-classification #ti #dataset-TLMD #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Sentiment Analysis for Tigrinya with TiRoBERTa
This model is a fine-tuned version of TiRoBERTa on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).
## Basic usage
## Training
### Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Results
It achieves the following results on the evaluation set:
- F1: 0.8477
- Precision: 0.7607
- Recall: 0.957
- Accuracy: 0.828
- Loss: 0.6796
### Framework versions
- Transformers 4.10.3
- Pytorch 1.9.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.1
If you use this model in your product or research, please cite as follows:
## References
|
[
"# Sentiment Analysis for Tigrinya with TiRoBERTa\n\nThis model is a fine-tuned version of TiRoBERTa on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).",
"## Basic usage",
"## Training",
"### Hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Results\n\nIt achieves the following results on the evaluation set:\n- F1: 0.8477\n- Precision: 0.7607\n- Recall: 0.957\n- Accuracy: 0.828\n- Loss: 0.6796",
"### Framework versions\n\n- Transformers 4.10.3\n- Pytorch 1.9.0+cu111\n- Datasets 1.10.2\n- Tokenizers 0.10.1\n\n\nIf you use this model in your product or research, please cite as follows:",
"## References"
] |
[
"TAGS\n#transformers #pytorch #roberta #text-classification #ti #dataset-TLMD #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Sentiment Analysis for Tigrinya with TiRoBERTa\n\nThis model is a fine-tuned version of TiRoBERTa on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).",
"## Basic usage",
"## Training",
"### Hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Results\n\nIt achieves the following results on the evaluation set:\n- F1: 0.8477\n- Precision: 0.7607\n- Recall: 0.957\n- Accuracy: 0.828\n- Loss: 0.6796",
"### Framework versions\n\n- Transformers 4.10.3\n- Pytorch 1.9.0+cu111\n- Datasets 1.10.2\n- Tokenizers 0.10.1\n\n\nIf you use this model in your product or research, please cite as follows:",
"## References"
] |
[
53,
50,
3,
2,
89,
50,
51,
3
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #text-classification #ti #dataset-TLMD #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Sentiment Analysis for Tigrinya with TiRoBERTa\n\nThis model is a fine-tuned version of TiRoBERTa on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).## Basic usage## Training### Hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0### Results\n\nIt achieves the following results on the evaluation set:\n- F1: 0.8477\n- Precision: 0.7607\n- Recall: 0.957\n- Accuracy: 0.828\n- Loss: 0.6796### Framework versions\n\n- Transformers 4.10.3\n- Pytorch 1.9.0+cu111\n- Datasets 1.10.2\n- Tokenizers 0.10.1\n\n\nIf you use this model in your product or research, please cite as follows:## References"
] |
[
-0.0880238488316536,
0.1263127624988556,
-0.0018613676074892282,
0.0893734022974968,
0.1107969805598259,
0.005070060957223177,
0.08105336874723434,
0.14100764691829681,
-0.04414255917072296,
0.07940275222063065,
0.07810820639133453,
0.07174544036388397,
0.06551416218280792,
0.1335236132144928,
-0.07213761657476425,
-0.21619075536727905,
0.010366247966885567,
0.004038081970065832,
0.014175912365317345,
0.14416851103305817,
0.10762247443199158,
-0.1143539771437645,
0.1193094253540039,
0.016213444992899895,
-0.1541822850704193,
0.02224132791161537,
0.06906870007514954,
-0.08746596425771713,
0.10552828758955002,
0.0412253700196743,
0.09264591336250305,
0.03573942929506302,
0.07839600741863251,
-0.1054748147726059,
0.003663082607090473,
0.03942846134305,
-0.008784540928900242,
0.06821882724761963,
0.11839766055345535,
-0.030039599165320396,
0.147997185587883,
-0.10514622926712036,
0.044960036873817444,
0.06968044489622116,
-0.13756754994392395,
-0.10012432187795639,
-0.08486239612102509,
0.039856307208538055,
0.04155158996582031,
0.09130127727985382,
-0.07149137556552887,
0.17144447565078735,
-0.0677347183227539,
0.1316535323858261,
0.2104833424091339,
-0.23180106282234192,
-0.07580915093421936,
0.0470886304974556,
0.01859918422996998,
0.07330452650785446,
-0.09196120500564575,
0.048788782209157944,
0.06446629762649536,
-0.014448719099164009,
0.0848507359623909,
-0.04747837781906128,
-0.04657638445496559,
-0.014621471986174583,
-0.13456693291664124,
-0.06875666230916977,
0.23161140084266663,
0.09368947148323059,
-0.06246531009674072,
-0.061482857912778854,
-0.0964265689253807,
-0.09375105798244476,
-0.0342446006834507,
-0.041714541614055634,
0.05132388323545456,
-0.05460934713482857,
-0.10857944190502167,
0.003554633120074868,
-0.09049595892429352,
-0.038939837366342545,
-0.0651470273733139,
0.031087834388017654,
0.005141181871294975,
0.0614786297082901,
-0.047627437859773636,
0.03432866558432579,
-0.07888451218605042,
-0.11975447833538055,
-0.033359020948410034,
-0.019531235098838806,
-0.08712723851203918,
-0.08491438627243042,
-0.024211859330534935,
-0.035826895385980606,
0.021257981657981873,
0.10722189396619797,
0.02007382921874523,
0.07284435629844666,
-0.034465596079826355,
-0.038316648453474045,
0.005933459848165512,
0.22284290194511414,
-0.09069082885980606,
-0.06787075847387314,
0.0011253730626776814,
0.02332654409110546,
-0.006596272811293602,
0.006528361234813929,
-0.0386686772108078,
-0.05276583507657051,
0.12514299154281616,
0.028441153466701508,
-0.01872311718761921,
0.05591559410095215,
-0.06574184447526932,
-0.030773108825087547,
-0.018712423741817474,
-0.108387291431427,
0.032181087881326675,
0.004122098907828331,
-0.0886150524020195,
0.07318216562271118,
0.006008722819387913,
0.0025741385761648417,
-0.03467412292957306,
0.01670416258275509,
-0.11333736777305603,
0.016615405678749084,
-0.06155768036842346,
-0.1139805018901825,
0.02921634167432785,
-0.11078407615423203,
-0.011452986858785152,
-0.13888631761074066,
-0.11128617823123932,
-0.024480191990733147,
0.019635621458292007,
-0.08315245807170868,
-0.004149810876697302,
-0.056458909064531326,
-0.058718014508485794,
0.03292668238282204,
0.013045202940702438,
0.023534642532467842,
-0.04474471136927605,
0.08213142305612564,
0.05601619556546211,
0.08062124997377396,
0.040176261216402054,
0.043064724653959274,
-0.10526592284440994,
0.027744492515921593,
-0.1294192522764206,
0.1051124706864357,
-0.05175332725048065,
0.037634044885635376,
-0.16068969666957855,
-0.10543583333492279,
0.023562368005514145,
-0.025301173329353333,
0.039908770471811295,
0.15554852783679962,
-0.2124277651309967,
-0.015332137234508991,
0.17678165435791016,
-0.07575137913227081,
-0.07436049729585648,
0.1397545486688614,
-0.021323425695300102,
0.031355343759059906,
0.09907089918851852,
0.15533676743507385,
0.04295230284333229,
-0.11590240895748138,
-0.0381963886320591,
-0.0277713555842638,
-0.013328117318451405,
0.042296841740608215,
0.09187595546245575,
0.023363038897514343,
0.01679178699851036,
-0.014667980372905731,
-0.013955860398709774,
-0.05345075577497482,
-0.08913763612508774,
-0.06608189642429352,
-0.04407971352338791,
-0.04074982553720474,
0.0391504243016243,
0.005723881535232067,
0.02544543147087097,
-0.06536753475666046,
-0.07075522094964981,
0.05434919893741608,
0.107060506939888,
-0.0379294827580452,
0.006928783841431141,
-0.1253177523612976,
0.1386573612689972,
-0.09812530875205994,
-0.04645783081650734,
-0.18232668936252594,
-0.042921360582113266,
0.03554212674498558,
0.018237167969346046,
0.017338290810585022,
0.06752756237983704,
0.058001041412353516,
0.0029813179280608892,
-0.038344696164131165,
0.00921144150197506,
-0.05769752711057663,
-0.011218775063753128,
-0.0123305544257164,
-0.11987851560115814,
-0.0019511785358190536,
-0.003989392425864935,
0.15403969585895538,
-0.18595391511917114,
-0.034075506031513214,
0.12209154665470123,
0.10474404692649841,
0.04689396545290947,
-0.06518014520406723,
-0.01067781075835228,
-0.00320301647298038,
-0.011722511611878872,
-0.07854153960943222,
0.03590252995491028,
-0.01740223355591297,
-0.07062092423439026,
-0.012279537506401539,
-0.10161282867193222,
0.04174233600497246,
0.11116230487823486,
0.0015397388488054276,
-0.11667078733444214,
0.01764526031911373,
-0.029515491798520088,
0.005630088970065117,
-0.0649709701538086,
-0.016281912103295326,
0.09260261058807373,
0.006836811546236277,
0.08053547888994217,
-0.1203257292509079,
-0.03584190458059311,
0.05138740688562393,
-0.04825577884912491,
-0.04765110835433006,
0.12587158381938934,
0.04359312355518341,
-0.24540017545223236,
0.06557789444923401,
0.049009058624506,
-0.040065981447696686,
0.11612240970134735,
-0.040135707706213,
-0.09370601177215576,
-0.05876082926988602,
-0.006845742929726839,
0.005320659838616848,
0.0840158462524414,
-0.07422332465648651,
-0.0020481087267398834,
0.051147062331438065,
0.009272708557546139,
-0.03956233337521553,
-0.11994893103837967,
0.010808903723955154,
0.04780900105834007,
-0.060212913900613785,
-0.10022793710231781,
0.05205667018890381,
0.01997246779501438,
0.1148827001452446,
0.026746906340122223,
-0.0034427412319928408,
-0.016370870172977448,
-0.03237542882561684,
-0.0844319611787796,
0.14741450548171997,
-0.052868884056806564,
-0.18927760422229767,
-0.1217477023601532,
-0.040668852627277374,
-0.07681639492511749,
0.004858435597270727,
0.013651599176228046,
-0.0857577845454216,
-0.07636759430170059,
-0.0720418244600296,
0.029472216963768005,
-0.040334850549697876,
0.008056934922933578,
0.020660022273659706,
0.015380753204226494,
0.05646607279777527,
-0.11437850445508957,
-0.04136735573410988,
-0.03538885712623596,
-0.0929199829697609,
0.05245838686823845,
-0.0164340790361166,
0.1068594753742218,
0.06641500443220139,
-0.049818553030490875,
0.04029569402337074,
-0.040865492075681686,
0.27753743529319763,
-0.0939321517944336,
0.04313239827752113,
0.15951299667358398,
0.04502600431442261,
0.058518875390291214,
0.1864883303642273,
0.024259179830551147,
-0.10926907509565353,
0.03960111364722252,
0.0637616217136383,
-0.01991172879934311,
-0.20052076876163483,
-0.019904593005776405,
-0.04644006863236427,
-0.026366762816905975,
0.11368384212255478,
-0.000857429055031389,
0.004783785901963711,
0.08651401847600937,
0.01276710256934166,
0.021793395280838013,
-0.002277543069794774,
0.08013853430747986,
0.16846399009227753,
0.058602429926395416,
0.11900089681148529,
-0.04177042096853256,
0.0031512537971138954,
0.09831566363573074,
-0.05463363602757454,
0.23125655949115753,
-0.013537736609578133,
0.173477903008461,
0.04498980566859245,
0.12346871942281723,
0.012856384739279747,
0.04408305883407593,
0.005547890439629555,
0.003686670446768403,
-0.01203385554254055,
-0.05805767700076103,
-0.056507404893636703,
0.015556280501186848,
0.04290962591767311,
0.03419319540262222,
-0.048620786517858505,
0.023056158795952797,
0.07629645615816116,
0.21529173851013184,
0.08109589666128159,
-0.3982623517513275,
-0.08609353005886078,
0.00871360395103693,
-0.06256292015314102,
-0.06582798808813095,
-0.004815845750272274,
0.052734844386577606,
-0.12919321656227112,
0.07436364889144897,
-0.04695647954940796,
0.09342671930789948,
-0.06101546809077263,
-0.015445446595549583,
0.015710748732089996,
0.06310275942087173,
-0.028714574873447418,
0.07566403597593307,
-0.20057101547718048,
0.26078349351882935,
0.006191771477460861,
0.033905431628227234,
-0.031240850687026978,
-0.02709238976240158,
0.008169252425432205,
0.045197322964668274,
0.1477455496788025,
0.018820062279701233,
0.02741868607699871,
-0.16573236882686615,
-0.08681829273700714,
0.016315875574946404,
0.09112035483121872,
-0.05008048564195633,
0.12211424857378006,
-0.08446422964334488,
-0.022963793948292732,
0.002615417819470167,
-0.00008003838593140244,
-0.08753836154937744,
-0.05107114464044571,
-0.009321551769971848,
0.013395249843597412,
0.040210459381341934,
-0.023898957297205925,
-0.08122671395540237,
-0.07630091160535812,
0.10528170317411423,
0.02439984120428562,
-0.0487561896443367,
-0.13512058556079865,
0.08178357779979706,
0.14380571246147156,
-0.08193229138851166,
0.08552335202693939,
-0.009014260023832321,
0.1149127408862114,
0.048260193318128586,
-0.0672989934682846,
0.06486451625823975,
-0.041894346475601196,
-0.13723334670066833,
-0.008006222546100616,
0.1340699940919876,
0.011612293310463428,
0.050132304430007935,
0.023462383076548576,
0.06715225428342819,
0.0019633581396192312,
-0.07519689202308655,
-0.03593026474118233,
0.03938431292772293,
0.059738557785749435,
0.018667878583073616,
0.026355886831879616,
-0.08516263216733932,
-0.11053086817264557,
-0.013605602085590363,
0.11672348529100418,
0.2387966513633728,
-0.08849497884511948,
0.033718861639499664,
0.010471630841493607,
-0.01846797578036785,
-0.14081396162509918,
0.05115780606865883,
0.08427400141954422,
0.020452557131648064,
0.04140887409448624,
-0.05581511929631233,
0.056032732129096985,
0.04456274211406708,
-0.002016606042161584,
-0.04891581833362579,
-0.2624993622303009,
-0.13394302129745483,
0.06146632879972458,
0.12866167724132538,
0.06471729278564453,
-0.0846734344959259,
-0.04221475124359131,
-0.025012491270899773,
-0.10168888419866562,
0.10460472851991653,
-0.011765843257308006,
0.10069094598293304,
0.008889662101864815,
0.12474638223648071,
0.037475425750017166,
-0.01341262273490429,
0.1243111789226532,
0.020311955362558365,
0.048804692924022675,
-0.05844178795814514,
-0.046401701867580414,
0.08835327625274658,
-0.061078086495399475,
0.08078578859567642,
0.06229769438505173,
0.06785425543785095,
-0.09115327149629593,
-0.03623403236269951,
-0.07365141808986664,
0.04274425283074379,
-0.03525304049253464,
-0.044890955090522766,
-0.053636837750673294,
0.03980620205402374,
0.05154281482100487,
-0.07918451726436615,
-0.019895559176802635,
-0.06115323677659035,
-0.0408613495528698,
0.08495742082595825,
0.09142839163541794,
0.048967745155096054,
-0.07475493848323822,
0.023281320929527283,
-0.008214670233428478,
0.03341884911060333,
-0.12291914969682693,
0.05156741663813591,
0.11004991084337234,
0.04169441759586334,
0.16124847531318665,
0.017346300184726715,
-0.0492171049118042,
0.010899623855948448,
0.05588923767209053,
-0.09571683406829834,
-0.13401246070861816,
-0.021010911092162132,
-0.11401903629302979,
-0.130174458026886,
-0.04222556948661804,
0.12694530189037323,
-0.050119202584028244,
-0.01704672910273075,
0.00640483433380723,
0.024014800786972046,
-0.011022894643247128,
0.10041923820972443,
0.007294066250324249,
0.04260930418968201,
-0.10055587440729141,
0.08996233344078064,
0.06617852300405502,
-0.1322416514158249,
0.03062756359577179,
0.0446099154651165,
-0.09736929833889008,
-0.027100695297122,
0.04326252266764641,
0.07871454954147339,
-0.0897061675786972,
-0.026012608781456947,
-0.06231694296002388,
-0.08339105546474457,
0.05498385801911354,
0.1711035817861557,
0.08549666404724121,
0.031231926754117012,
-0.03151319921016693,
-0.0035390518605709076,
-0.09168923646211624,
0.09803560376167297,
0.14024744927883148,
0.02437763474881649,
-0.18049687147140503,
0.06944142282009125,
-0.02602788619697094,
-0.006205595098435879,
-0.03053954988718033,
0.006986938416957855,
-0.10343357920646667,
-0.018807467073202133,
-0.024763327091932297,
0.08109129965305328,
-0.04970673844218254,
0.03727417439222336,
-0.028682108968496323,
-0.06791727989912033,
-0.08610665053129196,
0.004433437250554562,
-0.07240404188632965,
-0.03147709369659424,
0.02913966216146946,
0.0825008675456047,
-0.1613328754901886,
-0.014731034636497498,
0.06294368207454681,
-0.061055738478899,
0.08898661285638809,
0.07780945301055908,
0.003019446274265647,
0.039077408611774445,
-0.07417771220207214,
0.014408733695745468,
0.04418657347559929,
0.014103504829108715,
0.05936820060014725,
-0.14935174584388733,
0.03182753548026085,
-0.012937076389789581,
0.031139180064201355,
0.0055104028433561325,
0.035754527896642685,
-0.10539737343788147,
-0.0467546209692955,
-0.04661159962415695,
-0.07740320265293121,
-0.08483825623989105,
0.0686444416642189,
0.09334496408700943,
0.03272399306297302,
0.16329610347747803,
-0.06933650374412537,
0.011605305597186089,
-0.1897856891155243,
0.0011518746614456177,
-0.012339508160948753,
-0.04020509123802185,
-0.13256333768367767,
-0.0730985701084137,
0.08064638078212738,
-0.05233623459935188,
0.04234019294381142,
0.039949074387550354,
0.08939500153064728,
0.037596166133880615,
0.03998258337378502,
-0.06581466645002365,
0.001663634437136352,
0.18323664367198944,
0.03201817721128464,
-0.030591852962970734,
0.023844201117753983,
0.011407416313886642,
0.041555870324373245,
0.04131978005170822,
0.10255980491638184,
0.18778358399868011,
-0.05319041386246681,
0.03323705494403839,
0.031180869787931442,
-0.030057307332754135,
-0.11261117458343506,
0.07193830609321594,
-0.12054137885570526,
0.07375197112560272,
-0.0398876890540123,
0.13687895238399506,
0.13393981754779816,
-0.1614581197500229,
0.07376711815595627,
-0.048875100910663605,
-0.08592899888753891,
-0.09558426588773727,
-0.17773599922657013,
-0.12570072710514069,
-0.11967175453901291,
0.03479911759495735,
-0.13813947141170502,
0.08853829652070999,
0.037950653582811356,
0.05061258375644684,
-0.02584823966026306,
0.17758598923683167,
-0.11097687482833862,
-0.08727799355983734,
0.0977063998579979,
0.026354217901825905,
-0.03393173962831497,
0.034883785992860794,
-0.010014253668487072,
0.03542547672986984,
0.030261380597949028,
0.06283698976039886,
0.004229709971696138,
-0.005561801139265299,
0.023829028010368347,
-0.0677768662571907,
-0.09843756258487701,
-0.004411669448018074,
0.03733949735760689,
0.010794831439852715,
-0.017583079636096954,
0.042528752237558365,
0.009432198479771614,
-0.009777726605534554,
0.2837207615375519,
-0.07639093697071075,
-0.03177175298333168,
-0.1625916063785553,
0.19814738631248474,
-0.00963557232171297,
0.014877064153552055,
0.04055039957165718,
-0.11722446978092194,
0.01386239379644394,
0.21982404589653015,
0.11548667401075363,
-0.004487756639719009,
-0.022846199572086334,
-0.026674283668398857,
0.006905372254550457,
-0.004730568267405033,
0.05584048107266426,
0.0013410592218860984,
0.03509306162595749,
-0.06163956597447395,
0.037981078028678894,
-0.022287774831056595,
-0.020686043426394463,
0.030991926789283752,
0.07679536193609238,
0.03407653048634529,
-0.01791234500706196,
-0.08976349234580994,
0.11253681778907776,
-0.01625910960137844,
-0.17242275178432465,
0.038132138550281525,
-0.12429890781641006,
-0.10962938517332077,
-0.019275259226560593,
-0.026220744475722313,
0.028376078233122826,
0.0785100981593132,
-0.020673153921961784,
-0.0210459902882576,
0.09462549537420273,
0.04259008541703224,
-0.1268470138311386,
-0.05631709471344948,
0.11482776701450348,
-0.026000777259469032,
0.12603580951690674,
-0.02594415284693241,
0.06457746773958206,
0.11906734853982925,
0.016650181263685226,
-0.08573874831199646,
0.05182600021362305,
0.06514717638492584,
-0.06005708500742912,
0.04418105259537697,
0.14950671792030334,
-0.008291373029351234,
0.051238127052783966,
0.05201020836830139,
-0.14004367589950562,
-0.0022721262648701668,
-0.049354564398527145,
0.03782644122838974,
-0.09513971209526062,
0.04674180597066879,
-0.10307971388101578,
0.15097059309482574,
0.2073334902524948,
-0.036917831748723984,
0.03222550079226494,
-0.09524960815906525,
0.04102693870663643,
0.06345602124929428,
0.01037078257650137,
-0.009790861047804356,
-0.1695556938648224,
0.000576595775783062,
-0.0038110583554953337,
0.014926844276487827,
-0.22102881968021393,
-0.06873337179422379,
-0.010476158931851387,
-0.028929157182574272,
-0.06721770763397217,
0.0975114032626152,
0.021421676501631737,
0.02058589644730091,
-0.009406150318682194,
-0.06663075089454651,
-0.0026849128771573305,
0.159774512052536,
-0.138111412525177,
-0.050917841494083405
] |
null | null |
transformers
|
# NewsSentiment: easy-to-use, high-quality target-dependent sentiment classification for news articles
## Important: [use our PyPI package](https://pypi.org/project/NewsSentiment/) instead of this model on the Hub
The Huggingface Hub architecture currently [does not support](https://github.com/huggingface/transformers/issues/14785) target-dependent sentiment classification since you cannot provide the required inputs, i.e., sentence and target. Thus, we recommend that you use our easy-to-use [PyPI package NewsSentiment](https://pypi.org/project/NewsSentiment/).
## Description
This model is the currently [best performing](https://aclanthology.org/2021.eacl-main.142.pdf)
targeted sentiment classifier for news articles. In contrast to regular sentiment
classification, targeted sentiment classification allows you to provide a target in a sentence.
Only for this target, the sentiment is then predicted. This is more reliable in many
cases, as demonstrated by the following simplistic example: "I like Bert, but I hate Robert."
This model is also available as an easy-to-use PyPI package named [`NewsSentiment`](https://pypi.org/project/NewsSentiment/) and
in its original GitHub repository named [`NewsMTSC`](https://github.com/fhamborg/NewsMTSC), where you will find the dataset the model was trained on, other models for sentiment classification, and a training and testing framework. More information on the model and the dataset (consisting of more than 10k sentences sampled from news articles, each
labeled and agreed upon by at least 5 annotators) can be found in our [EACL paper](https://aclanthology.org/2021.eacl-main.142.pdf). The
dataset, the model, and its source code can be viewed in our [GitHub repository](https://github.com/fhamborg/NewsMTSC).
We recommend to use our [PyPI package](https://pypi.org/project/NewsSentiment/) for sentiment classification since the Huggingface Hub platform seems to [not support](https://github.com/huggingface/transformers/issues/14785) target-dependent sentiment classification.
# How to cite
If you use the dataset or model, please cite our [paper](https://www.aclweb.org/anthology/2021.eacl-main.142/) ([PDF](https://www.aclweb.org/anthology/2021.eacl-main.142.pdf)):
```
@InProceedings{Hamborg2021b,
author = {Hamborg, Felix and Donnay, Karsten},
title = {NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles},
booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)},
year = {2021},
month = {Apr.},
location = {Virtual Event},
}
```
|
{"language": ["en"], "license": "apache-2.0", "tags": ["text-classification", "sentiment-analysis", "sentiment-classification", "targeted-sentiment-classification", "target-depentent-sentiment-classification"], "datasets": "fhamborg/news_sentiment_newsmtsc"}
|
text-classification
|
fhamborg/roberta-targeted-sentiment-classification-newsarticles
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"sentiment-analysis",
"sentiment-classification",
"targeted-sentiment-classification",
"target-depentent-sentiment-classification",
"en",
"dataset:fhamborg/news_sentiment_newsmtsc",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #text-classification #sentiment-analysis #sentiment-classification #targeted-sentiment-classification #target-depentent-sentiment-classification #en #dataset-fhamborg/news_sentiment_newsmtsc #license-apache-2.0 #endpoints_compatible #region-us
|
# NewsSentiment: easy-to-use, high-quality target-dependent sentiment classification for news articles
## Important: use our PyPI package instead of this model on the Hub
The Huggingface Hub architecture currently does not support target-dependent sentiment classification since you cannot provide the required inputs, i.e., sentence and target. Thus, we recommend that you use our easy-to-use PyPI package NewsSentiment.
## Description
This model is the currently best performing
targeted sentiment classifier for news articles. In contrast to regular sentiment
classification, targeted sentiment classification allows you to provide a target in a sentence.
Only for this target, the sentiment is then predicted. This is more reliable in many
cases, as demonstrated by the following simplistic example: "I like Bert, but I hate Robert."
This model is also available as an easy-to-use PyPI package named 'NewsSentiment' and
in its original GitHub repository named 'NewsMTSC', where you will find the dataset the model was trained on, other models for sentiment classification, and a training and testing framework. More information on the model and the dataset (consisting of more than 10k sentences sampled from news articles, each
labeled and agreed upon by at least 5 annotators) can be found in our EACL paper. The
dataset, the model, and its source code can be viewed in our GitHub repository.
We recommend to use our PyPI package for sentiment classification since the Huggingface Hub platform seems to not support target-dependent sentiment classification.
# How to cite
If you use the dataset or model, please cite our paper (PDF):
|
[
"# NewsSentiment: easy-to-use, high-quality target-dependent sentiment classification for news articles",
"## Important: use our PyPI package instead of this model on the Hub\nThe Huggingface Hub architecture currently does not support target-dependent sentiment classification since you cannot provide the required inputs, i.e., sentence and target. Thus, we recommend that you use our easy-to-use PyPI package NewsSentiment.",
"## Description\n\nThis model is the currently best performing \ntargeted sentiment classifier for news articles. In contrast to regular sentiment\nclassification, targeted sentiment classification allows you to provide a target in a sentence. \nOnly for this target, the sentiment is then predicted. This is more reliable in many\ncases, as demonstrated by the following simplistic example: \"I like Bert, but I hate Robert.\"\n\nThis model is also available as an easy-to-use PyPI package named 'NewsSentiment' and \nin its original GitHub repository named 'NewsMTSC', where you will find the dataset the model was trained on, other models for sentiment classification, and a training and testing framework. More information on the model and the dataset (consisting of more than 10k sentences sampled from news articles, each \nlabeled and agreed upon by at least 5 annotators) can be found in our EACL paper. The\ndataset, the model, and its source code can be viewed in our GitHub repository.\n\nWe recommend to use our PyPI package for sentiment classification since the Huggingface Hub platform seems to not support target-dependent sentiment classification.",
"# How to cite\nIf you use the dataset or model, please cite our paper (PDF):"
] |
[
"TAGS\n#transformers #pytorch #roberta #text-classification #sentiment-analysis #sentiment-classification #targeted-sentiment-classification #target-depentent-sentiment-classification #en #dataset-fhamborg/news_sentiment_newsmtsc #license-apache-2.0 #endpoints_compatible #region-us \n",
"# NewsSentiment: easy-to-use, high-quality target-dependent sentiment classification for news articles",
"## Important: use our PyPI package instead of this model on the Hub\nThe Huggingface Hub architecture currently does not support target-dependent sentiment classification since you cannot provide the required inputs, i.e., sentence and target. Thus, we recommend that you use our easy-to-use PyPI package NewsSentiment.",
"## Description\n\nThis model is the currently best performing \ntargeted sentiment classifier for news articles. In contrast to regular sentiment\nclassification, targeted sentiment classification allows you to provide a target in a sentence. \nOnly for this target, the sentiment is then predicted. This is more reliable in many\ncases, as demonstrated by the following simplistic example: \"I like Bert, but I hate Robert.\"\n\nThis model is also available as an easy-to-use PyPI package named 'NewsSentiment' and \nin its original GitHub repository named 'NewsMTSC', where you will find the dataset the model was trained on, other models for sentiment classification, and a training and testing framework. More information on the model and the dataset (consisting of more than 10k sentences sampled from news articles, each \nlabeled and agreed upon by at least 5 annotators) can be found in our EACL paper. The\ndataset, the model, and its source code can be viewed in our GitHub repository.\n\nWe recommend to use our PyPI package for sentiment classification since the Huggingface Hub platform seems to not support target-dependent sentiment classification.",
"# How to cite\nIf you use the dataset or model, please cite our paper (PDF):"
] |
[
91,
24,
72,
254,
20
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #text-classification #sentiment-analysis #sentiment-classification #targeted-sentiment-classification #target-depentent-sentiment-classification #en #dataset-fhamborg/news_sentiment_newsmtsc #license-apache-2.0 #endpoints_compatible #region-us \n# NewsSentiment: easy-to-use, high-quality target-dependent sentiment classification for news articles## Important: use our PyPI package instead of this model on the Hub\nThe Huggingface Hub architecture currently does not support target-dependent sentiment classification since you cannot provide the required inputs, i.e., sentence and target. Thus, we recommend that you use our easy-to-use PyPI package NewsSentiment.## Description\n\nThis model is the currently best performing \ntargeted sentiment classifier for news articles. In contrast to regular sentiment\nclassification, targeted sentiment classification allows you to provide a target in a sentence. \nOnly for this target, the sentiment is then predicted. This is more reliable in many\ncases, as demonstrated by the following simplistic example: \"I like Bert, but I hate Robert.\"\n\nThis model is also available as an easy-to-use PyPI package named 'NewsSentiment' and \nin its original GitHub repository named 'NewsMTSC', where you will find the dataset the model was trained on, other models for sentiment classification, and a training and testing framework. More information on the model and the dataset (consisting of more than 10k sentences sampled from news articles, each \nlabeled and agreed upon by at least 5 annotators) can be found in our EACL paper. The\ndataset, the model, and its source code can be viewed in our GitHub repository.\n\nWe recommend to use our PyPI package for sentiment classification since the Huggingface Hub platform seems to not support target-dependent sentiment classification.# How to cite\nIf you use the dataset or model, please cite our paper (PDF):"
] |
[
-0.032544128596782684,
0.17790600657463074,
-0.006950757000595331,
-0.014728084206581116,
0.11271203309297562,
0.03540344536304474,
0.08442728966474533,
0.09982691705226898,
0.07120093703269958,
0.08108674734830856,
-0.05694432929158211,
0.12277834117412567,
0.06895146518945694,
-0.002521297661587596,
-0.0055697024799883366,
-0.18724589049816132,
0.017989322543144226,
0.015902288258075714,
0.25824981927871704,
0.08759314566850662,
0.1075950488448143,
-0.011343544349074364,
0.05295218154788017,
0.01787068508565426,
-0.08739269524812698,
0.03744317963719368,
0.04493046924471855,
0.0000182837884494802,
0.057947635650634766,
0.018777204677462578,
0.12277545034885406,
-0.005812731571495533,
-0.04843482747673988,
-0.1309749037027359,
0.05130688473582268,
0.09967991709709167,
-0.02418259158730507,
0.06799352914094925,
0.04883202910423279,
-0.10931296646595001,
0.20889383554458618,
-0.23182521760463715,
0.02674699015915394,
0.07362533360719681,
-0.09788574278354645,
-0.1127287819981575,
-0.061010412871837616,
0.19123533368110657,
0.10495845973491669,
0.05110279470682144,
-0.03632335364818573,
0.16335922479629517,
-0.08003854751586914,
0.029723798856139183,
0.17850254476070404,
-0.08073975890874863,
-0.06798630207777023,
-0.013132492080330849,
-0.10491502285003662,
0.10004919767379761,
-0.10593759268522263,
0.001074991887435317,
-0.018286237493157387,
0.03194286674261093,
-0.0009135648724623024,
-0.023373540490865707,
-0.030413804575800896,
-0.027292698621749878,
-0.08598000556230545,
-0.08159808814525604,
0.08593334257602692,
0.003963892348110676,
-0.09781511127948761,
-0.13751579821109772,
-0.07061076164245605,
0.0418371818959713,
0.010330135002732277,
0.028902985155582428,
0.019814886152744293,
0.0239495150744915,
0.10736445337533951,
-0.06817463040351868,
-0.09067846089601517,
-0.007349223829805851,
-0.03340159356594086,
0.18759533762931824,
-0.02271372452378273,
0.06171126291155815,
0.056631460785865784,
0.13864706456661224,
-0.10201212763786316,
-0.05247090011835098,
-0.05631043016910553,
-0.08518975228071213,
-0.07586237043142319,
-0.019359273836016655,
-0.095302052795887,
-0.06544319540262222,
-0.029345044866204262,
0.06368744373321533,
-0.02639974281191826,
-0.017360294237732887,
-0.031129511073231697,
-0.002199843991547823,
0.12625430524349213,
0.0950702503323555,
-0.05181828513741493,
0.016661617904901505,
0.06301681697368622,
-0.008303898386657238,
0.13194458186626434,
0.030146030709147453,
0.012229821644723415,
-0.04013800621032715,
-0.006530642043799162,
0.05451401695609093,
0.03374973312020302,
0.06753095984458923,
-0.12684500217437744,
-0.04598042368888855,
0.11619089543819427,
-0.11888886988162994,
0.0011546837631613016,
0.053920190781354904,
-0.06818664819002151,
0.10142689943313599,
-0.0024056166876107454,
-0.07316026091575623,
-0.11008205264806747,
0.05797288566827774,
-0.05125077813863754,
0.025593968108296394,
-0.14778879284858704,
-0.09379658848047256,
0.06478065252304077,
-0.0808611735701561,
-0.05953293293714523,
-0.041545744985342026,
-0.15075862407684326,
-0.05703049898147583,
-0.05053415149450302,
-0.03341241925954819,
-0.07356881350278854,
-0.017540881410241127,
-0.0007364745833911002,
-0.04108981415629387,
-0.0019806181080639362,
-0.06785576045513153,
-0.017733938992023468,
0.0446256585419178,
-0.09651198238134384,
0.07823638617992401,
0.0027581879403442144,
-0.03126857802271843,
-0.11674430221319199,
-0.0002965452440548688,
-0.2622985541820526,
0.13867919147014618,
-0.14617076516151428,
0.063224196434021,
-0.03761890530586243,
-0.014001470990478992,
0.05094924941658974,
0.004187020473182201,
-0.04268858954310417,
0.1263611912727356,
-0.1054357960820198,
-0.04990857467055321,
0.05655030533671379,
-0.1936318576335907,
0.10735996067523956,
0.12623809278011322,
-0.063350610435009,
0.012923872098326683,
0.1609780639410019,
0.052342869341373444,
0.08341341465711594,
0.000014484311577689368,
-0.1600235551595688,
-0.02597115933895111,
-0.16357825696468353,
0.04989823326468468,
0.003350782673805952,
0.07624200731515884,
-0.04340808466076851,
0.003977496176958084,
-0.06568121165037155,
-0.0038540749810636044,
0.03648239001631737,
-0.019837290048599243,
-0.017712300643324852,
-0.00710474094375968,
-0.019038617610931396,
0.02322901040315628,
-0.08190876245498657,
0.0017752182902768254,
-0.09242389351129532,
0.07379543781280518,
0.12352171540260315,
0.0179993137717247,
0.006044717505574226,
-0.09365227073431015,
0.05212422087788582,
-0.0371563658118248,
-0.0042006950825452805,
-0.18137963116168976,
-0.060129620134830475,
0.03694901987910271,
-0.16002388298511505,
0.10687418282032013,
0.042116716504096985,
-0.025050761178135872,
-0.052959926426410675,
-0.042570870369672775,
-0.004678352270275354,
-0.044591620564460754,
-0.00197408115491271,
-0.010856610722839832,
-0.13154077529907227,
-0.008015087805688381,
-0.020434049889445305,
0.09500054270029068,
-0.05821371078491211,
0.05248677730560303,
0.21158170700073242,
0.08655990660190582,
0.008209090679883957,
-0.031003788113594055,
0.13074944913387299,
0.04119321331381798,
0.04365312308073044,
-0.003146231407299638,
0.048295821994543076,
0.0072210789658129215,
-0.04537302255630493,
0.0868351086974144,
-0.07001487165689468,
-0.22054237127304077,
0.07560291886329651,
-0.05454932898283005,
-0.10596592724323273,
0.0317264162003994,
-0.019365955144166946,
0.031938984990119934,
-0.06770457327365875,
-0.06827174872159958,
0.17490793764591217,
0.008415482938289642,
0.03451189398765564,
-0.08180715143680573,
-0.03476131707429886,
-0.05301458016037941,
-0.016513878479599953,
-0.008426537737250328,
0.10268985480070114,
0.014525435864925385,
-0.17037418484687805,
0.09099888801574707,
-0.015139011666178703,
-0.08813004195690155,
0.12697255611419678,
0.08293375372886658,
-0.07767487317323685,
-0.031763236969709396,
-0.052425816655159,
0.0015974659472703934,
0.057387031614780426,
-0.10110398381948471,
0.006881792563945055,
0.05267949402332306,
-0.08890911191701889,
-0.0028273568022996187,
-0.09079031646251678,
0.045727916061878204,
-0.021117866039276123,
0.00015480854199267924,
-0.03321712464094162,
-0.05759064853191376,
0.08811261504888535,
0.11113245785236359,
0.007702598348259926,
0.07476630061864853,
-0.007264688611030579,
-0.06971930712461472,
-0.09117477387189865,
0.08260015398263931,
-0.06377869844436646,
-0.23201870918273926,
-0.1119447872042656,
0.08242932707071304,
-0.06066830828785896,
-0.0026285010389983654,
0.0032146605663001537,
-0.09797023981809616,
-0.07199996709823608,
-0.05872205272316933,
-0.008006214164197445,
0.1064765676856041,
-0.09705773741006851,
-0.0854988619685173,
0.00562724843621254,
-0.0015914776595309377,
-0.07249029725790024,
-0.001045883633196354,
-0.10945957899093628,
-0.08855201303958893,
0.04689766839146614,
-0.011880161240696907,
0.042411088943481445,
0.09822933375835419,
0.02181236259639263,
-0.02017396315932274,
-0.04197773337364197,
0.1587052345275879,
-0.08166970312595367,
0.1396801620721817,
0.1369723528623581,
-0.008359136991202831,
0.10928545147180557,
0.09694629162549973,
0.05400923267006874,
-0.03811252489686012,
0.016496021300554276,
0.054255615919828415,
-0.009040924720466137,
-0.20406876504421234,
-0.09098993986845016,
-0.0397738553583622,
-0.07985317707061768,
0.021578263491392136,
0.028118349611759186,
0.03289755433797836,
0.08512639999389648,
-0.07528874278068542,
-0.04941600561141968,
-0.016564877703785896,
0.12347423285245895,
0.16903133690357208,
-0.02213122695684433,
0.001314595341682434,
-0.08685871213674545,
-0.004700780846178532,
0.1390712708234787,
-0.048392195254564285,
0.0884837880730629,
-0.09504740685224533,
0.14864718914031982,
0.03353188559412956,
0.07780716568231583,
0.07219448685646057,
0.03397594019770622,
-0.05428279936313629,
0.04009793698787689,
-0.05733947828412056,
-0.05229534953832626,
-0.12493038922548294,
0.0695611760020256,
-0.01544440258294344,
0.06994430720806122,
0.025240423157811165,
-0.19977852702140808,
0.11426694691181183,
0.302765429019928,
0.018231457099318504,
-0.15343834459781647,
-0.07042989879846573,
0.03883016109466553,
-0.025433747097849846,
-0.0532427541911602,
-0.05155697837471962,
0.054817378520965576,
-0.0753762349486351,
0.10860451310873032,
0.024624740704894066,
0.06468866765499115,
-0.17442834377288818,
0.022364893928170204,
-0.041737060993909836,
0.1353316754102707,
-0.02148415893316269,
0.08797784149646759,
-0.022849446162581444,
0.09873802214860916,
0.037294499576091766,
0.06713514029979706,
-0.07748914510011673,
-0.045900676399469376,
0.13406358659267426,
0.011581198312342167,
0.09128207713365555,
0.03260247781872749,
0.03705659136176109,
-0.07922186702489853,
-0.1064189150929451,
0.0035469108261168003,
0.00004269728378858417,
-0.09072692692279816,
0.07268527895212173,
0.025091078132390976,
-0.010575391352176666,
-0.05421726033091545,
-0.05957292765378952,
-0.007020099554210901,
-0.11669106781482697,
0.018373673781752586,
0.09237828105688095,
0.05202438309788704,
-0.028739390894770622,
-0.0440162792801857,
0.1259852647781372,
0.22823597490787506,
-0.12004373967647552,
-0.12657763063907623,
-0.11110994219779968,
0.01131971925497055,
-0.01883886568248272,
-0.04115865007042885,
-0.08429159224033356,
0.0025882194750010967,
0.18758624792099,
-0.017089491710066795,
-0.05167052894830704,
0.003386403201147914,
-0.02428802289068699,
-0.12842898070812225,
-0.030136631801724434,
0.11452687531709671,
0.10842761397361755,
0.041164446622133255,
0.015197033062577248,
0.02570255659520626,
0.007854843512177467,
-0.12747453153133392,
-0.0060482872650027275,
0.18592609465122223,
-0.06266170740127563,
0.26370540261268616,
-0.04677284508943558,
-0.2540757358074188,
-0.15429992973804474,
0.026770152151584625,
0.07349477708339691,
0.17683352530002594,
-0.06060785427689552,
0.09673361480236053,
0.13066977262496948,
-0.10857822746038437,
-0.21549451351165771,
-0.014299931935966015,
0.00008941583655541763,
-0.039609432220458984,
0.0057665458880364895,
-0.1516757607460022,
0.0885922983288765,
0.057914849370718,
0.012643663212656975,
-0.10486157238483429,
-0.05391179397702217,
-0.03751729801297188,
0.047174736857414246,
0.06699666380882263,
0.09122113883495331,
-0.036990679800510406,
-0.04532608762383461,
-0.00812908448278904,
0.043083157390356064,
0.23415438830852509,
-0.051960572600364685,
0.06432493776082993,
-0.04615721479058266,
0.13748769462108612,
0.06943324953317642,
-0.03810514882206917,
0.13212646543979645,
-0.0020828850101679564,
0.09921558946371078,
-0.07965406775474548,
-0.020872341468930244,
0.15073299407958984,
-0.07393636554479599,
0.08067910373210907,
0.07112425565719604,
-0.006313980091363192,
-0.146327942609787,
-0.06397140771150589,
-0.12809279561042786,
0.03475998714566231,
-0.019133590161800385,
-0.017592770978808403,
-0.09111041575670242,
0.09964483976364136,
0.023275023326277733,
-0.01386735774576664,
-0.05477159470319748,
-0.12129353731870651,
0.07317188382148743,
0.16530847549438477,
0.1971847116947174,
0.04374676197767258,
-0.0828208327293396,
-0.0017956007504835725,
-0.02537524327635765,
0.07922704517841339,
-0.10564634203910828,
0.007391351740807295,
0.07208364456892014,
0.02523242123425007,
0.08797353506088257,
-0.01761990785598755,
-0.13756242394447327,
0.054980650544166565,
0.027871454134583473,
-0.13980036973953247,
-0.09003499150276184,
-0.017748132348060608,
0.03330259397625923,
-0.08689272403717041,
-0.10061866044998169,
0.12615074217319489,
-0.046450644731521606,
-0.04988519474864006,
0.05414695665240288,
0.016746824607253075,
0.04777326434850693,
-0.042418379336595535,
-0.017098376527428627,
0.024346357211470604,
-0.07178228348493576,
0.049514103680849075,
0.09855176508426666,
-0.18468661606311798,
0.017671100795269012,
0.14527153968811035,
-0.09048546850681305,
-0.06854604184627533,
-0.12707555294036865,
0.06274280697107315,
-0.07961875945329666,
-0.03852362185716629,
-0.050715669989585876,
-0.10687372833490372,
0.034313514828681946,
0.24196693301200867,
0.05163861811161041,
0.04420485720038414,
-0.03881978988647461,
0.0021632907446473837,
-0.03253784030675888,
0.014186377637088299,
-0.02679150179028511,
-0.018780579790472984,
-0.026287177577614784,
0.03351326659321785,
0.06623421609401703,
0.01371794380247593,
-0.034689608961343765,
-0.07145584374666214,
-0.06590325385332108,
-0.030320176854729652,
-0.16238945722579956,
0.06507011502981186,
-0.04734416678547859,
0.03431094065308571,
-0.0039237746968865395,
0.04095458984375,
-0.021304884925484657,
0.005566473584622145,
-0.047399502247571945,
0.016151119023561478,
0.03358585387468338,
0.09864801913499832,
-0.12504860758781433,
-0.005241813138127327,
0.09166941046714783,
-0.04994749650359154,
0.11865893006324768,
0.06730585545301437,
-0.0786033347249031,
-0.03687947988510132,
-0.1461695432662964,
-0.03170974552631378,
0.0025012167170643806,
0.013109715655446053,
-0.016464494168758392,
-0.08751057088375092,
0.06048405170440674,
0.008605964481830597,
-0.03957812860608101,
0.02696479856967926,
0.12469933182001114,
-0.07348226755857468,
0.15441231429576874,
0.1834166944026947,
-0.05038087069988251,
-0.11852271109819412,
0.0004303043242543936,
0.1356881707906723,
-0.009502590633928776,
0.10595626384019852,
-0.04525448754429817,
0.01758270338177681,
-0.1301048845052719,
0.0033984933979809284,
-0.005806367378681898,
-0.016525031998753548,
-0.09660781919956207,
-0.038603510707616806,
0.05404524505138397,
0.014831828884780407,
0.17358022928237915,
0.006471982225775719,
-0.07720242440700531,
0.10199866443872452,
0.12534810602664948,
-0.10389658808708191,
-0.051639486104249954,
-0.11982771009206772,
-0.0019912715069949627,
-0.028391791507601738,
0.032179415225982666,
-0.09849002957344055,
-0.0329672135412693,
-0.016981786116957664,
0.08762896806001663,
0.11698243767023087,
0.28543028235435486,
0.01878136582672596,
0.03435317054390907,
0.09079845994710922,
0.00884277280420065,
0.043509095907211304,
-0.042255301028490067,
0.020621754229068756,
-0.029955539852380753,
0.12622426450252533,
0.10268411040306091,
-0.012500627897679806,
0.11657912284135818,
0.017252899706363678,
-0.03176107257604599,
-0.058288123458623886,
-0.25109514594078064,
-0.023720992729067802,
-0.01647193357348442,
-0.01086343452334404,
-0.11500845104455948,
0.009023101069033146,
0.10360242426395416,
-0.02233058586716652,
-0.043318428099155426,
0.02705891616642475,
-0.07800431549549103,
-0.09734944254159927,
0.1345341056585312,
-0.0028319666162133217,
0.018709387630224228,
-0.038670048117637634,
0.02727488987147808,
0.05667843297123909,
0.09292317926883698,
0.0628306195139885,
0.085719995200634,
0.11298311501741409,
-0.0485481396317482,
-0.1009359061717987,
-0.0941697359085083,
0.015992574393749237,
-0.02475077472627163,
0.00440113665536046,
0.1202615424990654,
0.028320230543613434,
-0.016248978674411774,
0.014477601274847984,
0.16418786346912384,
-0.03480822220444679,
0.016070984303951263,
-0.08442588150501251,
0.34278690814971924,
-0.09056270867586136,
-0.055570632219314575,
0.06090087443590164,
-0.09080129861831665,
0.020008279010653496,
0.091984324157238,
0.06708217412233353,
-0.022388063371181488,
0.011561705730855465,
-0.10815629363059998,
0.023119455203413963,
0.02318985015153885,
0.024418335407972336,
0.0382489413022995,
0.20478542149066925,
-0.08017157018184662,
0.21359778940677643,
-0.013917799107730389,
0.023684825748205185,
-0.03226502984762192,
-0.021615853533148766,
-0.032721128314733505,
-0.011289275251328945,
-0.10488778352737427,
0.11739864200353622,
-0.06454328447580338,
-0.20823824405670166,
0.1672024428844452,
-0.0711255893111229,
-0.0951094701886177,
0.015070678666234016,
0.015503454953432083,
0.04470735415816307,
0.05536719039082527,
0.012584347277879715,
-0.01836133375763893,
0.20235800743103027,
-0.03910775110125542,
-0.12758377194404602,
-0.0774863213300705,
0.14085471630096436,
-0.18859738111495972,
0.1446833610534668,
0.0073887999169528484,
0.022956734523177147,
0.11362776160240173,
-0.09765829890966415,
-0.14022529125213623,
0.06348580121994019,
-0.019386500120162964,
-0.22521233558654785,
-0.0250705536454916,
0.21545733511447906,
0.0218289103358984,
0.10885320603847504,
0.06942296028137207,
-0.10971865057945251,
0.031100956723093987,
-0.01722569204866886,
-0.024154655635356903,
-0.05832874774932861,
0.09444701671600342,
-0.08700332045555115,
0.05942542105913162,
0.1709144115447998,
-0.0032750156242400408,
-0.023845482617616653,
-0.07882216572761536,
-0.06104075163602829,
0.03672372177243233,
-0.026254046708345413,
0.01912038028240204,
-0.03535553812980652,
0.025797275826334953,
0.0821930393576622,
0.08926321566104889,
-0.1972712129354477,
-0.042445551604032516,
-0.024259375408291817,
-0.009828400798141956,
-0.007306062150746584,
0.027688663452863693,
-0.04140589386224747,
0.07605045288801193,
0.007547097746282816,
-0.17383401095867157,
0.04110873490571976,
0.08582614362239838,
-0.04242768511176109,
-0.04582126811146736
] |
null | null |
transformers
|
# BERT-DE-NER
## What is it?
This is a German BERT model fine-tuned for named entity recognition.
## Base model & training
This model is based on [bert-base-german-dbmdz-cased](https://huggingface.co/bert-base-german-dbmdz-cased) and has been fine-tuned
for NER on the training data from [GermEval2014](https://sites.google.com/site/germeval2014ner).
## Model results
The results on the test data from GermEval2014 are (entities only):
| Precision | Recall | F1-Score |
|----------:|-------:|---------:|
| 0.817 | 0.842 | 0.829 |
## How to use
```Python
>>> from transformers import pipeline
>>> classifier = pipeline('ner', model="fhswf/bert_de_ner")
>>> classifier('Von der Organisation „medico international“ hieß es, die EU entziehe sich seit vielen Jahren der Verantwortung für die Menschen an ihren Außengrenzen.')
[{'word': 'med', 'score': 0.9996621608734131, 'entity': 'B-ORG', 'index': 6},
{'word': '##ico', 'score': 0.9995362162590027, 'entity': 'I-ORG', 'index': 7},
{'word': 'international',
'score': 0.9996932744979858,
'entity': 'I-ORG',
'index': 8},
{'word': 'eu', 'score': 0.9997008442878723, 'entity': 'B-ORG', 'index': 14}]
```
|
{"language": "de", "license": "cc-by-sa-4.0", "tags": ["German", "de", "NER"], "datasets": ["germeval_14"]}
|
token-classification
|
fhswf/bert_de_ner
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"token-classification",
"German",
"de",
"NER",
"dataset:germeval_14",
"doi:10.57967/hf/0655",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"de"
] |
TAGS
#transformers #pytorch #tf #jax #safetensors #bert #token-classification #German #de #NER #dataset-germeval_14 #doi-10.57967/hf/0655 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
BERT-DE-NER
===========
What is it?
-----------
This is a German BERT model fine-tuned for named entity recognition.
Base model & training
---------------------
This model is based on bert-base-german-dbmdz-cased and has been fine-tuned
for NER on the training data from GermEval2014.
Model results
-------------
The results on the test data from GermEval2014 are (entities only):
How to use
----------
|
[] |
[
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #token-classification #German #de #NER #dataset-germeval_14 #doi-10.57967/hf/0655 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
86
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #token-classification #German #de #NER #dataset-germeval_14 #doi-10.57967/hf/0655 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
-0.12804314494132996,
0.09047764539718628,
-0.004133088979870081,
0.08044986426830292,
0.05427295342087746,
-0.007584650069475174,
0.13152341544628143,
0.03595489263534546,
0.05435584485530853,
0.0023362888023257256,
0.14358532428741455,
0.16527068614959717,
-0.010990745387971401,
0.1225588396191597,
-0.06051800027489662,
-0.18826468288898468,
0.0752175897359848,
0.0060549164190888405,
0.0013583051040768623,
0.1033514142036438,
0.11688113957643509,
-0.06874484568834305,
0.0773695781826973,
-0.0044049727730453014,
-0.12911060452461243,
0.05375135317444801,
0.08313602209091187,
-0.10324167460203171,
0.12987938523292542,
0.019405746832489967,
0.14092746376991272,
0.0974530354142189,
0.04553849995136261,
-0.06898819655179977,
0.008351796306669712,
-0.01376539096236229,
-0.12478858232498169,
0.051530126482248306,
0.052664585411548615,
-0.05530007556080818,
0.05106744170188904,
0.039165858179330826,
-0.030153492465615273,
0.045297250151634216,
-0.11525589972734451,
-0.18687719106674194,
-0.09256134182214737,
0.1681046038866043,
0.03722832351922989,
0.07602372765541077,
0.04580806568264961,
0.1852140575647354,
-0.1279129683971405,
0.07816307991743088,
0.12284635752439499,
-0.35062459111213684,
-0.00831269659101963,
0.13969750702381134,
0.006796873174607754,
-0.0052111330442130566,
-0.06324031949043274,
0.02539326436817646,
0.09079960733652115,
-0.013223525136709213,
0.04292247071862221,
-0.061062537133693695,
-0.06679603457450867,
0.056404225528240204,
-0.09422852098941803,
-0.015100871212780476,
0.2095562368631363,
0.007926538586616516,
-0.004450885578989983,
0.047577518969774246,
-0.054337259382009506,
-0.0642930418252945,
0.001102213398553431,
-0.023944050073623657,
-0.010178713127970695,
0.008153632283210754,
0.03051155060529709,
0.04676041752099991,
-0.1222725436091423,
-0.024388782680034637,
-0.21057406067848206,
0.21720284223556519,
0.021632498130202293,
0.05850737914443016,
-0.11210130900144577,
0.07438942790031433,
-0.05974812060594559,
-0.11652831733226776,
0.008611710742115974,
-0.06220162659883499,
0.003209800226613879,
-0.03127768263220787,
0.0014587012119591236,
0.059070050716400146,
0.070285864174366,
0.18811091780662537,
-0.004091321956366301,
-0.006936937104910612,
-0.016800101846456528,
0.0978156104683876,
-0.04957728460431099,
0.0572546124458313,
-0.05081308260560036,
-0.029378261417150497,
0.054365672171115875,
-0.09367652237415314,
0.03634236752986908,
0.0017811028519645333,
-0.1421343982219696,
-0.0495409332215786,
0.030826011672616005,
0.08065234869718552,
-0.04056595265865326,
0.06116928905248642,
-0.04677196219563484,
0.03384342044591904,
0.15900206565856934,
-0.04755163565278053,
0.009952628053724766,
0.008646063506603241,
0.002244270872324705,
-0.026470158249139786,
-0.030942847952246666,
0.00256239902228117,
0.002339481608942151,
0.18764175474643707,
-0.10981599986553192,
-0.016071578487753868,
-0.01182576548308134,
-0.07512534409761429,
0.09195831418037415,
-0.09279316663742065,
0.028483672067523003,
-0.2029980719089508,
-0.14530561864376068,
0.057921066880226135,
0.039195384830236435,
-0.0025610863231122494,
-0.005659396294504404,
0.004807429853826761,
-0.03567935526371002,
0.016592931002378464,
-0.06864748150110245,
-0.12013634294271469,
-0.08201419562101364,
0.05925459414720535,
-0.05624765902757645,
0.029909754171967506,
-0.1660478413105011,
-0.016047928482294083,
-0.14914250373840332,
-0.003942581824958324,
-0.09994614124298096,
-0.09625718742609024,
-0.14528408646583557,
0.10683095455169678,
-0.04926114156842232,
-0.05515620484948158,
-0.022513598203659058,
-0.00669202022254467,
0.0030912882648408413,
0.1266503483057022,
-0.12571369111537933,
-0.09189648926258087,
0.16639703512191772,
-0.16132646799087524,
-0.1454343944787979,
0.0930376872420311,
0.0068193417973816395,
-0.026589181274175644,
0.0510341078042984,
0.14907129108905792,
0.0760602056980133,
-0.17143529653549194,
-0.014840736985206604,
0.08946587145328522,
-0.13332407176494598,
-0.16136978566646576,
0.057922277599573135,
0.005991453770548105,
-0.17017894983291626,
0.04124997556209564,
-0.046613845974206924,
0.10605716705322266,
-0.042712368071079254,
-0.0600307323038578,
-0.012948996387422085,
-0.03459973260760307,
0.10257668048143387,
0.028561465442180634,
0.06974288821220398,
-0.09393393248319626,
0.0006843666196800768,
-0.0006816710229031742,
0.02776360511779785,
0.04768241196870804,
-0.013686971738934517,
-0.07705473154783249,
0.08619595319032669,
-0.04660818353295326,
-0.03169795870780945,
-0.07986606657505035,
-0.11277700960636139,
-0.0019576610065996647,
-0.010302688926458359,
0.022117823362350464,
0.1040591150522232,
0.083253413438797,
0.013065855950117111,
-0.05539825186133385,
-0.03884859383106232,
0.09621259570121765,
0.06771981716156006,
0.017945025116205215,
-0.15890640020370483,
0.05909787863492966,
-0.03530285507440567,
0.038708243519067764,
-0.08388686925172806,
-0.012414839118719101,
0.10874679684638977,
0.15509022772312164,
-0.006541332229971886,
0.0812222957611084,
-0.06399670988321304,
0.0208443496376276,
-0.04952472448348999,
0.03281613439321518,
0.07197496294975281,
0.024409094825387,
-0.035340216010808945,
0.19425731897354126,
-0.04129327833652496,
0.3893793225288391,
0.17161984741687775,
-0.12457629293203354,
-0.04624756798148155,
-0.010964973829686642,
-0.03804667666554451,
-0.006100450176745653,
0.006621694657951593,
0.0023892782628536224,
-0.0036385669372975826,
-0.047343119978904724,
0.1467357873916626,
-0.0761343389749527,
-0.04383610188961029,
0.06645511835813522,
-0.044159434735774994,
-0.0689198449254036,
0.10518256574869156,
0.09165611118078232,
-0.2291124314069748,
0.21298444271087646,
0.2553417980670929,
-0.042506955564022064,
0.13595005869865417,
-0.03125177323818207,
0.009607331827282906,
-0.02054748684167862,
-0.008795666508376598,
-0.010275493375957012,
0.1718612164258957,
-0.030921094119548798,
0.0019466174999251962,
0.05464717373251915,
0.03436063230037689,
0.02573537826538086,
-0.1290537267923355,
-0.0708441436290741,
-0.033156268298625946,
-0.002255738712847233,
-0.06181184947490692,
0.08921772986650467,
-0.0234217531979084,
0.1329735666513443,
-0.030426591634750366,
-0.21236218512058258,
0.11053115129470825,
0.011748035438358784,
-0.1063438430428505,
0.17405809462070465,
-0.12257945537567139,
-0.20160098373889923,
-0.13082823157310486,
-0.1069362610578537,
-0.08354910463094711,
0.0051803914830088615,
0.04706913232803345,
-0.043162211775779724,
-0.06290196627378464,
0.012980290688574314,
-0.06987535208463669,
-0.0558125376701355,
0.0362977460026741,
-0.09189421683549881,
0.03268461674451828,
0.007585288491100073,
-0.09783490747213364,
-0.053134795278310776,
-0.02610216662287712,
0.00997164472937584,
0.08399967849254608,
-0.042975835502147675,
0.10353374481201172,
0.09431709349155426,
-0.041560154408216476,
0.04469270631670952,
-0.04985000193119049,
0.13771586120128632,
-0.035990625619888306,
0.019589439034461975,
0.1008572056889534,
-0.022732030600309372,
0.06353098899126053,
0.18878009915351868,
0.06643952429294586,
-0.033044371753931046,
-0.01878516748547554,
-0.0564875453710556,
-0.05860895663499832,
-0.19504602253437042,
-0.1324685513973236,
-0.09354977309703827,
0.07434427738189697,
0.06716810911893845,
0.08112829178571701,
0.1278955340385437,
0.08057112991809845,
0.02575596421957016,
-0.02457180619239807,
-0.04378907382488251,
0.09111577272415161,
0.16292636096477509,
0.011603090912103653,
0.09705045819282532,
-0.07695572078227997,
-0.09166260808706284,
0.08852441608905792,
0.04582465440034866,
0.06598357856273651,
0.10171086341142654,
-0.00949634239077568,
0.04594400152564049,
0.20732158422470093,
0.12364201247692108,
0.12335658818483353,
0.02224697358906269,
-0.0838053748011589,
0.01596973091363907,
-0.046922121196985245,
0.035972241312265396,
0.03342881053686142,
-0.04985431209206581,
-0.041627492755651474,
-0.03507978841662407,
-0.1694582998752594,
0.08136706054210663,
0.03113146685063839,
0.10663992911577225,
-0.24101471900939941,
-0.00634419871494174,
-0.008823627606034279,
0.030980845913290977,
-0.018081557005643845,
0.08020411431789398,
-0.017319850623607635,
-0.055970970541238785,
0.10407686978578568,
-0.0455695204436779,
0.05131467804312706,
0.05554188787937164,
0.03680120036005974,
-0.003167426213622093,
-0.03610093891620636,
-0.008894582279026508,
0.10374482721090317,
-0.26517340540885925,
0.30712124705314636,
0.01953774504363537,
-0.03736644238233566,
-0.054920073598623276,
-0.04377957805991173,
0.014661245979368687,
0.2169664353132248,
0.1785053312778473,
0.061911631375551224,
-0.1191038116812706,
-0.08735193312168121,
-0.03324335068464279,
0.016552118584513664,
0.05152935907244682,
-0.0016014761058613658,
-0.051268141716718674,
-0.02174302563071251,
-0.04302787780761719,
0.008542720228433609,
0.027320008724927902,
-0.08633308857679367,
-0.07469438761472702,
0.05129377171397209,
0.09504786878824234,
0.016691654920578003,
-0.03201349079608917,
-0.06982879340648651,
-0.18827418982982635,
0.1963934749364853,
-0.15653853118419647,
-0.010295744054019451,
-0.12037254869937897,
-0.11256790906190872,
0.04163997620344162,
-0.09422623366117477,
0.04340481013059616,
-0.03636699169874191,
-0.00961042195558548,
-0.07738995552062988,
-0.15967485308647156,
0.1460374891757965,
-0.15777619183063507,
-0.03732391074299812,
-0.06905519962310791,
0.13024216890335083,
-0.045148611068725586,
0.018137186765670776,
0.010278316214680672,
0.018527161329984665,
-0.03275204822421074,
-0.10779567062854767,
0.02643507346510887,
-0.07597033679485321,
0.10980278998613358,
-0.012222504243254662,
-0.05488024652004242,
-0.11754067987203598,
0.034882910549640656,
-0.019306350499391556,
0.12518908083438873,
0.26941025257110596,
-0.09759854525327682,
0.10995543003082275,
0.24602001905441284,
-0.006028404459357262,
-0.3291236162185669,
-0.04946855083107948,
-0.12650641798973083,
-0.027844317257404327,
-0.04890074208378792,
-0.03982772305607796,
0.10284806042909622,
0.11128503829240799,
-0.1015084907412529,
0.07259027659893036,
-0.1190202534198761,
-0.10203733295202255,
0.1779988408088684,
0.023891162127256393,
0.3656960427761078,
-0.0550781786441803,
-0.05412821099162102,
-0.011061339639127254,
-0.20362547039985657,
0.11386055499315262,
-0.056747809052467346,
0.014579132199287415,
-0.02636387012898922,
-0.042249780148267746,
-0.00532890297472477,
-0.09391237050294876,
0.09888029843568802,
0.03621683642268181,
0.08468934893608093,
-0.09984194487333298,
-0.10974231362342834,
0.14457501471042633,
-0.006310918368399143,
0.07579737156629562,
-0.06425682455301285,
0.04950159043073654,
-0.010470587760210037,
-0.010966943576931953,
-0.0925515815615654,
0.14859430491924286,
-0.013503397814929485,
-0.10009276866912842,
-0.043386198580265045,
0.05966624617576599,
-0.038552165031433105,
-0.07094628363847733,
0.21499398350715637,
0.017180008813738823,
0.09152547270059586,
0.1126614585518837,
0.0840502381324768,
-0.12214957177639008,
-0.006646002177149057,
-0.036453474313020706,
-0.10170231014490128,
0.0930134579539299,
-0.072642020881176,
0.04645603522658348,
0.14713054895401,
0.00261800829321146,
0.08091167360544205,
0.0656166523694992,
0.002547347452491522,
-0.05685481056571007,
0.15240125358104706,
-0.2290976345539093,
0.023519504815340042,
-0.03247266635298729,
-0.028485722839832306,
0.09733112156391144,
0.17332115769386292,
0.0927792638540268,
-0.065082848072052,
0.016496431082487106,
0.02257632464170456,
-0.0283025112003088,
-0.05811752378940582,
0.07216668874025345,
0.07785242050886154,
0.03026314079761505,
-0.10777237266302109,
0.08194071799516678,
0.008218557573854923,
-0.12607312202453613,
-0.03682553768157959,
0.0370250903069973,
-0.17184339463710785,
-0.08421103656291962,
-0.05882130190730095,
0.11236133426427841,
-0.21071404218673706,
-0.10323677211999893,
-0.09344004839658737,
-0.08870483189821243,
0.03386665880680084,
0.21281763911247253,
0.09450957179069519,
0.0749259740114212,
0.01866094209253788,
-0.06392340362071991,
-0.04098862409591675,
0.028263354673981667,
-0.044858843088150024,
0.09045059233903885,
-0.175167977809906,
0.03592021390795708,
-0.02361399680376053,
0.10554035753011703,
-0.07443691790103912,
0.02766854502260685,
-0.15906570851802826,
0.021579772233963013,
-0.0661025419831276,
-0.033216442912817,
-0.08784608542919159,
-0.009239807724952698,
-0.0019322207663208246,
-0.1083182767033577,
-0.039869457483291626,
0.024824131280183792,
-0.1034337654709816,
0.06099922955036163,
0.06579743325710297,
0.046963538974523544,
-0.07146904617547989,
-0.031246989965438843,
0.052890196442604065,
0.006141304969787598,
0.1073569729924202,
0.1003815308213234,
-0.014612148515880108,
0.12186016142368317,
-0.15492980182170868,
-0.057437874376773834,
0.07773299515247345,
0.015242761932313442,
0.09390264004468918,
0.028025610372424126,
0.03434135764837265,
0.1078542098402977,
-0.02693135477602482,
0.06460276991128922,
-0.05398188531398773,
-0.11185839772224426,
0.04268399253487587,
0.02278856746852398,
-0.1515040099620819,
-0.0370185524225235,
-0.05191138759255409,
0.12389672547578812,
-0.026933806017041206,
0.1594977229833603,
-0.05008712410926819,
-0.004067521076649427,
-0.045573748648166656,
-0.009150567464530468,
-0.009672683663666248,
-0.17938901484012604,
-0.1201007068157196,
-0.04227360710501671,
-0.021901605650782585,
-0.031432587653398514,
0.2101593017578125,
0.03401460498571396,
-0.08230829983949661,
0.05104903131723404,
-0.007205793634057045,
0.01079822238534689,
0.02572050876915455,
0.1979089379310608,
0.010434435680508614,
-0.02015470527112484,
-0.13435098528862,
0.045071475207805634,
-0.02086772210896015,
-0.03889324516057968,
0.11381043493747711,
0.088589146733284,
-0.01867685653269291,
0.024799363687634468,
0.09776659309864044,
-0.08002211898565292,
-0.11211854219436646,
-0.12832379341125488,
-0.07291378080844879,
0.04480886086821556,
0.03565326705574989,
0.050688933581113815,
0.2138015776872635,
-0.034777965396642685,
-0.016621064394712448,
-0.023905334994196892,
-0.013591914437711239,
-0.14632359147071838,
-0.15478473901748657,
-0.08324504643678665,
-0.11635102331638336,
0.047975607216358185,
-0.021304655820131302,
-0.012274335138499737,
0.04412170127034187,
0.08995357900857925,
-0.03568580374121666,
0.06667201966047287,
-0.021570926532149315,
-0.023180436342954636,
0.04373178258538246,
0.019411258399486542,
-0.012314634397625923,
-0.03036484494805336,
-0.030741097405552864,
-0.09021209925413132,
-0.10789015889167786,
-0.04683401808142662,
-0.024759743362665176,
0.028789224103093147,
-0.007491447031497955,
-0.042671289294958115,
-0.05180228129029274,
-0.03739519044756889,
0.04130019620060921,
0.01307216752320528,
0.09032228589057922,
0.018217969685792923,
0.05486707761883736,
0.0443895123898983,
0.11875573545694351,
-0.034167248755693436,
-0.11755837500095367,
-0.04497230798006058,
0.26938673853874207,
0.03871440514922142,
0.1296014040708542,
0.009695835411548615,
-0.01537010446190834,
0.02067834883928299,
0.19818104803562164,
0.2728146016597748,
-0.006713382434099913,
0.06089284271001816,
0.002224845113232732,
0.012817339040338993,
0.03482618182897568,
0.07482034713029861,
0.03695093095302582,
0.2844969928264618,
-0.05290371552109718,
-0.032727017998695374,
-0.061906903982162476,
0.018251270055770874,
-0.04764947295188904,
0.10448817163705826,
-0.014021430164575577,
-0.08830896019935608,
-0.10500549525022507,
0.07765383273363113,
-0.06254616379737854,
0.08370767533779144,
0.07766515761613846,
-0.0904751792550087,
-0.07490759342908859,
0.006369021255522966,
0.12809674441814423,
0.026505770161747932,
0.03151208162307739,
-0.07116604596376419,
-0.03801567852497101,
-0.012602762319147587,
0.013941817916929722,
-0.190940260887146,
-0.10081013292074203,
0.029804378747940063,
0.07235945761203766,
0.17058086395263672,
-0.009303942322731018,
0.14846619963645935,
0.11259333044290543,
0.026541559025645256,
-0.10071279853582382,
0.08344076573848724,
0.03143656626343727,
-0.12735742330551147,
-0.02974783629179001,
-0.08207517862319946,
0.008388892747461796,
0.004459495190531015,
0.009002789855003357,
-0.11057843267917633,
0.05216709151864052,
0.014178529381752014,
-0.057735878974199295,
-0.0854107141494751,
0.11316776275634766,
-0.004866004455834627,
0.10216152667999268,
0.04085530713200569,
-0.002870462369173765,
-0.037825632840394974,
-0.07583494484424591,
0.021041395142674446,
0.09593956917524338,
-0.1008923128247261,
-0.00019580092339310795,
-0.061803802847862244,
0.004391353111714125,
0.07814780622720718,
0.013302411884069443,
-0.0906485840678215,
-0.021351922303438187,
-0.07334361225366592,
0.015496280044317245,
-0.10624536126852036,
0.02630317397415638,
0.0760180875658989,
0.030935006216168404,
0.009374115616083145,
-0.04977317899465561,
0.014950470067560673,
0.013498218730092049,
-0.0860883891582489,
-0.10790842771530151
] |
null | null |
transformers
|
# Fibruh Bot Model
|
{"tags": ["conversational"]}
|
text-generation
|
fibruh/DialoGPT-small-harrypotter
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Fibruh Bot Model
|
[
"# Fibruh Bot Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Fibruh Bot Model"
] |
[
51,
6
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Fibruh Bot Model"
] |
[
-0.02054092474281788,
0.0060601611621677876,
-0.006901804357767105,
0.014957175590097904,
0.1398673802614212,
-0.018231766298413277,
0.16717691719532013,
0.08531017601490021,
0.03206370770931244,
-0.00520884245634079,
0.11419239640235901,
0.15471729636192322,
0.020722365006804466,
0.19662441313266754,
-0.08506640791893005,
-0.23889797925949097,
0.08980731666088104,
0.022069357335567474,
-0.022051963955163956,
0.10393017530441284,
0.10167824476957321,
-0.044627733528614044,
0.09795907884836197,
-0.0014031928731128573,
-0.21481260657310486,
0.00862063653767109,
0.00703787337988615,
-0.0962039902806282,
0.09859427809715271,
0.11154258251190186,
0.0651988610625267,
-0.0021896171383559704,
-0.03867257386445999,
-0.08765716850757599,
0.045800596475601196,
-0.010846556164324284,
-0.05532103776931763,
0.06471759080886841,
0.018978817388415337,
-0.08392912149429321,
0.18291373550891876,
0.15469573438167572,
-0.007651656400412321,
0.060015782713890076,
-0.13943670690059662,
0.13177365064620972,
0.013665537349879742,
0.05558071658015251,
0.062452252954244614,
0.11077366769313812,
-0.03731391206383705,
0.1296105533838272,
-0.09399715065956116,
0.1285235583782196,
0.11696071177721024,
-0.3083558976650238,
-0.038880981504917145,
0.11238553375005722,
0.024114077910780907,
-0.005753087345510721,
-0.05992904677987099,
0.03451187536120415,
0.030490988865494728,
0.027988163754343987,
-0.0334644615650177,
-0.05683420971035957,
-0.12361423671245575,
-0.01912568509578705,
-0.09390340000391006,
-0.016947755590081215,
0.21793314814567566,
-0.03336193040013313,
0.03246986120939255,
-0.05864982306957245,
-0.11036688089370728,
0.016615770757198334,
-0.07379161566495895,
0.02912929281592369,
-0.08635320514440536,
0.060557689517736435,
0.059878796339035034,
-0.10597877949476242,
-0.11087381094694138,
-0.039302077144384384,
-0.15340033173561096,
0.20363323390483856,
0.03893130645155907,
0.058567021042108536,
-0.22744372487068176,
0.08003723621368408,
0.0006827301695011556,
-0.07057568430900574,
0.023566151037812233,
-0.09778366982936859,
0.0011344474041834474,
-0.006564122159034014,
-0.0574856735765934,
-0.10189799964427948,
0.09055901318788528,
0.15516990423202515,
-0.029949529096484184,
-0.010695518925786018,
-0.011071140877902508,
0.07112330198287964,
0.0360446460545063,
0.01258927397429943,
-0.02678513526916504,
0.008715096861124039,
0.04188620299100876,
-0.07171439379453659,
-0.026506619527935982,
-0.06591607630252838,
-0.18091729283332825,
0.012116805650293827,
0.012540040537714958,
0.03966842591762543,
0.036275092512369156,
0.1141154021024704,
-0.013167831115424633,
-0.03466781601309776,
0.00830940529704094,
-0.04086600989103317,
-0.014278273098170757,
0.020532624796032906,
-0.03728120028972626,
0.06647635996341705,
0.004790353123098612,
0.033723462373018265,
-0.1307990849018097,
0.029441172257065773,
-0.06831680238246918,
0.0006493101827800274,
-0.03127843886613846,
-0.07842464745044708,
-0.001471432507969439,
-0.09286385029554367,
0.028607990592718124,
-0.17051824927330017,
-0.13182060420513153,
-0.013309135101735592,
-0.004990553483366966,
-0.06821954995393753,
-0.09288053214550018,
-0.08113282173871994,
-0.06018686294555664,
0.043249987065792084,
-0.04172155633568764,
0.024738045409321785,
-0.061179257929325104,
0.07078509032726288,
-0.03701263293623924,
0.09600260108709335,
-0.14438830316066742,
0.05092964693903923,
-0.09652640670537949,
-0.054376374930143356,
-0.0776190385222435,
0.09747778624296188,
-0.026066826656460762,
0.1367160677909851,
-0.01888059265911579,
0.021692760288715363,
-0.09228942543268204,
0.0633685365319252,
-0.020338669419288635,
0.26109644770622253,
-0.088007852435112,
-0.12284304201602936,
0.3252178430557251,
-0.07539903372526169,
-0.10332819819450378,
0.12024621665477753,
0.003803761675953865,
0.05791575834155083,
0.12314796447753906,
0.14659683406352997,
-0.06809176504611969,
-0.028945786878466606,
0.049096185714006424,
0.08514589071273804,
-0.1050158441066742,
0.0003821029094979167,
-0.018809763714671135,
0.011261725798249245,
-0.08375049382448196,
0.02583959698677063,
0.07648435980081558,
0.10269526392221451,
-0.06796086579561234,
-0.027458278462290764,
-0.008152191527187824,
-0.040537137538194656,
0.0670042634010315,
-0.02730083465576172,
0.13954997062683105,
-0.023387381806969643,
-0.0334850437939167,
-0.04609757661819458,
0.014630134217441082,
-0.009564840234816074,
0.006041241344064474,
-0.11168672889471054,
0.12657767534255981,
0.00016736984252929688,
0.09482377767562866,
-0.16797307133674622,
-0.09554989635944366,
-0.01740243285894394,
0.09936825186014175,
0.07259557396173477,
0.11097092926502228,
0.0584411546587944,
-0.0006874877726659179,
0.02551731839776039,
0.030204424634575844,
0.13353285193443298,
-0.02695961482822895,
-0.10199784487485886,
-0.14318600296974182,
0.049792829900979996,
-0.062157247215509415,
0.1285780966281891,
-0.1208595559000969,
0.03746816888451576,
0.05588574334979057,
0.140562042593956,
0.018667209893465042,
0.03853398561477661,
0.030518127605319023,
-0.01956677995622158,
-0.06547888368368149,
0.009709946811199188,
0.08152422308921814,
-0.001864572986960411,
-0.08595171570777893,
0.1839154064655304,
-0.0887586772441864,
0.1797301471233368,
0.19590818881988525,
-0.16595424711704254,
-0.030789820477366447,
-0.04559647664427757,
-0.0448848232626915,
0.016818953678011894,
0.027421167120337486,
-0.04328833520412445,
0.18932363390922546,
0.010202210396528244,
0.18063132464885712,
-0.03260466828942299,
-0.013894516043365002,
0.012535182759165764,
-0.060402803122997284,
0.0034605758264660835,
0.08647362142801285,
0.13617971539497375,
-0.14719344675540924,
0.14661453664302826,
0.0480252243578434,
0.029987961053848267,
0.18596580624580383,
0.043784257024526596,
-0.009569271467626095,
0.06367213279008865,
0.0228286013007164,
-0.03345385566353798,
-0.02307376265525818,
-0.2675307095050812,
-0.056633953005075455,
0.05532900616526604,
-0.024931032210588455,
0.10548810660839081,
-0.08737482875585556,
-0.016976619139313698,
-0.02902773581445217,
-0.016773661598563194,
0.07522870600223541,
0.09113594144582748,
0.04541074484586716,
0.1499042510986328,
-0.006971906404942274,
-0.12471182644367218,
0.03970231115818024,
0.011172793805599213,
-0.10156187415122986,
0.17636099457740784,
-0.08638527989387512,
-0.3367501199245453,
-0.106815867125988,
-0.15633074939250946,
-0.08631296455860138,
0.053926050662994385,
0.06508902460336685,
-0.10367749631404877,
-0.04407041519880295,
-0.0200908612459898,
0.13228683173656464,
0.00982468668371439,
0.023884600028395653,
-0.0264015831053257,
0.03075389750301838,
-0.08735869079828262,
-0.07901948690414429,
-0.053264640271663666,
-0.056516312062740326,
-0.053866639733314514,
0.12444523721933365,
-0.11016272753477097,
0.0842958390712738,
0.1648026704788208,
0.05654427036643028,
0.06600849330425262,
-0.011688042432069778,
0.28111088275909424,
-0.11587799340486526,
0.034452516585588455,
0.1569298803806305,
-0.025612488389015198,
0.03245137259364128,
0.1503949761390686,
0.012768693268299103,
-0.11411390453577042,
0.043003156781196594,
-0.013302493840456009,
-0.06709770113229752,
-0.15752819180488586,
-0.14682114124298096,
-0.11516682803630829,
0.0730576291680336,
0.06821820884943008,
0.07504346966743469,
0.13167314231395721,
0.0632668286561966,
-0.03901960700750351,
0.046755775809288025,
0.05992037430405617,
0.07542041689157486,
0.12394502758979797,
-0.05259482190012932,
0.15186339616775513,
-0.027110248804092407,
-0.1573304831981659,
0.09951586276292801,
0.01673121377825737,
0.1280941367149353,
0.05578579753637314,
0.04965003579854965,
0.026752719655632973,
0.04523799568414688,
0.14781002700328827,
0.10824347287416458,
-0.002274185884743929,
-0.05220767855644226,
-0.017494644969701767,
-0.04534400627017021,
-0.0400933139026165,
0.06706061214208603,
0.03385132923722267,
-0.12964719533920288,
-0.042671602219343185,
-0.05043454095721245,
0.07718508690595627,
0.15713819861412048,
0.1034584641456604,
-0.21785283088684082,
-0.06748662889003754,
0.05736361816525459,
-0.06541907787322998,
-0.117362380027771,
0.07413795590400696,
0.053598061203956604,
-0.16631212830543518,
0.027832141146063805,
-0.031537167727947235,
0.13944090902805328,
-0.0629776194691658,
0.08181792497634888,
-0.09698087722063065,
-0.05804531276226044,
0.008944845758378506,
0.07976392656564713,
-0.30714792013168335,
0.1614106297492981,
-0.009219711646437645,
-0.035226769745349884,
-0.10188416391611099,
0.008842556737363338,
0.03259841352701187,
0.11633233726024628,
0.09907831996679306,
-0.007043886464089155,
0.06389738619327545,
-0.14281277358531952,
-0.07832028716802597,
0.02107367292046547,
0.07271529734134674,
-0.061067014932632446,
0.037055131047964096,
-0.027139538899064064,
-0.007188768591731787,
-0.04716707393527031,
-0.06392709165811539,
0.009059270843863487,
-0.17334772646427155,
0.08966939896345139,
0.07697566598653793,
0.06387123465538025,
0.0036181246396154165,
-0.03688129782676697,
0.0048948186449706554,
0.23429138958454132,
-0.08229024708271027,
-0.10610897839069366,
-0.10047464072704315,
0.039937395602464676,
0.01011992059648037,
-0.05535491555929184,
0.01852934993803501,
-0.07213114202022552,
0.06679368764162064,
-0.08038758486509323,
-0.18169459700584412,
0.1078367605805397,
-0.11178022623062134,
-0.04369218647480011,
-0.01129565667361021,
0.16224607825279236,
-0.0003537957090884447,
0.003474791534245014,
0.05509263277053833,
-0.012647513300180435,
-0.10094652324914932,
-0.10741076618432999,
-0.026538971811532974,
0.027276556938886642,
-0.01363074779510498,
0.06760641187429428,
-0.0497036911547184,
-0.08880622684955597,
-0.0593172125518322,
0.012749158777296543,
0.29378005862236023,
0.11654918640851974,
-0.028478849679231644,
0.16273227334022522,
0.12244842201471329,
-0.03703119605779648,
-0.28337153792381287,
-0.09514617174863815,
-0.07360010594129562,
-0.0019204391865059733,
-0.12653669714927673,
-0.18073517084121704,
0.10337173938751221,
-0.0560440868139267,
-0.010731109417974949,
0.16072998940944672,
-0.28253114223480225,
-0.08543334156274796,
0.20047850906848907,
0.0007755894330330193,
0.36843231320381165,
-0.09686824679374695,
-0.0963141992688179,
-0.057926829904317856,
-0.15719351172447205,
0.1609615832567215,
-0.05247688293457031,
0.09713013470172882,
-0.0015486031770706177,
0.1653023660182953,
0.06049752235412598,
-0.002008670475333929,
0.060891058295965195,
0.039133913815021515,
-0.041397809982299805,
-0.10475795716047287,
-0.04800160229206085,
0.03785950317978859,
0.04009474441409111,
0.09364870935678482,
-0.05409209802746773,
0.015974879264831543,
-0.11920168995857239,
-0.05523005127906799,
-0.08154217153787613,
0.06759990751743317,
0.015012312680482864,
-0.039099231362342834,
0.00024737464264035225,
-0.034843992441892624,
-0.018662648275494576,
0.05323125049471855,
0.0841713398694992,
-0.0664314553141594,
0.18158210813999176,
0.13879738748073578,
0.08487163484096527,
-0.20782749354839325,
0.002991945017129183,
-0.01287622470408678,
-0.0361926443874836,
0.0972142294049263,
-0.03905738145112991,
0.02695612795650959,
0.1134973093867302,
-0.04235714673995972,
0.10399158298969269,
0.0858689621090889,
-0.025098249316215515,
-0.004006530623883009,
0.10567548871040344,
-0.27543312311172485,
-0.05886450782418251,
-0.0630120113492012,
0.007819228805601597,
0.07504501193761826,
0.08435938507318497,
0.23631548881530762,
-0.029646603390574455,
-0.03099423460662365,
-0.0018371040932834148,
0.023270118981599808,
-0.03655245527625084,
0.07807359844446182,
-0.0063048116862773895,
0.025012236088514328,
-0.1767134815454483,
0.0023491547908633947,
0.0170024074614048,
-0.0688042864203453,
0.02853623405098915,
0.14124351739883423,
-0.14599189162254333,
-0.12196870148181915,
-0.08989793807268143,
0.12349474430084229,
-0.1175236627459526,
-0.000208066136110574,
-0.04205668717622757,
-0.17343483865261078,
0.08231718838214874,
0.10811573266983032,
0.05966328829526901,
0.08059205114841461,
-0.07314576953649521,
-0.03055236116051674,
-0.024226920679211617,
-0.003945943899452686,
0.01988561823964119,
-0.0188748799264431,
-0.03966570645570755,
0.0849919319152832,
-0.028492268174886703,
0.11854968965053558,
-0.10005642473697662,
-0.08033940941095352,
-0.17038191854953766,
0.025160863995552063,
-0.09626351296901703,
-0.06873961538076401,
-0.08045404404401779,
-0.036412108689546585,
-0.025346871465444565,
-0.033052876591682434,
-0.04442790150642395,
-0.051741715520620346,
-0.10683134943246841,
0.02701585739850998,
-0.04873550310730934,
0.01511183101683855,
-0.1058424785733223,
0.04662603512406349,
0.04840393364429474,
-0.051427531987428665,
0.16558729112148285,
0.16863246262073517,
-0.10019190609455109,
0.08981532603502274,
-0.08785772323608398,
-0.06240786239504814,
0.10485748201608658,
0.014907154254615307,
0.08716090023517609,
0.07449769228696823,
0.015665633603930473,
0.04971419647336006,
0.0754883661866188,
0.06868968158960342,
0.08522450923919678,
-0.08377418667078018,
0.012966837733983994,
-0.016831927001476288,
-0.09851735830307007,
-0.047400329262018204,
-0.0025259670801460743,
0.05262375995516777,
0.03797999396920204,
0.12074457854032516,
-0.0463029220700264,
0.06925714761018753,
-0.04543263092637062,
0.03991274908185005,
0.014298860915005207,
-0.16827628016471863,
-0.013691944070160389,
-0.07802575826644897,
0.04036417603492737,
-0.019289450719952583,
0.19781653583049774,
0.044277794659137726,
0.040794242173433304,
0.039233844727277756,
0.07326565682888031,
-0.0020784286316484213,
0.03109659068286419,
0.07022666931152344,
0.13278751075267792,
-0.03843729570508003,
-0.11839074641466141,
0.05377732589840889,
0.04620156064629555,
-0.0028453771956264973,
0.16848231852054596,
-0.023043356835842133,
0.016809236258268356,
0.09272562712430954,
0.03804932162165642,
0.016474293544888496,
-0.09795012325048447,
-0.07385808229446411,
-0.114338219165802,
0.08123935759067535,
-0.04706500843167305,
0.15578429400920868,
0.16322258114814758,
-0.01703689806163311,
0.023038027808070183,
-0.006891343276947737,
-0.05814795568585396,
-0.15696626901626587,
-0.18866267800331116,
-0.0677606612443924,
-0.09875614941120148,
0.0014564793091267347,
-0.1202983558177948,
0.01885920576751232,
-0.008966847322881222,
0.07636278122663498,
-0.05715671926736832,
0.12034548074007034,
0.02452409826219082,
-0.08146598935127258,
0.11088798195123672,
-0.02794240042567253,
0.021994901821017265,
-0.026497632265090942,
-0.01313924603164196,
-0.08609374612569809,
0.06678742170333862,
0.004876204300671816,
0.059191759675741196,
-0.048799701035022736,
0.0420667938888073,
-0.11096532642841339,
-0.08503828942775726,
-0.03717925399541855,
0.038888752460479736,
-0.03734418749809265,
0.07532758265733719,
0.03037356585264206,
-0.014066151343286037,
0.0104675879701972,
0.25222453474998474,
-0.05669653043150902,
-0.06786466389894485,
-0.11620496213436127,
0.0892639309167862,
0.0023209836799651384,
0.11109703779220581,
-0.048986125737428665,
0.009744838811457157,
-0.1313861757516861,
0.34067651629447937,
0.31213146448135376,
-0.15874060988426208,
0.026303332298994064,
0.008770797401666641,
0.03763553500175476,
0.10028288513422012,
0.11257784068584442,
0.09365607798099518,
0.32111597061157227,
-0.04765033349394798,
0.009368274360895157,
-0.010018882341682911,
-0.05188518390059471,
-0.07580859959125519,
0.07624384015798569,
0.06098327040672302,
-0.03222336992621422,
-0.060719817876815796,
0.09095753729343414,
-0.25155380368232727,
0.035012971609830856,
-0.1469227522611618,
-0.17555385828018188,
-0.09588733315467834,
-0.0021804808638989925,
0.023925703018903732,
0.04510371759533882,
0.11560217291116714,
-0.02137620374560356,
-0.07465262711048126,
0.0861370712518692,
0.0034080788027495146,
-0.1827721893787384,
-0.06928936392068863,
0.0890139639377594,
-0.11687792837619781,
-0.01794487051665783,
-0.03958752378821373,
0.03467218950390816,
0.07078681141138077,
0.028621666133403778,
-0.04214077442884445,
0.02619308792054653,
-0.015764925628900528,
-0.05276542156934738,
0.02325773611664772,
0.08629066497087479,
0.023285796865820885,
-0.058692581951618195,
0.07674992829561234,
-0.20002521574497223,
0.026121187955141068,
-0.050532013177871704,
-0.023720979690551758,
-0.014101486653089523,
0.061833806335926056,
-0.07178539782762527,
0.04565555602312088,
0.15377277135849,
-0.028500741347670555,
-0.002289581811055541,
-0.02803810127079487,
0.009568972513079643,
-0.04179970175027847,
-0.09406792372465134,
-0.11369867622852325,
-0.19985349476337433,
-0.08528169989585876,
0.0042427643202245235,
0.004113374277949333,
-0.20352542400360107,
0.00044098886428400874,
-0.15171808004379272,
0.08631807565689087,
-0.1337599903345108,
0.12001870572566986,
0.1308569461107254,
0.01818307861685753,
-0.013860580511391163,
-0.046954523772001266,
0.03291443735361099,
0.07544753700494766,
-0.09456972777843475,
-0.11128916591405869
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# biobert_v1.1_pubmed-finetuned-ner-finetuned-ner
This model is a fine-tuned version of [fidukm34/biobert_v1.1_pubmed-finetuned-ner](https://huggingface.co/fidukm34/biobert_v1.1_pubmed-finetuned-ner) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0715
- Precision: 0.8464
- Recall: 0.8872
- F1: 0.8663
- Accuracy: 0.9829
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 340 | 0.0715 | 0.8464 | 0.8872 | 0.8663 | 0.9829 |
### Framework versions
- Transformers 4.8.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["ncbi_disease"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "biobert_v1.1_pubmed-finetuned-ner-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "ncbi_disease", "type": "ncbi_disease", "args": "ncbi_disease"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9829142288061745}}]}]}
|
token-classification
|
fidukm34/biobert_v1.1_pubmed-finetuned-ner-finetuned-ner
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:ncbi_disease",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-ncbi_disease #autotrain_compatible #endpoints_compatible #region-us
|
biobert\_v1.1\_pubmed-finetuned-ner-finetuned-ner
=================================================
This model is a fine-tuned version of fidukm34/biobert\_v1.1\_pubmed-finetuned-ner on the ncbi\_disease dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0715
* Precision: 0.8464
* Recall: 0.8872
* F1: 0.8663
* Accuracy: 0.9829
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.8.1
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.1\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-ncbi_disease #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.1\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
54,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-ncbi_disease #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.8.1\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
-0.10828348249197006,
0.08748280256986618,
-0.0015680468641221523,
0.11407843977212906,
0.1996324509382248,
0.047830432653427124,
0.0866067185997963,
0.11536251753568649,
-0.0987713560461998,
0.01888362132012844,
0.13790856301784515,
0.18294154107570648,
-0.007212372031062841,
0.11529628187417984,
-0.05578701198101044,
-0.25161635875701904,
-0.009623861871659756,
0.056279610842466354,
-0.07588288187980652,
0.1335364729166031,
0.08623307943344116,
-0.15041936933994293,
0.08482345193624496,
-0.0036661941558122635,
-0.2398713231086731,
0.01223980262875557,
0.03168183192610741,
-0.04819338396191597,
0.1517566293478012,
0.0023273362312465906,
0.14737524092197418,
-0.002999771386384964,
0.09771252423524857,
-0.16161833703517914,
0.007191214710474014,
0.04399672523140907,
0.022624561563134193,
0.089588962495327,
0.051383089274168015,
-0.01017994899302721,
0.1073124036192894,
-0.10069914907217026,
0.05937274172902107,
0.01687842793762684,
-0.13024687767028809,
-0.2289334535598755,
-0.06940079480409622,
0.015843579545617104,
0.056628596037626266,
0.09533309936523438,
-0.00392229063436389,
0.15882106125354767,
-0.1089223325252533,
0.09151192754507065,
0.21109648048877716,
-0.2616707384586334,
-0.0656462237238884,
0.03661680966615677,
-0.006112308707088232,
0.06846781075000763,
-0.11037441343069077,
-0.017262665554881096,
0.05158436298370361,
0.04200497642159462,
0.11844227463006973,
-0.04432210326194763,
-0.11251405626535416,
0.029587240889668465,
-0.1426405906677246,
-0.01893465965986252,
0.12935291230678558,
0.02977728098630905,
-0.02115163765847683,
-0.0004361402243375778,
-0.050270866602659225,
-0.15266001224517822,
-0.030313344672322273,
-0.022529205307364464,
0.02827194333076477,
-0.0557534396648407,
-0.08137773722410202,
0.021954329684376717,
-0.08484787493944168,
-0.07354741543531418,
-0.0637282282114029,
0.16222597658634186,
0.02844739891588688,
0.010413738898932934,
-0.011794130317866802,
0.10475493222475052,
0.01641802117228508,
-0.11716073006391525,
0.02956988848745823,
0.03259808197617531,
-0.032787155359983444,
-0.06880391389131546,
-0.06815899163484573,
-0.017596935853362083,
0.005315771326422691,
0.0964035764336586,
-0.031725287437438965,
0.033319566398859024,
0.041497018188238144,
0.020500341430306435,
-0.0863063707947731,
0.18388591706752777,
-0.059929948300123215,
-0.026606449857354164,
0.0017767880344763398,
0.04599948227405548,
-0.0173394363373518,
0.0034778702538460493,
-0.11601540446281433,
-0.01959596388041973,
0.123465396463871,
0.01582939736545086,
-0.09462857991456985,
0.06869535893201828,
-0.05972924828529358,
-0.0421459786593914,
0.02277367003262043,
-0.08411812037229538,
0.040023788809776306,
-0.004480279516428709,
-0.0757366344332695,
-0.020388895645737648,
0.004884581547230482,
0.015474687330424786,
-0.00653814384713769,
0.13271281123161316,
-0.10255352407693863,
0.028684064745903015,
-0.09859097003936768,
-0.11158963292837143,
-0.0037510863039642572,
-0.08770284056663513,
0.03701214864850044,
-0.09505551308393478,
-0.15354089438915253,
-0.009487737901508808,
0.029818939045071602,
-0.024328524246811867,
-0.05214913561940193,
-0.059818435460329056,
-0.061578959226608276,
0.008613700978457928,
-0.00918836984783411,
0.15926624834537506,
-0.058767836540937424,
0.1051781103014946,
0.041391242295503616,
0.06988482922315598,
-0.06103071570396423,
0.05342795327305794,
-0.09543289989233017,
0.002790050348266959,
-0.18764622509479523,
0.0495753176510334,
-0.04511749744415283,
0.061471372842788696,
-0.08113288134336472,
-0.10934854298830032,
0.03636845573782921,
-0.016826843842864037,
0.07905079424381256,
0.0989607647061348,
-0.15251311659812927,
-0.07854057848453522,
0.13592015206813812,
-0.04720999300479889,
-0.11377382278442383,
0.11166680604219437,
-0.07568629831075668,
0.03762500733137131,
0.07079754024744034,
0.14981813728809357,
0.07407638430595398,
-0.053610432893037796,
0.019263381138443947,
-0.016706496477127075,
0.04102775454521179,
-0.0718429684638977,
0.06449754536151886,
0.019900640472769737,
-0.04391689971089363,
0.027687424793839455,
-0.02449570596218109,
0.06481959670782089,
-0.12108457833528519,
-0.08524578809738159,
-0.030529508367180824,
-0.11279062181711197,
0.07699642330408096,
0.0749601349234581,
0.080438993871212,
-0.09412094205617905,
-0.05224340036511421,
0.08768156915903091,
0.08829804509878159,
-0.042928460985422134,
0.01297790091484785,
-0.060777824372053146,
0.03800174221396446,
-0.0685412585735321,
-0.034828849136829376,
-0.1892300248146057,
-0.042256247252225876,
0.008933013305068016,
0.047581423074007034,
0.00412924587726593,
0.013268425129354,
0.0687311515212059,
0.07050437480211258,
-0.061976224184036255,
-0.012777497060596943,
-0.016584722325205803,
0.004497231915593147,
-0.1521824151277542,
-0.18605631589889526,
-0.0360385924577713,
-0.021828263998031616,
0.1211550310254097,
-0.2102872133255005,
0.013359605334699154,
-0.042068660259246826,
0.0813889279961586,
0.005845492240041494,
-0.02104233019053936,
-0.03978809714317322,
0.09489402174949646,
-0.03635290265083313,
-0.042165908962488174,
0.06812059134244919,
-0.021900473162531853,
-0.09488990157842636,
-0.058382850140333176,
-0.08304745703935623,
0.1672106832265854,
0.11958112567663193,
-0.14587946236133575,
-0.08393088728189468,
-0.006764853373169899,
-0.05500894784927368,
-0.023289164528250694,
-0.04612884297966957,
0.03335696458816528,
0.1815965175628662,
-0.014234795235097408,
0.1468317061662674,
-0.05229996517300606,
-0.034593235701322556,
0.014068312011659145,
-0.01812470145523548,
0.033472154289484024,
0.119961678981781,
0.12477719038724899,
-0.10573068261146545,
0.1307351142168045,
0.14149226248264313,
-0.08859344571828842,
0.11948902159929276,
-0.02061411552131176,
-0.07035855948925018,
-0.01956709288060665,
-0.05113913491368294,
0.0028238992672413588,
0.09927607327699661,
-0.1533508449792862,
-0.00478597404435277,
0.02487223595380783,
0.023274408653378487,
0.005884496960788965,
-0.20564717054367065,
-0.05309991538524628,
0.0294602382928133,
-0.020483488216996193,
-0.01709035597741604,
-0.01993373967707157,
0.02506677806377411,
0.12018010765314102,
0.014651850797235966,
-0.0824388712644577,
0.03941689059138298,
0.012143030762672424,
-0.08268623799085617,
0.21596765518188477,
-0.09092343598604202,
-0.13670100271701813,
-0.11125612258911133,
-0.08442771434783936,
-0.0444415919482708,
0.02447151392698288,
0.036397919058799744,
-0.10737083107233047,
-0.020973658189177513,
-0.03712260350584984,
0.01848444901406765,
-0.018722347915172577,
0.05037352815270424,
0.0053598531521856785,
-0.007079537957906723,
0.0637555941939354,
-0.090276338160038,
-0.005013041198253632,
-0.07050523906946182,
-0.07611310482025146,
0.05585654079914093,
0.02379574626684189,
0.11901948601007462,
0.16318121552467346,
-0.02687607705593109,
0.011906652711331844,
-0.02950567938387394,
0.2364150881767273,
-0.061383042484521866,
-0.04541495442390442,
0.10641172528266907,
-0.016691036522388458,
0.0355987511575222,
0.0993441566824913,
0.07358699291944504,
-0.0963999554514885,
0.013784047216176987,
0.03426521643996239,
-0.033201344311237335,
-0.19747020304203033,
-0.058653756976127625,
-0.05553312227129936,
-0.05393087491393089,
0.09560129791498184,
0.011135280132293701,
0.035603124648332596,
0.07568783313035965,
0.05236275866627693,
0.08648286014795303,
-0.07232218980789185,
0.054097261279821396,
0.09944110363721848,
0.04563603922724724,
0.12514807283878326,
-0.023967863991856575,
-0.07720313966274261,
0.034246932715177536,
-0.031536467373371124,
0.216258242726326,
-0.0009912033565342426,
0.11010786890983582,
0.04392845928668976,
0.18518228828907013,
0.00956118293106556,
0.0945134088397026,
0.01313513983041048,
-0.05247310921549797,
-0.006312496494501829,
-0.029363485053181648,
-0.056643616408109665,
0.007416965905576944,
-0.041994404047727585,
0.06573081761598587,
-0.1304842233657837,
-0.01519047562032938,
0.047137051820755005,
0.21321804821491241,
0.05778912082314491,
-0.33916008472442627,
-0.07907712459564209,
-0.007319278549402952,
-0.015369645319879055,
-0.0351555272936821,
0.007422339636832476,
0.09614048153162003,
-0.10265342146158218,
0.016889916732907295,
-0.057892829179763794,
0.0922207161784172,
-0.055990055203437805,
0.04944315925240517,
0.047007977962493896,
0.08738311380147934,
-0.01339617371559143,
0.08626100420951843,
-0.3050023019313812,
0.2676725685596466,
0.011705324985086918,
0.05823429301381111,
-0.07638812810182571,
-0.027151048183441162,
0.037034258246421814,
0.08963309973478317,
0.049615394324064255,
0.0020923512056469917,
0.001788492314517498,
-0.24492155015468597,
-0.04649941250681877,
0.032462459057569504,
0.10166066884994507,
-0.03979215770959854,
0.09524045139551163,
-0.022046508267521858,
0.004197305534034967,
0.07324160635471344,
-0.02367464266717434,
-0.05634278059005737,
-0.06943301111459732,
-0.02025449089705944,
0.034608639776706696,
-0.02570217289030552,
-0.0500703863799572,
-0.10076946765184402,
-0.10109799355268478,
0.15719063580036163,
-0.001314158900640905,
-0.021262338384985924,
-0.1344350278377533,
0.1211644634604454,
0.09112174063920975,
-0.08609870821237564,
0.03369355946779251,
0.011897209100425243,
0.05724423751235008,
0.04400297999382019,
-0.06761091947555542,
0.10307223349809647,
-0.0640924945473671,
-0.16818398237228394,
-0.07310240715742111,
0.09046817570924759,
0.05018208548426628,
0.06722723692655563,
-0.0017472583567723632,
0.02320907451212406,
-0.029210321605205536,
-0.08288342505693436,
0.042589519172906876,
-0.019951505586504936,
0.08191025257110596,
0.06132907047867775,
-0.07250089198350906,
0.03902142867445946,
-0.062014710158109665,
-0.01978340558707714,
0.19527333974838257,
0.22728495299816132,
-0.09727936238050461,
-0.00004417899253894575,
0.024159671738743782,
-0.06287132948637009,
-0.19471430778503418,
0.07321047782897949,
0.08259717375040054,
0.005077207926660776,
0.03292771056294441,
-0.18326328694820404,
0.16203977167606354,
0.10689619928598404,
0.002692384412512183,
0.1069900393486023,
-0.275687575340271,
-0.12087439745664597,
0.11731553077697754,
0.15795086324214935,
0.1585666984319687,
-0.12065953016281128,
-0.008623986504971981,
-0.02079998515546322,
-0.10393955558538437,
0.10858019441366196,
-0.027435047551989555,
0.11732495576143265,
-0.038439493626356125,
0.10110196471214294,
0.01658722385764122,
-0.05043184384703636,
0.11239725351333618,
0.04436596855521202,
0.10235490649938583,
-0.048329442739486694,
-0.07215046882629395,
0.04103988781571388,
-0.03160755708813667,
-0.020553365349769592,
-0.030126484110951424,
0.024155640974640846,
-0.13428521156311035,
-0.022835617884993553,
-0.09118688106536865,
0.03776686266064644,
-0.022094106301665306,
-0.06791602820158005,
-0.03008902072906494,
0.028533868491649628,
0.042028170078992844,
-0.016722125932574272,
0.10633813589811325,
0.011198687367141247,
0.14135566353797913,
0.07105830311775208,
0.07785072922706604,
-0.07264045625925064,
-0.02685687690973282,
0.0028477536980062723,
-0.015516647137701511,
0.05653649941086769,
-0.09636204689741135,
0.03898690268397331,
0.14594246447086334,
0.028024718165397644,
0.13249816000461578,
0.09544787555932999,
-0.005552826449275017,
0.004855652805417776,
0.06571564823389053,
-0.15961956977844238,
-0.033067356795072556,
-0.009774229489266872,
-0.09579870104789734,
-0.11451957374811172,
0.04506542161107063,
0.08324721455574036,
-0.05739818885922432,
-0.014182034879922867,
-0.025850705802440643,
-0.010419710539281368,
-0.07423564046621323,
0.21469752490520477,
0.07096613198518753,
0.052072152495384216,
-0.1097894236445427,
0.04758425056934357,
0.062169525772333145,
-0.05635981634259224,
-0.007355814334005117,
0.058023035526275635,
-0.09625343233346939,
-0.036599427461624146,
0.10229307413101196,
0.20715327560901642,
-0.08488603681325912,
-0.021072281524538994,
-0.12082027643918991,
-0.11053496599197388,
0.078946053981781,
0.17181207239627838,
0.1161452904343605,
0.01216717530041933,
-0.0592157244682312,
0.0024459706619381905,
-0.1504276692867279,
0.07857910543680191,
0.024583421647548676,
0.08458898216485977,
-0.12760771811008453,
0.19067436456680298,
-0.012627508491277695,
0.05654923990368843,
-0.032343775033950806,
0.02227255515754223,
-0.11035244911909103,
0.020592540502548218,
-0.11935686320066452,
-0.03853412717580795,
-0.010913074016571045,
-0.0011909824097529054,
0.002475240733474493,
-0.07747234404087067,
-0.04812723025679588,
-0.0008502210839651525,
-0.12202350050210953,
-0.023861845955252647,
0.04831687733530998,
0.03968987613916397,
-0.10712511092424393,
-0.05484806373715401,
0.027144744992256165,
-0.05419868603348732,
0.07254965603351593,
0.056755006313323975,
0.0313163585960865,
0.05941840633749962,
-0.12234926968812943,
-0.012688416987657547,
0.0718083456158638,
0.016093501821160316,
0.08829990029335022,
-0.07364112883806229,
-0.0006401846767403185,
-0.0009077807771973312,
0.07524457573890686,
0.03355580195784569,
0.08969372510910034,
-0.1253948211669922,
-0.0036540962755680084,
-0.0247617419809103,
-0.0886283740401268,
-0.06054333224892616,
0.02372380904853344,
0.08063284307718277,
0.005329761654138565,
0.18103671073913574,
-0.0637102946639061,
0.04253874719142914,
-0.20047569274902344,
-0.0161909107118845,
-0.025359228253364563,
-0.10809469223022461,
-0.1292904019355774,
-0.06394683569669724,
0.08128925412893295,
-0.03596222773194313,
0.12840259075164795,
0.016423048451542854,
0.05641588941216469,
0.016779396682977676,
-0.01839214749634266,
0.027335017919540405,
0.022028131410479546,
0.21664117276668549,
0.058639753609895706,
-0.05305653437972069,
0.07926280051469803,
0.07513521611690521,
0.10637211799621582,
0.1419340819120407,
0.20195263624191284,
0.13314472138881683,
-0.02392386831343174,
0.062237340956926346,
0.006717668380588293,
-0.062098730355501175,
-0.1573352962732315,
-0.0074918512254953384,
-0.057887107133865356,
0.07797666639089584,
-0.007277084980159998,
0.2014690637588501,
0.03714670613408089,
-0.17419005930423737,
0.038873638957738876,
-0.05098367854952812,
-0.09602060914039612,
-0.10585279017686844,
-0.007564553525298834,
-0.08665549755096436,
-0.13610218465328217,
0.0018309823935851455,
-0.12063556164503098,
0.013021215796470642,
0.12841622531414032,
0.009778400883078575,
-0.014115855097770691,
0.15978558361530304,
0.032281361520290375,
0.05024125799536705,
0.05352574959397316,
0.004068615380674601,
-0.01664920710027218,
-0.0967511460185051,
-0.060651879757642746,
-0.01958225481212139,
-0.019181394949555397,
0.04031331092119217,
-0.07585480064153671,
-0.08379638195037842,
0.029635272920131683,
-0.02045416831970215,
-0.1085350289940834,
0.01757769100368023,
0.012078915722668171,
0.05225890874862671,
0.035967085510492325,
0.009102500043809414,
0.025665177032351494,
-0.025369025766849518,
0.21479396522045135,
-0.07052582502365112,
-0.05532735586166382,
-0.08945823460817337,
0.3084160387516022,
0.046529773622751236,
0.0029297040309756994,
0.027099289000034332,
-0.055076390504837036,
0.01435511652380228,
0.24800407886505127,
0.17637832462787628,
-0.11539044231176376,
-0.013856184668838978,
-0.004928064066916704,
-0.013911922462284565,
-0.005810739938169718,
0.134504035115242,
0.1190212294459343,
0.018199512735009193,
-0.09898018836975098,
-0.04710051789879799,
-0.0645177811384201,
-0.009752205573022366,
-0.030324874445796013,
0.04317288473248482,
0.06512641161680222,
0.009283186867833138,
-0.060429561883211136,
0.047832950949668884,
-0.06397529691457748,
-0.07761631160974503,
0.09319692850112915,
-0.1997661143541336,
-0.15818768739700317,
-0.025857819244265556,
0.05873224511742592,
-0.0027178749442100525,
0.06187080964446068,
-0.03075733222067356,
-0.006767265032976866,
0.05599977448582649,
-0.02352835237979889,
-0.09498559683561325,
-0.10304141044616699,
0.10578858107328415,
-0.10163641721010208,
0.1980302333831787,
-0.04538717493414879,
0.0680089145898819,
0.12178079038858414,
0.05786598101258278,
-0.07873383909463882,
0.05549734830856323,
0.033186301589012146,
-0.07613673061132431,
0.03708450868725777,
0.10663487762212753,
-0.03336620703339577,
0.060261115431785583,
0.028638185933232307,
-0.14672236144542694,
0.02289450354874134,
-0.07683783024549484,
-0.04796402156352997,
-0.032614536583423615,
-0.050501126796007156,
-0.04654325917363167,
0.13199304044246674,
0.22605913877487183,
-0.022761022672057152,
0.025662191212177277,
-0.0815550908446312,
0.0078065176494419575,
0.05418967828154564,
0.04134735092520714,
-0.09505309909582138,
-0.23366959393024445,
0.008371897041797638,
0.07034701853990555,
-0.03622637689113617,
-0.23038071393966675,
-0.08268734812736511,
-0.014697044156491756,
-0.07555188983678818,
-0.07152324914932251,
0.09116849303245544,
0.04113416001200676,
0.054718878120183945,
-0.057065825909376144,
-0.0835181400179863,
-0.06990299373865128,
0.160805806517601,
-0.15373541414737701,
-0.0951625406742096
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# biobert_v1.1_pubmed-finetuned-ner
This model is a fine-tuned version of [monologg/biobert_v1.1_pubmed](https://huggingface.co/monologg/biobert_v1.1_pubmed) on the ncbi_disease dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0657
- Precision: 0.8338
- Recall: 0.8933
- F1: 0.8625
- Accuracy: 0.9827
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 340 | 0.0612 | 0.8268 | 0.85 | 0.8382 | 0.9806 |
| 0.0987 | 2.0 | 680 | 0.0604 | 0.8397 | 0.8848 | 0.8616 | 0.9829 |
| 0.0272 | 3.0 | 1020 | 0.0657 | 0.8338 | 0.8933 | 0.8625 | 0.9827 |
### Framework versions
- Transformers 4.8.1
- Pytorch 1.9.0
- Datasets 1.6.2
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "datasets": ["ncbi_disease"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "biobert_v1.1_pubmed-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "ncbi_disease", "type": "ncbi_disease", "args": "ncbi_disease"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9827274990663513}}]}]}
|
token-classification
|
fidukm34/biobert_v1.1_pubmed-finetuned-ner
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:ncbi_disease",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-ncbi_disease #autotrain_compatible #endpoints_compatible #region-us
|
biobert\_v1.1\_pubmed-finetuned-ner
===================================
This model is a fine-tuned version of monologg/biobert\_v1.1\_pubmed on the ncbi\_disease dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0657
* Precision: 0.8338
* Recall: 0.8933
* F1: 0.8625
* Accuracy: 0.9827
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.8.1
* Pytorch 1.9.0
* Datasets 1.6.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.1\n* Pytorch 1.9.0\n* Datasets 1.6.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-ncbi_disease #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.8.1\n* Pytorch 1.9.0\n* Datasets 1.6.2\n* Tokenizers 0.10.3"
] |
[
54,
98,
4,
31
] |
[
"passage: TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-ncbi_disease #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.8.1\n* Pytorch 1.9.0\n* Datasets 1.6.2\n* Tokenizers 0.10.3"
] |
[
-0.10672702640295029,
0.08427079021930695,
-0.0016531774308532476,
0.1126013845205307,
0.19956055283546448,
0.047248102724552155,
0.0848192572593689,
0.11916397511959076,
-0.09826157242059708,
0.013320855796337128,
0.13649055361747742,
0.18618656694889069,
-0.0075025055557489395,
0.11551611125469208,
-0.055852390825748444,
-0.2517082989215851,
-0.009739570319652557,
0.058988217264413834,
-0.0770891010761261,
0.13282255828380585,
0.08833616971969604,
-0.14883436262607574,
0.08162663131952286,
-0.0024446402676403522,
-0.2386464923620224,
0.018034249544143677,
0.03340545669198036,
-0.0479116253554821,
0.15266053378582,
0.0000934064737521112,
0.14295829832553864,
-0.001907667494378984,
0.0952625647187233,
-0.15975825488567352,
0.008486474864184856,
0.04293252155184746,
0.024236073717474937,
0.08959704637527466,
0.05722976475954056,
-0.008552778512239456,
0.11031326651573181,
-0.09794547408819199,
0.06034513935446739,
0.016728835180401802,
-0.1266794055700302,
-0.24137786030769348,
-0.06690065562725067,
0.010996103286743164,
0.0570601150393486,
0.09544061869382858,
-0.004098992329090834,
0.1707286685705185,
-0.108748659491539,
0.09311644732952118,
0.21254907548427582,
-0.25854769349098206,
-0.06374563276767731,
0.03667503595352173,
-0.001097173779271543,
0.06923959404230118,
-0.11161624640226364,
-0.014086327515542507,
0.04986868053674698,
0.04535039886832237,
0.11861265450716019,
-0.04473774507641792,
-0.11100718379020691,
0.030147789046168327,
-0.14295004308223724,
-0.014008691534399986,
0.13426631689071655,
0.028481649234890938,
-0.01831628568470478,
-0.003680028021335602,
-0.048046309500932693,
-0.13562600314617157,
-0.026783110573887825,
-0.027654321864247322,
0.02800757624208927,
-0.05831591412425041,
-0.08304351568222046,
0.019588716328144073,
-0.08394619077444077,
-0.07552863657474518,
-0.0606713630259037,
0.16393479704856873,
0.03106824681162834,
0.013240222819149494,
-0.010707179084420204,
0.1041635200381279,
0.02002878673374653,
-0.11735992878675461,
0.02837131917476654,
0.029238257557153702,
-0.03119952604174614,
-0.06622743606567383,
-0.07256452739238739,
-0.019983328878879547,
0.009269500151276588,
0.09260982275009155,
-0.03783135861158371,
0.03182968869805336,
0.041552525013685226,
0.017655672505497932,
-0.08668921887874603,
0.1776106357574463,
-0.05908270180225372,
-0.024134764447808266,
-0.00032658883719705045,
0.05053946003317833,
-0.019426463171839714,
0.00573198776692152,
-0.11337704211473465,
-0.018981358036398888,
0.12381690740585327,
0.016866834834218025,
-0.09668269753456116,
0.06738199293613434,
-0.06014217063784599,
-0.046318765729665756,
0.02523704804480076,
-0.08525850623846054,
0.037218112498521805,
-0.005054676905274391,
-0.0781455710530281,
-0.020969541743397713,
0.0049341581761837006,
0.014751376584172249,
-0.008424187079071999,
0.12799863517284393,
-0.0990067720413208,
0.025664811953902245,
-0.09906245023012161,
-0.10981573909521103,
-0.0071005430072546005,
-0.07870779186487198,
0.038670022040605545,
-0.09286963939666748,
-0.1580428034067154,
-0.008138841018080711,
0.028770005330443382,
-0.02454383485019207,
-0.05125315859913826,
-0.05606583505868912,
-0.05866139382123947,
0.006888755597174168,
-0.011556421406567097,
0.16154251992702484,
-0.05915123596787453,
0.10653512179851532,
0.043843045830726624,
0.07105859369039536,
-0.06377442926168442,
0.05259094014763832,
-0.09724822640419006,
-0.0005446928553283215,
-0.18149302899837494,
0.04652892053127289,
-0.045798130333423615,
0.05604856461286545,
-0.08000338077545166,
-0.11068809032440186,
0.03921541944146156,
-0.01888257823884487,
0.08023381233215332,
0.0964537188410759,
-0.14303328096866608,
-0.07987835258245468,
0.13376884162425995,
-0.047016441822052,
-0.11759518086910248,
0.11192383617162704,
-0.07372983545064926,
0.037208687514066696,
0.07275907695293427,
0.15603382885456085,
0.07642093300819397,
-0.0509769544005394,
0.021985098719596863,
-0.01778264157474041,
0.04187226668000221,
-0.06990591436624527,
0.06768472492694855,
0.021222064271569252,
-0.04402913898229599,
0.03113456815481186,
-0.026580888777971268,
0.06738757342100143,
-0.1214926615357399,
-0.08372237533330917,
-0.02992032840847969,
-0.11634337902069092,
0.0784149169921875,
0.07584843039512634,
0.08095633238554001,
-0.09709474444389343,
-0.04723665863275528,
0.0899478867650032,
0.09200753271579742,
-0.04161261394619942,
0.011908884160220623,
-0.061560630798339844,
0.03333212807774544,
-0.06497779488563538,
-0.03441198915243149,
-0.18878698348999023,
-0.045406438410282135,
0.011071396060287952,
0.04456445947289467,
0.0027773543260991573,
0.012960287742316723,
0.06918113678693771,
0.07361533492803574,
-0.06636618077754974,
-0.014624858275055885,
-0.017863962799310684,
0.002659368794411421,
-0.15023112297058105,
-0.18703396618366241,
-0.03317726030945778,
-0.025335153564810753,
0.11228464543819427,
-0.2061360627412796,
0.013272001408040524,
-0.05333087220788002,
0.08644858747720718,
0.003808350069448352,
-0.020811008289456367,
-0.03782019391655922,
0.09152773767709732,
-0.03633216395974159,
-0.040006306022405624,
0.07059220224618912,
-0.02482105977833271,
-0.09548649191856384,
-0.05506708845496178,
-0.08057677745819092,
0.17008888721466064,
0.11702147126197815,
-0.14737771451473236,
-0.08014897257089615,
-0.008687055669724941,
-0.05576865002512932,
-0.024373963475227356,
-0.048210907727479935,
0.0301829744130373,
0.1778150051832199,
-0.01476109866052866,
0.1477523148059845,
-0.052714087069034576,
-0.03459271788597107,
0.008785096928477287,
-0.017880531027913094,
0.03355628624558449,
0.11716662347316742,
0.11669693887233734,
-0.11244554817676544,
0.12831805646419525,
0.14921003580093384,
-0.08972985297441483,
0.12619204819202423,
-0.022504009306430817,
-0.07028155773878098,
-0.01580537110567093,
-0.05159576237201691,
0.0013789944350719452,
0.0969388410449028,
-0.14780771732330322,
-0.001242180122062564,
0.026229744777083397,
0.022424448281526566,
0.005754076410084963,
-0.20422177016735077,
-0.05501094087958336,
0.027399107813835144,
-0.02105552889406681,
-0.012775992974638939,
-0.014578251168131828,
0.024507442489266396,
0.12195464223623276,
0.011831333860754967,
-0.08707182854413986,
0.03824414312839508,
0.011196671053767204,
-0.08409716933965683,
0.21624691784381866,
-0.09135342389345169,
-0.13928987085819244,
-0.10702420771121979,
-0.08310884982347488,
-0.049639903008937836,
0.024042200297117233,
0.03776613995432854,
-0.1111331433057785,
-0.021351218223571777,
-0.03640398755669594,
0.016158999875187874,
-0.01904108375310898,
0.05060630291700363,
0.005755830090492964,
-0.00669540511444211,
0.06828734278678894,
-0.09246547520160675,
-0.003168126568198204,
-0.07262647897005081,
-0.07481208443641663,
0.05340670049190521,
0.020878486335277557,
0.11682728677988052,
0.15910841524600983,
-0.023763507604599,
0.012367027811706066,
-0.030880870297551155,
0.23356172442436218,
-0.05838026851415634,
-0.04909094050526619,
0.11526849865913391,
-0.013415638357400894,
0.03418769687414169,
0.09427370131015778,
0.07218115776777267,
-0.09706895798444748,
0.016718262806534767,
0.035941384732723236,
-0.031057748943567276,
-0.2028001993894577,
-0.056909434497356415,
-0.05632077157497406,
-0.05732457712292671,
0.09158777445554733,
0.012725350446999073,
0.03551885485649109,
0.07337925583124161,
0.04983534663915634,
0.07724890857934952,
-0.06851286441087723,
0.05425894260406494,
0.10166425257921219,
0.04551594331860542,
0.13042980432510376,
-0.02048599347472191,
-0.07727621495723724,
0.03384732827544212,
-0.032152120023965836,
0.2173365205526352,
-0.0036506117321550846,
0.10153952240943909,
0.04536767303943634,
0.1824190616607666,
0.005906893406063318,
0.09251779317855835,
0.014278561808168888,
-0.05185038596391678,
-0.005982000380754471,
-0.02940792217850685,
-0.052441056817770004,
0.006282910238951445,
-0.041184164583683014,
0.06609587371349335,
-0.12788166105747223,
-0.016775520518422127,
0.04820733517408371,
0.2071400135755539,
0.05776066333055496,
-0.3379681408405304,
-0.07583253085613251,
-0.008026775903999805,
-0.01620742678642273,
-0.03478265181183815,
0.007363904733210802,
0.09167125076055527,
-0.10250139981508255,
0.02258392609655857,
-0.05983216315507889,
0.09259297698736191,
-0.04807266965508461,
0.05022258684039116,
0.04474843293428421,
0.09098747372627258,
-0.013426254503428936,
0.08927680552005768,
-0.309724360704422,
0.26636552810668945,
0.011525997892022133,
0.0580553263425827,
-0.07500280439853668,
-0.027285445481538773,
0.04114017263054848,
0.09438776224851608,
0.045193254947662354,
0.0018640565685927868,
0.007388670463114977,
-0.24057768285274506,
-0.043017953634262085,
0.03564636781811714,
0.10268200933933258,
-0.03835606575012207,
0.08995064347982407,
-0.023227769881486893,
0.007144388277083635,
0.07397592812776566,
-0.024527914822101593,
-0.06213478371500969,
-0.06624506413936615,
-0.01903393119573593,
0.034170933067798615,
-0.027411213144659996,
-0.0496494360268116,
-0.1018831804394722,
-0.10115973651409149,
0.15966881811618805,
0.009798558428883553,
-0.022473342716693878,
-0.13585789501667023,
0.12460137158632278,
0.09150730073451996,
-0.08407243341207504,
0.028332069516181946,
0.011925285682082176,
0.05586066469550133,
0.045300837606191635,
-0.07034949958324432,
0.10139071941375732,
-0.06735768169164658,
-0.17027254402637482,
-0.07237354665994644,
0.09182770550251007,
0.051098424941301346,
0.06841395050287247,
0.00046263381955213845,
0.021839838474988937,
-0.030494702979922295,
-0.08161476254463196,
0.04860459268093109,
-0.022254230454564095,
0.08134106546640396,
0.06545400619506836,
-0.07407540827989578,
0.039058882743120193,
-0.0642024576663971,
-0.024112680926918983,
0.1903090924024582,
0.23280930519104004,
-0.09186821430921555,
0.0025589512661099434,
0.030962444841861725,
-0.06139957159757614,
-0.19392526149749756,
0.07211311161518097,
0.08794286847114563,
0.0023818197660148144,
0.039497364312410355,
-0.18396691977977753,
0.1607334315776825,
0.10383295267820358,
0.0009596662130206823,
0.09880737215280533,
-0.2690892815589905,
-0.12188071757555008,
0.11809950321912766,
0.16044899821281433,
0.15789608657360077,
-0.11923873424530029,
-0.006804258096963167,
-0.019500304013490677,
-0.0961984395980835,
0.10656623542308807,
-0.033736977726221085,
0.11745467782020569,
-0.04251864179968834,
0.09839979559183121,
0.016614621505141258,
-0.04804433509707451,
0.11420764774084091,
0.03795742243528366,
0.10250195860862732,
-0.04620341211557388,
-0.0596780925989151,
0.044442079961299896,
-0.02994878962635994,
-0.01928839273750782,
-0.03604228049516678,
0.026522137224674225,
-0.13545367121696472,
-0.023225970566272736,
-0.09701082855463028,
0.03991074115037918,
-0.01823825016617775,
-0.0700104609131813,
-0.03069264069199562,
0.029485879465937614,
0.04319152235984802,
-0.017246123403310776,
0.10607326775789261,
0.012714744545519352,
0.14536695182323456,
0.07483777403831482,
0.08119414001703262,
-0.07193338125944138,
-0.027783242985606194,
0.0007570874877274036,
-0.015339790843427181,
0.05685657262802124,
-0.09322764724493027,
0.038337770849466324,
0.14444252848625183,
0.025996727868914604,
0.13324692845344543,
0.09655841439962387,
-0.008493609726428986,
0.0035495799966156483,
0.06709956377744675,
-0.16136334836483002,
-0.02939753048121929,
-0.012228448875248432,
-0.09432786703109741,
-0.11748712509870529,
0.047778189182281494,
0.08210145682096481,
-0.05706128478050232,
-0.014028307981789112,
-0.026022890582680702,
-0.009834100492298603,
-0.07507860660552979,
0.22419174015522003,
0.07099621742963791,
0.0524250864982605,
-0.1090191900730133,
0.0468764528632164,
0.05746904015541077,
-0.05100574344396591,
-0.006974291987717152,
0.060668062418699265,
-0.09710393846035004,
-0.03699721768498421,
0.10691576451063156,
0.20674459636211395,
-0.08674225956201553,
-0.0239975918084383,
-0.12335171550512314,
-0.10838793218135834,
0.07649610936641693,
0.1625586748123169,
0.11613935977220535,
0.006071569863706827,
-0.05967715010046959,
0.0005950757185928524,
-0.14893858134746552,
0.08235647529363632,
0.023582592606544495,
0.08666246384382248,
-0.12696823477745056,
0.1867409497499466,
-0.011116084642708302,
0.05772382766008377,
-0.035254694521427155,
0.023035645484924316,
-0.10790220648050308,
0.021483059972524643,
-0.12212380766868591,
-0.043889615684747696,
-0.006386746186763048,
-0.0029266069177538157,
0.0024456793908029795,
-0.08248106390237808,
-0.04738974571228027,
0.0030450026970356703,
-0.12308844178915024,
-0.02638969011604786,
0.05112454667687416,
0.03849680349230766,
-0.10932835936546326,
-0.05883095785975456,
0.026551879942417145,
-0.053501781076192856,
0.0707046240568161,
0.05516988784074783,
0.028845401480793953,
0.06140650808811188,
-0.11909963190555573,
-0.01810149848461151,
0.07305937260389328,
0.019919324666261673,
0.08370009064674377,
-0.07314138114452362,
-0.0027598950546234846,
0.0022334775421768427,
0.07471680641174316,
0.035932473838329315,
0.09095387905836105,
-0.12294183671474457,
-0.007497639860957861,
-0.02871735952794552,
-0.08758436888456345,
-0.05808878317475319,
0.02215060219168663,
0.07641156762838364,
0.011213903315365314,
0.18354658782482147,
-0.06552332639694214,
0.03997586667537689,
-0.20243437588214874,
-0.016586776822805405,
-0.0257033109664917,
-0.11101478338241577,
-0.13079248368740082,
-0.05951357260346413,
0.08272436261177063,
-0.036494750529527664,
0.12990421056747437,
0.014549546875059605,
0.0590205080807209,
0.018439138308167458,
-0.015485924668610096,
0.03690417483448982,
0.017747705802321434,
0.2157946228981018,
0.05647643655538559,
-0.04904934763908386,
0.08273374289274216,
0.07538094371557236,
0.10608521848917007,
0.13950115442276,
0.20174264907836914,
0.1332876980304718,
-0.026256712153553963,
0.058650314807891846,
0.009067270904779434,
-0.06046120449900627,
-0.15901876986026764,
-0.020700938999652863,
-0.049561526626348495,
0.07819990813732147,
-0.004783842246979475,
0.19544358551502228,
0.042729735374450684,
-0.17479750514030457,
0.040721647441387177,
-0.052320681512355804,
-0.09629242867231369,
-0.10662055760622025,
-0.0009435050887987018,
-0.08546803891658783,
-0.1397094577550888,
0.002421734156087041,
-0.12219838798046112,
0.015361030586063862,
0.12921053171157837,
0.012009707279503345,
-0.01561321783810854,
0.1605815440416336,
0.03742823377251625,
0.04859543219208717,
0.05165103077888489,
-0.00041161212720908225,
-0.016455061733722687,
-0.09004095196723938,
-0.06047032028436661,
-0.01822829060256481,
-0.018216101452708244,
0.039007943123579025,
-0.07820168137550354,
-0.08922664821147919,
0.026647690683603287,
-0.022642191499471664,
-0.10882972180843353,
0.019293373450636864,
0.012392621487379074,
0.051066674292087555,
0.03793095424771309,
0.01146802306175232,
0.02503432333469391,
-0.02496933750808239,
0.21875113248825073,
-0.07162266969680786,
-0.05257219448685646,
-0.08751078695058823,
0.31498801708221436,
0.04741674289107323,
0.0015575698344036937,
0.0268173236399889,
-0.05445760861039162,
0.00783589668571949,
0.24633875489234924,
0.17631082236766815,
-0.116778664290905,
-0.01606695167720318,
-0.008803834207355976,
-0.013904853723943233,
-0.004590451251715422,
0.13939246535301208,
0.1207457110285759,
0.012746420688927174,
-0.09805244207382202,
-0.04454107582569122,
-0.06503275036811829,
-0.009912194684147835,
-0.034229669719934464,
0.04429022967815399,
0.07113383710384369,
0.0065573775209486485,
-0.059057872742414474,
0.04674409329891205,
-0.05962645262479782,
-0.07182731479406357,
0.08308858424425125,
-0.19355997443199158,
-0.15850870311260223,
-0.029636388644576073,
0.058776967227458954,
-0.004131822846829891,
0.05949316918849945,
-0.030577324330806732,
0.0002164314064430073,
0.04831778258085251,
-0.025475304573774338,
-0.09540353715419769,
-0.10780853033065796,
0.11239488422870636,
-0.10851974040269852,
0.19474747776985168,
-0.043062563985586166,
0.0730976089835167,
0.1203158050775528,
0.05199964717030525,
-0.07690246403217316,
0.057883065193891525,
0.032447364181280136,
-0.07880683243274689,
0.03921332210302353,
0.11203181743621826,
-0.030515996739268303,
0.05952804163098335,
0.026985226199030876,
-0.14385986328125,
0.01848122850060463,
-0.07697722315788269,
-0.049470674246549606,
-0.03268585354089737,
-0.04519512876868248,
-0.04689362272620201,
0.13083447515964508,
0.22629113495349884,
-0.02322257310152054,
0.025173403322696686,
-0.08546677976846695,
0.009923839941620827,
0.05648145079612732,
0.035351529717445374,
-0.0948251485824585,
-0.23629164695739746,
0.007704526651650667,
0.07264170795679092,
-0.0365375280380249,
-0.2315303385257721,
-0.08241590112447739,
-0.017127158120274544,
-0.07748793065547943,
-0.07099942117929459,
0.09084253758192062,
0.03763600438833237,
0.05204366147518158,
-0.0550951324403286,
-0.08768784254789352,
-0.07036572694778442,
0.1570354551076889,
-0.16029420495033264,
-0.09347447752952576
] |
null | null |
transformers
|
This model can measure semantic similarity between pairs of texts containing figurative language. As far as we know,
this model works slightly better than sup-simCSE-roberta-base. For example :
**sentence 1**: I have been in seventh heaven since Harry entered my life .
**sentence 2**: I have been in very happy since Harry entered my life.
the cosin score of simcse: 0.897
the cosin score of us: 0.897
-------------------------------------------------------------------
**sentence 1**: I have been in seventh heaven since Harry entered my life .
**sentence 2**: I have been in pain since Harry entered my life .
the cosin score of simcse: 0.846
the cosin score of us: 0.753
--------------------------------------------------
It's still a big challenge for us to measure semantic similarity of figurative language from the sentence embedding perspective.
unsupvised models may useless as the key is to infer the literal meaning of the figurative expression, since the annotated is rare.
|
{}
| null |
figurative-nlp/se4fig-roberta-base
|
[
"transformers",
"pytorch",
"roberta",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #roberta #endpoints_compatible #region-us
|
This model can measure semantic similarity between pairs of texts containing figurative language. As far as we know,
this model works slightly better than sup-simCSE-roberta-base. For example :
sentence 1: I have been in seventh heaven since Harry entered my life .
sentence 2: I have been in very happy since Harry entered my life.
the cosin score of simcse: 0.897
the cosin score of us: 0.897
-------------------------------------------------------------------
sentence 1: I have been in seventh heaven since Harry entered my life .
sentence 2: I have been in pain since Harry entered my life .
the cosin score of simcse: 0.846
the cosin score of us: 0.753
--------------------------------------------------
It's still a big challenge for us to measure semantic similarity of figurative language from the sentence embedding perspective.
unsupvised models may useless as the key is to infer the literal meaning of the figurative expression, since the annotated is rare.
|
[] |
[
"TAGS\n#transformers #pytorch #roberta #endpoints_compatible #region-us \n"
] |
[
24
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #endpoints_compatible #region-us \n"
] |
[
-0.04710838571190834,
0.000004481258201849414,
-0.01084833312779665,
-0.028830038383603096,
0.13689196109771729,
0.024661021307110786,
0.021276047453284264,
0.06774231046438217,
0.07277411967515945,
-0.005768520291894674,
0.12997958064079285,
0.23497812449932098,
-0.05278132110834122,
0.012769986875355244,
-0.058594558387994766,
-0.24939481914043427,
0.07595402747392654,
0.08731918036937714,
-0.08350498974323273,
0.10799740999937057,
0.05421356484293938,
-0.0927719920873642,
0.054529819637537,
-0.028123930096626282,
-0.11991901695728302,
0.0522734597325325,
0.02667221426963806,
-0.08790246397256851,
0.1232864186167717,
0.019574476405978203,
0.1794423609972,
0.025344358757138252,
-0.12528492510318756,
-0.1700793355703354,
0.03414492681622505,
0.00017859147919807583,
-0.06686453521251678,
0.02657829038798809,
0.044179003685712814,
-0.12301196157932281,
0.057532187551259995,
0.0489761121571064,
0.008118457160890102,
0.03497127443552017,
-0.1776191145181656,
-0.16942951083183289,
-0.05893833190202713,
0.028112027794122696,
0.023665081709623337,
0.07402801513671875,
0.019056716933846474,
0.17335262894630432,
-0.15199004113674164,
0.08120588213205338,
0.18331138789653778,
-0.28230294585227966,
0.0015046729240566492,
0.08527936041355133,
0.06626160442829132,
0.04926927015185356,
0.011503299698233604,
0.05494994297623634,
0.006573413033038378,
0.015392723493278027,
-0.038082290440797806,
-0.08980979025363922,
-0.05154621973633766,
0.08386038988828659,
-0.08894415944814682,
-0.09970521926879883,
0.2075752317905426,
-0.03570309281349182,
0.04820539429783821,
0.04827036336064339,
-0.09897863864898682,
-0.03811374679207802,
0.0005226113717071712,
0.00046723868581466377,
-0.02164444699883461,
0.06373990327119827,
0.0032088239677250385,
-0.027601467445492744,
-0.11205756664276123,
0.04216599091887474,
-0.22245104610919952,
0.28457048535346985,
0.026442578062415123,
0.0912148579955101,
-0.2189355343580246,
0.05204150453209877,
-0.04516136646270752,
-0.07567647844552994,
0.022055523470044136,
-0.10415781289339066,
0.030584361404180527,
0.014985884539783001,
-0.0726739913225174,
0.029076840728521347,
0.07451772689819336,
0.18171283602714539,
0.029848651960492134,
0.023849116638302803,
0.037326473742723465,
0.10956461727619171,
0.021593017503619194,
0.09593676775693893,
-0.004522756207734346,
-0.0062623959966003895,
0.038684386759996414,
-0.17025139927864075,
0.013620704412460327,
-0.037897635251283646,
-0.08988464623689651,
-0.07785378396511078,
0.012553787790238857,
0.11354856938123703,
0.03250754252076149,
0.007742214482277632,
-0.07710288465023041,
0.001371672609820962,
0.017974281683564186,
-0.04751988500356674,
-0.031038520857691765,
-0.013762025162577629,
0.02762053720653057,
0.21292611956596375,
-0.03568822890520096,
-0.014079419896006584,
-0.019960032775998116,
0.10149375349283218,
-0.07446283847093582,
-0.020848069339990616,
-0.041397370398044586,
-0.014479035511612892,
0.05333780497312546,
-0.14959795773029327,
0.0894417017698288,
-0.16235913336277008,
-0.07721706479787827,
0.023479359224438667,
0.04314779117703438,
0.004523617215454578,
0.012278388254344463,
0.012087945826351643,
-0.009219380095601082,
-0.027236631140112877,
-0.06194603443145752,
-0.06089937686920166,
-0.05625390261411667,
0.11111260950565338,
0.008455435745418072,
0.06341143697500229,
-0.10391087830066681,
0.05662205442786217,
-0.09099423885345459,
0.034544508904218674,
-0.1375199556350708,
-0.02523764967918396,
-0.02645164355635643,
0.1923660933971405,
0.0068205008283257484,
-0.05464669689536095,
-0.12457500398159027,
0.048568207770586014,
-0.038346800953149796,
0.15382003784179688,
-0.010524366982281208,
-0.11424499750137329,
0.2527368664741516,
-0.09813989698886871,
-0.16236662864685059,
0.05221249535679817,
0.005846796091645956,
0.007414969149976969,
0.07120196521282196,
0.16906669735908508,
0.042660776525735855,
-0.057577621191740036,
0.08609550446271896,
0.1047014519572258,
-0.16269704699516296,
-0.1719195693731308,
0.03675294667482376,
-0.03488772362470627,
-0.09545575827360153,
0.04062844440340996,
0.02270287647843361,
0.09206480532884598,
-0.08907764405012131,
-0.015806129202246666,
-0.01633630134165287,
-0.025665801018476486,
0.05208549275994301,
0.05835433676838875,
0.08001566678285599,
-0.07454166561365128,
0.005366346798837185,
-0.021103912964463234,
0.0089016268029809,
0.04055006429553032,
0.05169028416275978,
-0.043948616832494736,
0.1355406641960144,
-0.05684935301542282,
0.002631255192682147,
-0.21775834262371063,
-0.08198042213916779,
-0.031147386878728867,
0.09329741448163986,
-0.023606708273291588,
0.16439582407474518,
0.0935794934630394,
-0.11291755735874176,
0.005576607305556536,
-0.015517151914536953,
0.10377269238233566,
0.012552604079246521,
0.007448928896337748,
-0.03710265830159187,
0.037671931087970734,
-0.08019174635410309,
-0.06017129495739937,
-0.013761534355580807,
-0.0042076194658875465,
0.11294644325971603,
0.11935915052890778,
0.007766587194055319,
0.0414375476539135,
-0.02214924804866314,
0.06110641360282898,
-0.0232370775192976,
0.0158044695854187,
0.09678713232278824,
-0.01638456992805004,
-0.06773242354393005,
0.1486222892999649,
-0.07785765081644058,
0.3427961468696594,
0.19542205333709717,
-0.3026086688041687,
0.010744023136794567,
0.00817910023033619,
-0.01671556755900383,
0.026477953419089317,
0.11432955414056778,
0.0012866508914157748,
0.056610532104969025,
0.032953374087810516,
0.10734401643276215,
-0.019625410437583923,
-0.02903112582862377,
0.008584939874708652,
-0.06553634256124496,
-0.03852369263768196,
0.09696078300476074,
0.07031720131635666,
-0.13535082340240479,
0.1570144146680832,
0.24322782456874847,
0.0343286469578743,
0.10578674077987671,
-0.05931145325303078,
-0.016244295984506607,
0.03694572672247887,
0.011412939988076687,
-0.03407431021332741,
0.025389118120074272,
-0.21651425957679749,
-0.043358366936445236,
0.07186431437730789,
0.005580555181950331,
0.09065420180559158,
-0.13706739246845245,
-0.07837424427270889,
0.026302676647901535,
0.03360499441623688,
-0.04703304171562195,
0.11487317830324173,
0.03388232737779617,
0.077121801674366,
0.023567313328385353,
-0.04248740151524544,
0.09480869770050049,
0.008020560257136822,
-0.029814818874001503,
0.1654738038778305,
-0.10702735930681229,
-0.2581709027290344,
-0.12193218618631363,
-0.14464305341243744,
0.030673744156956673,
0.013289694674313068,
0.07178103923797607,
-0.09570969641208649,
-0.02794502303004265,
0.11040882021188736,
0.06391425430774689,
-0.15068218111991882,
0.028558287769556046,
-0.033212799578905106,
0.06838270276784897,
-0.09712500125169754,
-0.0651562362909317,
-0.06374204158782959,
-0.07063358277082443,
-0.04265620559453964,
0.11264993250370026,
-0.12369513511657715,
0.10658242553472519,
0.1185801699757576,
0.053718872368335724,
0.06899958103895187,
-0.0026584325823932886,
0.14750708639621735,
-0.08715782314538956,
-0.06181985139846802,
0.21091774106025696,
-0.017197037115693092,
0.09724331647157669,
0.11277004331350327,
0.03381453827023506,
-0.07095205038785934,
-0.029416784644126892,
-0.06745780259370804,
-0.12051685899496078,
-0.2196408361196518,
-0.10431885719299316,
-0.1338929682970047,
0.001790988608263433,
-0.013106169179081917,
0.03685977682471275,
0.09675391018390656,
0.08901132643222809,
0.0550701804459095,
-0.09611242264509201,
-0.05373142287135124,
0.06117139011621475,
0.2261544018983841,
-0.024348463863134384,
0.0807882696390152,
-0.08071843534708023,
-0.09673191606998444,
0.08415748924016953,
0.06262189149856567,
0.20184406638145447,
0.09570007771253586,
0.011600117199122906,
0.07747740298509598,
0.15804089605808258,
0.14802859723567963,
0.12967321276664734,
0.004285544157028198,
-0.03386882692575455,
-0.01980484649538994,
0.008402297273278236,
-0.06879641860723495,
0.008582867681980133,
0.0945887565612793,
-0.1460021585226059,
-0.04694917052984238,
-0.21670974791049957,
0.07566370069980621,
0.05846427008509636,
0.033341988921165466,
-0.14540016651153564,
-0.00019760403665713966,
0.07043024152517319,
0.006035317201167345,
-0.038673847913742065,
0.08767110854387283,
-0.04047485813498497,
-0.13432176411151886,
0.05549551174044609,
-0.03985515609383583,
0.10120426118373871,
-0.026484539732336998,
0.07381577044725418,
-0.06528782844543457,
-0.10097641497850418,
0.062186695635318756,
0.07416008412837982,
-0.22962474822998047,
0.2966843247413635,
-0.022008458152413368,
-0.07843590527772903,
-0.047935355454683304,
-0.0402735136449337,
0.00031782049336470664,
0.14924046397209167,
0.11227667331695557,
0.03040711209177971,
-0.08607617765665054,
-0.11602409929037094,
0.05855415388941765,
0.027639638632535934,
0.1261444091796875,
-0.02718510292470455,
-0.03371329978108406,
-0.011890764348208904,
-0.0054674530401825905,
-0.03813306242227554,
-0.0011421740055084229,
0.09245268255472183,
-0.13451752066612244,
0.043690603226423264,
-0.005595468450337648,
0.015751486644148827,
-0.0058060502633452415,
0.000216820408240892,
-0.04368269816040993,
0.12863071262836456,
-0.041234876960515976,
-0.056774456053972244,
-0.08750397711992264,
-0.15236316621303558,
0.13593828678131104,
-0.10860603302717209,
0.0916966125369072,
-0.09828807413578033,
-0.07356434315443039,
-0.07884984463453293,
-0.1789647787809372,
0.11347857862710953,
-0.09571298211812973,
0.027581993490457535,
-0.025902552530169487,
0.19910907745361328,
-0.06354320794343948,
-0.011006711050868034,
-0.009784103371202946,
0.030833037570118904,
-0.13190630078315735,
-0.0889718309044838,
0.014048268087208271,
-0.026677189394831657,
0.04725491255521774,
0.06850610673427582,
-0.010306584648787975,
0.04110793396830559,
0.00722151156514883,
-0.0022256039083003998,
0.2132948487997055,
0.20776315033435822,
-0.04205271974205971,
0.12124003469944,
0.16055002808570862,
-0.02298271469771862,
-0.2685646414756775,
-0.09160544723272324,
-0.17717303335666656,
-0.04701691120862961,
-0.017774688079953194,
-0.14274154603481293,
0.11054682731628418,
0.04712479189038277,
-0.02545398473739624,
0.11969882249832153,
-0.2559846341609955,
-0.04124341532588005,
0.1493636518716812,
0.0062699345871806145,
0.5546489953994751,
-0.11287706345319748,
-0.08298387378454208,
0.01305436436086893,
-0.24928133189678192,
0.07992938905954361,
0.01904345117509365,
0.059819381684064865,
-0.030170824378728867,
0.08720887452363968,
0.03699829801917076,
-0.08040692657232285,
0.12438756972551346,
0.0074538118205964565,
0.024170564487576485,
-0.07946096360683441,
-0.1280781477689743,
0.0872870683670044,
-0.004839536268264055,
-0.03662366047501564,
0.07352577149868011,
0.02766229584813118,
-0.13575823605060577,
-0.024937165901064873,
-0.12918852269649506,
0.0509490966796875,
0.046772826462984085,
-0.03127678856253624,
-0.028668509796261787,
-0.02079327218234539,
-0.03304222971200943,
0.01052573136985302,
0.2621735632419586,
-0.034418486058712006,
0.1412678211927414,
-0.007903030142188072,
0.05526409298181534,
-0.16902287304401398,
-0.08229594677686691,
-0.07681068032979965,
-0.054233379662036896,
0.06747926026582718,
-0.07158241420984268,
0.02895243652164936,
0.13923749327659607,
-0.014868827536702156,
-0.01971399411559105,
0.10571489483118057,
-0.006689982954412699,
-0.0020358276087790728,
0.14539019763469696,
-0.2069898396730423,
-0.05753513053059578,
-0.021022606641054153,
-0.04989248514175415,
0.1327422708272934,
0.11654264479875565,
0.0983550176024437,
0.07786483317613602,
-0.03430752828717232,
-0.02362983115017414,
-0.028949681669473648,
-0.08090628683567047,
0.0024792233016341925,
0.06885196268558502,
0.025992391631007195,
-0.11667148768901825,
0.05747832730412483,
-0.012274257838726044,
-0.23964636027812958,
-0.052203960716724396,
0.1062576100230217,
-0.13067859411239624,
-0.09600488841533661,
-0.07801525294780731,
0.04109029099345207,
-0.20162880420684814,
-0.034601181745529175,
-0.04159528389573097,
-0.10255556553602219,
0.05963057279586792,
0.24780724942684174,
0.10143034905195236,
0.09888434410095215,
-0.01028646994382143,
-0.016449730843305588,
0.021191705018281937,
-0.08732561767101288,
0.004237749148160219,
0.004638320300728083,
-0.09378271549940109,
-0.02855258248746395,
-0.01363504771143198,
0.15873940289020538,
-0.07890082895755768,
-0.08274190872907639,
-0.16358937323093414,
0.08995359390974045,
-0.09812886267900467,
-0.11397140473127365,
-0.12412403523921967,
-0.06893793493509293,
0.0037182122468948364,
-0.09338416159152985,
-0.04137818142771721,
-0.02767200581729412,
-0.13551916182041168,
0.07086745649576187,
0.023581858724355698,
-0.0037772138603031635,
-0.05501500517129898,
-0.039861805737018585,
0.1472712904214859,
-0.042508505284786224,
0.09451454877853394,
0.17123942077159882,
-0.07602482289075851,
0.12351230531930923,
-0.10503007471561432,
-0.1372130960226059,
0.10087032616138458,
-0.0001759071456035599,
0.07120119035243988,
0.06956369429826736,
0.020766686648130417,
0.06875994801521301,
0.030996467918157578,
0.05662810802459717,
-0.034365203231573105,
-0.12265201658010483,
0.031028853729367256,
0.03192706033587456,
-0.1781196892261505,
-0.033246178179979324,
-0.08630622923374176,
0.15495331585407257,
0.028343068435788155,
0.0859447568655014,
0.022196872159838676,
0.10975440591573715,
-0.01211862824857235,
0.007993047125637531,
-0.0023370154667645693,
-0.19463440775871277,
0.03239840641617775,
-0.05557585507631302,
-0.0024593796115368605,
0.0033398494124412537,
0.2839198708534241,
-0.0705515444278717,
0.023442646488547325,
0.02998867630958557,
0.06523754447698593,
0.012018566019833088,
0.025090577080845833,
0.21156036853790283,
0.10683322697877884,
-0.039572279900312424,
-0.09842078387737274,
0.08749271929264069,
-0.011929450556635857,
-0.0594974085688591,
0.12458037585020065,
0.13864046335220337,
0.08676675707101822,
0.08204910904169083,
-0.0033394009806215763,
0.070549875497818,
-0.06136783957481384,
-0.25319474935531616,
0.011225651018321514,
0.012381847016513348,
0.016442246735095978,
0.09178762137889862,
0.16398729383945465,
-0.004069536458700895,
0.0749489888548851,
-0.01615913212299347,
0.0006451079971157014,
-0.13821111619472504,
-0.08257102966308594,
-0.047923535108566284,
-0.10142721980810165,
0.029083652421832085,
-0.05849800631403923,
-0.007420131005346775,
0.17096444964408875,
0.0392775796353817,
-0.03622451052069664,
0.09747672080993652,
0.0760326161980629,
-0.0670693889260292,
0.03528399392962456,
-0.007171704433858395,
0.021475624293088913,
0.04873443394899368,
-0.012379645369946957,
-0.1494901478290558,
-0.07176815718412399,
-0.06488432735204697,
0.037990935146808624,
-0.10138802975416183,
-0.01745467633008957,
-0.13597668707370758,
-0.09903085976839066,
-0.05588725209236145,
0.08903270214796066,
-0.048549771308898926,
0.0984780415892601,
-0.02612706646323204,
0.006907667499035597,
0.016333235427737236,
0.19976969063282013,
-0.05262446030974388,
-0.04221903532743454,
0.001148911309428513,
0.16957782208919525,
0.05555977299809456,
0.09394799172878265,
-0.011725390329957008,
0.026208948343992233,
-0.06366859376430511,
0.33636584877967834,
0.2535180151462555,
-0.0233058612793684,
0.04417799040675163,
0.05260763689875603,
0.04840950295329094,
0.11710292100906372,
0.09676902741193771,
0.10052920877933502,
0.3192276656627655,
-0.0892692431807518,
-0.04494080692529678,
-0.031999826431274414,
0.013973445631563663,
-0.10555802285671234,
0.05037616938352585,
0.03306281939148903,
-0.07167352735996246,
-0.07398556917905807,
0.12090129405260086,
-0.17856739461421967,
0.11545499414205551,
0.08242010325193405,
-0.22637870907783508,
-0.05336158722639084,
-0.0596175380051136,
0.15627482533454895,
-0.002246581017971039,
0.12675566971302032,
-0.04257085174322128,
-0.15510323643684387,
0.03285594657063484,
0.062185365706682205,
-0.2798806130886078,
-0.09374892711639404,
0.11671829223632812,
0.06078186258673668,
-0.02176550216972828,
-0.03682553395628929,
-0.0003849651257041842,
0.07988492399454117,
0.05872725695371628,
-0.031531307846307755,
0.017113979905843735,
0.04315922036767006,
-0.08697821199893951,
-0.09998752921819687,
-0.019003579393029213,
0.011787851341068745,
-0.1190737634897232,
0.04075232893228531,
-0.15655088424682617,
0.04439463093876839,
0.01169892493635416,
-0.00949098076671362,
0.0003081741160713136,
0.016252411529421806,
-0.06897782534360886,
0.01970541663467884,
0.047393329441547394,
0.0031528547406196594,
-0.020613694563508034,
-0.04386329650878906,
-0.013762234710156918,
0.06974862515926361,
-0.0728449895977974,
-0.15882854163646698,
-0.005591145716607571,
-0.09538131207227707,
0.11189249902963638,
-0.030698299407958984,
-0.08759639412164688,
-0.01687025837600231,
-0.053370025008916855,
0.05895037204027176,
-0.11432106047868729,
0.04182504862546921,
0.026789119467139244,
0.040701985359191895,
0.021567801013588905,
-0.013804933987557888,
0.046552740037441254,
0.05624568089842796,
-0.12087129056453705,
-0.07611918449401855
] |
null | null |
transformers
|
This model can convert the literal expression to figurative/metaphorical expression. Below is the usage of our model:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("figurative-nlp/t5-figurative-generation")
model = AutoModelForSeq2SeqLM.from_pretrained("figurative-nlp/t5-figurative-generation")
input_ids = tokenizer(
"research is <m> very difficult </m> for me.", return_tensors="pt"
).input_ids # Batch size 1
outputs = model.generate(input_ids,beam_search = 5)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
#result : research is a tough nut to crack for me.
For example (the <m> and </m> is the mark that inform the model which literal expression we want to convert it as figurative expression):
**Input**: as of a cloud that softly <m> covers </m> the sun.
**Output**: as of a cloud that softly drapes over the sun.
**Input**: that car coming around the corner <m> surprised me. </m>
**Output**: that car coming around the corner knocked my socks off.
Note: the figurative language here includes metaphor, idiom and simile. We don't guarantee that the results generated results are satisfactory to you. We are trying to improve the effect of the model.
|
{}
|
text2text-generation
|
figurative-nlp/t5-figurative-generation
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This model can convert the literal expression to figurative/metaphorical expression. Below is the usage of our model:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("figurative-nlp/t5-figurative-generation")
model = AutoModelForSeq2SeqLM.from_pretrained("figurative-nlp/t5-figurative-generation")
input_ids = tokenizer(
"research is <m> very difficult </m> for me.", return_tensors="pt"
).input_ids # Batch size 1
outputs = model.generate(input_ids,beam_search = 5)
result = URL(outputs[0], skip_special_tokens=True)
#result : research is a tough nut to crack for me.
For example (the <m> and </m> is the mark that inform the model which literal expression we want to convert it as figurative expression):
Input: as of a cloud that softly <m> covers </m> the sun.
Output: as of a cloud that softly drapes over the sun.
Input: that car coming around the corner <m> surprised me. </m>
Output: that car coming around the corner knocked my socks off.
Note: the figurative language here includes metaphor, idiom and simile. We don't guarantee that the results generated results are satisfactory to you. We are trying to improve the effect of the model.
|
[
"# Batch size 1\n outputs = model.generate(input_ids,beam_search = 5)\n result = URL(outputs[0], skip_special_tokens=True)\n #result : research is a tough nut to crack for me.\n\n\n\nFor example (the <m> and </m> is the mark that inform the model which literal expression we want to convert it as figurative expression):\n\n Input: as of a cloud that softly <m> covers </m> the sun.\n \n Output: as of a cloud that softly drapes over the sun. \n \n Input: that car coming around the corner <m> surprised me. </m>\n \n Output: that car coming around the corner knocked my socks off.\n \n \n Note: the figurative language here includes metaphor, idiom and simile. We don't guarantee that the results generated results are satisfactory to you. We are trying to improve the effect of the model."
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Batch size 1\n outputs = model.generate(input_ids,beam_search = 5)\n result = URL(outputs[0], skip_special_tokens=True)\n #result : research is a tough nut to crack for me.\n\n\n\nFor example (the <m> and </m> is the mark that inform the model which literal expression we want to convert it as figurative expression):\n\n Input: as of a cloud that softly <m> covers </m> the sun.\n \n Output: as of a cloud that softly drapes over the sun. \n \n Input: that car coming around the corner <m> surprised me. </m>\n \n Output: that car coming around the corner knocked my socks off.\n \n \n Note: the figurative language here includes metaphor, idiom and simile. We don't guarantee that the results generated results are satisfactory to you. We are trying to improve the effect of the model."
] |
[
48,
234
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Batch size 1\n outputs = model.generate(input_ids,beam_search = 5)\n result = URL(outputs[0], skip_special_tokens=True)\n #result : research is a tough nut to crack for me.\n\n\n\nFor example (the <m> and </m> is the mark that inform the model which literal expression we want to convert it as figurative expression):\n\n Input: as of a cloud that softly <m> covers </m> the sun.\n \n Output: as of a cloud that softly drapes over the sun. \n \n Input: that car coming around the corner <m> surprised me. </m>\n \n Output: that car coming around the corner knocked my socks off.\n \n \n Note: the figurative language here includes metaphor, idiom and simile. We don't guarantee that the results generated results are satisfactory to you. We are trying to improve the effect of the model."
] |
[
-0.06365357339382172,
-0.12393070012331009,
-0.004857082851231098,
0.08249246329069138,
0.07526891678571701,
-0.06349966675043106,
-0.042893391102552414,
0.11404943466186523,
-0.04838695377111435,
0.09703658521175385,
0.15946032106876373,
0.18535950779914856,
0.01588977873325348,
0.04518076777458191,
-0.018859688192605972,
-0.24704736471176147,
0.028027184307575226,
-0.04932507500052452,
0.00859746616333723,
0.13465535640716553,
0.10902566462755203,
-0.028029117733240128,
0.10610029846429825,
0.0026793121360242367,
-0.08965987712144852,
-0.1056646928191185,
0.015160585753619671,
0.018963878974318504,
0.07461625337600708,
0.1458427906036377,
-0.00983419455587864,
0.03485066071152687,
-0.06568443775177002,
-0.13099107146263123,
0.001374538172967732,
0.00499274767935276,
0.001244590850546956,
0.028235748410224915,
0.04740998521447182,
0.05891619995236397,
0.19163720309734344,
-0.060659486800432205,
-0.0571589432656765,
0.09349654614925385,
-0.11322227120399475,
-0.04131751134991646,
-0.05644731596112251,
-0.03816575929522514,
0.09865886718034744,
0.054452620446681976,
-0.07263708114624023,
0.27590566873550415,
-0.12757447361946106,
0.13184170424938202,
0.11479400098323822,
-0.1571039855480194,
-0.048641666769981384,
0.0021917650010436773,
0.0752210021018982,
-0.023842625319957733,
-0.06991810351610184,
0.006898988503962755,
-0.01643921621143818,
0.025330504402518272,
0.006673096679151058,
0.016632821410894394,
0.07380330562591553,
0.05592525750398636,
-0.14886215329170227,
-0.06341855973005295,
0.21852658689022064,
0.0033718799240887165,
-0.09525321424007416,
-0.13308462500572205,
-0.027952468022704124,
0.08758164942264557,
0.015713047236204147,
-0.09766639024019241,
-0.00276086013764143,
0.026785118505358696,
-0.038690678775310516,
-0.02699846401810646,
-0.12308558076620102,
0.007020196877419949,
-0.12991313636302948,
0.06615564227104187,
0.003072560764849186,
-0.00014492035552393645,
-0.07376590371131897,
0.07450200617313385,
-0.09903460741043091,
-0.08318287879228592,
-0.09684477001428604,
-0.1293146163225174,
0.059353433549404144,
-0.021230611950159073,
-0.08181191235780716,
-0.11487734317779541,
0.006144566927105188,
0.086587093770504,
0.10977156460285187,
-0.00623915484175086,
-0.0064255655743181705,
0.06709933280944824,
0.09457855671644211,
0.09947797656059265,
-0.05040694773197174,
-0.0015143414493650198,
0.033854950219392776,
-0.009328768588602543,
-0.01594374142587185,
-0.03407038003206253,
-0.04047146067023277,
0.04765336588025093,
0.03508441150188446,
0.016828976571559906,
0.13682053983211517,
0.11187390983104706,
-0.08926398307085037,
-0.059454791247844696,
0.016308492049574852,
-0.0912105068564415,
-0.06287714838981628,
-0.03668468818068504,
-0.08247125148773193,
0.051664985716342926,
0.04392636939883232,
-0.009648262523114681,
-0.06656534224748611,
-0.05524840205907822,
-0.07939036935567856,
0.004107743501663208,
-0.07323987782001495,
-0.06448862701654434,
0.048413943499326706,
0.0002838531509041786,
-0.02819679118692875,
-0.10589901357889175,
-0.04719497263431549,
-0.06404520571231842,
0.05693846568465233,
-0.056930456310510635,
-0.043726079165935516,
-0.06299320608377457,
-0.06239108741283417,
0.02601511776447296,
0.01726585440337658,
-0.062267422676086426,
0.002558442996814847,
0.08120366185903549,
0.03952842578291893,
0.04682208225131035,
-0.07440990954637527,
0.051761504262685776,
-0.15066032111644745,
0.004858092404901981,
-0.23541195690631866,
0.11524467170238495,
-0.04053562507033348,
0.018206551671028137,
-0.09072413295507431,
-0.020043162629008293,
0.02298598550260067,
0.052224986255168915,
0.01982920803129673,
0.2047537863254547,
-0.17546555399894714,
0.005370637867599726,
0.1428612619638443,
-0.04677046462893486,
-0.09205611050128937,
0.16106905043125153,
-0.053235504776239395,
0.18661247193813324,
0.08193414658308029,
-0.05069512128829956,
0.019195450469851494,
-0.07221696525812149,
-0.05866864323616028,
0.042979318648576736,
-0.1430879384279251,
0.08381303399801254,
0.08444498479366302,
-0.040028996765613556,
-0.08745238929986954,
0.04460838437080383,
-0.10159789770841599,
0.039372947067022324,
-0.05639972537755966,
-0.040949661284685135,
-0.023700648918747902,
0.016810933127999306,
0.07409138232469559,
-0.0018036521505564451,
0.0007113868487067521,
-0.036530543118715286,
-0.14213357865810394,
0.01113819144666195,
-0.030434520915150642,
0.017025373876094818,
0.033159177750349045,
-0.09380028396844864,
0.08918841928243637,
-0.06600472331047058,
0.02348538301885128,
-0.14378756284713745,
-0.06307633966207504,
0.0008175966795533895,
0.016909169033169746,
0.07900013029575348,
0.03325323760509491,
0.028086906298995018,
0.0267235916107893,
0.03768647462129593,
0.06443112343549728,
0.014161046594381332,
-0.0030426857993006706,
-0.07286176085472107,
-0.10368051379919052,
0.05061402544379234,
-0.026826729997992516,
-0.0023830574937164783,
-0.028164919465780258,
0.034639872610569,
0.1655610054731369,
-0.03460076451301575,
-0.009803209453821182,
-0.05402291566133499,
-0.018192406743764877,
-0.021751079708337784,
-0.05354774743318558,
0.012907342985272408,
0.09424720704555511,
0.005002425517886877,
-0.10661601275205612,
0.21402394771575928,
-0.15279190242290497,
-0.10329922288656235,
0.1245322898030281,
-0.1716911941766739,
-0.07367566227912903,
-0.03756342828273773,
-0.06942127645015717,
-0.0464702770113945,
0.03946318477392197,
-0.1022334173321724,
0.10311268270015717,
0.0015801425324752927,
0.04662022739648819,
-0.07474072277545929,
-0.05627219378948212,
0.058163948357105255,
-0.07425343245267868,
-0.021055618301033974,
0.04155130684375763,
0.053054310381412506,
-0.12844714522361755,
0.07282425463199615,
0.16737380623817444,
0.018434150144457817,
0.13183149695396423,
0.0025951447896659374,
-0.07317941635847092,
-0.0974108949303627,
0.13260602951049805,
-0.00030037364922463894,
0.011258845217525959,
-0.09807323664426804,
-0.02702893689274788,
0.05727367103099823,
-0.015513373538851738,
0.042548779398202896,
-0.08069030940532684,
0.026104643940925598,
0.08461010456085205,
0.06307633966207504,
-0.018677523359656334,
0.04317878559231758,
-0.017112834379076958,
0.04301301762461662,
0.025598933920264244,
-0.007705483585596085,
0.12113098800182343,
0.005572743713855743,
-0.10607504844665527,
0.14975115656852722,
-0.091079480946064,
-0.3044165074825287,
-0.17574815452098846,
-0.06796089559793472,
-0.04052337631583214,
0.035221658647060394,
0.05802140012383461,
-0.04110909253358841,
-0.0399533174932003,
-0.04481114074587822,
0.12568838894367218,
0.0037818150594830513,
-0.0049717361107468605,
-0.1504065990447998,
-0.00867832824587822,
0.006979320663958788,
-0.10138364881277084,
-0.01659282110631466,
-0.012018080800771713,
-0.07776615023612976,
0.10937822610139847,
-0.05858820304274559,
0.12143074721097946,
0.05320011079311371,
0.02969210222363472,
-0.019190305843949318,
-0.04412999004125595,
0.2875700891017914,
-0.031292930245399475,
0.06920883059501648,
0.13470400869846344,
-0.0770820900797844,
0.10330241918563843,
0.0998132973909378,
0.010230151005089283,
-0.06945645809173584,
0.05424240604043007,
-0.0010442417114973068,
-0.09128981083631516,
-0.14940139651298523,
-0.047912199050188065,
-0.06256367266178131,
-0.015404594130814075,
-0.05881268158555031,
-0.016028400510549545,
0.04657858610153198,
0.11769124120473862,
-0.005588404834270477,
0.06545121967792511,
-0.030723191797733307,
0.08592067658901215,
0.11285260319709778,
-0.07042589038610458,
0.13368575274944305,
0.030707992613315582,
-0.06708905100822449,
0.11193427443504333,
-0.08966420590877533,
0.14439477026462555,
0.0015850267373025417,
0.08501870930194855,
0.13043348491191864,
0.15910851955413818,
0.016425834968686104,
0.005592652130872011,
0.00303669017739594,
-0.0011183873284608126,
-0.08019854128360748,
-0.10004386305809021,
-0.11008957773447037,
0.06459902971982956,
0.11883325129747391,
0.016033072024583817,
-0.08498387783765793,
0.05537126958370209,
0.05705898255109787,
0.09818174690008163,
-0.013232654891908169,
-0.1934218555688858,
-0.07715164870023727,
0.0011143009178340435,
-0.0063454569317400455,
-0.0639541745185852,
0.05170689523220062,
0.039793673902750015,
-0.11962367594242096,
-0.08145463466644287,
-0.08399130403995514,
0.06469669938087463,
0.010069651529192924,
0.09355755150318146,
-0.026280643418431282,
0.014851483516395092,
0.024081187322735786,
0.09266316890716553,
-0.24152590334415436,
0.13189886510372162,
0.022570617496967316,
-0.03481745347380638,
-0.07870394736528397,
0.057868942618370056,
0.05549066886305809,
-0.04518169164657593,
0.19485385715961456,
0.00806523859500885,
-0.02085593342781067,
-0.13733556866645813,
-0.09050463140010834,
0.021674608811736107,
0.18202783167362213,
-0.07142587751150131,
0.11078120023012161,
-0.03774465620517731,
0.04970851168036461,
0.01306625921279192,
0.21129664778709412,
-0.07224621623754501,
-0.10745604336261749,
0.07223482429981232,
-0.11193037033081055,
0.002131694694980979,
-0.039176713675260544,
-0.033623192459344864,
-0.043765973299741745,
0.1851828545331955,
-0.11284594982862473,
-0.059579282999038696,
-0.11352556198835373,
-0.007034335285425186,
0.07050689309835434,
-0.08688335865736008,
0.0006954598939046264,
-0.035881176590919495,
-0.04364541172981262,
0.025304516777396202,
-0.06046760082244873,
0.15057314932346344,
-0.09263595938682556,
-0.127589613199234,
-0.0639301985502243,
0.11759265512228012,
0.05212002247571945,
0.041668545454740524,
-0.031068578362464905,
0.06226570904254913,
-0.06567215919494629,
-0.12455850839614868,
0.010887742973864079,
0.09556560963392258,
-0.019335150718688965,
0.04467788711190224,
-0.007622951176017523,
0.009654891677200794,
-0.07129699736833572,
-0.01830858364701271,
0.13165803253650665,
0.18686766922473907,
-0.06504907459020615,
0.14497943222522736,
0.11931753158569336,
-0.0703810453414917,
-0.2601728141307831,
-0.06711556017398834,
0.02246258035302162,
0.037234652787446976,
0.13991159200668335,
-0.08908361196517944,
0.0689583495259285,
0.01698782853782177,
-0.03244258463382721,
-0.011536444537341595,
-0.17741413414478302,
-0.09493041038513184,
0.12635517120361328,
0.041326846927404404,
0.16087386012077332,
-0.11086343228816986,
-0.07724824547767639,
0.07163313031196594,
-0.03894736245274544,
0.11673564463853836,
-0.026136765256524086,
0.07169976085424423,
0.02314017340540886,
0.033254314213991165,
0.08172410726547241,
-0.04338093101978302,
0.09601395577192307,
-0.052317045629024506,
-0.007661448325961828,
-0.13416928052902222,
-0.0759212002158165,
0.041677121073007584,
-0.038262613117694855,
0.1228988990187645,
-0.020670104771852493,
0.04466784745454788,
-0.1178957000374794,
-0.040687862783670425,
-0.08492836356163025,
0.06562226265668869,
-0.07441746443510056,
-0.07889726758003235,
-0.05797002837061882,
-0.01936706155538559,
0.08771965652704239,
-0.014874950051307678,
0.02113328129053116,
-0.06374114751815796,
0.000044163498387206346,
0.2345752865076065,
0.1342877894639969,
0.010160770267248154,
-0.16485296189785004,
-0.007849499583244324,
-0.008399340324103832,
0.07222898304462433,
-0.1299191415309906,
0.021420899778604507,
0.07322356849908829,
-0.014841508120298386,
0.07001446932554245,
0.0728922188282013,
-0.0774146020412445,
0.011770457029342651,
0.03846874088048935,
-0.0950385257601738,
-0.11050914227962494,
-0.1553163081407547,
-0.02899615839123726,
-0.07237151265144348,
-0.04653070494532585,
0.09286768734455109,
-0.08722228556871414,
0.01991962641477585,
-0.009585076943039894,
0.06914504617452621,
0.0701741874217987,
0.059437524527311325,
0.08614213019609451,
0.018415994942188263,
-0.0554540753364563,
-0.023826882243156433,
0.001889639999717474,
-0.1779353767633438,
0.07447990030050278,
0.023586280643939972,
-0.04571303725242615,
-0.06952781230211258,
0.049699362367391586,
-0.08793555200099945,
-0.0649498999118805,
0.0071395328268408775,
0.003917254041880369,
-0.04562242701649666,
0.06513329595327377,
0.1121121495962143,
0.10614603012800217,
0.08424212783575058,
-0.053723447024822235,
0.023274598643183708,
-0.019619451835751534,
0.12812061607837677,
0.0220117699354887,
-0.01920296810567379,
-0.010863486677408218,
0.04605351388454437,
-0.020693758502602577,
0.062411315739154816,
-0.06352504342794418,
-0.07275337725877762,
-0.10965748876333237,
-0.0334329754114151,
-0.026126623153686523,
0.04709339886903763,
-0.04586610198020935,
-0.030100969597697258,
0.012127387337386608,
0.02153690718114376,
0.033875588327646255,
0.013767142780125141,
-0.07129895687103271,
-0.026496265083551407,
-0.015386868268251419,
0.0982237160205841,
-0.07218731194734573,
0.011936523951590061,
0.13060270249843597,
-0.05382011830806732,
0.039167970418930054,
-0.03094806894659996,
-0.090217724442482,
0.046092111617326736,
-0.1339418590068817,
0.003299018833786249,
-0.003069708589464426,
-0.023676402866840363,
0.012485377490520477,
-0.09210821241140366,
-0.0068197972141206264,
0.026510581374168396,
0.023162439465522766,
-0.016967928037047386,
0.10629986226558685,
-0.1304495483636856,
0.046477191150188446,
0.08619112521409988,
-0.05291090905666351,
-0.06906044483184814,
0.0021020828280597925,
-0.07883159816265106,
0.021561840549111366,
0.11311720311641693,
-0.026049748063087463,
0.024123376235365868,
-0.12288360297679901,
0.011022485792636871,
0.031040631234645844,
0.018799249082803726,
-0.06292276084423065,
-0.0579792819917202,
0.030505357310175896,
-0.048442550003528595,
0.06570788472890854,
0.016214560717344284,
-0.010241873562335968,
0.01609637215733528,
0.15956048667430878,
0.03674813732504845,
-0.042633723467588425,
0.04647869989275932,
0.021148255094885826,
-0.0061432551592588425,
-0.057975221425294876,
0.047157272696495056,
-0.0708102434873581,
-0.16853520274162292,
0.12668341398239136,
0.02694733440876007,
0.01878299005329609,
0.13828913867473602,
0.04568184167146683,
0.05888235941529274,
-0.07133779674768448,
-0.000011787756193371024,
0.05699476599693298,
0.049503739923238754,
-0.039455752819776535,
0.14065171778202057,
0.11518748104572296,
-0.01743849739432335,
0.10283783823251724,
-0.018455609679222107,
-0.037577852606773376,
-0.12043607234954834,
-0.21542750298976898,
-0.05106654018163681,
-0.02776707522571087,
-0.0030930577777326107,
-0.06878586113452911,
0.05978359654545784,
0.045449063181877136,
0.020855864509940147,
-0.04721003770828247,
0.11629743874073029,
-0.13093340396881104,
-0.12196588516235352,
0.0342019684612751,
-0.09122742712497711,
0.008201674558222294,
-0.00870970543473959,
0.00178502534981817,
-0.0306510329246521,
0.0665239468216896,
0.07585511356592178,
0.05705946683883667,
0.03168534114956856,
-0.015567431226372719,
-0.12897220253944397,
-0.10983909666538239,
-0.03262420743703842,
0.0645490437746048,
-0.005236913915723562,
0.045466821640729904,
-0.015191225335001945,
-0.023906825110316277,
-0.011254558339715004,
0.10328133404254913,
-0.05185489356517792,
-0.08826041966676712,
-0.10188668221235275,
0.22805245220661163,
0.014708172529935837,
0.03385581821203232,
-0.02045450732111931,
-0.07496129721403122,
0.03527028113603592,
0.17561355233192444,
0.18362636864185333,
-0.03408478572964668,
-0.014622814953327179,
0.031506553292274475,
0.02367205172777176,
0.04722194746136665,
0.15169569849967957,
0.031227365136146545,
0.38120943307876587,
-0.0671495571732521,
0.08360617607831955,
-0.0429835170507431,
-0.04644845798611641,
0.007435040082782507,
0.03493458405137062,
0.05623011663556099,
-0.008731786161661148,
-0.03853217884898186,
0.1040455549955368,
-0.09202338755130768,
0.01977928727865219,
-0.061873190104961395,
-0.024549094960093498,
-0.021923406049609184,
-0.018665539100766182,
0.06805551052093506,
0.03765644505620003,
0.08817655593156815,
0.0035530636087059975,
-0.023686151951551437,
0.13866595923900604,
-0.0042418260127305984,
-0.1988290548324585,
0.14357589185237885,
0.1084677055478096,
-0.08207782357931137,
0.075953908264637,
0.036830149590969086,
0.20381499826908112,
0.0937977135181427,
0.034778300672769547,
-0.09281352162361145,
0.06680525839328766,
0.019506091251969337,
-0.12241567671298981,
0.06480325013399124,
0.11163681000471115,
0.06748226284980774,
0.04261741042137146,
0.0593135729432106,
-0.0793655514717102,
0.0654422789812088,
-0.03455284237861633,
-0.02559659443795681,
-0.09856884926557541,
0.048688359558582306,
-0.12118954211473465,
0.12266243249177933,
0.19002549350261688,
-0.004688942804932594,
-0.032454900443553925,
-0.08604561537504196,
-0.02128591202199459,
-0.0528583899140358,
0.03838116303086281,
-0.03136169910430908,
-0.0950496718287468,
0.0007240382255986333,
0.07379327714443207,
-0.0036951140500605106,
-0.2082260549068451,
-0.09219090640544891,
0.03412114828824997,
0.04332944378256798,
-0.0062530506402254105,
0.0939989760518074,
0.11883634328842163,
0.034104954451322556,
-0.05336460843682289,
-0.012550989165902138,
-0.043507397174835205,
0.07221900671720505,
-0.05735839530825615,
-0.05674823001027107
] |
null | null |
transformers
|
This model can convert the figurative/metaphorical expression to the literal expression. Below is the usage of our model:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("figurative-nlp/t5-figurative-paraphrase")
model = AutoModelForSeq2SeqLM.from_pretrained("figurative-nlp/t5-figurative-paraphrase")
input_ids = tokenizer(
"paraphrase the sentence : i will talk this story to you from A to Z", return_tensors="pt"
).input_ids # Batch size 1
outputs = model.generate(input_ids,num_beams = 5)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
#result : i will talk this story to you from beginning to end..
For example:
**Input**: He is always bang on when he makes a speech.
**Output**: He is always presice when he makes a speech.
**Input**: He always buy what he said.
**Output**: He always agree with what he said.
**Input**: Your team will be done like dinner if they play against the all-star team.
**Output**: Your team will be defeated if they play against the all-star team. (the one is not particularly accurate)
Note: the figurative language here includes metaphor, idiom and simile. We don't guarantee that the results generated results are satisfactory to you. We are trying to improve the effect of the model.
|
{}
|
text2text-generation
|
figurative-nlp/t5-figurative-paraphrase
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This model can convert the figurative/metaphorical expression to the literal expression. Below is the usage of our model:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("figurative-nlp/t5-figurative-paraphrase")
model = AutoModelForSeq2SeqLM.from_pretrained("figurative-nlp/t5-figurative-paraphrase")
input_ids = tokenizer(
"paraphrase the sentence : i will talk this story to you from A to Z", return_tensors="pt"
).input_ids # Batch size 1
outputs = model.generate(input_ids,num_beams = 5)
result = URL(outputs[0], skip_special_tokens=True)
#result : i will talk this story to you from beginning to end..
For example:
Input: He is always bang on when he makes a speech.
Output: He is always presice when he makes a speech.
Input: He always buy what he said.
Output: He always agree with what he said.
Input: Your team will be done like dinner if they play against the all-star team.
Output: Your team will be defeated if they play against the all-star team. (the one is not particularly accurate)
Note: the figurative language here includes metaphor, idiom and simile. We don't guarantee that the results generated results are satisfactory to you. We are trying to improve the effect of the model.
|
[
"# Batch size 1\n outputs = model.generate(input_ids,num_beams = 5)\n result = URL(outputs[0], skip_special_tokens=True)\n #result : i will talk this story to you from beginning to end..\n \n\n\n\nFor example:\n\n Input: He is always bang on when he makes a speech.\n \n Output: He is always presice when he makes a speech.\n \n Input: He always buy what he said.\n \n Output: He always agree with what he said. \n \n Input: Your team will be done like dinner if they play against the all-star team.\n \n Output: Your team will be defeated if they play against the all-star team. (the one is not particularly accurate)\n \n \n \n Note: the figurative language here includes metaphor, idiom and simile. We don't guarantee that the results generated results are satisfactory to you. We are trying to improve the effect of the model."
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Batch size 1\n outputs = model.generate(input_ids,num_beams = 5)\n result = URL(outputs[0], skip_special_tokens=True)\n #result : i will talk this story to you from beginning to end..\n \n\n\n\nFor example:\n\n Input: He is always bang on when he makes a speech.\n \n Output: He is always presice when he makes a speech.\n \n Input: He always buy what he said.\n \n Output: He always agree with what he said. \n \n Input: Your team will be done like dinner if they play against the all-star team.\n \n Output: Your team will be defeated if they play against the all-star team. (the one is not particularly accurate)\n \n \n \n Note: the figurative language here includes metaphor, idiom and simile. We don't guarantee that the results generated results are satisfactory to you. We are trying to improve the effect of the model."
] |
[
48,
207
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Batch size 1\n outputs = model.generate(input_ids,num_beams = 5)\n result = URL(outputs[0], skip_special_tokens=True)\n #result : i will talk this story to you from beginning to end..\n \n\n\n\nFor example:\n\n Input: He is always bang on when he makes a speech.\n \n Output: He is always presice when he makes a speech.\n \n Input: He always buy what he said.\n \n Output: He always agree with what he said. \n \n Input: Your team will be done like dinner if they play against the all-star team.\n \n Output: Your team will be defeated if they play against the all-star team. (the one is not particularly accurate)\n \n \n \n Note: the figurative language here includes metaphor, idiom and simile. We don't guarantee that the results generated results are satisfactory to you. We are trying to improve the effect of the model."
] |
[
0.009485136717557907,
-0.0914163812994957,
-0.004909302107989788,
0.051167238503694534,
0.0599968284368515,
-0.09070752561092377,
0.0495334155857563,
0.12437533587217331,
0.018630947917699814,
0.009418496862053871,
0.10556137561798096,
0.18199720978736877,
0.025037212297320366,
-0.011750400997698307,
0.014821485616266727,
-0.2629458010196686,
0.02921941503882408,
0.022863486781716347,
0.07969728857278824,
0.11409694701433182,
0.09200819581747055,
-0.04019659757614136,
0.10011935979127884,
0.027050349861383438,
-0.11179913580417633,
-0.03825526684522629,
0.0471140593290329,
0.013875072821974754,
0.0773206502199173,
0.04027353972196579,
-0.008127867244184017,
-0.00007593804184580222,
-0.11101335287094116,
-0.15057940781116486,
-0.0015069817891344428,
0.029685402289032936,
0.045487020164728165,
-0.0343700535595417,
-0.02242547646164894,
-0.037515152245759964,
0.22860966622829437,
0.002137009287253022,
0.02598707377910614,
0.09101767092943192,
-0.17020608484745026,
-0.011772945523262024,
0.003274659626185894,
0.040422920137643814,
0.11252458393573761,
0.08231822401285172,
-0.08293566852807999,
0.17628422379493713,
-0.06307654827833176,
0.08485535532236099,
-0.02696056105196476,
-0.20195059478282928,
-0.023463279008865356,
0.019009266048669815,
0.0241654422134161,
0.03086904250085354,
-0.07187780737876892,
-0.0027965744957327843,
0.01937606744468212,
0.0872906818985939,
-0.07159990072250366,
-0.008652836084365845,
-0.009215512312948704,
0.06331001967191696,
-0.12766945362091064,
-0.06608191877603531,
0.3047293722629547,
-0.011630520224571228,
-0.06445536017417908,
-0.1351747214794159,
-0.0022289033513516188,
0.004671045113354921,
0.0217848289757967,
-0.08635009080171585,
-0.005806824192404747,
0.06635905057191849,
0.03410590812563896,
0.08937055617570877,
-0.16657598316669464,
0.05698220059275627,
-0.15468230843544006,
0.03680572658777237,
-0.004254528321325779,
0.022135324776172638,
-0.11814515292644501,
0.04273614287376404,
-0.02195744216442108,
-0.12363937497138977,
-0.06147243455052376,
-0.11192286759614944,
0.017732318490743637,
-0.013794858008623123,
-0.11714661866426468,
-0.163129985332489,
-0.02305946871638298,
-0.01654520444571972,
0.05769219249486923,
0.022662119939923286,
0.022178253158926964,
0.07269157469272614,
0.1374279409646988,
0.12711675465106964,
-0.03745532035827637,
0.05810695141553879,
-0.0006582440109923482,
-0.003468524431809783,
0.05584610626101494,
-0.0305345356464386,
-0.08326108008623123,
0.09936907142400742,
0.04184303432703018,
-0.019163765013217926,
0.040941741317510605,
0.16285303235054016,
-0.11869177967309952,
-0.036268264055252075,
-0.01830316334962845,
-0.04481055960059166,
-0.09052545577287674,
-0.012634726241230965,
-0.10948149114847183,
0.08141719549894333,
0.01406524796038866,
0.019777147099375725,
-0.13511089980602264,
-0.0771130919456482,
-0.08108746260404587,
0.018946446478366852,
-0.054496560245752335,
-0.061775241047143936,
0.060539424419403076,
0.118726447224617,
-0.00552127743139863,
-0.11211936920881271,
-0.05525438115000725,
-0.03311837092041969,
0.039947863668203354,
-0.07417361438274384,
-0.046258002519607544,
-0.08759255707263947,
0.019401630386710167,
-0.021869005635380745,
0.022016027942299843,
-0.13952243328094482,
-0.03841642290353775,
0.060152068734169006,
0.06237328425049782,
0.049838729202747345,
-0.025658665224909782,
0.07721655070781708,
-0.22725948691368103,
-0.028520189225673676,
-0.17990514636039734,
0.1305428296327591,
0.013769755139946938,
0.007374989800155163,
-0.08118721842765808,
-0.0016816389979794621,
-0.09394028782844543,
0.06483571976423264,
-0.049409475177526474,
0.22055606544017792,
-0.2538507878780365,
-0.03614914044737816,
0.1532078981399536,
-0.07097399234771729,
-0.20035183429718018,
0.171103373169899,
-0.04218016937375069,
0.19039411842823029,
0.11218544095754623,
0.04853440448641777,
-0.004863197449594736,
-0.10149393230676651,
0.016982045024633408,
0.010430410504341125,
-0.03119870088994503,
0.13274279236793518,
0.06422534584999084,
-0.008050852455198765,
-0.0077213020995259285,
0.038062065839767456,
0.048510078340768814,
-0.007840173318982124,
-0.05368933826684952,
-0.05444065108895302,
-0.013548753224313259,
0.06869550049304962,
0.03474481776356697,
0.022439029067754745,
-0.00936882570385933,
-0.032746825367212296,
-0.10937171429395676,
0.01114229392260313,
-0.03505108878016472,
-0.0026358964387327433,
-0.008347447961568832,
-0.08871444314718246,
0.10243532806634903,
-0.035044848918914795,
0.029573118314146996,
-0.12628614902496338,
0.014061927795410156,
0.013495122082531452,
0.11187611520290375,
0.1315045803785324,
0.12864351272583008,
0.03210146725177765,
-0.018139010295271873,
0.062331970781087875,
0.02292449213564396,
0.03465932980179787,
0.053617000579833984,
-0.029862534254789352,
-0.09340609610080719,
0.10527921468019485,
-0.05304909124970436,
0.16308794915676117,
-0.07963987439870834,
0.02657955512404442,
0.028785619884729385,
0.02941756322979927,
-0.019766321405768394,
-0.015273778699338436,
-0.0010289547499269247,
0.02023588865995407,
-0.07561071217060089,
0.02004523202776909,
0.10630354285240173,
-0.00976594164967537,
-0.12115509808063507,
0.2368149608373642,
-0.26372095942497253,
-0.05563504993915558,
0.15287397801876068,
-0.25771257281303406,
-0.04527895525097847,
-0.0485050305724144,
-0.05506319925189018,
-0.03274920582771301,
0.08207158744335175,
-0.08277107775211334,
0.1769721508026123,
0.00046358295367099345,
0.04452560842037201,
-0.0919165313243866,
0.004065778572112322,
0.06615204364061356,
-0.11824152618646622,
0.009429548867046833,
0.07262556999921799,
-0.009216142818331718,
-0.15392255783081055,
0.149582639336586,
0.15167415142059326,
0.05744192376732826,
0.18065403401851654,
0.008693343959748745,
-0.0166271161288023,
-0.06676959991455078,
0.08982154726982117,
-0.05440264567732811,
0.027389343827962875,
-0.23348507285118103,
-0.023224402219057083,
0.011881801299750805,
-0.037953853607177734,
0.030020322650671005,
-0.10593049973249435,
0.004010726697742939,
0.0803222581744194,
0.05203670263290405,
-0.020157234743237495,
0.07434506714344025,
-0.01589779742062092,
0.11326584219932556,
-0.020315812900662422,
0.023478612303733826,
0.0880257859826088,
0.01082814671099186,
-0.14390705525875092,
0.15493282675743103,
-0.13326099514961243,
-0.29935240745544434,
-0.08504730463027954,
-0.03211847320199013,
-0.07686321437358856,
0.03974949195981026,
0.06514289975166321,
-0.0813085213303566,
-0.009484441950917244,
-0.0431307777762413,
0.12448842078447342,
-0.01211849320679903,
-0.09543059021234512,
-0.10382480174303055,
-0.029964538291096687,
-0.07893985509872437,
-0.1049051284790039,
-0.07334211468696594,
-0.0046565597876906395,
-0.06204169988632202,
0.09192225337028503,
-0.15765924751758575,
0.02555680461227894,
0.23281225562095642,
0.022758102044463158,
0.02797546237707138,
-0.065304234623909,
0.21021778881549835,
0.012445013038814068,
0.053709208965301514,
0.19630831480026245,
-0.035730861127376556,
0.06470426172018051,
0.08834614604711533,
-0.007804710417985916,
-0.008670558221638203,
0.04325924441218376,
-0.02256106585264206,
-0.05529336258769035,
-0.08443921059370041,
-0.09716436266899109,
-0.12358838319778442,
0.06952250748872757,
-0.01266813836991787,
-0.0257599875330925,
0.07459313422441483,
0.0870116576552391,
-0.05963223800063133,
-0.03845662996172905,
0.04149360954761505,
0.09008656442165375,
0.17588427662849426,
-0.09625199437141418,
0.08258326351642609,
-0.025614067912101746,
-0.07848235964775085,
0.026559321209788322,
0.04401440545916557,
-0.0604507140815258,
0.037467896938323975,
0.06297130882740021,
0.1273951381444931,
-0.0040076314471662045,
-0.02920496091246605,
0.029055653139948845,
-0.08207585662603378,
-0.03432512283325195,
-0.10768914222717285,
-0.12028567492961884,
-0.17745889723300934,
0.08773890882730484,
0.07095673680305481,
0.008281048387289047,
-0.0747438296675682,
-0.0054547651670873165,
0.0918664038181305,
0.015610234811902046,
0.06365900486707687,
-0.2534484565258026,
-0.10276287794113159,
0.0489029735326767,
0.05189157649874687,
-0.04316335171461105,
0.060805823653936386,
0.059305377304553986,
-0.07459744065999985,
0.04007056728005409,
-0.0427064448595047,
0.05989859998226166,
0.08295506238937378,
0.11172378063201904,
-0.07838205248117447,
-0.04539841413497925,
0.01465862337499857,
0.10284949094057083,
-0.33403050899505615,
0.041465312242507935,
-0.014788168482482433,
-0.07694950699806213,
-0.11445970833301544,
-0.021772796288132668,
-0.037508998066186905,
0.008473987691104412,
0.16475017368793488,
0.05346781760454178,
-0.054411083459854126,
-0.09909576177597046,
0.018652524799108505,
0.006655696779489517,
0.05945533141493797,
0.016797950491309166,
0.09885811060667038,
-0.03777364641427994,
0.052928660064935684,
0.04267190024256706,
0.16485798358917236,
0.008932476863265038,
-0.168002188205719,
0.06510613858699799,
-0.09841480106115341,
-0.059073708951473236,
0.02254299446940422,
0.01298344973474741,
0.004309672396630049,
0.08976779133081436,
-0.041866663843393326,
-0.10727497935295105,
-0.044668037444353104,
-0.05063561350107193,
-0.032637763768434525,
-0.05677478387951851,
-0.06280826032161713,
-0.09768816828727722,
0.02133883163332939,
-0.028308061882853508,
-0.08902082592248917,
0.09874645620584488,
-0.053122859448194504,
-0.05418058857321739,
-0.1109178364276886,
0.12126090377569199,
0.0006996926385909319,
0.05885084345936775,
-0.02284977212548256,
0.01454282645136118,
-0.08842771500349045,
-0.08750125765800476,
-0.036006782203912735,
0.022325526922941208,
0.02187175117433071,
0.023593386635184288,
-0.06156061589717865,
0.0006693632458336651,
-0.1478087157011032,
-0.009942147880792618,
0.14339472353458405,
0.07096323370933533,
-0.04568993300199509,
0.09485321491956711,
0.00295228511095047,
-0.08564621210098267,
-0.22558672726154327,
-0.07811553031206131,
-0.009842117317020893,
0.0003385400050319731,
0.02809949778020382,
-0.03475681692361832,
0.09398868680000305,
-0.047717928886413574,
0.030823906883597374,
0.0008334251469932497,
-0.20542342960834503,
-0.09901425987482071,
0.11581995338201523,
0.042916011065244675,
0.1995195597410202,
-0.09824914485216141,
0.014404798857867718,
0.05605795234441757,
-0.08900818973779678,
0.20551207661628723,
-0.03349032998085022,
0.058853473514318466,
0.03772887960076332,
0.10098648816347122,
0.05881449580192566,
0.013304171152412891,
0.03529350459575653,
0.03140013664960861,
0.005459868814796209,
-0.07979787886142731,
-0.13260574638843536,
0.09627663344144821,
-0.02464878000319004,
0.10657308995723724,
-0.08219404518604279,
0.0006211561267264187,
-0.17258696258068085,
-0.018053460866212845,
-0.08530674874782562,
0.0276594590395689,
-0.1017427146434784,
-0.07683733105659485,
-0.007945798337459564,
0.035509075969457626,
0.09439487755298615,
-0.04881465062499046,
0.057306207716464996,
-0.13754817843437195,
0.12099847197532654,
0.31868356466293335,
0.09750120341777802,
0.028851168230175972,
-0.07854937762022018,
-0.001081310911104083,
0.0022247780580073595,
0.05137686803936958,
-0.1438937783241272,
0.0064629619009792805,
0.08133580535650253,
0.006132874172180891,
0.09895703941583633,
0.08100575953722,
-0.06649928539991379,
-0.007619540672749281,
0.07709954679012299,
-0.1101108193397522,
-0.1597951054573059,
-0.05291382595896721,
0.01844971626996994,
0.003387544071301818,
-0.04701898247003555,
0.07244223356246948,
-0.1352354735136032,
0.007067394442856312,
0.03499792143702507,
0.0891433134675026,
-0.03009955585002899,
0.0688735693693161,
-0.033436961472034454,
0.021766414865851402,
-0.047730498015880585,
-0.0072787427343428135,
-0.0031033032573759556,
-0.24063915014266968,
0.07204506546258926,
-0.03948948159813881,
-0.024736329913139343,
-0.06915096193552017,
-0.05562221258878708,
0.06458554416894913,
-0.123476043343544,
0.004449708387255669,
-0.022250181064009666,
-0.17929020524024963,
0.014417954720556736,
0.1790231615304947,
0.05524907633662224,
0.09628819674253464,
-0.02587573230266571,
0.031967394053936005,
-0.010430483147501945,
0.12638917565345764,
0.09944064170122147,
-0.05628770589828491,
0.003341787029057741,
0.09390261024236679,
0.02259356901049614,
0.10420092940330505,
-0.06463257223367691,
-0.12176760286092758,
-0.1416175216436386,
0.012858300469815731,
-0.06451545655727386,
0.01421821117401123,
-0.06766267120838165,
-0.02849677950143814,
0.00202006078325212,
0.04359455034136772,
0.027591848745942116,
-0.028851376846432686,
-0.03960704058408737,
0.0686303898692131,
0.023597411811351776,
0.10318351536989212,
-0.07804109156131744,
-0.004232428502291441,
0.09405513107776642,
-0.08243443071842194,
-0.011978579685091972,
0.06572235375642776,
-0.16183249652385712,
0.06553155928850174,
-0.1493905782699585,
0.054627932608127594,
0.0663740411400795,
0.013150876387953758,
0.012435121461749077,
-0.05475052446126938,
0.008675966411828995,
0.06029858812689781,
0.019218651577830315,
-0.03333656117320061,
0.14601945877075195,
-0.08328261971473694,
0.030885258689522743,
0.11816693097352982,
-0.10080970078706741,
-0.10475743561983109,
0.047806475311517715,
-0.14666999876499176,
0.049326084554195404,
0.1791495531797409,
-0.03871547430753708,
0.03577058017253876,
-0.14667156338691711,
-0.010707163251936436,
0.05066045746207237,
-0.02465244010090828,
-0.030434997752308846,
-0.060640446841716766,
0.008042451925575733,
-0.02646070159971714,
0.1059964969754219,
0.02702265791594982,
0.029517538845539093,
0.015508216805756092,
0.14238591492176056,
0.0058775111101567745,
0.024929098784923553,
-0.007595639675855637,
-0.06280265003442764,
-0.02844955213367939,
-0.15026608109474182,
0.04046611860394478,
-0.028692085295915604,
-0.018163098022341728,
0.15953752398490906,
0.11412619054317474,
0.005433354992419481,
0.09163902699947357,
0.06496289372444153,
0.13761122524738312,
-0.022128403186798096,
-0.06109771132469177,
0.06414294987916946,
0.032020069658756256,
-0.048722896724939346,
0.1360791027545929,
0.20160269737243652,
-0.07140447199344635,
0.07559728622436523,
-0.10991454124450684,
-0.023819278925657272,
-0.06205524876713753,
-0.2470875382423401,
0.010576804168522358,
-0.07373357564210892,
-0.02036307193338871,
-0.08240445703268051,
0.047049619257450104,
-0.005472456570714712,
0.07624351233243942,
-0.11484972387552261,
0.08937534689903259,
-0.0447210967540741,
-0.1293264627456665,
-0.03630063310265541,
-0.005558209028095007,
0.06227714940905571,
0.03326315060257912,
0.07860536128282547,
-0.009422969073057175,
0.058702509850263596,
0.04671564698219299,
0.09261805564165115,
0.05005734786391258,
-0.008609551005065441,
-0.14819400012493134,
-0.06300011277198792,
-0.027795573696494102,
-0.00460901390761137,
-0.06859104335308075,
0.06093894690275192,
0.014211817644536495,
-0.03691663220524788,
0.009081919677555561,
0.06450729072093964,
-0.0662127360701561,
-0.18708831071853638,
-0.09884992986917496,
0.441694438457489,
0.0035548668820410967,
0.03580063581466675,
-0.010829072445631027,
-0.05197076126933098,
-0.045889802277088165,
0.18429936468601227,
0.19106879830360413,
-0.06695400923490524,
-0.017137249931693077,
-0.055559687316417694,
0.06042623519897461,
0.03067588061094284,
0.10731000453233719,
0.007465923670679331,
0.3290935158729553,
-0.07217785716056824,
0.1569349616765976,
-0.06497301906347275,
-0.02985863760113716,
-0.0006007064948789775,
0.0917336568236351,
0.12132605165243149,
-0.019816840067505836,
-0.051321279257535934,
0.07752752304077148,
-0.1839865744113922,
0.042246971279382706,
-0.12587787210941315,
-0.0905459001660347,
0.024452747777104378,
-0.030499346554279327,
0.06924131512641907,
-0.0004938138881698251,
0.13532140851020813,
0.0132384542375803,
-0.048340681940317154,
0.05792197957634926,
0.02349155582487583,
-0.08430460095405579,
0.14040224254131317,
0.09728408604860306,
-0.0866958498954773,
-0.018465226516127586,
0.021593410521745682,
0.1580948680639267,
0.09965167939662933,
0.10518650710582733,
0.016719786450266838,
0.0693412572145462,
-0.036773622035980225,
0.03287747502326965,
-0.03440610319375992,
0.00010445771476952359,
0.05464746430516243,
0.06298860162496567,
0.06205414608120918,
-0.16484282910823822,
0.09216490387916565,
0.013731035403907299,
-0.015326120890676975,
-0.07595989853143692,
0.11022955179214478,
-0.03044068068265915,
0.10960487276315689,
0.1872866451740265,
0.00003222638042643666,
-0.06050534546375275,
-0.07835237681865692,
-0.04295216500759125,
-0.02144794538617134,
0.04664546623826027,
-0.03241841122508049,
-0.07179317623376846,
0.016974162310361862,
0.03784782811999321,
0.02258731797337532,
-0.28719696402549744,
-0.04463513195514679,
0.07127409428358078,
0.013137031346559525,
0.042523451149463654,
0.10967141389846802,
0.09322451055049896,
0.07339618355035782,
-0.0880882740020752,
0.0054304515942931175,
0.036323729902505875,
0.16665376722812653,
0.036739531904459,
-0.08417531102895737
] |
null | null | null |
import requests
API_URL = "https://api-inference.huggingface.co/models/huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad"
headers = {"Authorization": "Bearer api_UXqrzQBiZKXaWxstVwEKcYvHQpGSGiQGbr"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
output = query({
"inputs": {
"question": "What's my name?",
"context": "My name is Clara and I live in Berkeley.",
},
})
|
{}
| null |
fihtrotuld/123
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
import requests
API_URL = "URL
headers = {"Authorization": "Bearer api_UXqrzQBiZKXaWxstVwEKcYvHQpGSGiQGbr"}
def query(payload):
response = URL(API_URL, headers=headers, json=payload)
return URL()
output = query({
"inputs": {
"question": "What's my name?",
"context": "My name is Clara and I live in Berkeley.",
},
})
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
[
0.024608636274933815,
-0.026205500587821007,
-0.009666500613093376,
-0.10395516455173492,
0.08638657629489899,
0.059816278517246246,
0.01882290467619896,
0.020661840215325356,
0.23975107073783875,
-0.005599027033895254,
0.1219947561621666,
0.0015615287702530622,
-0.037353623658418655,
0.03733762726187706,
-0.0035912662278860807,
-0.17583473026752472,
0.03876631706953049,
-0.018274923786520958,
0.01843859627842903,
0.026470553129911423,
-0.07776834815740585,
-0.07564429938793182,
0.015296397730708122,
-0.10247814655303955,
-0.083692267537117,
0.11002834886312485,
0.031466204673051834,
-0.019670886918902397,
0.10779199749231339,
-0.04243955761194229,
0.18699054419994354,
-0.011512263678014278,
-0.11213519424200058,
-0.2536850869655609,
0.021806683391332626,
-0.01765260472893715,
-0.08747660368680954,
0.01506110467016697,
0.0665089413523674,
-0.09014441072940826,
-0.0588928684592247,
0.0795099288225174,
-0.01132340170443058,
0.04246443510055542,
-0.27593839168548584,
-0.12684126198291779,
-0.05297930911183357,
-0.1421966552734375,
0.08651168644428253,
0.04035491496324539,
0.008764253929257393,
0.15506891906261444,
-0.20897391438484192,
0.004104613792151213,
0.08255259692668915,
-0.2538507878780365,
0.05591634660959244,
0.17671173810958862,
0.03623908758163452,
0.18037272989749908,
0.0060391901060938835,
0.11029672622680664,
0.0716743916273117,
-0.024263937026262283,
-0.17590197920799255,
-0.08127854019403458,
-0.04696211963891983,
0.16642488539218903,
-0.06727185100317001,
-0.14248386025428772,
0.34701237082481384,
0.00015008423360995948,
0.009657775051891804,
0.16921205818653107,
-0.059524230659008026,
-0.09972117841243744,
0.07259953022003174,
0.016484731808304787,
0.018492350354790688,
0.1471305936574936,
0.16307872533798218,
-0.0458691343665123,
-0.13837823271751404,
-0.018630273640155792,
-0.22798998653888702,
0.17510560154914856,
-0.03248048573732376,
0.13137903809547424,
-0.27447956800460815,
0.01684025302529335,
-0.2570667266845703,
0.0032130838371813297,
0.04178816080093384,
-0.06004921346902847,
-0.0226522795855999,
-0.013265985064208508,
-0.08018817007541656,
0.004899587947875261,
0.06192673370242119,
0.1266920566558838,
-0.06128726154565811,
0.06128238886594772,
-0.09319206327199936,
0.141696035861969,
0.07166698575019836,
0.07868369668722153,
0.13037432730197906,
0.041205424815416336,
-0.07187089323997498,
-0.21872246265411377,
-0.0026476888451725245,
-0.06275863200426102,
-0.09502086788415909,
-0.0020165652967989445,
-0.11606067419052124,
0.17244569957256317,
-0.030802514404058456,
-0.09825427830219269,
-0.11208184063434601,
0.09148659557104111,
-0.032992321997880936,
-0.03437839448451996,
-0.03552987426519394,
-0.020977836102247238,
0.019381176680326462,
0.04704452306032181,
-0.1548958420753479,
-0.005131472367793322,
0.07039852440357208,
0.11502562463283539,
-0.1346137970685959,
-0.003783059772104025,
-0.07908964157104492,
0.03039063885807991,
0.07654735445976257,
-0.16510222852230072,
0.03158547356724739,
-0.1124754324555397,
-0.07531405985355377,
0.002912673633545637,
-0.015710093080997467,
-0.016202643513679504,
0.166526660323143,
-0.0020451415330171585,
0.0714716836810112,
-0.026345307007431984,
-0.05890209600329399,
-0.11243434250354767,
-0.08489254862070084,
0.05390460044145584,
0.03670717030763626,
0.03266148269176483,
-0.2193479984998703,
0.014805203303694725,
-0.12762966752052307,
0.1360815018415451,
-0.10566820204257965,
-0.04705966264009476,
-0.022842247039079666,
0.20562705397605896,
0.037286072969436646,
0.08762791007757187,
-0.22171171009540558,
0.039756543934345245,
-0.05404696613550186,
0.18480908870697021,
-0.1502426266670227,
-0.0799463614821434,
0.20813211798667908,
-0.07964949309825897,
-0.10115210711956024,
0.021235812455415726,
0.020391687750816345,
0.026287272572517395,
0.0766737088561058,
0.4564172327518463,
-0.09766800701618195,
-0.09146861732006073,
0.10178250074386597,
0.17055274546146393,
-0.12427149713039398,
-0.1827561855316162,
0.06446871906518936,
-0.16666454076766968,
-0.1973118633031845,
0.0018917324487119913,
0.09222044050693512,
0.038269978016614914,
-0.07875611633062363,
-0.020746968686580658,
0.06325206160545349,
-0.0007678253459744155,
0.09095914661884308,
0.03755716234445572,
0.09034032374620438,
-0.08716782182455063,
0.11115926504135132,
-0.05017651244997978,
0.004037132486701012,
0.1343354731798172,
0.027325427159667015,
-0.03223329409956932,
0.08694463223218918,
-0.0485352948307991,
0.05295134335756302,
-0.1662379503250122,
-0.15068690478801727,
0.03398871049284935,
0.06283251196146011,
0.03186952322721481,
0.1280253529548645,
0.08141885697841644,
-0.10732853412628174,
0.022690722718834877,
-0.004228927195072174,
0.058398615568876266,
0.03891623765230179,
0.006107209715992212,
0.008764320984482765,
0.0961301177740097,
-0.10607069730758667,
-0.13589619100093842,
-0.07336436957120895,
-0.014715781435370445,
0.14371353387832642,
-0.0302802175283432,
0.07690227776765823,
-0.004240254405885935,
0.00013200697139836848,
0.06930823624134064,
0.08137880265712738,
0.016412746161222458,
0.08971183747053146,
-0.05237193778157234,
-0.05160155147314072,
0.10863113403320312,
-0.13533565402030945,
0.17837053537368774,
0.14053137600421906,
-0.20532016456127167,
0.029453208670020103,
-0.06838275492191315,
0.03670361638069153,
-0.008162540383636951,
0.0975119024515152,
-0.08272241055965424,
-0.02106042578816414,
0.013134466484189034,
0.0052274600602686405,
-0.013007243163883686,
0.017682146281003952,
-0.07295988500118256,
-0.07787393033504486,
-0.10233919322490692,
0.08436838537454605,
0.11562882363796234,
-0.10282530635595322,
0.14214380085468292,
0.4384984076023102,
0.11495281755924225,
0.21582984924316406,
-0.09581480920314789,
-0.0412987545132637,
0.007486371789127588,
0.0001535322517156601,
-0.04476691037416458,
0.08031861484050751,
-0.15973517298698425,
-0.038901735097169876,
0.027348900213837624,
0.07128690183162689,
0.11475157737731934,
-0.14959022402763367,
-0.09639324247837067,
-0.00793045200407505,
0.0022841424215584993,
-0.1249532699584961,
0.023905446752905846,
-0.03974650055170059,
0.04015624523162842,
0.07232289016246796,
-0.021535737439990044,
0.13939237594604492,
-0.04166141897439957,
-0.0639561116695404,
0.07585346698760986,
-0.2017085999250412,
-0.23179671168327332,
-0.12309670448303223,
-0.14680525660514832,
0.04366797208786011,
0.05154111236333847,
0.01726446859538555,
-0.17635835707187653,
-0.015074856579303741,
0.07706750929355621,
0.07820965349674225,
-0.20886357128620148,
-0.022814949974417686,
-0.004290030337870121,
0.0895976573228836,
-0.10227091610431671,
-0.0017130117630586028,
-0.04419664293527603,
-0.10150232166051865,
0.0017003051470965147,
0.07279510796070099,
-0.137485533952713,
0.13807645440101624,
0.21589438617229462,
0.07225540280342102,
0.07359948754310608,
-0.019093448296189308,
0.09936179965734482,
-0.10856141895055771,
-0.16549113392829895,
0.08348225057125092,
-0.06234746053814888,
0.047262318432331085,
0.17534415423870087,
0.03307317942380905,
-0.13904969394207,
-0.015682822093367577,
-0.0402069091796875,
-0.15603256225585938,
-0.238995760679245,
-0.09178274869918823,
-0.1182505264878273,
0.16442428529262543,
0.0009358620154671371,
0.06651917099952698,
0.08258313685655594,
-0.022042419761419296,
0.16447891294956207,
-0.07379321753978729,
-0.07578866183757782,
-0.006978808436542749,
0.12375060468912125,
-0.056660156697034836,
-0.03080669604241848,
-0.10566964000463486,
-0.008295975625514984,
0.1151021271944046,
0.15304014086723328,
0.12214863300323486,
0.2957419455051422,
0.08268889784812927,
0.026645636186003685,
0.08958091586828232,
0.17622539401054382,
0.09495089203119278,
0.07838419824838638,
-0.045413073152303696,
-0.014814783819019794,
0.014317171648144722,
-0.04022889584302902,
0.010141594335436821,
0.14683100581169128,
-0.2679629921913147,
-0.006678564939647913,
-0.2710230350494385,
0.0965198427438736,
-0.10913380235433578,
0.11837165057659149,
-0.01015760749578476,
0.10194015502929688,
0.11082887649536133,
0.03233652561903,
-0.03858073800802231,
0.16613617539405823,
0.08450309932231903,
-0.11277695000171661,
0.001758623169735074,
0.03737903758883476,
0.09715615212917328,
-0.02818971499800682,
0.12721189856529236,
-0.11048974841833115,
-0.1464834064245224,
0.013753619976341724,
0.07152791321277618,
-0.15373679995536804,
0.3138748109340668,
0.012069208547472954,
-0.13481520116329193,
-0.01481647603213787,
-0.09957809001207352,
-0.006440147757530212,
0.1254177987575531,
0.09333524852991104,
0.07935678958892822,
-0.2185502052307129,
-0.13339371979236603,
0.05872276425361633,
-0.00575496768578887,
0.22408108413219452,
-0.034034017473459244,
-0.11356475204229355,
-0.027013886719942093,
0.04241163283586502,
-0.06043251231312752,
0.08524788916110992,
0.023536119610071182,
-0.08113526552915573,
-0.032957352697849274,
0.05323701351881027,
0.012368366122245789,
0.00524376705288887,
0.09360801428556442,
0.020107939839363098,
-0.0009265501867048442,
0.01785753294825554,
0.047885000705718994,
-0.0675911232829094,
-0.1984109878540039,
0.09357594698667526,
-0.05215044692158699,
0.0015536568826064467,
-0.08013670891523361,
-0.15122665464878082,
-0.08837161958217621,
-0.16009655594825745,
0.12540200352668762,
-0.034406669437885284,
0.12700119614601135,
-0.06619787961244583,
0.17341409623622894,
-0.07871770113706589,
0.04481020197272301,
-0.047349292784929276,
0.050332702696323395,
-0.007268077693879604,
-0.07756082713603973,
0.16585899889469147,
-0.15564003586769104,
0.01809087023139,
0.19572502374649048,
-0.018915493041276932,
0.07177707552909851,
0.021322092041373253,
-0.0636206790804863,
0.23147478699684143,
0.3014698624610901,
0.008138049393892288,
0.1665448248386383,
0.3018903136253357,
-0.07466315478086472,
-0.2642788887023926,
-0.05505012720823288,
-0.2841376066207886,
-0.05371501296758652,
0.10716094076633453,
-0.22523896396160126,
0.06986407935619354,
0.14383509755134583,
-0.06471995264291763,
0.30228954553604126,
-0.21825523674488068,
0.012589273042976856,
0.15434536337852478,
-0.08868814259767532,
0.5515313148498535,
-0.1133413165807724,
-0.17677772045135498,
-0.008122089318931103,
-0.08741296827793121,
0.10602109134197235,
-0.0340677872300148,
0.06877441704273224,
0.013465235009789467,
0.04797380417585373,
0.048932258039712906,
-0.03111894056200981,
0.22701001167297363,
0.008710170164704323,
0.09015397727489471,
-0.07378865778446198,
-0.18624304234981537,
0.11639340221881866,
-0.04359482601284981,
-0.08891059458255768,
0.0849778801202774,
-0.05942516401410103,
-0.11078983545303345,
0.04663389176130295,
-0.07950539886951447,
-0.024862350896000862,
0.08423490077257156,
-0.04678233340382576,
-0.042606171220541,
-0.008054176345467567,
-0.1618063747882843,
-0.0002289071271661669,
0.31360217928886414,
-0.07096036523580551,
0.16695955395698547,
0.03677211329340935,
0.00038613268407061696,
-0.11027684062719345,
0.030288029462099075,
-0.05203165486454964,
-0.021576624363660812,
0.09578979015350342,
-0.11096979677677155,
0.03204701095819473,
0.14160704612731934,
-0.04864364117383957,
0.05846960097551346,
0.09256096184253693,
-0.0849417969584465,
0.007583672646433115,
0.17753590643405914,
-0.17537221312522888,
-0.1273445188999176,
-0.006135711446404457,
-0.09862716495990753,
0.14055661857128143,
0.04394126310944557,
0.05191568285226822,
0.16669964790344238,
0.03967129811644554,
-0.029474308714270592,
-0.02817419543862343,
-0.1153380498290062,
-0.0201893113553524,
0.040153320878744125,
0.00045633706031367183,
-0.08791285753250122,
0.2262638509273529,
0.06409153342247009,
-0.1328488290309906,
-0.051157206296920776,
0.2161225974559784,
-0.06805316358804703,
-0.04911920800805092,
-0.223562553524971,
0.10752306133508682,
-0.07112517952919006,
-0.0965060144662857,
0.05453834682703018,
-0.02270081453025341,
0.005106312222778797,
0.181985542178154,
0.03941008821129799,
0.11070270836353302,
0.03738937899470329,
-0.02448922023177147,
0.15798696875572205,
-0.142850860953331,
-0.14191335439682007,
-0.025354057550430298,
-0.08757315576076508,
-0.13844476640224457,
-0.026804137974977493,
0.1617041826248169,
-0.09177309274673462,
-0.14772607386112213,
-0.2621181011199951,
0.10968475043773651,
-0.16432365775108337,
-0.10192688554525375,
-0.03469514101743698,
-0.08968492597341537,
0.0696166530251503,
0.030301768332719803,
-0.03093348816037178,
-0.06706760823726654,
-0.18593791127204895,
0.0816768929362297,
0.06349513679742813,
0.045533183962106705,
-0.017847947776317596,
0.0067379772663116455,
0.1720137596130371,
0.025955144315958023,
0.10040043294429779,
0.16762186586856842,
0.011397695168852806,
0.2246655523777008,
-0.1671202927827835,
-0.11496317386627197,
0.1336962729692459,
-0.026543032377958298,
0.06762003898620605,
0.16792191565036774,
-0.0772583931684494,
0.015526676550507545,
-0.028136352077126503,
0.07066910713911057,
-0.11003983020782471,
-0.105624258518219,
0.007937257178127766,
0.02567129209637642,
-0.2755882740020752,
-0.005599735304713249,
-0.19717298448085785,
0.14788752794265747,
0.02579621411859989,
0.03297143429517746,
0.10257530212402344,
0.10404334217309952,
0.08312062919139862,
-0.0017710148822516203,
0.03226327523589134,
-0.1176818460226059,
0.02753005363047123,
-0.059239376336336136,
-0.020663779228925705,
0.017624232918024063,
0.36952024698257446,
-0.03603357449173927,
-0.046802736818790436,
0.003710439894348383,
0.1307835876941681,
-0.02139742486178875,
0.017395347356796265,
0.13209912180900574,
0.12607666850090027,
-0.08595693111419678,
-0.1504845917224884,
0.04888554662466049,
-0.04565655067563057,
-0.02836887165904045,
0.1464131623506546,
0.05905961990356445,
0.1050296202301979,
0.0908031314611435,
-0.014463032595813274,
-0.00318976235575974,
0.012856799177825451,
-0.15486004948616028,
0.06223496049642563,
-0.010558074340224266,
0.012565906159579754,
0.017934376373887062,
0.15238402783870697,
-0.005540105979889631,
0.07739730179309845,
-0.09889880567789078,
0.004208535887300968,
-0.13498884439468384,
-0.07913459837436676,
0.03617347031831741,
-0.13393273949623108,
0.04141177982091904,
-0.01871878281235695,
0.029611799865961075,
0.30386561155319214,
0.02558239921927452,
-0.020639164373278618,
0.12512871623039246,
-0.1214587539434433,
-0.12050267308950424,
-0.001594188273884356,
-0.029960084706544876,
0.0791488066315651,
-0.02633434161543846,
-0.0997740775346756,
-0.1001306027173996,
-0.15166029334068298,
-0.09759195148944855,
0.05182836204767227,
-0.04993441700935364,
-0.059362251311540604,
-0.17634081840515137,
-0.05707859992980957,
-0.05147340148687363,
0.14025864005088806,
-0.12263951450586319,
0.15159130096435547,
-0.014490418136119843,
0.004084470681846142,
0.04405883327126503,
0.1950942426919937,
-0.03644494712352753,
0.08714226633310318,
0.0154351145029068,
0.1522706001996994,
-0.05119588226079941,
0.14720745384693146,
-0.10931728035211563,
-0.04014137014746666,
-0.06710435450077057,
0.21513493359088898,
0.25630924105644226,
-0.06136954948306084,
-0.008937356993556023,
-0.012760217301547527,
0.058654606342315674,
0.1073930487036705,
0.16049085557460785,
0.002326392102986574,
0.2802925705909729,
-0.03133585304021835,
0.04815128445625305,
0.02901598811149597,
0.013607407920062542,
-0.06336209923028946,
0.03397751972079277,
0.07539387792348862,
-0.035039983689785004,
-0.1412304788827896,
0.15837742388248444,
-0.21980468928813934,
0.18157227337360382,
0.11640069633722305,
-0.19996967911720276,
-0.013728445395827293,
-0.04882071167230606,
0.1689416468143463,
-0.0856364443898201,
0.1637246012687683,
-0.0903693437576294,
-0.2108195722103119,
-0.2056000679731369,
0.03867346793413162,
-0.34623071551322937,
-0.254462867975235,
0.10422009229660034,
0.1488201916217804,
0.04015883058309555,
-0.018507536500692368,
-0.019967829808592796,
-0.018367022275924683,
0.04877542704343796,
-0.0067357709631323814,
0.06014643982052803,
0.031397558748722076,
-0.02988368645310402,
-0.24127542972564697,
-0.029804671183228493,
0.023964406922459602,
-0.07093082368373871,
0.07464958727359772,
-0.06874357163906097,
-0.022495782002806664,
0.08059766888618469,
-0.03066304884850979,
0.03298592567443848,
-0.035373736172914505,
-0.16326889395713806,
0.027529051527380943,
0.03900543600320816,
0.036012712866067886,
0.00634160777553916,
0.0008072225609794259,
-0.03455270454287529,
0.0644603744149208,
-0.16716794669628143,
-0.16015739738941193,
0.14140215516090393,
-0.06745140254497528,
0.2779497504234314,
-0.05812826007604599,
-0.0809100940823555,
0.04766704887151718,
-0.03426874056458473,
0.1807648241519928,
-0.07756473124027252,
0.047254521399736404,
0.12766779959201813,
0.011127962730824947,
0.03121316432952881,
-0.3092964291572571,
0.11082969605922699,
-0.000795336440205574,
-0.006093299947679043,
-0.07581598311662674
] |
null | null |
transformers
|
# GPT2 base style transfer paraphraser
This is the trained base-model from the paper [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700) by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author.
## Citation
If you found this model useful, please cite the original work:
```
@inproceedings{style20,
author={Kalpesh Krishna and John Wieting and Mohit Iyyer},
Booktitle = {Empirical Methods in Natural Language Processing},
Year = "2020",
Title={Reformulating Unsupervised Style Transfer as Paraphrase Generation},
}
```
|
{}
|
text-generation
|
filco306/gpt2-base-style-paraphraser
|
[
"transformers",
"pytorch",
"text-generation",
"arxiv:2010.05700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.05700"
] |
[] |
TAGS
#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us
|
# GPT2 base style transfer paraphraser
This is the trained base-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author.
If you found this model useful, please cite the original work:
|
[
"# GPT2 base style transfer paraphraser\n\nThis is the trained base-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you found this model useful, please cite the original work:"
] |
[
"TAGS\n#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# GPT2 base style transfer paraphraser\n\nThis is the trained base-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you found this model useful, please cite the original work:"
] |
[
42,
86
] |
[
"passage: TAGS\n#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us \n# GPT2 base style transfer paraphraser\n\nThis is the trained base-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you found this model useful, please cite the original work:"
] |
[
0.013750361278653145,
-0.05992263928055763,
-0.0020720260217785835,
0.018457723781466484,
0.1187732070684433,
0.04755192995071411,
0.14381417632102966,
0.0647866502404213,
-0.16433635354042053,
-0.07416094094514847,
0.1195310726761818,
0.1185111328959465,
0.024418147280812263,
0.029853908345103264,
0.054724302142858505,
-0.3969886898994446,
0.036711666733026505,
0.07962452620267868,
-0.006822725757956505,
0.11160330474376678,
0.12008243799209595,
-0.0479733869433403,
0.1040782779455185,
0.047265008091926575,
-0.08154014497995377,
0.034506410360336304,
-0.006047294940799475,
-0.02755415067076683,
0.12311143428087234,
0.09933309257030487,
0.07492220401763916,
0.059427376836538315,
0.043256502598524094,
-0.034829989075660706,
0.05518190562725067,
0.019082801416516304,
-0.07144754379987717,
0.06538105010986328,
0.05110480636358261,
-0.04645918682217598,
0.3422531485557556,
0.0768551155924797,
-0.06324512511491776,
-0.0025835521519184113,
-0.14049768447875977,
-0.15301421284675598,
-0.04251424968242645,
0.10840665549039841,
0.11865878850221634,
0.06217266619205475,
-0.034182749688625336,
0.0821395069360733,
0.006913655437529087,
0.08913207799196243,
0.1595626026391983,
-0.3331655263900757,
-0.05980385094881058,
0.016130773350596428,
0.033370114862918854,
0.0639718621969223,
0.0017692653927952051,
0.07105240970849991,
0.026052378118038177,
0.01926284469664097,
0.008188107050955296,
-0.11722112447023392,
-0.08805369585752487,
-0.0038587802555412054,
-0.11996608972549438,
0.039205364882946014,
0.28073206543922424,
-0.04297495633363724,
-0.030046910047531128,
0.005782090127468109,
-0.11165623366832733,
0.18890507519245148,
-0.016855301335453987,
-0.09133902192115784,
-0.023474648594856262,
0.0375455766916275,
0.027187181636691093,
-0.1991901397705078,
-0.11404278874397278,
-0.07447493076324463,
-0.1447550356388092,
0.046116575598716736,
0.012759208679199219,
0.029725398868322372,
-0.0537421889603138,
0.1267697811126709,
-0.10040957480669022,
-0.03869275003671646,
0.03341387212276459,
-0.13454480469226837,
0.1608559936285019,
0.040004849433898926,
-0.05782332643866539,
-0.09866800904273987,
-0.05237719416618347,
0.1197042465209961,
0.1771593689918518,
-0.02349644899368286,
0.041538868099451065,
0.07459049671888351,
0.027746932581067085,
0.06867726892232895,
-0.12857265770435333,
-0.015226558782160282,
0.10142385959625244,
-0.05924326181411743,
0.0632186159491539,
-0.020296653732657433,
-0.15705612301826477,
-0.03894951939582825,
0.05375896394252777,
-0.005048366263508797,
-0.012801812961697578,
0.13925763964653015,
0.02211247757077217,
-0.05276907607913017,
0.15305359661579132,
-0.033361248672008514,
-0.07961539179086685,
-0.11066709458827972,
0.0007726845797151327,
0.05949771776795387,
0.11937299370765686,
0.04742376133799553,
-0.07143791764974594,
-0.01747709885239601,
-0.09075193852186203,
-0.014349985867738724,
-0.040025677531957626,
-0.08692233264446259,
-0.05750957503914833,
-0.12817923724651337,
0.013525160029530525,
-0.1414431929588318,
-0.20679910480976105,
-0.006588574964553118,
0.012588800862431526,
0.007703725248575211,
-0.0656416192650795,
-0.04702853038907051,
0.05984213203191757,
-0.018016861751675606,
-0.04097757115960121,
-0.003381662303581834,
-0.04855268448591232,
0.05682417005300522,
-0.08184020221233368,
0.06296423077583313,
-0.17655164003372192,
0.06117276847362518,
-0.11670596152544022,
-0.017771560698747635,
-0.23623058199882507,
-0.006418554112315178,
0.008585413917899132,
0.04207276552915573,
-0.02641349472105503,
-0.08159512281417847,
-0.08252856135368347,
0.020018134266138077,
0.010011964477598667,
0.17220400273799896,
-0.05614521726965904,
-0.1190788596868515,
0.17819266021251678,
-0.13588246703147888,
-0.07787403464317322,
0.0890408605337143,
-0.006398854777216911,
0.15369845926761627,
0.0867791622877121,
0.16577912867069244,
0.05301521718502045,
-0.05134453624486923,
0.07859960198402405,
0.060064710676670074,
-0.10554170608520508,
-0.007347809616476297,
0.02982850931584835,
-0.0004535342159215361,
-0.1429460048675537,
0.023012889549136162,
-0.02421506494283676,
0.06287872791290283,
-0.04409771412611008,
0.004091882146894932,
0.017629049718379974,
-0.0007588631706312299,
-0.00915017630904913,
0.028483880683779716,
0.07465335726737976,
0.010605028830468655,
-0.03767919912934303,
0.003608018858358264,
0.015202010981738567,
-0.07594817876815796,
0.054517704993486404,
-0.02967843785881996,
0.07817480713129044,
0.004996248055249453,
0.04155753552913666,
-0.11074905842542648,
0.025890661403536797,
-0.04409957677125931,
0.16614723205566406,
0.06405635178089142,
0.1445743292570114,
0.041118886321783066,
0.0021445159800350666,
-0.08420073986053467,
0.01308731734752655,
0.0829315111041069,
-0.02740531601011753,
-0.07379937171936035,
-0.07823006063699722,
0.049592435359954834,
0.0021386255975812674,
0.05449988320469856,
-0.07807909697294235,
0.03476967662572861,
-0.05527612566947937,
0.04598069190979004,
-0.007372342981398106,
0.06369877606630325,
0.03606230020523071,
0.006310099270194769,
-0.05575701966881752,
-0.010833065025508404,
0.11665275692939758,
0.004916511941701174,
-0.07764565199613571,
0.2453075498342514,
-0.14285501837730408,
0.0808039978146553,
0.13072864711284637,
-0.2504570782184601,
-0.02340061031281948,
-0.028180880472064018,
-0.058518048375844955,
-0.01441502571105957,
0.05285003408789635,
0.016767248511314392,
0.1538037210702896,
-0.05072147399187088,
0.1567903608083725,
-0.08502404391765594,
0.006682331673800945,
-0.008753673173487186,
-0.10213004052639008,
0.00613397266715765,
0.07455483824014664,
0.10328900814056396,
-0.05525609850883484,
0.062439970672130585,
0.2052268087863922,
-0.008841309696435928,
0.1691088080406189,
0.04495679587125778,
-0.04328140988945961,
0.0016937218606472015,
-0.11800782382488251,
-0.027809301391243935,
-0.002952954499050975,
-0.07820762693881989,
-0.053355276584625244,
0.07306299358606339,
0.02165926620364189,
0.08912814408540726,
-0.10078463703393936,
-0.03001159243285656,
-0.02827148325741291,
-0.0017997854156419635,
0.0267011858522892,
0.0952862948179245,
-0.05418914183974266,
0.09744727611541748,
-0.03812416270375252,
0.042641784995794296,
0.022275462746620178,
0.04363499581813812,
-0.08337409049272537,
0.14235509932041168,
-0.10848855972290039,
-0.2439207285642624,
-0.16448085010051727,
-0.020841937512159348,
-0.03693705052137375,
0.05329018458724022,
0.06393874436616898,
-0.0207977257668972,
-0.04890217259526253,
-0.06339453160762787,
0.07701539993286133,
-0.036044325679540634,
-0.0379532165825367,
-0.10245560109615326,
-0.03704134002327919,
-0.02992086298763752,
-0.08895087242126465,
-0.0462680421769619,
-0.04122856259346008,
-0.14770813286304474,
0.11773746460676193,
-0.16345016658306122,
0.11102008819580078,
0.08018162101507187,
0.031583022326231,
0.001267682877369225,
-0.03891909494996071,
0.18396717309951782,
-0.04864808917045593,
0.11763443052768707,
0.2205885499715805,
0.020405102521181107,
0.09236924350261688,
0.04181554913520813,
-0.05790312960743904,
-0.03933520242571831,
0.03516096994280815,
-0.03433258458971977,
-0.07988763600587845,
-0.08564102649688721,
-0.06499731540679932,
-0.059617724269628525,
0.039237260818481445,
0.01464013196527958,
0.012789384461939335,
0.08694244176149368,
0.1337451934814453,
-0.060773734003305435,
0.035630252212285995,
0.04335745424032211,
0.0827249214053154,
0.1687263399362564,
-0.08435431867837906,
0.1776808500289917,
-0.06228801608085632,
-0.09548106789588928,
0.12689466774463654,
-0.05393547937273979,
0.2074388861656189,
0.035265929996967316,
-0.0316329151391983,
0.04105101898312569,
0.14991135895252228,
0.09034407883882523,
0.16852402687072754,
-0.026987919583916664,
-0.012521597556769848,
-0.09510573744773865,
-0.00931328535079956,
-0.04284464940428734,
0.09245537966489792,
-0.04826471954584122,
-0.1390528380870819,
-0.024093493819236755,
-0.06200771778821945,
0.020275654271245003,
0.17822778224945068,
0.07810399681329727,
-0.14845290780067444,
-0.007771592121571302,
0.018674122169613838,
0.021390914916992188,
-0.11526899039745331,
0.009067615494132042,
-0.04509347677230835,
-0.12202824652194977,
0.07793394476175308,
-0.02960711158812046,
0.09601493179798126,
-0.07002538442611694,
0.03345378860831261,
-0.1478070318698883,
-0.042429372668266296,
-0.020663900300860405,
0.1141853854060173,
-0.1449088156223297,
0.22198571264743805,
-0.008767959661781788,
-0.036330338567495346,
-0.058596014976501465,
-0.05719399452209473,
0.07987405359745026,
0.22802956402301788,
0.11179405450820923,
0.012072020210325718,
-0.012447669170796871,
-0.03941759467124939,
-0.06197396293282509,
0.07880523055791855,
0.12177153676748276,
-0.08995037525892258,
0.032200660556554794,
-0.065863236784935,
0.029092583805322647,
0.007674444932490587,
0.06910084187984467,
-0.09472547471523285,
-0.11358793824911118,
0.05833575874567032,
-0.04304802417755127,
0.11323472112417221,
-0.012848319485783577,
-0.049617212265729904,
-0.059509553015232086,
0.1533442586660385,
-0.023223696276545525,
-0.12592962384223938,
-0.07192973047494888,
-0.05161420628428459,
0.023689955472946167,
-0.07110351324081421,
0.05444900318980217,
-0.07288802415132523,
-0.05365629866719246,
-0.008687223307788372,
-0.14859546720981598,
0.07739532738924026,
-0.06119794398546219,
-0.06327339261770248,
0.04556160792708397,
0.05870039388537407,
0.056763794273138046,
-0.0019746108446270227,
0.027067752555012703,
0.01672236993908882,
-0.11312682181596756,
-0.09427251666784286,
-0.00244038924574852,
0.033564552664756775,
0.09096800535917282,
-0.03495155647397041,
-0.13002721965312958,
0.08654576539993286,
-0.06762199103832245,
-0.014287600293755531,
0.1374514400959015,
0.16810166835784912,
-0.02838251367211342,
0.09170544147491455,
0.17323555052280426,
-0.1004548892378807,
-0.286796510219574,
-0.2017260640859604,
-0.0646519735455513,
-0.010587770491838455,
0.012627274729311466,
-0.17823271453380585,
0.08027992397546768,
0.09720940887928009,
0.0019299853593111038,
0.03244075924158096,
-0.05608674883842468,
-0.12515026330947876,
0.16902397572994232,
0.04111159220337868,
0.431844025850296,
-0.13357725739479065,
-0.002490031998604536,
-0.04037316516041756,
-0.10119974613189697,
0.0749296098947525,
-0.00899962056428194,
0.09899590164422989,
-0.054569732397794724,
0.03621464967727661,
-0.011969656683504581,
0.007626652717590332,
0.07725406438112259,
-0.09009978175163269,
0.040304046124219894,
-0.1562637984752655,
-0.018004821613430977,
0.13108128309249878,
0.021680057048797607,
0.03703532740473747,
-0.03648938611149788,
0.0406775064766407,
-0.1358451098203659,
-0.04912810027599335,
-0.0647435411810875,
0.0620594397187233,
0.0489824078977108,
-0.11531946063041687,
-0.05519340932369232,
0.07708235085010529,
-0.07722699642181396,
0.017302891239523888,
0.09312178939580917,
-0.1220649778842926,
0.08476652950048447,
0.01751450076699257,
0.11577074974775314,
-0.10654620081186295,
0.12955515086650848,
-0.04948858916759491,
-0.06873518228530884,
0.04050082713365555,
-0.07302606850862503,
0.016372215002775192,
0.08583738654851913,
-0.051805656403303146,
0.09355815500020981,
0.04427974298596382,
0.017870398238301277,
0.05067010596394539,
0.10467065870761871,
-0.2559959590435028,
-0.09335928410291672,
-0.07612083107233047,
-0.10055140405893326,
0.0205475352704525,
0.0730813592672348,
0.16054219007492065,
-0.018795808777213097,
-0.05158328264951706,
0.00026757229352369905,
-0.006135595962405205,
-0.03377862647175789,
0.007597430143505335,
0.06340200453996658,
-0.053728215396404266,
-0.043661490082740784,
0.06435627490282059,
0.018205462023615837,
-0.04668495059013367,
0.0041085402481257915,
0.005950717721134424,
-0.1293187290430069,
-0.07972460240125656,
-0.10940411686897278,
0.07156385481357574,
-0.14506983757019043,
-0.02907879464328289,
-0.06903046369552612,
-0.036054328083992004,
0.010705772787332535,
0.06842464208602905,
0.07546650618314743,
0.03131021931767464,
-0.04266900569200516,
0.019198700785636902,
-0.010300539433956146,
-0.01426120474934578,
0.05731624364852905,
-0.027531703934073448,
-0.05008647218346596,
0.09547151625156403,
0.036644864827394485,
0.13474369049072266,
-0.10670612752437592,
-0.044007398188114166,
-0.06533388793468475,
-0.029776472598314285,
-0.09096364676952362,
-0.08135712146759033,
-0.10943886637687683,
-0.06803601235151291,
-0.030692780390381813,
-0.03634583577513695,
-0.04896632209420204,
-0.049258679151535034,
-0.09281034767627716,
0.0388457253575325,
-0.03129547834396362,
0.011077165603637695,
-0.023507332429289818,
-0.02540726773440838,
0.08345986157655716,
0.016727406531572342,
0.08786550164222717,
0.10363143682479858,
-0.03137346729636192,
0.07654687762260437,
-0.04098505899310112,
0.02388474904000759,
0.03032994642853737,
0.021669358015060425,
-0.024419039487838745,
0.04822828993201256,
-0.030218565836548805,
0.0314931683242321,
0.023997066542506218,
0.026194678619503975,
0.056319937109947205,
-0.07844351977109909,
0.10993591696023941,
-0.010661006905138493,
-0.0786665678024292,
-0.05785574018955231,
0.02491172030568123,
-0.055170901119709015,
0.11457587033510208,
0.13668742775917053,
-0.08335626870393753,
0.056184593588113785,
-0.09368637949228287,
0.03829327970743179,
0.00538790924474597,
-0.1350865215063095,
0.08923289179801941,
-0.17902381718158722,
-0.018245434388518333,
0.048568468540906906,
0.22403183579444885,
0.02735406532883644,
0.002608750481158495,
-0.010939150117337704,
0.1067764163017273,
0.0195232592523098,
-0.06326501071453094,
0.1471760869026184,
0.0764826163649559,
-0.005665059667080641,
-0.08820649236440659,
0.16391034424304962,
-0.03903910890221596,
0.0008538537076674402,
0.13376152515411377,
-0.013023106381297112,
-0.04424602910876274,
0.07326901704072952,
-0.045043572783470154,
0.03606729209423065,
0.0011494739446789026,
-0.10812780261039734,
0.05062323063611984,
0.09751714766025543,
-0.007900194264948368,
0.16067960858345032,
0.17378847301006317,
-0.02089555189013481,
0.01641986519098282,
0.0811007022857666,
-0.053142860531806946,
-0.18515925109386444,
-0.2011861652135849,
-0.07694341987371445,
-0.16265027225017548,
0.0044499547220766544,
-0.07355701923370361,
-0.020404648035764694,
0.017624160274863243,
0.04861212521791458,
-0.04369155317544937,
0.04146954044699669,
-0.08401310443878174,
-0.1498236507177353,
0.11302924156188965,
-0.03747376799583435,
-0.012435189448297024,
-0.10747148096561432,
-0.021486550569534302,
-0.03052043542265892,
0.031158527359366417,
0.0038796563167124987,
-0.002970887813717127,
-0.005941919516772032,
-0.005491259507834911,
-0.07573540508747101,
-0.07960803806781769,
-0.10073322802782059,
0.09989240765571594,
0.008271077647805214,
0.05091048404574394,
-0.021231817081570625,
-0.012451093643903732,
0.007533238735049963,
0.16287358105182648,
-0.0024994162376970053,
-0.08346512913703918,
-0.054752010852098465,
0.23052345216274261,
-0.041034996509552,
0.03953161463141441,
-0.007086144294589758,
-0.008780651725828648,
0.05056745931506157,
0.3489843010902405,
0.2939869165420532,
-0.05830987170338631,
-0.012219320982694626,
0.015054005198180676,
0.037602901458740234,
0.11704828590154648,
0.09264302253723145,
0.0939563512802124,
0.124755859375,
-0.09769944101572037,
-0.06403517723083496,
-0.0994562953710556,
0.03767608478665352,
-0.07371874898672104,
0.022984826937317848,
0.0815645307302475,
-0.05183139443397522,
0.003960517235100269,
0.07503179460763931,
-0.11042144894599915,
-0.025547174736857414,
-0.07821720838546753,
-0.021362580358982086,
-0.01959351822733879,
-0.0066222818568348885,
-0.022681206464767456,
-0.0002599340514279902,
0.08445645123720169,
-0.04284762591123581,
0.02404039539396763,
0.17107367515563965,
0.05474244803190231,
-0.14265339076519012,
-0.0270738173276186,
0.11716505140066147,
-0.09959753602743149,
-0.018818646669387817,
-0.04953784868121147,
-0.031019283458590508,
0.04677808657288551,
-0.011546926572918892,
0.0050789909437298775,
0.10985392332077026,
-0.037058986723423004,
0.013461362570524216,
0.0061198268085718155,
-0.011323179118335247,
0.04603487253189087,
-0.14593221247196198,
0.008734765462577343,
-0.06835594773292542,
0.10097550600767136,
-0.03714747726917267,
-0.11194220930337906,
-0.010706620290875435,
0.11204720288515091,
-0.1283383071422577,
0.11811964213848114,
0.11515623331069946,
-0.009524021297693253,
-0.017875973135232925,
-0.06555429846048355,
-0.03985926881432533,
0.044309742748737335,
-0.05195445567369461,
0.006526122335344553,
-0.03475583717226982,
-0.07024364918470383,
-0.005254313815385103,
-0.02656594105064869,
-0.20970962941646576,
0.0008766975370235741,
-0.10985470563173294,
-0.02385885640978813,
-0.06054385378956795,
0.10071856528520584,
0.00942966341972351,
0.006651732139289379,
0.008018107153475285,
0.01534009724855423,
0.01523386500775814,
0.030269628390669823,
-0.12061221897602081,
-0.061883751302957535
] |
null | null |
transformers
|
# GPT2 Bible style transfer paraphraser
This is the trained Bible model from the paper [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700) by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author.
## Citation
If you found this model useful, please cite the original work:
```
@inproceedings{style20,
author={Kalpesh Krishna and John Wieting and Mohit Iyyer},
Booktitle = {Empirical Methods in Natural Language Processing},
Year = "2020",
Title={Reformulating Unsupervised Style Transfer as Paraphrase Generation},
}
```
|
{}
|
text-generation
|
filco306/gpt2-bible-paraphraser
|
[
"transformers",
"pytorch",
"text-generation",
"arxiv:2010.05700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.05700"
] |
[] |
TAGS
#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us
|
# GPT2 Bible style transfer paraphraser
This is the trained Bible model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author.
If you found this model useful, please cite the original work:
|
[
"# GPT2 Bible style transfer paraphraser\n\nThis is the trained Bible model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you found this model useful, please cite the original work:"
] |
[
"TAGS\n#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# GPT2 Bible style transfer paraphraser\n\nThis is the trained Bible model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you found this model useful, please cite the original work:"
] |
[
42,
85
] |
[
"passage: TAGS\n#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us \n# GPT2 Bible style transfer paraphraser\n\nThis is the trained Bible model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you found this model useful, please cite the original work:"
] |
[
0.008305924013257027,
-0.03973846510052681,
-0.003372024744749069,
0.05806238204240799,
0.13137325644493103,
0.06484947353601456,
0.12290965020656586,
0.04146311804652214,
-0.14555440843105316,
-0.07846622169017792,
0.13893850147724152,
0.11217566579580307,
0.015983376652002335,
-0.06750229001045227,
0.05936179310083389,
-0.3549312949180603,
-0.016817009076476097,
0.08740349858999252,
-0.01189098134636879,
0.11436436325311661,
0.06922072917222977,
-0.032709814608097076,
0.12487857043743134,
-0.010948406532406807,
-0.03805657476186752,
0.0005588846397586167,
-0.019320659339427948,
0.008327041752636433,
0.10729968547821045,
0.07920023053884506,
0.019397806376218796,
0.03131134808063507,
0.025021882727742195,
-0.056875813752412796,
0.05540992692112923,
0.004217834677547216,
-0.04868666082620621,
0.016491087153553963,
-0.006537549197673798,
-0.02899795211851597,
0.3271481394767761,
0.07565227895975113,
-0.08255735039710999,
0.008972766809165478,
-0.182783842086792,
-0.0944875180721283,
-0.0707181766629219,
0.08909010142087936,
0.07805322855710983,
0.07598358392715454,
-0.017169252038002014,
0.1083243191242218,
-0.00879758968949318,
0.09442522376775742,
0.1856999695301056,
-0.28456804156303406,
-0.053290143609046936,
0.07160693407058716,
0.02075793966650963,
0.1013682559132576,
-0.014560322277247906,
0.08666403591632843,
0.038314249366521835,
0.08012477308511734,
0.021032527089118958,
-0.13589662313461304,
-0.083590567111969,
-0.017186054959893227,
-0.15640123188495636,
-0.047347087413072586,
0.2677987515926361,
-0.08531028032302856,
-0.023789290338754654,
0.008694382384419441,
-0.10266274213790894,
0.21597374975681305,
0.0016615435015410185,
-0.06692527234554291,
-0.030366946011781693,
-0.00374214886687696,
-0.015757763758301735,
-0.21290913224220276,
-0.10622745007276535,
-0.08827559649944305,
-0.16858690977096558,
0.03270262852311134,
0.04150121659040451,
0.001693239202722907,
-0.0779092013835907,
0.13955476880073547,
-0.10733934491872787,
-0.07308395951986313,
0.01061960682272911,
-0.13595151901245117,
0.21142680943012238,
0.028326740488409996,
-0.06303523480892181,
-0.0364399254322052,
-0.1125146672129631,
0.12739083170890808,
0.1685454249382019,
-0.010885234922170639,
0.03245419263839722,
0.03660941869020462,
0.029119621962308884,
0.09269934892654419,
0.002500817645341158,
0.007902562618255615,
0.0736808329820633,
0.010176361538469791,
0.07936229556798935,
0.02644808031618595,
-0.15076513588428497,
-0.10268726199865341,
0.021979251876473427,
0.05269191041588783,
-0.020884307101368904,
0.1504354476928711,
0.051642172038555145,
-0.05341482162475586,
0.12057942152023315,
-0.032470911741256714,
-0.06792603433132172,
-0.10662633180618286,
0.036452341824769974,
0.021517010405659676,
0.1447121649980545,
-0.030169324949383736,
-0.10900002717971802,
-0.042523451149463654,
-0.052394166588783264,
-0.015294250100851059,
-0.030629849061369896,
-0.0613565519452095,
-0.028932714834809303,
-0.12179384380578995,
0.017739344388246536,
-0.1424207240343094,
-0.22581300139427185,
0.015665506944060326,
-0.03391537442803383,
0.04899173602461815,
-0.06698451936244965,
-0.023080699145793915,
0.0609099417924881,
-0.022242454811930656,
-0.028648799285292625,
-0.005963734816759825,
-0.031053973361849785,
0.09091765433549881,
-0.08997523039579391,
0.04206457361578941,
-0.06952480971813202,
0.06750282645225525,
-0.13681286573410034,
-0.02720601111650467,
-0.21632695198059082,
-0.0032959855161607265,
0.0016393822152167559,
0.044844429939985275,
0.01029716432094574,
-0.07441121339797974,
-0.12169193476438522,
0.04662661626935005,
0.01759941875934601,
0.1934603750705719,
-0.09589926898479462,
-0.13812404870986938,
0.20390619337558746,
-0.146718829870224,
-0.13687372207641602,
0.09799925237894058,
0.02761523239314556,
0.14892610907554626,
0.10493289679288864,
0.15532618761062622,
0.09933175891637802,
-0.03953059762716293,
0.055698707699775696,
0.09093176573514938,
-0.06079813838005066,
-0.003261320758610964,
0.009189347736537457,
-0.021291831508278847,
-0.07007648050785065,
-0.009231170639395714,
0.025151919573545456,
0.07426541298627853,
-0.039946939796209335,
0.027043871581554413,
0.006780016236007214,
-0.005727365147322416,
-0.06642560660839081,
0.04117681831121445,
0.009150714613497257,
0.03221861273050308,
-0.008982417173683643,
-0.023484358564019203,
0.015141547657549381,
-0.06692139059305191,
0.07526715844869614,
0.04990590363740921,
0.0885646864771843,
-0.02108510583639145,
0.05450740456581116,
-0.08198311179876328,
0.023552700877189636,
-0.040003709495067596,
0.13315296173095703,
0.10463165491819382,
0.0630105659365654,
0.07804304361343384,
-0.0015050357906147838,
-0.09599708020687103,
-0.0021107769571244717,
0.08936303853988647,
-0.05828368663787842,
-0.0917293131351471,
-0.012034188024699688,
0.05142467841506004,
0.038854505866765976,
-0.011066412553191185,
-0.08757699280977249,
0.06671492010354996,
-0.035973913967609406,
0.060974083840847015,
-0.028538398444652557,
0.062380265444517136,
0.0611446388065815,
-0.002345707733184099,
-0.03180702403187752,
-0.04058057442307472,
0.10305462777614594,
-0.015397843904793262,
-0.1020093485713005,
0.24272994697093964,
-0.21962083876132965,
0.05858318507671356,
0.1432473063468933,
-0.28060030937194824,
-0.017961472272872925,
0.004007262643426657,
-0.05640343204140663,
-0.007306431885808706,
0.052089352160692215,
0.03347594290971756,
0.1595337837934494,
-0.04696737602353096,
0.14893613755702972,
-0.06283526122570038,
-0.012023955583572388,
0.00723235122859478,
-0.10768424719572067,
-0.0005175775149837136,
0.131626158952713,
0.13659697771072388,
-0.0957767516374588,
0.06865319609642029,
0.3202785849571228,
0.02002371847629547,
0.1404842883348465,
0.007016391027718782,
-0.04719514027237892,
-0.0007856906740926206,
-0.16838614642620087,
-0.029876381158828735,
-0.06603993475437164,
-0.000962820544373244,
-0.02859593741595745,
0.0658690482378006,
0.07173299789428711,
0.09914819151163101,
-0.08169595897197723,
-0.011371186934411526,
0.014554175548255444,
0.021448418498039246,
0.0089789517223835,
0.1269833892583847,
-0.059255052357912064,
0.07277101278305054,
-0.09181398153305054,
0.09144458919763565,
0.03406567499041557,
0.07352989166975021,
-0.06938239187002182,
0.16720621287822723,
-0.08249402791261673,
-0.2082410752773285,
-0.1894540637731552,
-0.07086619734764099,
0.014503750950098038,
0.047530535608530045,
0.10638348758220673,
-0.003507602261379361,
-0.05228454992175102,
-0.061310555785894394,
0.15651649236679077,
-0.08934175223112106,
-0.034089818596839905,
-0.14693869650363922,
-0.12190677225589752,
-0.021960148587822914,
-0.08810696750879288,
-0.059696778655052185,
-0.010661878623068333,
-0.24139344692230225,
0.12347084283828735,
-0.17479968070983887,
0.08712233603000641,
0.023097025230526924,
0.06653450429439545,
-0.0068261572159826756,
-0.044941458851099014,
0.21543194353580475,
-0.04922036826610565,
0.09815140068531036,
0.26939815282821655,
-0.0075205727480351925,
0.11047541350126266,
-0.021259872242808342,
-0.062062378972768784,
-0.03119955025613308,
0.00578059908002615,
-0.014842290431261063,
-0.10672570019960403,
-0.09508584439754486,
-0.023203449323773384,
-0.05015888437628746,
0.03264206275343895,
0.006748206447809935,
0.0032044334802776575,
0.06423858553171158,
0.11937755346298218,
-0.10218606144189835,
0.0479196235537529,
0.01586853340268135,
0.09905900061130524,
0.24762757122516632,
-0.07018066942691803,
0.1395370215177536,
-0.06767137348651886,
-0.0754735991358757,
0.10728809237480164,
-0.09582701325416565,
0.212960883975029,
0.02494698204100132,
-0.004989670589566231,
0.06689084321260452,
0.08660201728343964,
0.07409483194351196,
0.12307634204626083,
-0.03837614506483078,
0.00400252640247345,
-0.08970285207033157,
-0.024319011718034744,
-0.07883032411336899,
0.03461875021457672,
-0.05273483321070671,
-0.12179690599441528,
-0.07931309193372726,
-0.03924236446619034,
0.020370392128825188,
0.10287807881832123,
0.07758952677249908,
-0.14269216358661652,
-0.03697631508111954,
0.023084433749318123,
0.013602993451058865,
-0.12174011021852493,
0.04223036393523216,
-0.05809438228607178,
-0.09934747964143753,
0.11589241027832031,
0.011309024877846241,
0.10520794242620468,
-0.0799465924501419,
0.05868351086974144,
-0.18906964361667633,
-0.08272946625947952,
-0.03804689273238182,
0.11551184207201004,
-0.23481442034244537,
0.19348393380641937,
0.0009008378838188946,
-0.050718989223241806,
-0.035311076790094376,
-0.07392328977584839,
0.11493224650621414,
0.2563713490962982,
0.06021585687994957,
0.026985010132193565,
0.04559791833162308,
-0.047828078269958496,
-0.02329607866704464,
0.07263840734958649,
0.10730648040771484,
-0.0928564965724945,
0.04659666121006012,
-0.057506926357746124,
0.0468854084610939,
-0.023557791486382484,
0.110519640147686,
-0.05199979990720749,
-0.0826120525598526,
0.06480376422405243,
-0.04090375825762749,
0.05222481116652489,
-0.018004491925239563,
-0.061124641448259354,
-0.0724397748708725,
0.03592643141746521,
0.00003148695031995885,
-0.10398516803979874,
-0.03771303594112396,
-0.058567922562360764,
0.058625008910894394,
-0.1070433035492897,
0.027607934549450874,
-0.06405428797006607,
-0.09808143973350525,
-0.026606032624840736,
-0.12411964684724808,
0.03739464655518532,
-0.04804285615682602,
-0.0684896856546402,
0.07833743840456009,
0.1445256918668747,
0.04353262856602669,
0.02058597467839718,
-0.00033638474997133017,
-0.010136853903532028,
-0.09188754111528397,
-0.07960640639066696,
0.03326788544654846,
0.07022121548652649,
0.07488247007131577,
-0.011940479278564453,
-0.09481638669967651,
0.10372518748044968,
-0.08363789319992065,
0.007643468677997589,
0.10264943540096283,
0.1780722439289093,
0.0390394926071167,
0.05370311066508293,
0.18697237968444824,
-0.11344204097986221,
-0.29889214038848877,
-0.2486944943666458,
-0.07896661758422852,
-0.005507468245923519,
0.06792435795068741,
-0.15309341251850128,
0.077220618724823,
0.03489485755562782,
0.009201837703585625,
0.046662602573633194,
-0.07862347364425659,
-0.12486706674098969,
0.14989545941352844,
0.0047248112969100475,
0.361068457365036,
-0.11688264459371567,
-0.011604554019868374,
0.007180838380008936,
-0.140174999833107,
0.0739206001162529,
-0.036627933382987976,
0.11850231885910034,
-0.06570783257484436,
0.13048666715621948,
-0.002728288760408759,
-0.001184397959150374,
0.0702122375369072,
-0.04424293339252472,
0.02842617593705654,
-0.1810552477836609,
-0.030873265117406845,
0.0998707115650177,
-0.02074524387717247,
-0.023701230064034462,
-0.13023871183395386,
0.04354449734091759,
-0.12888772785663605,
-0.057589299976825714,
-0.05181814730167389,
0.05250459536910057,
0.00442340224981308,
-0.1044352725148201,
-0.00490383617579937,
0.05511845275759697,
-0.06304359436035156,
0.026356831192970276,
0.05768898129463196,
-0.11109193414449692,
0.039474718272686005,
-0.01379190944135189,
0.134126678109169,
-0.07582508027553558,
0.10337177664041519,
-0.1272243857383728,
-0.05795310065150261,
0.03436168283224106,
-0.10273699462413788,
0.035768941044807434,
0.09131094813346863,
-0.06314490735530853,
0.08824033290147781,
0.03776443749666214,
0.023729702457785606,
0.014429954811930656,
0.04828735068440437,
-0.20210906863212585,
-0.11227551847696304,
-0.06753724813461304,
-0.10420233011245728,
-0.03490949049592018,
0.07856805622577667,
0.1817464530467987,
0.025717442855238914,
-0.05179598554968834,
-0.009749109856784344,
-0.008201945573091507,
0.0006282090907916427,
0.032212067395448685,
0.00984873715788126,
-0.049063634127378464,
-0.028462843969464302,
0.034663040190935135,
-0.01815779320895672,
-0.038518182933330536,
-0.0033548055216670036,
0.0568331740796566,
-0.1101434975862503,
-0.11087661981582642,
-0.11382604390382767,
0.05279375612735748,
-0.1608356535434723,
0.012379552237689495,
0.013362077064812183,
-0.02895609848201275,
0.03564724698662758,
0.10068017244338989,
0.08427969366312027,
-0.0015025250613689423,
-0.08089488744735718,
0.026707777753472328,
0.03421318903565407,
-0.0017396766925230622,
0.04297937825322151,
-0.03641263768076897,
-0.05849868059158325,
0.13848170638084412,
0.02467372454702854,
0.1224135011434555,
-0.08095947653055191,
-0.05306434631347656,
-0.022747162729501724,
-0.006667975801974535,
-0.0905207097530365,
-0.06538698077201843,
-0.07436541467905045,
-0.0240006223320961,
-0.03831141069531441,
-0.053009890019893646,
-0.05148252472281456,
-0.05472598969936371,
-0.0946464017033577,
0.041893407702445984,
-0.019902518019080162,
0.06416524946689606,
0.011335582472383976,
-0.04697161167860031,
0.09636369347572327,
0.018583817407488823,
0.07212989032268524,
0.09729884564876556,
-0.034705061465501785,
0.10348097234964371,
-0.025393258780241013,
0.007548977620899677,
0.02628750167787075,
0.050823695957660675,
-0.07598720490932465,
0.14160412549972534,
-0.044619377702474594,
0.008555802516639233,
-0.005251480266451836,
0.01801278628408909,
-0.007243734784424305,
-0.056354183703660965,
0.12762171030044556,
0.04919303581118584,
-0.07924963533878326,
-0.04069165885448456,
0.02778562344610691,
-0.11784834414720535,
0.13270075619220734,
0.12824571132659912,
-0.07457662373781204,
0.052054986357688904,
-0.1344502568244934,
0.045824840664863586,
0.014921354129910469,
-0.1771755814552307,
0.07091644406318665,
-0.19944453239440918,
0.0033013392239809036,
0.04468650743365288,
0.14691567420959473,
-0.0004539117799140513,
0.0036489004269242287,
-0.021628370508551598,
0.17992481589317322,
0.040084339678287506,
-0.12922360002994537,
0.11270330101251602,
0.09932352602481842,
-0.04214083403348923,
-0.09988028556108475,
0.11763360351324081,
-0.04954713210463524,
-0.01009352970868349,
0.13576897978782654,
-0.0074730198830366135,
-0.04798578843474388,
0.03102785535156727,
-0.056377388536930084,
0.03501594066619873,
0.006204967852681875,
-0.21812081336975098,
0.0936395674943924,
0.11030114442110062,
-0.01374108251184225,
0.15833237767219543,
0.11767998337745667,
0.03390930965542793,
0.002952712820842862,
0.0442926287651062,
-0.012869266793131828,
-0.1720385104417801,
-0.2238338440656662,
-0.028323853388428688,
-0.0649469718337059,
0.020280320197343826,
-0.03200876712799072,
-0.007136964239180088,
0.05302419140934944,
0.052192747592926025,
-0.04998651519417763,
0.07215338945388794,
-0.03917689621448517,
-0.14442306756973267,
0.06125559285283089,
-0.0519774965941906,
-0.00809419248253107,
-0.11037015914916992,
-0.01282741129398346,
-0.006829104386270046,
0.010234338231384754,
0.0005910309264436364,
-0.046181391924619675,
0.009520665742456913,
-0.018514662981033325,
-0.11953777819871902,
-0.07413443177938461,
-0.13189052045345306,
0.10990697890520096,
-0.0012022521113976836,
0.01634190045297146,
-0.03626689314842224,
-0.0031648091971874237,
0.004388490226119757,
0.14760391414165497,
0.02028665505349636,
-0.16785666346549988,
0.027425922453403473,
0.25158512592315674,
-0.0023223916068673134,
0.0019346754997968674,
-0.003972876351326704,
0.01974627748131752,
0.05508695915341377,
0.33873769640922546,
0.3126797378063202,
-0.06345474720001221,
-0.02207186445593834,
0.057475943118333817,
0.04183607175946236,
0.1352991908788681,
0.03246413543820381,
0.06796935200691223,
0.1374824345111847,
-0.08646287769079208,
-0.03283149003982544,
-0.08615667372941971,
0.023953473195433617,
-0.0036185544449836016,
0.03453914821147919,
0.037059981375932693,
-0.042118482291698456,
0.056752439588308334,
0.031512148678302765,
-0.07394341379404068,
-0.04514465108513832,
-0.1132713109254837,
-0.04583274945616722,
0.014466069638729095,
-0.017293797805905342,
-0.013498838059604168,
0.0611911378800869,
0.07393103092908859,
-0.05941370874643326,
0.020626623183488846,
0.12928584218025208,
0.041107241064310074,
-0.15060757100582123,
-0.005927056074142456,
0.12439627200365067,
-0.1197829321026802,
-0.02248062565922737,
-0.048827171325683594,
-0.005036769434809685,
0.03619186952710152,
0.0033532450906932354,
0.04255229979753494,
0.10805834829807281,
-0.007791349198669195,
0.0337010957300663,
-0.004602937027812004,
-0.008871200494468212,
0.019907450303435326,
-0.14683784544467926,
0.002665773732587695,
-0.10560508072376251,
0.09384280443191528,
0.04904941841959953,
-0.12865103781223297,
0.03356723487377167,
0.06626513600349426,
-0.0730820819735527,
0.09541358798742294,
0.13194593787193298,
0.0019138603238388896,
-0.04069958254694939,
-0.07079257816076279,
-0.0598929338157177,
0.0775369182229042,
-0.04844927042722702,
0.018557626754045486,
0.02073109894990921,
-0.0865095928311348,
-0.04557851701974869,
-0.04375327751040459,
-0.19694627821445465,
-0.023989880457520485,
-0.08075577765703201,
-0.010262727737426758,
-0.08799277991056442,
0.09963543713092804,
-0.0065833041444420815,
0.006165105849504471,
0.038601990789175034,
0.06971213966608047,
0.006086495239287615,
0.0294656939804554,
-0.122481569647789,
-0.04141152650117874
] |
null | null |
transformers
|
# GPT2 Romantic poetry style transfer paraphraser
This is the trained Romantic poetry-model from the paper [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700) by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author.
## Citation
If you found this model useful, please cite the original work:
```
@inproceedings{style20,
author={Kalpesh Krishna and John Wieting and Mohit Iyyer},
Booktitle = {Empirical Methods in Natural Language Processing},
Year = "2020",
Title={Reformulating Unsupervised Style Transfer as Paraphrase Generation},
}
```
|
{}
|
text-generation
|
filco306/gpt2-romantic-poetry-paraphraser
|
[
"transformers",
"pytorch",
"text-generation",
"arxiv:2010.05700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.05700"
] |
[] |
TAGS
#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us
|
# GPT2 Romantic poetry style transfer paraphraser
This is the trained Romantic poetry-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author.
If you found this model useful, please cite the original work:
|
[
"# GPT2 Romantic poetry style transfer paraphraser\n\nThis is the trained Romantic poetry-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you found this model useful, please cite the original work:"
] |
[
"TAGS\n#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# GPT2 Romantic poetry style transfer paraphraser\n\nThis is the trained Romantic poetry-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you found this model useful, please cite the original work:"
] |
[
42,
90
] |
[
"passage: TAGS\n#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us \n# GPT2 Romantic poetry style transfer paraphraser\n\nThis is the trained Romantic poetry-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you found this model useful, please cite the original work:"
] |
[
0.06093309819698334,
-0.009271158836781979,
-0.004061062354594469,
0.07652349770069122,
0.0922376736998558,
0.039792560040950775,
0.04867467284202576,
0.0439453050494194,
-0.17755354940891266,
-0.07942917197942734,
0.14914816617965698,
0.12239902466535568,
0.008480689488351345,
-0.07204777747392654,
0.02974790334701538,
-0.37025687098503113,
-0.023728514090180397,
0.04569126293063164,
0.07487791776657104,
0.11134239286184311,
0.09732063859701157,
-0.00950046069920063,
0.124265655875206,
0.048305120319128036,
0.002574503654614091,
-0.016552358865737915,
-0.01030045561492443,
0.0009753429912962019,
0.10170501470565796,
0.07847058773040771,
0.009550373069941998,
0.07340500503778458,
0.008102341555058956,
-0.07949498295783997,
0.036820217967033386,
-0.02248193696141243,
-0.07570790499448776,
0.002970967208966613,
0.12082666158676147,
-0.11794398725032806,
0.26989439129829407,
0.009233725257217884,
-0.02537251077592373,
-0.002554569160565734,
-0.11407096683979034,
-0.06861431151628494,
-0.08915820717811584,
0.08516806364059448,
0.07861566543579102,
0.12335440516471863,
-0.0055229077115654945,
0.098491370677948,
0.018751516938209534,
0.045122891664505005,
0.15902936458587646,
-0.3055051267147064,
-0.0700211301445961,
0.00962748471647501,
0.06472795456647873,
0.09851635992527008,
-0.07619225978851318,
0.0710332915186882,
-0.013503557071089745,
0.07252918928861618,
0.049199383705854416,
-0.1287125051021576,
-0.013525302521884441,
0.029169751331210136,
-0.12638923525810242,
0.037347082048654556,
0.24888619780540466,
-0.06509985774755478,
-0.04147947579622269,
0.014448156580328941,
-0.11073873192071915,
0.1685696840286255,
-0.036234669387340546,
-0.08300191909074783,
-0.015981877222657204,
0.008409315720200539,
0.058760110288858414,
-0.19965584576129913,
-0.15358172357082367,
-0.03479462116956711,
-0.10070402175188065,
-0.05682508647441864,
0.0029644130263477564,
0.007702544331550598,
-0.02053888514637947,
0.07944771647453308,
-0.0913209393620491,
0.005847111809998751,
0.05627599358558655,
-0.13526292145252228,
0.20691931247711182,
0.04512470215559006,
-0.08411527425050735,
-0.16343414783477783,
-0.0830678939819336,
0.09841115027666092,
0.2164180725812912,
-0.004715280141681433,
0.11155769228935242,
0.03484567627310753,
0.07044430077075958,
0.07337906211614609,
-0.0022042104974389076,
-0.02797766961157322,
0.07839251309633255,
-0.04344803839921951,
0.09621400386095047,
0.008002545684576035,
-0.1329270601272583,
-0.1005764827132225,
0.0524841845035553,
-0.027169032022356987,
-0.01994229666888714,
0.11415588110685349,
0.0105950478464365,
-0.021562449634075165,
0.16193732619285583,
0.02815566211938858,
-0.05324427783489227,
-0.0816929042339325,
0.026800693944096565,
0.07659540325403214,
0.1013733446598053,
0.005014225374907255,
-0.1006057858467102,
0.011455053463578224,
-0.08603972941637039,
-0.029970217496156693,
0.0351378507912159,
-0.050950534641742706,
-0.031182214617729187,
-0.1393224000930786,
0.028308138251304626,
-0.17269162833690643,
-0.172084242105484,
-0.03622407093644142,
0.028158973902463913,
-0.016944916918873787,
-0.08135367184877396,
-0.06526839733123779,
0.06875976175069809,
0.008034758269786835,
-0.02722381055355072,
-0.05288352072238922,
-0.03730771318078041,
0.03980785980820656,
-0.14559291303157806,
0.054646044969558716,
-0.1692306399345398,
0.07560447603464127,
-0.15261635184288025,
-0.039293836802244186,
-0.15699049830436707,
-0.02434067241847515,
0.05613568425178528,
0.011236773803830147,
-0.01681944914162159,
-0.06116615980863571,
-0.009999134577810764,
-0.00922793336212635,
-0.0347352996468544,
0.1893591433763504,
-0.0870341882109642,
-0.08583154529333115,
0.14957241714000702,
-0.09311352670192719,
-0.062432557344436646,
0.13706807792186737,
0.019395072013139725,
0.11544521152973175,
0.07105519622564316,
0.20993982255458832,
0.0673953965306282,
-0.017290793359279633,
0.06844206899404526,
0.0590088777244091,
-0.08411956578493118,
0.013192160986363888,
0.02989887073636055,
0.034077126532793045,
-0.0704183429479599,
0.032056305557489395,
0.021697960793972015,
-0.002184914657846093,
0.02351086400449276,
-0.002514118095859885,
-0.022738561034202576,
0.022888444364070892,
-0.030166208744049072,
-0.010390021838247776,
0.0481836199760437,
-0.029302503913640976,
-0.026364244520664215,
-0.08913028985261917,
0.003098191926255822,
-0.0067704906687140465,
0.09989435225725174,
0.029627561569213867,
0.09987696260213852,
0.124280646443367,
0.036025408655405045,
-0.058228082954883575,
0.09428705275058746,
-0.0398002564907074,
0.07661397010087967,
0.029246347025036812,
0.09546034783124924,
0.041943736374378204,
0.013103564269840717,
-0.06011396273970604,
0.04916984587907791,
0.09159466624259949,
-0.04478470981121063,
-0.08966688066720963,
-0.06249367445707321,
0.10197918117046356,
0.008458338677883148,
0.08651230484247208,
-0.13023628294467926,
0.022062525153160095,
-0.030679214745759964,
-0.009720664471387863,
-0.025151856243610382,
0.06463799625635147,
0.03210832551121712,
0.0033997874706983566,
-0.06359107792377472,
0.04785347357392311,
0.12845246493816376,
0.03269093856215477,
-0.10239556431770325,
0.28520357608795166,
-0.14289093017578125,
-0.03256642073392868,
0.12703031301498413,
-0.28719523549079895,
-0.013852214440703392,
-0.08988139033317566,
-0.05528322234749794,
-0.0054498822428286076,
0.13053318858146667,
0.07022653520107269,
0.13647228479385376,
-0.04532730579376221,
0.1594783067703247,
-0.07791285216808319,
0.029761143028736115,
-0.004241885617375374,
-0.14504943788051605,
-0.06412094831466675,
0.15430091321468353,
0.0026542285922914743,
-0.02011916972696781,
0.08423344790935516,
0.2395399808883667,
-0.02436375804245472,
0.1705143302679062,
0.07946228981018066,
-0.008987710811197758,
-0.023614004254341125,
-0.13975289463996887,
-0.04159840941429138,
-0.028169648721814156,
-0.08877667039632797,
-0.018363798037171364,
0.07192560285329819,
0.008011993020772934,
0.07890528440475464,
-0.11609882116317749,
-0.08794290572404861,
-0.01987527124583721,
0.05030694603919983,
0.03907719627022743,
0.124847412109375,
-0.03451823815703392,
0.06015295162796974,
-0.019749393686652184,
0.0940634086728096,
0.018154695630073547,
0.058394305408000946,
-0.08250568807125092,
0.09861365705728531,
-0.1632848083972931,
-0.2789528965950012,
-0.14484260976314545,
0.015338033437728882,
-0.031188592314720154,
0.020674679428339005,
0.09166973829269409,
-0.040001023560762405,
-0.039901699870824814,
-0.10072586685419083,
0.07757516205310822,
-0.07558128982782364,
-0.10109375417232513,
-0.17069752514362335,
-0.002931819763034582,
-0.08262189477682114,
-0.03164589777588844,
-0.04678395390510559,
-0.022562101483345032,
-0.13638292253017426,
0.1490473449230194,
-0.15237079560756683,
0.11461032927036285,
0.03372877463698387,
0.05019175633788109,
-0.05760536715388298,
-0.018378091976046562,
0.1446942538022995,
-0.02463792823255062,
0.09810155630111694,
0.19165241718292236,
0.045681748539209366,
0.11271065473556519,
-0.0013993749162182212,
-0.0630321055650711,
-0.0167080145329237,
0.03515097126364708,
-0.015690378844738007,
-0.050934381783008575,
-0.09703582525253296,
-0.08412379771471024,
-0.031444162130355835,
0.11889299005270004,
-0.02203535847365856,
0.03398459032177925,
0.03309696167707443,
0.14844980835914612,
-0.052089422941207886,
-0.008368991315364838,
-0.01729508861899376,
0.11066828668117523,
0.2901153564453125,
-0.09148053824901581,
0.1265455037355423,
-0.0706382468342781,
-0.03296983242034912,
0.1501614898443222,
-0.03669137880206108,
0.09815199673175812,
0.03285163268446922,
0.028798988088965416,
0.02681879885494709,
0.225234255194664,
0.037139300256967545,
0.037030890583992004,
0.036624811589717865,
-0.014927802607417107,
-0.11310015618801117,
-0.03340105339884758,
-0.05901904031634331,
0.12251205742359161,
-0.05774983763694763,
-0.13828077912330627,
-0.028669295832514763,
-0.06254621595144272,
0.03615569695830345,
0.13437332212924957,
0.06571847945451736,
-0.09069201350212097,
0.0049178930930793285,
0.021190686151385307,
0.014792677015066147,
-0.08988756686449051,
0.024755386635661125,
-0.10335201025009155,
-0.17654944956302643,
0.09978511929512024,
-0.045460477471351624,
0.07339868694543839,
-0.07851429283618927,
0.042171888053417206,
-0.16014347970485687,
-0.08791683614253998,
0.013815738260746002,
0.09608634561300278,
-0.16593770682811737,
0.17504021525382996,
0.00985680054873228,
-0.053066644817590714,
-0.021208414807915688,
-0.10181693732738495,
0.04903245344758034,
0.17477184534072876,
0.1062440425157547,
0.015475071966648102,
0.07546151429414749,
0.03308187052607536,
-0.034001804888248444,
0.05559242144227028,
0.11596830189228058,
-0.08405273407697678,
0.023202842101454735,
-0.07507826387882233,
0.021562552079558372,
-0.05897953361272812,
0.16117557883262634,
-0.05120162293314934,
-0.15435642004013062,
0.011811564676463604,
-0.028166847303509712,
-0.0066804904490709305,
0.021899722516536713,
-0.04627617076039314,
-0.00010511586151551455,
0.0521492101252079,
0.10034338384866714,
-0.1066003367304802,
-0.022148214280605316,
-0.14211711287498474,
-0.024625709280371666,
-0.045157380402088165,
0.034405410289764404,
-0.11031492054462433,
-0.10624400526285172,
-0.05071324482560158,
-0.10080042481422424,
0.04877282679080963,
-0.025104418396949768,
-0.060903675854206085,
0.02241179347038269,
0.10549993813037872,
0.08626455813646317,
0.0099274180829525,
-0.004274019971489906,
0.037372034043073654,
-0.14978496730327606,
-0.09206385165452957,
0.02555658482015133,
-0.002787573728710413,
0.057074204087257385,
-0.07051236927509308,
-0.12485429644584656,
0.05055028945207596,
-0.10322850942611694,
-0.07831577211618423,
0.1441868543624878,
0.18015453219413757,
0.006150558590888977,
0.14727844297885895,
0.17698878049850464,
-0.12538032233715057,
-0.28832295536994934,
-0.16804857552051544,
-0.09445612877607346,
-0.049640458077192307,
0.04479887709021568,
-0.17653800547122955,
-0.009518474340438843,
0.05994760990142822,
0.028405217453837395,
0.00703987292945385,
-0.08787816762924194,
-0.10157589614391327,
0.09761427342891693,
0.04052838683128357,
0.4180953800678253,
-0.13197672367095947,
-0.007396066095679998,
-0.045960161834955215,
-0.11670627444982529,
0.07472871989011765,
0.11508069932460785,
0.1182357594370842,
-0.11240985989570618,
0.12135742604732513,
-0.018053492531180382,
0.03650783747434616,
0.06803370267152786,
-0.1213630810379982,
0.030896620824933052,
-0.1623450219631195,
0.019647911190986633,
0.12967455387115479,
0.014557995833456516,
0.01466756034642458,
-0.10071369260549545,
0.024270905181765556,
-0.07907707989215851,
-0.04971013218164444,
-0.14739945530891418,
0.02424836903810501,
0.00733063044026494,
-0.11300267279148102,
-0.05992256850004196,
0.102203868329525,
-0.04737648367881775,
-0.015954922884702682,
-0.00008565965254092589,
-0.06734839826822281,
0.013422906398773193,
-0.021029658615589142,
0.05836429446935654,
-0.003721074666827917,
-0.003424004651606083,
-0.04700767248868942,
-0.06919196993112564,
0.01018136739730835,
-0.05378687009215355,
-0.017596902325749397,
0.10421845316886902,
-0.049505580216646194,
0.03398364782333374,
0.02203579805791378,
-0.010679611004889011,
0.011157295666635036,
0.08115693926811218,
-0.29971522092819214,
-0.16029249131679535,
-0.0743517279624939,
-0.15586130321025848,
0.08500813692808151,
-0.012646275572478771,
0.18092985451221466,
-0.020838383585214615,
-0.07022791355848312,
0.0011239700252190232,
-0.0061824508011341095,
0.016615262255072594,
-0.047743044793605804,
-0.008283313363790512,
-0.08768139779567719,
0.014862489886581898,
0.08417785167694092,
0.01054696086794138,
-0.18064816296100616,
0.02452138625085354,
0.13321949541568756,
-0.09548702836036682,
-0.08076319843530655,
-0.10164749622344971,
0.009214021265506744,
-0.22017131745815277,
0.04494781419634819,
-0.03058832883834839,
-0.014930537901818752,
0.0022680622059851885,
0.12489401549100876,
0.07433074712753296,
0.04518634453415871,
-0.05981599539518356,
0.017504535615444183,
-0.004345160908997059,
-0.006538773886859417,
0.07724597305059433,
-0.07138478010892868,
-0.039316851645708084,
0.05341160297393799,
0.00966237485408783,
0.12213141471147537,
-0.08169001340866089,
-0.06455643475055695,
-0.030198264867067337,
-0.06448625773191452,
-0.15353886783123016,
-0.04749871417880058,
-0.12241333723068237,
-0.06279617547988892,
-0.07559611648321152,
0.041487038135528564,
-0.058430835604667664,
-0.09992063790559769,
-0.06316787749528885,
-0.0028184298425912857,
-0.01352617982774973,
0.04573154076933861,
0.005853801034390926,
-0.06314381957054138,
0.07390734553337097,
0.02378206141293049,
0.05789214000105858,
0.13633359968662262,
-0.040461182594299316,
0.05797740817070007,
-0.052912622690200806,
0.06710425019264221,
0.01873525232076645,
0.01697869971394539,
-0.020322050899267197,
0.05813619866967201,
-0.08222328871488571,
-0.0023805107921361923,
0.020098721608519554,
0.0506206639111042,
0.15832796692848206,
-0.09740842133760452,
0.09875927865505219,
0.06700411438941956,
-0.12097849696874619,
-0.09189438074827194,
-0.007185259833931923,
-0.05763992667198181,
0.07013123482465744,
0.14495423436164856,
-0.0979200005531311,
0.07446954399347305,
-0.08203084766864777,
0.03766656666994095,
0.003925674129277468,
-0.10974206030368805,
0.015806660056114197,
-0.12373848259449005,
-0.03343450650572777,
0.027816126123070717,
0.16805122792720795,
0.037968944758176804,
-0.09082620590925217,
0.025237640365958214,
0.17213311791419983,
-0.0028227015864104033,
-0.08907175809144974,
0.01861211284995079,
0.07904774695634842,
-0.0914682075381279,
-0.09853054583072662,
0.15644370019435883,
-0.030138054862618446,
0.04360102862119675,
0.07794693112373352,
0.015374802052974701,
0.08103679120540619,
0.10502547770738602,
-0.06984666734933853,
0.08800662308931351,
-0.018749568611383438,
-0.07666194438934326,
0.057376112788915634,
0.1570294201374054,
-0.04811936616897583,
0.21644623577594757,
0.13337311148643494,
0.005862596910446882,
0.031008176505565643,
0.02622072957456112,
-0.0747418999671936,
-0.15259872376918793,
-0.24524562060832977,
-0.05276134982705116,
-0.06667471677064896,
-0.014321105554699898,
-0.013598248362541199,
-0.011838310398161411,
-0.01902443915605545,
0.04572387784719467,
-0.08378975838422775,
0.01943550445139408,
-0.017093772068619728,
-0.1730465590953827,
0.17238928377628326,
-0.010797522030770779,
0.010367391631007195,
-0.03928833827376366,
0.03258479759097099,
-0.048861145973205566,
-0.03042268007993698,
-0.010526168160140514,
0.028140611946582794,
0.028275206685066223,
-0.018676193431019783,
-0.10008184611797333,
-0.07054433226585388,
-0.10425127297639847,
0.11296544224023819,
0.025287296622991562,
0.09286224842071533,
-0.02100943960249424,
-0.013466594740748405,
0.020179247483611107,
0.21031343936920166,
0.05247991904616356,
0.06194151192903519,
-0.003986714873462915,
0.22509469091892242,
-0.05843483284115791,
0.007760543841868639,
0.048207592219114304,
0.05956343933939934,
0.021396612748503685,
0.27043741941452026,
0.3208360970020294,
-0.06800255179405212,
-0.01512544322758913,
0.03117801435291767,
0.04518464207649231,
0.11193611472845078,
0.06788375973701477,
0.10619881004095078,
0.13965126872062683,
-0.13011015951633453,
-0.024785250425338745,
-0.16683931648731232,
0.057660967111587524,
-0.04439791291952133,
0.0984058678150177,
0.08446823060512543,
0.005895896814763546,
0.005772273987531662,
0.027593448758125305,
-0.1424027681350708,
0.03230616822838783,
-0.09247590601444244,
-0.080180823802948,
-0.014928744174540043,
0.010818757116794586,
-0.004203849472105503,
-0.01002788357436657,
0.14792393147945404,
0.018125299364328384,
0.042214345186948776,
0.13710513710975647,
0.018834849819540977,
-0.1491178274154663,
0.014177672564983368,
0.1508243978023529,
-0.14915049076080322,
0.02934953384101391,
-0.04674520716071129,
-0.05902143195271492,
0.03247736766934395,
0.039688024669885635,
0.035940080881118774,
0.10144861787557602,
-0.019576897844672203,
0.017486197873950005,
-0.006522420793771744,
0.009699194692075253,
0.05010233446955681,
-0.08360018581151962,
0.049883414059877396,
0.015868699178099632,
0.13664574921131134,
0.04193562641739845,
-0.09229236841201782,
-0.003497295780107379,
0.16897143423557281,
-0.175393208861351,
0.0765770971775055,
0.14855219423770905,
0.0078091430477797985,
-0.03371846303343773,
-0.05994412302970886,
-0.08081699162721634,
0.008640806190669537,
-0.07769595086574554,
0.07051540911197662,
0.026459859684109688,
-0.017048191279172897,
0.04883025586605072,
-0.007199474144726992,
-0.2783292829990387,
-0.03793090209364891,
-0.09358936548233032,
0.008708391338586807,
-0.07315729558467865,
0.08079925924539566,
-0.07259323447942734,
-0.0013712123036384583,
0.028953157365322113,
0.03639381751418114,
0.030122919008135796,
-0.024268317967653275,
-0.057076115161180496,
-0.08612003922462463
] |
null | null |
transformers
|
# GPT2 Shakespeare style transfer paraphraser
This is the trained Shakespeare-model from the paper [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700) by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author.
## Citation
If you found this model useful, please cite the original work:
```
@inproceedings{style20,
author={Kalpesh Krishna and John Wieting and Mohit Iyyer},
Booktitle = {Empirical Methods in Natural Language Processing},
Year = "2020",
Title={Reformulating Unsupervised Style Transfer as Paraphrase Generation},
}
```
|
{}
|
text-generation
|
filco306/gpt2-shakespeare-paraphraser
|
[
"transformers",
"pytorch",
"text-generation",
"arxiv:2010.05700",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.05700"
] |
[] |
TAGS
#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# GPT2 Shakespeare style transfer paraphraser
This is the trained Shakespeare-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author.
If you found this model useful, please cite the original work:
|
[
"# GPT2 Shakespeare style transfer paraphraser\n\nThis is the trained Shakespeare-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you found this model useful, please cite the original work:"
] |
[
"TAGS\n#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# GPT2 Shakespeare style transfer paraphraser\n\nThis is the trained Shakespeare-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you found this model useful, please cite the original work:"
] |
[
46,
86
] |
[
"passage: TAGS\n#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# GPT2 Shakespeare style transfer paraphraser\n\nThis is the trained Shakespeare-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you found this model useful, please cite the original work:"
] |
[
0.02701636590063572,
-0.04901103675365448,
-0.0026243729516863823,
0.07207337766885757,
0.07302892208099365,
-0.01815352775156498,
0.1308077573776245,
0.10094726085662842,
-0.06257222592830658,
-0.0506180040538311,
0.1287839561700821,
0.13698776066303253,
-0.013233469799160957,
-0.044544003903865814,
0.07810093462467194,
-0.36854586005210876,
0.005724932532757521,
0.05779976770281792,
-0.04970385134220123,
0.09733741730451584,
0.12675796449184418,
-0.05754271149635315,
0.12235143780708313,
0.044889871031045914,
-0.003716984298080206,
0.027062350884079933,
0.006313442252576351,
-0.01894051395356655,
0.1840222328901291,
0.11531370878219604,
0.027822105213999748,
0.05178891494870186,
0.01242048479616642,
-0.07886824011802673,
0.04786929115653038,
0.05331816524267197,
-0.044031061232089996,
0.02682267501950264,
0.08232177793979645,
-0.05191205441951752,
0.3343004882335663,
0.11620340496301651,
-0.06789392232894897,
-0.01368080172687769,
-0.09923567622900009,
-0.044754061847925186,
-0.024626072496175766,
0.07658862322568893,
0.060384903103113174,
0.13226385414600372,
-0.023763412609696388,
0.15420019626617432,
-0.013196993619203568,
0.10112802684307098,
0.18770411610603333,
-0.3377891480922699,
-0.026460425928235054,
-0.06274642050266266,
0.12539303302764893,
0.12399426847696304,
-0.06528130918741226,
0.08698711544275284,
-0.027471086010336876,
0.05243891850113869,
0.015419675968587399,
-0.13102327287197113,
-0.15275025367736816,
0.03705829009413719,
-0.13618874549865723,
0.06724727898836136,
0.24520352482795715,
-0.04540201649069786,
-0.0319666787981987,
0.007875487208366394,
-0.0782141238451004,
0.24454021453857422,
-0.004292852245271206,
-0.09612706303596497,
-0.002499530091881752,
0.023222247138619423,
0.042521700263023376,
-0.24552999436855316,
-0.14444582164287567,
-0.0036269030533730984,
-0.1585206389427185,
-0.02711743675172329,
0.012294831685721874,
0.030066030099987984,
-0.06297847628593445,
0.06382694095373154,
-0.11821690946817398,
0.005083923693746328,
0.04980061575770378,
-0.13405300676822662,
0.18722693622112274,
-0.0017529140459373593,
-0.043680351227521896,
-0.09155882149934769,
-0.09916011244058609,
0.0721188560128212,
0.1625823676586151,
-0.021464183926582336,
0.04982462525367737,
0.0973162055015564,
0.04790881276130676,
0.09169377386569977,
-0.14865216612815857,
0.035162538290023804,
0.10782710462808609,
0.0021822417620569468,
0.06534761935472488,
0.0450592041015625,
-0.15785112977027893,
-0.08257167041301727,
0.013121713884174824,
-0.025747165083885193,
0.030542440712451935,
0.10576322674751282,
0.021924737840890884,
-0.005750694312155247,
0.13406871259212494,
-0.018365995958447456,
-0.01428916398435831,
-0.08169372379779816,
-0.0309230238199234,
0.11400347948074341,
0.15048789978027344,
0.03476911783218384,
-0.11221456527709961,
-0.019907664507627487,
-0.12278346717357635,
0.026107829064130783,
0.06515107303857803,
-0.12759678065776825,
0.009709097445011139,
-0.11817978322505951,
0.006730717606842518,
-0.133883535861969,
-0.13324663043022156,
-0.0037788173649460077,
-0.013872661627829075,
0.0399014875292778,
-0.03376210853457451,
-0.025823548436164856,
0.024776771664619446,
-0.04214693605899811,
-0.025490012019872665,
-0.06396910548210144,
-0.03231281787157059,
0.04749371111392975,
-0.14922109246253967,
0.04315624013543129,
-0.15397295355796814,
0.0743425041437149,
-0.1729404777288437,
-0.06404146552085876,
-0.23768845200538635,
-0.007539535406976938,
0.04365498945116997,
-0.02820291742682457,
-0.05232051759958267,
-0.05871855840086937,
-0.05530066043138504,
0.052510812878608704,
-0.035524606704711914,
0.16475878655910492,
-0.10571948438882828,
-0.1332978755235672,
0.14194121956825256,
-0.06371435523033142,
-0.12616924941539764,
0.12664175033569336,
0.02177722379565239,
0.112393319606781,
0.11095435917377472,
0.17213021218776703,
0.0705256313085556,
-0.09297677874565125,
0.002398291602730751,
0.11480099707841873,
-0.09609788656234741,
0.015498175285756588,
0.02637757919728756,
-0.013685747981071472,
0.0566716194152832,
0.013152709230780602,
-0.06407973915338516,
0.009977851063013077,
0.0009496381971985102,
0.026392873376607895,
0.0007329861400648952,
0.0165017768740654,
-0.0011302503990009427,
0.0244156327098608,
0.04933692142367363,
0.013570593670010567,
-0.0732078105211258,
-0.0037797221448272467,
-0.028786398470401764,
-0.0776284784078598,
0.15868374705314636,
0.017101919278502464,
0.019560586661100388,
0.11363600939512253,
0.040397610515356064,
-0.1352970153093338,
0.05793938785791397,
-0.0339675135910511,
0.11652418226003647,
0.04449661448597908,
0.18702879548072815,
0.03441501036286354,
0.024752434343099594,
-0.07317346334457397,
0.06410465389490128,
0.06259150058031082,
-0.043994154781103134,
-0.11758291721343994,
-0.12481849640607834,
0.034593354910612106,
-0.04253609851002693,
-0.0011298934696242213,
-0.08729010820388794,
0.033955805003643036,
-0.07490694522857666,
0.022480176761746407,
-0.012921973131597042,
0.05291375517845154,
0.038657695055007935,
0.014479881152510643,
-0.08546358346939087,
0.004190466366708279,
0.10821294784545898,
-0.023893313482403755,
-0.11349166184663773,
0.22972291707992554,
-0.18195059895515442,
0.06351405382156372,
0.15510773658752441,
-0.2570785880088806,
0.013709882274270058,
0.00019885686924681067,
-0.0901242345571518,
-0.01703605428338051,
0.06137963756918907,
-0.01934046857059002,
0.16989050805568695,
-0.0033429667819291353,
0.13823355734348297,
-0.09009191393852234,
-0.006737078074365854,
-0.0388055145740509,
-0.10225075483322144,
-0.04181618615984917,
0.13703510165214539,
0.06287245452404022,
0.017626116052269936,
0.04965677857398987,
0.25117918848991394,
-0.05725830793380737,
0.15840496122837067,
0.06158680096268654,
-0.010940748266875744,
0.04901162534952164,
-0.13949018716812134,
-0.06303001195192337,
-0.06715868413448334,
-0.0668865293264389,
-0.03232339769601822,
0.04466323181986809,
0.022087670862674713,
0.07269924879074097,
-0.1027207300066948,
-0.05197252333164215,
-0.026248697191476822,
-0.00441765459254384,
0.014317487366497517,
0.11329050362110138,
-0.06804817914962769,
0.09676110744476318,
-0.042295947670936584,
0.04667135700583458,
0.007723554037511349,
0.06633235514163971,
-0.1171383485198021,
0.12848520278930664,
-0.15939101576805115,
-0.1868056207895279,
-0.12467217445373535,
0.0037721246480941772,
-0.006660050712525845,
0.06423164904117584,
0.0712067037820816,
0.00638547632843256,
-0.04988202452659607,
-0.11818166077136993,
0.12517262995243073,
-0.10077369213104248,
-0.09329980611801147,
-0.14213407039642334,
0.005630675703287125,
-0.08085229247808456,
-0.08446095883846283,
-0.031065091490745544,
-0.04435630142688751,
-0.17384850978851318,
0.043359383940696716,
-0.16160032153129578,
0.05861799418926239,
0.06906045973300934,
0.021685166284441948,
-0.04403415322303772,
-0.046583086252212524,
0.1378881335258484,
-0.023091992363333702,
0.15101277828216553,
0.2171347737312317,
0.03610430657863617,
0.08555929362773895,
0.016498703509569168,
-0.03926260024309158,
0.051462091505527496,
0.001312111970037222,
-0.030454155057668686,
-0.0326036736369133,
-0.1536955088376999,
-0.052297983318567276,
-0.056420598179101944,
0.1543339043855667,
-0.01008662674576044,
0.0391884371638298,
-0.009734856896102428,
0.06856697052717209,
-0.07016557455062866,
0.024769172072410583,
0.10580836981534958,
0.07023786008358002,
0.1875118613243103,
-0.06604479998350143,
0.14761914312839508,
-0.03933396190404892,
-0.04288233816623688,
0.10495901107788086,
-0.038604188710451126,
0.09960013628005981,
0.04247894883155823,
-0.008712617680430412,
-0.012850545346736908,
0.07869217544794083,
0.03272053971886635,
0.10101622343063354,
0.04463261738419533,
-0.015707699581980705,
-0.07910655438899994,
-0.027986811473965645,
0.011418798007071018,
0.0879189595580101,
0.04441060125827789,
-0.17978492379188538,
0.0016576239140704274,
0.0308837927877903,
0.0546053946018219,
0.04869294911623001,
0.1087566465139389,
-0.12928910553455353,
-0.006532907485961914,
0.016936615109443665,
-0.026880815625190735,
-0.07117991149425507,
0.028494076803326607,
-0.04852871224284172,
-0.16335435211658478,
0.06222042068839073,
0.00815914012491703,
0.07960289716720581,
-0.06029266491532326,
0.05511310696601868,
-0.17776095867156982,
-0.08638846129179001,
-0.01299138180911541,
0.07212676107883453,
-0.14271169900894165,
0.1842445731163025,
0.024625733494758606,
-0.022302981466054916,
-0.01143577042967081,
-0.08272119611501694,
0.07232088595628738,
0.21952150762081146,
0.14003735780715942,
0.004438538104295731,
0.08782888948917389,
-0.01703326590359211,
-0.034583013504743576,
0.06919926404953003,
0.09402530640363693,
-0.08155182749032974,
0.03051820583641529,
-0.08969256281852722,
0.016284391283988953,
-0.008260301314294338,
0.18715138733386993,
-0.06945975124835968,
-0.13472875952720642,
0.026693405583500862,
0.03224117308855057,
0.014122149907052517,
0.028764892369508743,
-0.07228633761405945,
-0.0841018334031105,
0.09910966455936432,
-0.09591390192508698,
-0.12463847547769547,
-0.028168631717562675,
0.003928625490516424,
0.03947560489177704,
-0.05648078769445419,
0.014829909428954124,
-0.10401515662670135,
-0.03635542094707489,
-0.10816948115825653,
-0.10425001382827759,
0.06762947142124176,
-0.02531616762280464,
-0.045174211263656616,
0.009171836078166962,
0.14635148644447327,
0.042389970272779465,
0.04667286574840546,
-0.010263608768582344,
0.025378072634339333,
-0.14768721163272858,
-0.07105036079883575,
0.022906169295310974,
0.02090977504849434,
0.0844910591840744,
-0.006406958680599928,
-0.19011864066123962,
0.10519984364509583,
-0.07034590095281601,
-0.014202497899532318,
0.150223046541214,
0.14654411375522614,
-0.0043995375744998455,
0.0725686103105545,
0.1852918118238449,
-0.09690709412097931,
-0.34886857867240906,
-0.15009009838104248,
-0.06439904868602753,
-0.051509130746126175,
0.06816302239894867,
-0.1447925716638565,
0.047448620200157166,
0.046434562653303146,
0.02274272032082081,
0.029730089008808136,
-0.06669218093156815,
-0.1399035006761551,
0.14389768242835999,
-0.04313076287508011,
0.3935287296772003,
-0.10295821726322174,
0.02388661354780197,
-0.03712354227900505,
-0.07365087419748306,
0.004109193570911884,
-0.036733973771333694,
0.10138608515262604,
-0.07741378247737885,
0.05850154161453247,
-0.03212185949087143,
0.02480228990316391,
0.027538523077964783,
-0.01983404904603958,
-0.0029685546178370714,
-0.0911422073841095,
-0.05924196168780327,
0.18137022852897644,
-0.00041734776459634304,
0.038895703852176666,
-0.05849447846412659,
0.005220190621912479,
-0.05985652655363083,
-0.006267338991165161,
-0.16026589274406433,
0.07485891878604889,
0.014506732113659382,
-0.050551023334264755,
-0.07418254017829895,
0.06981799006462097,
-0.09440673142671585,
0.034479133784770966,
0.125230610370636,
-0.12637417018413544,
0.009873665869235992,
0.07459546625614166,
0.13404923677444458,
-0.01171258557587862,
0.01612757332623005,
0.005753787234425545,
-0.06474276632070541,
-0.010113983415067196,
-0.08813423663377762,
0.016961010172963142,
0.1059885248541832,
-0.05008156970143318,
0.03699365258216858,
0.060760870575904846,
-0.009886082261800766,
0.013604216277599335,
0.05944495275616646,
-0.26134079694747925,
-0.1392788141965866,
-0.058760866522789,
-0.10983622074127197,
-0.028758911415934563,
0.04678913950920105,
0.21420922875404358,
-0.045887500047683716,
-0.048417262732982635,
0.039436399936676025,
-0.00007100321090547368,
0.00616230396553874,
0.012216460891067982,
0.04679441452026367,
-0.07100678235292435,
0.0024558345321565866,
0.12022833526134491,
0.022520562633872032,
-0.11104778945446014,
0.0056821927428245544,
0.0834365263581276,
-0.14712440967559814,
-0.10942111164331436,
-0.14344345033168793,
0.04645543545484543,
-0.17732393741607666,
0.02174113504588604,
-0.016985004767775536,
-0.028162483125925064,
-0.011310662142932415,
0.08377539366483688,
0.046980053186416626,
0.06097886711359024,
-0.09162171930074692,
-0.008986308239400387,
-0.012192830443382263,
0.04898016154766083,
0.0633038654923439,
-0.05621377006173134,
-0.009466918185353279,
0.12703335285186768,
-0.020763801410794258,
0.15466080605983734,
-0.06813198328018188,
-0.013703311793506145,
-0.003638632595539093,
-0.032826345413923264,
-0.0832466334104538,
-0.09134668111801147,
-0.0809844359755516,
-0.10049521178007126,
-0.06361965090036392,
-0.02471182309091091,
-0.06381266564130783,
-0.09508943557739258,
-0.06475674360990524,
-0.012677815742790699,
-0.04455075412988663,
0.03924541920423508,
-0.09212737530469894,
-0.030177239328622818,
0.08804911375045776,
0.012127600610256195,
0.07779960334300995,
0.11865004152059555,
-0.04078451171517372,
0.07005288451910019,
0.028973916545510292,
-0.009408027864992619,
-0.013028000481426716,
0.0029347799718379974,
-0.012159403413534164,
0.09079873561859131,
-0.09177651256322861,
-0.017050184309482574,
0.046914249658584595,
0.015933174639940262,
0.03911438211798668,
-0.0470898300409317,
0.15365827083587646,
0.012808307074010372,
-0.07041852176189423,
-0.060417238622903824,
0.020298944786190987,
-0.05762719362974167,
0.08662224560976028,
0.13200244307518005,
-0.07889851927757263,
0.05764683708548546,
-0.11283260583877563,
0.0665547251701355,
0.013567086309194565,
-0.1322593092918396,
0.09579037129878998,
-0.12812933325767517,
-0.016076454892754555,
-0.015915870666503906,
0.16069546341896057,
0.05317182093858719,
0.001926719443872571,
0.05352059751749039,
0.0869293361902237,
-0.03394756838679314,
-0.04065905138850212,
0.04282001405954361,
0.0421704463660717,
-0.07768471539020538,
-0.12103686481714249,
0.1119794249534607,
0.002284341724589467,
0.07318412512540817,
0.15340463817119598,
0.01692652516067028,
0.03539988771080971,
0.10331537574529648,
-0.053552646189928055,
0.06506646424531937,
-0.04419104382395744,
-0.1714709997177124,
0.09387920796871185,
0.11222036927938461,
-0.03522525727748871,
0.15017931163311005,
0.1968325674533844,
-0.017603619024157524,
0.0002318763145012781,
0.0561385378241539,
-0.08703698962926865,
-0.1998228132724762,
-0.17648252844810486,
-0.02562548965215683,
-0.0751471295952797,
-0.023418815806508064,
-0.050940848886966705,
0.006353278644382954,
-0.0887511596083641,
0.07943140715360641,
-0.05093792453408241,
0.05125721916556358,
-0.058171600103378296,
-0.20126761496067047,
0.14804242551326752,
-0.03405065834522247,
0.037162113934755325,
-0.018420647829771042,
0.044035106897354126,
-0.03159599006175995,
-0.06848960369825363,
-0.014431593008339405,
0.024659767746925354,
-0.03230104595422745,
0.010745652951300144,
-0.11822107434272766,
-0.10181549936532974,
-0.11831224709749222,
0.11665508151054382,
-0.025151832029223442,
0.030846819281578064,
-0.022052675485610962,
-0.040327027440071106,
0.008769149892032146,
0.17991064488887787,
-0.003372304607182741,
0.025265607982873917,
0.02842653915286064,
0.27498236298561096,
0.0099951708689332,
0.0087610287591815,
-0.010527504608035088,
0.0021838191896677017,
-0.01103608775883913,
0.3298097848892212,
0.3172343373298645,
-0.11287956684827805,
-0.00009774767386261374,
-0.005596433766186237,
0.04355059191584587,
0.16446936130523682,
0.011421417817473412,
0.07364068925380707,
0.11580201238393784,
-0.136884406208992,
-0.029990840703248978,
-0.09327199310064316,
0.04483288899064064,
-0.07937857508659363,
0.09248430281877518,
0.08823472261428833,
-0.04919242486357689,
-0.011598551645874977,
0.031144078820943832,
-0.09320205450057983,
-0.11372558772563934,
-0.12432823330163956,
-0.07149743288755417,
0.037324994802474976,
0.014567744918167591,
-0.004018436186015606,
0.010082351043820381,
0.14125721156597137,
-0.014095543883740902,
0.01013936847448349,
0.11789515614509583,
0.014737071469426155,
-0.18887168169021606,
0.03206045925617218,
0.12124040722846985,
-0.09115411341190338,
-0.06340930610895157,
-0.04787728190422058,
0.004590553231537342,
0.042104266583919525,
0.024451278150081635,
0.022739853709936142,
0.06469877064228058,
0.0015851602656766772,
0.06413911283016205,
-0.031943947076797485,
-0.02063778229057789,
0.051507964730262756,
-0.12068740278482437,
0.03332364186644554,
-0.038931865245103836,
0.10569152981042862,
-0.006330830976366997,
-0.155548557639122,
-0.008754348382353783,
0.1352933943271637,
-0.10096841305494308,
0.09467252343893051,
0.1165751963853836,
-0.00018388318130746484,
-0.039313290268182755,
-0.03951622545719147,
-0.10453994572162628,
0.04790842905640602,
-0.03506264090538025,
0.012526747770607471,
-0.017820114269852638,
-0.027074452489614487,
-0.009510600939393044,
-0.007900734432041645,
-0.2350473254919052,
-0.012925572693347931,
-0.07520198822021484,
-0.027866777032613754,
-0.07913515716791153,
0.0786212608218193,
-0.0258152037858963,
-0.02361362613737583,
0.0186165738850832,
0.05475170537829399,
0.06483536958694458,
0.00789865106344223,
-0.09707430750131607,
-0.0831015408039093
] |
null | null |
transformers
|
# GPT2 Switchboard style transfer paraphraser
This is the trained Switchboard-model from the paper [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700) by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author.
## Citation
If you found this model useful, please cite the original work:
```
@inproceedings{style20,
author={Kalpesh Krishna and John Wieting and Mohit Iyyer},
Booktitle = {Empirical Methods in Natural Language Processing},
Year = "2020",
Title={Reformulating Unsupervised Style Transfer as Paraphrase Generation},
}
```
|
{}
|
text-generation
|
filco306/gpt2-switchboard-paraphraser
|
[
"transformers",
"pytorch",
"text-generation",
"arxiv:2010.05700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.05700"
] |
[] |
TAGS
#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us
|
# GPT2 Switchboard style transfer paraphraser
This is the trained Switchboard-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author.
If you found this model useful, please cite the original work:
|
[
"# GPT2 Switchboard style transfer paraphraser\n\nThis is the trained Switchboard-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you found this model useful, please cite the original work:"
] |
[
"TAGS\n#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# GPT2 Switchboard style transfer paraphraser\n\nThis is the trained Switchboard-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you found this model useful, please cite the original work:"
] |
[
42,
88
] |
[
"passage: TAGS\n#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us \n# GPT2 Switchboard style transfer paraphraser\n\nThis is the trained Switchboard-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you found this model useful, please cite the original work:"
] |
[
0.022642415016889572,
-0.11738639324903488,
-0.002246162388473749,
0.007448453456163406,
0.10888098180294037,
0.018335117027163506,
0.20456822216510773,
0.03829142078757286,
-0.08497948199510574,
-0.09004364162683487,
0.0849495679140091,
0.2261548787355423,
0.034389678388834,
0.06545420736074448,
0.030224768444895744,
-0.4006049931049347,
0.07474670559167862,
0.11042039841413498,
-0.054353971034288406,
0.12049680948257446,
0.10912417620420456,
-0.08497270196676254,
0.07423272728919983,
0.05310049280524254,
-0.08370495587587357,
0.01928161084651947,
0.009360787458717823,
-0.041160471737384796,
0.13413205742835999,
0.14659401774406433,
0.1316431611776352,
0.06802725791931152,
-0.004525582771748304,
-0.07239806652069092,
0.05886968597769737,
0.03743726387619972,
-0.07288910448551178,
0.07045766711235046,
0.02923368290066719,
-0.017462214455008507,
0.254877507686615,
0.02291157655417919,
-0.05560559406876564,
0.006345269735902548,
-0.10960095375776291,
-0.19269227981567383,
-0.05219312012195587,
0.14051903784275055,
0.1118771880865097,
0.057508111000061035,
-0.0180948618799448,
0.14699523150920868,
0.01432221382856369,
0.10465841740369797,
0.1938154697418213,
-0.28291648626327515,
-0.04751686379313469,
-0.051057763397693634,
0.07156911492347717,
0.03105710819363594,
-0.03050016611814499,
0.08881689608097076,
0.008022880181670189,
0.0005134799866937101,
-0.014306689612567425,
-0.1152268722653389,
-0.09604617953300476,
-0.020416829735040665,
-0.1237889751791954,
0.03991718962788582,
0.16442568600177765,
-0.00408377218991518,
-0.009344466961920261,
-0.06496486067771912,
-0.12340514361858368,
0.1913955956697464,
0.003424821887165308,
-0.1133149042725563,
-0.02684805728495121,
0.020283319056034088,
-0.0033952621743083,
-0.20783506333827972,
-0.11999139934778214,
-0.08346356451511383,
-0.18282724916934967,
0.0975966528058052,
0.007239514961838722,
0.03157760575413704,
-0.06953167170286179,
0.1006716638803482,
0.11374282091856003,
-0.027482710778713226,
0.0014395113103091717,
-0.11789499223232269,
0.1116148978471756,
0.03726179525256157,
-0.016480827704072,
-0.14169305562973022,
-0.050744228065013885,
0.11529292911291122,
0.1551656574010849,
-0.02203327976167202,
0.023150643333792686,
0.07524846494197845,
0.005667230114340782,
0.09580899775028229,
-0.12927114963531494,
0.053095743060112,
0.09809595346450806,
-0.07128257304430008,
0.08680868148803711,
-0.052476827055215836,
-0.17169669270515442,
-0.05613669008016586,
0.0968203991651535,
0.04353488236665726,
0.014956237748265266,
0.14528876543045044,
0.02693178318440914,
-0.06687236577272415,
0.21653102338314056,
-0.054548732936382294,
-0.022364981472492218,
-0.08632650226354599,
0.016071969643235207,
0.12444297224283218,
0.1024957224726677,
0.015911106020212173,
-0.08870800584554672,
-0.0357937291264534,
-0.06674393266439438,
0.019852329045534134,
-0.06454191356897354,
-0.12080361694097519,
-0.020509235560894012,
-0.018099823966622353,
0.00640456285327673,
-0.139913871884346,
-0.182319775223732,
-0.0012142028426751494,
-0.020691459998488426,
0.036479007452726364,
-0.043912794440984726,
-0.013903956860303879,
0.054948147386312485,
-0.016208196058869362,
-0.05548727512359619,
0.03469403088092804,
-0.04101261496543884,
0.06585001200437546,
-0.10867410153150558,
0.05414057523012161,
-0.18478929996490479,
0.02948821149766445,
-0.07743344455957413,
-0.04549996554851532,
-0.28582972288131714,
0.019896896556019783,
-0.013134561479091644,
0.040484681725502014,
-0.018155500292778015,
-0.07895899564027786,
-0.021606912836432457,
0.043466079980134964,
-0.016882209107279778,
0.10562688112258911,
-0.043265052139759064,
-0.13226206600666046,
0.2339337021112442,
-0.18050874769687653,
-0.08419204503297806,
0.08633887767791748,
-0.02097904123365879,
0.091373011469841,
0.1010892391204834,
0.1080191358923912,
0.06515611708164215,
-0.09547848999500275,
0.05370946601033211,
0.057203520089387894,
-0.15630389750003815,
-0.02539169229567051,
0.024155190214514732,
0.035110291093587875,
-0.03184066340327263,
0.0027696851175278425,
0.04000042751431465,
0.09550079703330994,
-0.030412381514906883,
0.01645014062523842,
0.028987478464841843,
0.018416285514831543,
-0.0620482861995697,
0.054954227060079575,
0.05196451023221016,
0.02968391589820385,
-0.06250818073749542,
0.15082058310508728,
-0.003393556224182248,
-0.09488832205533981,
0.07262309640645981,
-0.07061731815338135,
0.04120803624391556,
0.020796669647097588,
0.011492366902530193,
-0.08917070925235748,
-0.022287411615252495,
-0.05999390035867691,
0.1171201765537262,
0.03162715211510658,
0.21193456649780273,
0.05051356554031372,
-0.018534110859036446,
-0.05872368440032005,
0.012634686194360256,
0.0783151164650917,
-0.002158329589292407,
-0.0610286220908165,
-0.07898653298616409,
0.04239106923341751,
0.0023705351632088423,
0.02616543509066105,
-0.09791235625743866,
0.023762086406350136,
0.011326886713504791,
0.10749811679124832,
-0.013003519736230373,
0.07055395096540451,
0.02971665747463703,
-0.005767948925495148,
-0.06584261357784271,
-0.031376779079437256,
0.11370203644037247,
0.011250685900449753,
-0.07954201102256775,
0.26582100987434387,
-0.1570323258638382,
0.1399509310722351,
0.15330272912979126,
-0.2666454017162323,
0.0014702858170494437,
-0.047252215445041656,
-0.06865891069173813,
-0.016542579978704453,
0.0441620759665966,
0.02130441553890705,
0.1677967756986618,
-0.015063175931572914,
0.14011451601982117,
-0.07449161261320114,
0.0010186423314735293,
-0.01316735241562128,
-0.06178255379199982,
0.013700713403522968,
0.07382962852716446,
0.034241486340761185,
-0.08135712146759033,
0.07414090633392334,
0.10650529712438583,
0.015940794721245766,
0.21824954450130463,
0.02612394094467163,
-0.01919833943247795,
-0.002136810449883342,
-0.11896029114723206,
-0.008797424845397472,
0.010557484813034534,
-0.07055193930864334,
-0.0523611456155777,
0.08217769116163254,
0.0016193254850804806,
0.08116817474365234,
-0.1184573620557785,
-0.016593655571341515,
-0.025164693593978882,
-0.01273305993527174,
0.08795814961194992,
0.09320486336946487,
-0.019560500979423523,
0.07108665257692337,
-0.04754824936389923,
0.05860380083322525,
0.024497192353010178,
0.04185350239276886,
-0.07250665128231049,
0.14905786514282227,
-0.08832987397909164,
-0.3322668671607971,
-0.1772657334804535,
0.003030468011274934,
-0.003167979186400771,
0.008714917115867138,
0.050908226519823074,
0.011470352299511433,
-0.039865732192993164,
-0.049158163368701935,
0.13887088000774384,
-0.06258565932512283,
-0.01831367425620556,
-0.06145209074020386,
-0.037042275071144104,
-0.06433772295713425,
-0.11672250926494598,
-0.020194821059703827,
-0.032783154398202896,
-0.15365007519721985,
0.09721054136753082,
-0.13492287695407867,
0.09788398444652557,
0.09894762933254242,
0.016822826117277145,
0.013072081841528416,
-0.07032529264688492,
0.0956190899014473,
-0.05921079218387604,
0.15851092338562012,
0.2248091846704483,
0.027807429432868958,
0.0857292115688324,
0.025922948494553566,
-0.0708838552236557,
-0.021391626447439194,
0.021048566326498985,
-0.025146065279841423,
-0.07056461274623871,
-0.09844384342432022,
-0.044069234281778336,
-0.04502435401082039,
0.06730055063962936,
-0.017730340361595154,
0.007474629674106836,
0.11475808918476105,
0.1255791038274765,
-0.08516295999288559,
0.011088586412370205,
0.05105188861489296,
0.07567932456731796,
0.19849665462970734,
-0.0804227888584137,
0.2001882791519165,
-0.0859890803694725,
-0.09880276769399643,
0.1301833689212799,
-0.09266162663698196,
0.11428830772638321,
0.0014957196544855833,
-0.002328042406588793,
0.028651511296629906,
0.08504685014486313,
0.1043994203209877,
0.19571144878864288,
-0.004670526832342148,
-0.014718194492161274,
-0.08062386512756348,
-0.005470534320920706,
-0.049416035413742065,
0.06939609348773956,
-0.013067792169749737,
-0.12670724093914032,
0.0007128393044695258,
-0.05950154364109039,
0.03337537497282028,
0.12483973801136017,
0.07287448644638062,
-0.13617323338985443,
-0.04791087284684181,
-0.01465320959687233,
0.011249165050685406,
-0.12453842163085938,
-0.017465144395828247,
-0.05473931506276131,
-0.11058297753334045,
0.09604816138744354,
-0.05547701567411423,
0.0844327062368393,
-0.14626161754131317,
0.006641899701207876,
-0.17657141387462616,
-0.004737436771392822,
-0.030709879472851753,
0.1253185272216797,
-0.18332301080226898,
0.16110876202583313,
-0.0055365292355418205,
0.0016860044561326504,
-0.08983462303876877,
-0.06304091960191727,
0.05387907475233078,
0.1618829220533371,
0.06855082511901855,
0.0005644942866638303,
-0.025134919211268425,
-0.027706241235136986,
-0.09048416465520859,
0.07147464156150818,
0.08156842738389969,
-0.012206566520035267,
0.014414523728191853,
-0.08843598514795303,
0.034702517092227936,
0.03165803104639053,
0.08668308705091476,
-0.08866729587316513,
-0.1415327787399292,
0.057592034339904785,
0.023097867146134377,
0.08956625312566757,
-0.026542935520410538,
-0.09300887584686279,
-0.01511935330927372,
0.18557536602020264,
-0.07266969978809357,
-0.11452574282884598,
-0.07885165512561798,
-0.011913081631064415,
0.015941740944981575,
-0.09049446880817413,
0.04686351493000984,
-0.10287566483020782,
-0.0006447622436098754,
-0.03622869774699211,
-0.1283024400472641,
0.08758639544248581,
-0.04592147096991539,
-0.09234953671693802,
0.0467219278216362,
0.051070135086774826,
0.04922120273113251,
-0.010843510739505291,
0.030465079471468925,
-0.024405116215348244,
-0.09370525926351547,
-0.10949843376874924,
-0.0190024022012949,
0.07398839294910431,
0.07885278016328812,
-0.03609587252140045,
-0.11066088825464249,
0.08897160738706589,
-0.044485460966825485,
-0.048488978296518326,
0.13046279549598694,
0.15611673891544342,
-0.026681626215577126,
0.08836951106786728,
0.16568315029144287,
-0.07763678580522537,
-0.304328590631485,
-0.21194028854370117,
-0.08980677276849747,
-0.01708904840052128,
-0.03483607620000839,
-0.1505921483039856,
0.0941460132598877,
0.06285879015922546,
0.0089745307341218,
0.08364295959472656,
-0.005041896365582943,
-0.14351511001586914,
0.185830220580101,
0.0571383535861969,
0.4420553147792816,
-0.14974991977214813,
-0.006131554022431374,
-0.055266525596380234,
-0.12714041769504547,
0.14216208457946777,
0.006668915506452322,
0.0963878259062767,
-0.05315380170941353,
0.03273598104715347,
-0.0251858439296484,
-0.021737780421972275,
0.06093349680304527,
-0.13434350490570068,
0.045087046921253204,
-0.16133250296115875,
-0.08466305583715439,
0.16636261343955994,
0.01283263973891735,
-0.01470585260540247,
-0.06428475677967072,
0.02653101645410061,
-0.09849897027015686,
-0.0889301747083664,
-0.08264951407909393,
0.09380706399679184,
0.04508129507303238,
-0.11898621171712875,
-0.09694314002990723,
0.06563432514667511,
-0.06825072318315506,
0.013852962292730808,
0.15616759657859802,
-0.09113336354494095,
0.014326432719826698,
0.07516892999410629,
0.0796278566122055,
-0.12393952906131744,
0.12486490607261658,
-0.07225216180086136,
-0.08010170608758926,
0.04003065824508667,
0.032901108264923096,
0.039032720029354095,
0.0847206860780716,
-0.042034126818180084,
0.08239147812128067,
0.07225411385297775,
-0.012808933854103088,
0.04009174555540085,
0.06967524439096451,
-0.22772136330604553,
-0.14336912333965302,
-0.03855522722005844,
-0.0558478944003582,
0.05278780683875084,
0.07788543403148651,
0.18917341530323029,
0.004117815289646387,
-0.04451772943139076,
0.00723509956151247,
-0.0211196169257164,
0.004454941023141146,
0.007227658294141293,
0.049590423703193665,
-0.03295280039310455,
-0.03912712261080742,
0.040825579315423965,
-0.001552210538648069,
-0.07979843020439148,
0.011853854171931744,
-0.022412799298763275,
-0.12963475286960602,
-0.07717487961053848,
-0.04742728918790817,
0.08995708078145981,
-0.1845005303621292,
-0.06709663569927216,
-0.0999038890004158,
-0.06269064545631409,
0.01709909737110138,
0.16388432681560516,
0.05997678264975548,
0.05985400080680847,
0.004151548724621534,
0.03673626482486725,
-0.032420914620161057,
0.004566321615129709,
0.005149309057742357,
-0.039930738508701324,
-0.07022411376237869,
0.17923825979232788,
0.04697965458035469,
0.10092081129550934,
-0.10532942414283752,
-0.02279108762741089,
-0.10151800513267517,
-0.036771949380636215,
-0.08067748695611954,
-0.04327571392059326,
-0.1001540869474411,
-0.07129678875207901,
-0.01809588260948658,
-0.057125575840473175,
-0.03364451974630356,
-0.03641520440578461,
-0.07256254553794861,
0.047742437571287155,
-0.022925008088350296,
-0.010057821869850159,
-0.08858848363161087,
-0.06069422513246536,
0.05588924512267113,
0.01889350451529026,
0.0704902783036232,
0.1054399237036705,
-0.024889709427952766,
0.06555971503257751,
-0.06506132334470749,
0.03248805180191994,
0.03483687713742256,
0.01978292129933834,
-0.00731669319793582,
0.03182939812541008,
-0.034951724112033844,
0.05862117558717728,
0.062002018094062805,
0.03233820199966431,
0.04993513971567154,
-0.05747581645846367,
0.09427832812070847,
-0.027592582628130913,
-0.07000426948070526,
-0.033509161323308945,
0.05697111785411835,
-0.047239139676094055,
0.10423005372285843,
0.12382502853870392,
-0.07423530519008636,
0.06034292280673981,
-0.1297309398651123,
0.04147164151072502,
0.027012277394533157,
-0.11288491636514664,
0.21641337871551514,
-0.1451132893562317,
-0.006515130400657654,
0.053177230060100555,
0.2689671218395233,
0.03415258228778839,
-0.030952004715800285,
0.002690284512937069,
0.027753004804253578,
-0.023406604304909706,
-0.03945702314376831,
0.20092326402664185,
0.04364423453807831,
0.005558432545512915,
-0.044276606291532516,
0.11582451313734055,
-0.015514004044234753,
0.09667299687862396,
0.16795961558818817,
0.04437324404716492,
-0.08438693732023239,
0.09686567634344101,
-0.0646430253982544,
0.034156862646341324,
0.02238159067928791,
-0.15113569796085358,
0.034215204417705536,
0.08365444839000702,
-0.011444099247455597,
0.16471824049949646,
0.16106002032756805,
0.005905826110392809,
0.012531641870737076,
0.12240254878997803,
-0.05033181980252266,
-0.1949419230222702,
-0.19412146508693695,
-0.08571022003889084,
-0.13439060747623444,
0.011405148543417454,
-0.05050419643521309,
-0.016312221065163612,
-0.02027803286910057,
0.06260848045349121,
-0.021232927218079567,
0.034041568636894226,
-0.027003750205039978,
-0.10077159851789474,
0.09377912431955338,
-0.018512427806854248,
-0.04931368678808212,
-0.08370032161474228,
-0.011009970679879189,
-0.017137087881565094,
0.03465517237782478,
0.0018367973389104009,
-0.0074594165198504925,
-0.01228038128465414,
0.023770883679389954,
-0.045403290539979935,
-0.08613073825836182,
-0.08805680274963379,
0.09009590744972229,
0.015436596237123013,
0.03593553230166435,
-0.01040665153414011,
-0.008753988891839981,
-0.0031955039594322443,
0.13028129935264587,
0.014013171195983887,
-0.1323324292898178,
-0.005376783199608326,
0.20474179089069366,
-0.05520537868142128,
0.05816357210278511,
-0.005352458916604519,
0.0018984157359227538,
0.023162512108683586,
0.35678595304489136,
0.21955205500125885,
-0.04250945895910263,
-0.004354351665824652,
0.012960080057382584,
0.03737300634384155,
0.10847661644220352,
0.07780147343873978,
0.07205279916524887,
0.149680033326149,
-0.04436871036887169,
-0.10702673345804214,
-0.08800570666790009,
-0.0042454954236745834,
-0.05195667967200279,
0.013200550340116024,
0.05347788333892822,
-0.02870689146220684,
-0.010391072370111942,
0.04359276592731476,
-0.12460317462682724,
-0.13507352769374847,
-0.029168741777539253,
-0.014709661714732647,
0.010247999802231789,
-0.0051087019965052605,
0.06963715702295303,
-0.03066602721810341,
0.08279264718294144,
-0.01785392127931118,
0.03230632469058037,
0.1936235874891281,
0.0507538765668869,
-0.19031788408756256,
0.049614448100328445,
0.0819375291466713,
-0.1526619791984558,
-0.027277128770947456,
-0.05882665514945984,
0.010936671867966652,
0.08069464564323425,
-0.016799775883555412,
0.008081309497356415,
0.10300139337778091,
-0.04894994571805,
-0.017898106947541237,
0.013252484612166882,
-0.004116067197173834,
0.024564698338508606,
-0.1791403591632843,
0.00229322980158031,
-0.11444398015737534,
0.11953458935022354,
-0.05691909044981003,
-0.17157678306102753,
-0.019017739221453667,
0.09093740582466125,
-0.11491447687149048,
0.09216663241386414,
0.09703948348760605,
-0.003862573066726327,
0.004213476087898016,
-0.06797097623348236,
-0.04936083033680916,
0.03717855364084244,
-0.024426626041531563,
-0.011610785499215126,
-0.024837752804160118,
-0.089357890188694,
-0.027564827352762222,
-0.000809425488114357,
-0.21083670854568481,
0.02022072859108448,
-0.12795060873031616,
-0.09240861982107162,
-0.06158694997429848,
0.09922584146261215,
-0.04016387462615967,
0.02784065157175064,
0.01087264809757471,
0.0536983460187912,
0.01161572802811861,
0.04256861284375191,
-0.11967433989048004,
-0.04457447677850723
] |
null | null |
transformers
|
# GPT2 Tweet style transfer paraphraser
This is the trained Tweet-model from the paper [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://arxiv.org/abs/2010.05700) by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author.
## Citation
If you found this model useful, please cite the original work:
```
@inproceedings{style20,
author={Kalpesh Krishna and John Wieting and Mohit Iyyer},
Booktitle = {Empirical Methods in Natural Language Processing},
Year = "2020",
Title={Reformulating Unsupervised Style Transfer as Paraphrase Generation},
}
```
|
{}
|
text-generation
|
filco306/gpt2-tweet-paraphraser
|
[
"transformers",
"pytorch",
"text-generation",
"arxiv:2010.05700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.05700"
] |
[] |
TAGS
#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us
|
# GPT2 Tweet style transfer paraphraser
This is the trained Tweet-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author.
If you found this model useful, please cite the original work:
|
[
"# GPT2 Tweet style transfer paraphraser\n\nThis is the trained Tweet-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you found this model useful, please cite the original work:"
] |
[
"TAGS\n#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# GPT2 Tweet style transfer paraphraser\n\nThis is the trained Tweet-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you found this model useful, please cite the original work:"
] |
[
42,
86
] |
[
"passage: TAGS\n#transformers #pytorch #text-generation #arxiv-2010.05700 #autotrain_compatible #endpoints_compatible #region-us \n# GPT2 Tweet style transfer paraphraser\n\nThis is the trained Tweet-model from the paper Reformulating Unsupervised Style Transfer as Paraphrase Generation by Krishna K. et al. Note that I (the uploader) am not the author of the paper. Permission to upload to Huggingface was given by the main author. \n\n\nIf you found this model useful, please cite the original work:"
] |
[
0.05668855085968971,
-0.10278899222612381,
-0.002465823432430625,
0.03156713768839836,
0.11637859791517258,
0.05127875134348869,
0.12912504374980927,
0.06077665835618973,
-0.18035657703876495,
-0.08447448164224625,
0.1297200322151184,
0.1412062644958496,
0.003176213474944234,
0.04706540331244469,
0.06896302103996277,
-0.3922452926635742,
0.006557496264576912,
0.05754178389906883,
-0.010238359682261944,
0.1448097676038742,
0.1245616003870964,
-0.02306441403925419,
0.12247098237276077,
0.042243942618370056,
-0.07712240517139435,
0.029252173379063606,
0.03125029429793358,
-0.056569959968328476,
0.13354170322418213,
0.10367219895124435,
0.055434707552194595,
0.06671323627233505,
0.010472850874066353,
-0.0007056938484311104,
0.05850588530302048,
0.055540524423122406,
-0.0816497951745987,
0.04071122407913208,
0.05791693553328514,
-0.06771925836801529,
0.34739935398101807,
0.0700894370675087,
-0.028466803953051567,
0.01977379620075226,
-0.148725688457489,
-0.10259272158145905,
-0.050769172608852386,
0.07653408497571945,
0.11282599717378616,
0.06758233904838562,
-0.030717337504029274,
0.18258461356163025,
0.008330954238772392,
0.09983936697244644,
0.16133524477481842,
-0.3605079650878906,
-0.05371362715959549,
-0.03354770690202713,
0.03399644419550896,
0.09266535192728043,
0.01898527517914772,
0.09085189551115036,
0.029354296624660492,
0.03856174275279045,
0.03484269976615906,
-0.12393766641616821,
-0.072785384953022,
-0.013649002648890018,
-0.12233222275972366,
0.028417913243174553,
0.25493136048316956,
-0.021100036799907684,
-0.03395870700478554,
0.0037902132607996464,
-0.09947798401117325,
0.19160419702529907,
-0.00010846284567378461,
-0.1213354766368866,
-0.030302373692393303,
0.028469571843743324,
-0.028183216229081154,
-0.1809450387954712,
-0.1354893445968628,
-0.04200959950685501,
-0.1742219179868698,
0.013113963417708874,
0.009546710178256035,
0.02440195530653,
-0.11801935732364655,
0.1134592667222023,
-0.06720998883247375,
-0.018228286877274513,
0.051532186567783356,
-0.13008835911750793,
0.12041718512773514,
0.015773504972457886,
-0.03970940783619881,
-0.1487029790878296,
-0.050121236592531204,
0.10813423246145248,
0.139365091919899,
-0.030966300517320633,
0.06484504044055939,
0.04483195021748543,
0.04096882790327072,
0.04436248540878296,
-0.15279464423656464,
-0.019994031637907028,
0.09060288965702057,
-0.10904768854379654,
0.012064489535987377,
-0.01670369692146778,
-0.11150107532739639,
-0.035231854766607285,
0.03868945688009262,
-0.004322746302932501,
0.0024831315968185663,
0.16339440643787384,
0.022376636043190956,
-0.048843901604413986,
0.13986051082611084,
-0.02721293270587921,
-0.0756719708442688,
-0.1289910227060318,
-0.003427941119298339,
0.07515165209770203,
0.10675450414419174,
0.04505658149719238,
-0.08695980161428452,
-0.004748477600514889,
-0.11071810871362686,
-0.02889484167098999,
0.003636582987383008,
-0.08671397715806961,
-0.04404282569885254,
-0.11728588491678238,
-0.016721239313483238,
-0.18833483755588531,
-0.17095094919204712,
0.0007797405123710632,
-0.021699992939829826,
0.02397131733596325,
-0.055697642266750336,
-0.026381289586424828,
0.04996245354413986,
0.004473026841878891,
-0.02028512768447399,
-0.0125264348462224,
-0.047564972192049026,
0.08786123245954514,
-0.15279340744018555,
0.05264798924326897,
-0.1684277355670929,
0.04935995861887932,
-0.14105837047100067,
-0.03645451366901398,
-0.19576439261436462,
0.01217577513307333,
0.038657788187265396,
0.0957513228058815,
-0.05861686170101166,
-0.09338276833295822,
-0.07412995398044586,
0.01387199480086565,
0.001901063951663673,
0.1660199612379074,
-0.060527339577674866,
-0.1149367094039917,
0.1700265109539032,
-0.11324049532413483,
-0.10319492965936661,
0.13666217029094696,
0.0007653991924598813,
0.17571543157100677,
0.12576231360435486,
0.15050990879535675,
0.11089082062244415,
-0.08050357550382614,
0.07936681807041168,
0.030195064842700958,
-0.1783342808485031,
0.026221664622426033,
0.0019257664680480957,
0.028138240799307823,
-0.13882538676261902,
-0.0022514648735523224,
0.03480719402432442,
0.08417599648237228,
-0.06976691633462906,
0.025730803608894348,
0.03362029790878296,
-0.010481465607881546,
0.00046422911691479385,
-0.007869064807891846,
0.04766617715358734,
-0.021574854850769043,
-0.05586671456694603,
-0.03309488669037819,
0.002290057484060526,
-0.04220899939537048,
0.047573767602443695,
-0.06124451011419296,
0.022111553698778152,
0.07473590224981308,
0.035971928387880325,
-0.06864389032125473,
-0.008032453246414661,
-0.050436895340681076,
0.17242202162742615,
0.10832589119672775,
0.13122178614139557,
0.03323563188314438,
-0.02555042877793312,
-0.07503361999988556,
0.015818212181329727,
0.07897680997848511,
-0.024307481944561005,
-0.07078823447227478,
-0.055800050497055054,
0.10851316899061203,
0.01065815333276987,
0.016539014875888824,
-0.04745347052812576,
0.01766236498951912,
-0.015341670252382755,
0.046299684792757034,
0.012210562825202942,
0.0820210725069046,
0.04013419523835182,
-0.0014534508809447289,
-0.04729899391531944,
-0.031082499772310257,
0.07693253457546234,
0.00235338369384408,
-0.09066437184810638,
0.2840561270713806,
-0.15661264955997467,
0.10136295855045319,
0.1312950700521469,
-0.23610937595367432,
-0.07729008793830872,
0.019230257719755173,
-0.06850288808345795,
-0.02142888866364956,
0.05271073430776596,
0.0020451920572668314,
0.1397758573293686,
-0.06530438363552094,
0.14740803837776184,
-0.08654658496379852,
-0.021027596667408943,
0.0245070718228817,
-0.1000945195555687,
-0.027936330065131187,
0.06393317133188248,
0.0620359368622303,
-0.14554060995578766,
0.04146670550107956,
0.22554297745227814,
0.03393517807126045,
0.19523589313030243,
0.058122582733631134,
-0.0346553660929203,
-0.0018438943661749363,
-0.13089394569396973,
-0.024785438552498817,
-0.05139085650444031,
-0.02293773926794529,
-0.027000054717063904,
0.06692229211330414,
0.03795018047094345,
0.06915058195590973,
-0.06309203058481216,
-0.06041004881262779,
-0.014631900936365128,
0.013658266514539719,
0.030562400817871094,
0.11716115474700928,
-0.03186109662055969,
0.12272331863641739,
-0.05464477464556694,
0.053017206490039825,
0.019996779039502144,
0.0630362257361412,
-0.10652939975261688,
0.13103537261486053,
-0.14097866415977478,
-0.342902272939682,
-0.13340646028518677,
0.014263007789850235,
0.02424582839012146,
0.023381391540169716,
0.09131719917058945,
-0.064763642847538,
-0.03893610090017319,
-0.06116459518671036,
0.09458022564649582,
-0.05768357217311859,
0.01289746630936861,
-0.1236233115196228,
-0.04064418748021126,
-0.029872242361307144,
-0.07670476287603378,
-0.038071077316999435,
-0.028588756918907166,
-0.19043824076652527,
0.15938639640808105,
-0.1749953329563141,
0.10961820930242538,
0.08704333007335663,
-0.007939756847918034,
0.004721236880868673,
-0.08036772906780243,
0.18001314997673035,
-0.08144678175449371,
0.15415622293949127,
0.1853632926940918,
0.0575232058763504,
0.11382598429918289,
-0.010522530414164066,
-0.0789099931716919,
-0.04810347035527229,
0.07677056640386581,
-0.011874324642121792,
-0.09156601876020432,
-0.09539745002985,
-0.06877711415290833,
-0.017726963385939598,
0.0766742005944252,
0.020464323461055756,
0.01362189557403326,
0.1333678662776947,
0.11256422102451324,
-0.060441888868808746,
0.02099321037530899,
0.049886349588632584,
0.0817718356847763,
0.13144735991954803,
-0.052521251142024994,
0.16445346176624298,
-0.08298142999410629,
-0.08727779239416122,
0.18021441996097565,
-0.09989045560359955,
0.14083240926265717,
0.025037432089447975,
-0.04638231545686722,
0.03828182816505432,
0.14425812661647797,
0.08322574943304062,
0.15195298194885254,
-0.026306798681616783,
-0.009439566172659397,
-0.09866547584533691,
-0.01213833224028349,
-0.01831134594976902,
0.07152687758207321,
-0.03631354868412018,
-0.17165210843086243,
-0.02348908968269825,
-0.00045172718819230795,
0.048127684742212296,
0.15848709642887115,
0.07747603207826614,
-0.16686701774597168,
-0.032763633877038956,
-0.03163047879934311,
-0.023904399946331978,
-0.09261715412139893,
0.0301663875579834,
0.024772590026259422,
-0.1210344210267067,
0.12369629740715027,
-0.01417612936347723,
0.07567168027162552,
-0.01961526833474636,
0.04056777432560921,
-0.09087996184825897,
-0.05537664517760277,
-0.05371522158384323,
0.1080629900097847,
-0.21953506767749786,
0.19595827162265778,
-0.0189495999366045,
-0.04440674930810928,
-0.06429938226938248,
-0.10034593939781189,
0.049753062427043915,
0.19086384773254395,
0.11197235435247421,
0.04015324264764786,
0.10153821110725403,
-0.024021556600928307,
-0.06141194328665733,
0.049262095242738724,
0.13801054656505585,
-0.059624023735523224,
0.028077172115445137,
-0.07158304005861282,
0.02434507943689823,
0.007087967824190855,
0.06870593130588531,
-0.06648526340723038,
-0.07354125380516052,
0.04924731329083443,
-0.01828497089445591,
0.06482936441898346,
0.013428138568997383,
-0.03798963874578476,
-0.05233382433652878,
0.09251630306243896,
-0.010830933228135109,
-0.12005047500133514,
-0.08846964687108994,
-0.03393533453345299,
0.017724158242344856,
-0.08529943227767944,
0.049071650952100754,
-0.04528793320059776,
-0.09979774802923203,
-0.01621522195637226,
-0.16626609861850739,
0.0775807797908783,
-0.04779711365699768,
-0.08103583753108978,
0.041266150772571564,
0.08557715266942978,
0.06662097573280334,
-0.001473532523959875,
0.04314468428492546,
0.0024835048243403435,
-0.11022668331861496,
-0.10776946693658829,
0.013947296887636185,
-0.034736670553684235,
0.055851049721241,
-0.04275117069482803,
-0.06061304733157158,
0.05560089647769928,
-0.09800810366868973,
-0.017529020085930824,
0.1312301903963089,
0.1505429744720459,
-0.04594165459275246,
0.11611278355121613,
0.1486346274614334,
-0.07767589390277863,
-0.25039318203926086,
-0.2073422521352768,
-0.07086265832185745,
-0.039530813694000244,
0.04569463059306145,
-0.1320418268442154,
0.06539476662874222,
0.10189425945281982,
0.046192068606615067,
0.008134175091981888,
-0.10157859325408936,
-0.1369234174489975,
0.12093634158372879,
0.01997406780719757,
0.42753756046295166,
-0.1250196397304535,
0.010401822626590729,
-0.021539151668548584,
-0.0777151882648468,
0.16931328177452087,
-0.017437687143683434,
0.0768052339553833,
-0.04145905375480652,
0.09864524006843567,
-0.012795009650290012,
0.027613719925284386,
0.03128639981150627,
-0.07440368086099625,
0.03098875842988491,
-0.1704263836145401,
-0.02984638139605522,
0.18634465336799622,
0.03603058308362961,
0.045380521565675735,
-0.009882797487080097,
0.05361612141132355,
-0.14698541164398193,
-0.03778575360774994,
-0.1030382588505745,
0.051194097846746445,
0.03899192437529564,
-0.123642198741436,
-0.07042597234249115,
0.07138543576002121,
-0.03731396421790123,
-0.027689199894666672,
0.015561357140541077,
-0.1047254279255867,
0.07744307816028595,
-0.037813253700733185,
0.11962711811065674,
-0.10509627312421799,
0.09884278476238251,
-0.07407151162624359,
-0.08567918837070465,
0.026108987629413605,
-0.08487500250339508,
0.02228423021733761,
0.06574342399835587,
-0.04578550532460213,
0.08611130714416504,
0.024600118398666382,
-0.005425869487226009,
0.052450306713581085,
0.12577565014362335,
-0.2754335403442383,
-0.08214418590068817,
-0.07896355539560318,
-0.08848758041858673,
0.08154620975255966,
0.06933047622442245,
0.19356544315814972,
-0.03022688627243042,
-0.047762926667928696,
0.0015826335875317454,
-0.0006674600881524384,
-0.033003952354192734,
0.03067842684686184,
0.04881814867258072,
-0.07676666229963303,
-0.027985958382487297,
0.04811609163880348,
0.03386806696653366,
-0.07602550834417343,
0.020917044952511787,
0.05977104604244232,
-0.14622727036476135,
-0.07382962107658386,
-0.10326982289552689,
-0.03758401796221733,
-0.1606399118900299,
-0.0003135268634650856,
-0.01516014151275158,
0.007424753159284592,
0.02550889551639557,
0.12206148356199265,
0.032727956771850586,
0.06872215121984482,
-0.02680238150060177,
0.009404825046658516,
0.0023645777255296707,
-0.03689257428050041,
0.06392975151538849,
-0.03803348168730736,
-0.0750659853219986,
0.19379788637161255,
0.024946196004748344,
0.1601323038339615,
-0.1072850227355957,
-0.03231823816895485,
-0.06478050351142883,
-0.04256724938750267,
-0.017362480983138084,
-0.06154698133468628,
-0.09199638664722443,
-0.06722292304039001,
-0.02527294121682644,
-0.05497658997774124,
-0.042158424854278564,
-0.06664980947971344,
-0.07345438748598099,
0.03235650807619095,
-0.002595064230263233,
0.020205941051244736,
-0.043303728103637695,
-0.033875588327646255,
0.09084955602884293,
0.04030309244990349,
0.12303349375724792,
0.09033546596765518,
-0.07531844824552536,
0.07168598473072052,
-0.04217313602566719,
0.01244452502578497,
0.027783066034317017,
0.0019631723407655954,
-0.01059019099920988,
0.08712289482355118,
-0.020871082320809364,
0.01596781238913536,
0.029730189591646194,
0.04423009976744652,
0.0571102648973465,
-0.08517146855592728,
0.11650225520133972,
0.024141451343894005,
-0.08766687661409378,
-0.07721635699272156,
-0.014792684465646744,
-0.09702851623296738,
0.06345497071743011,
0.1514984369277954,
-0.08421991020441055,
0.04948687553405762,
-0.11633428931236267,
0.008292303420603275,
0.016211412847042084,
-0.11161722242832184,
0.10892216116189957,
-0.133244127035141,
-0.012097116559743881,
0.021664060652256012,
0.15461434423923492,
0.05896759033203125,
-0.01718560792505741,
0.014585879631340504,
0.1264851838350296,
-0.05390501767396927,
-0.06882958859205246,
0.08215885609388351,
0.039161037653684616,
-0.023176806047558784,
-0.13165508210659027,
0.10484813153743744,
-0.05277705192565918,
-0.010289396159350872,
0.16089418530464172,
-0.028741730377078056,
-0.03470791503787041,
0.028977425768971443,
-0.054532624781131744,
0.062119513750076294,
-0.01550187636166811,
-0.15335384011268616,
0.017120426520705223,
0.10095611959695816,
0.006075035780668259,
0.10322210937738419,
0.19636283814907074,
-0.006839865352958441,
0.017534101381897926,
0.08201700448989868,
-0.03622725233435631,
-0.14894673228263855,
-0.2344929277896881,
-0.08266494423151016,
-0.1363135576248169,
-0.0037640375085175037,
-0.06733135879039764,
0.016174597665667534,
-0.010045618750154972,
0.08998008817434311,
-0.04792684316635132,
0.03958480805158615,
-0.10663482546806335,
-0.1734389066696167,
0.1427154541015625,
-0.02082776464521885,
0.003464869922026992,
-0.06096256151795387,
-0.003964845556765795,
-0.04536774009466171,
0.07001999020576477,
-0.011945576407015324,
-0.005068403668701649,
-0.0007907156832516193,
-0.005154336802661419,
-0.11426493525505066,
-0.08533066511154175,
-0.08666534721851349,
0.09445460140705109,
-0.025546958670020103,
-0.0775238499045372,
0.004957588855177164,
0.000842711771838367,
0.005330846644937992,
0.19258199632167816,
0.02908729761838913,
-0.10828651487827301,
-0.05933300033211708,
0.22616825997829437,
-0.04966205358505249,
0.024389639496803284,
0.00775837991386652,
-0.000744677847251296,
0.034714631736278534,
0.33829259872436523,
0.300274133682251,
-0.06084439158439636,
-0.004779998678714037,
-0.024616533890366554,
0.04390595480799675,
0.15151174366474152,
0.13677723705768585,
0.08577099442481995,
0.15424364805221558,
-0.060261309146881104,
-0.040187980979681015,
-0.11392954736948013,
0.05347117409110069,
-0.030619828030467033,
0.027292702347040176,
0.06221918761730194,
-0.0333847813308239,
0.024227024987339973,
0.10528843849897385,
-0.10421743988990784,
-0.05108844116330147,
-0.11435656994581223,
-0.02134745568037033,
-0.003400310641154647,
0.0017637547571212053,
-0.03668805584311485,
-0.0033874334767460823,
0.09485501796007156,
-0.013950132764875889,
0.021579422056674957,
0.10314711928367615,
0.07915391772985458,
-0.18157851696014404,
-0.008540243841707706,
0.12640786170959473,
-0.13843023777008057,
-0.03662322461605072,
-0.031524114310741425,
-0.020140845328569412,
0.039353929460048676,
-0.01583857461810112,
0.011886500753462315,
0.10962451994419098,
-0.02668215148150921,
0.06610333919525146,
0.009200544096529484,
-0.021122856065630913,
0.06962212920188904,
-0.15249255299568176,
0.011853729374706745,
-0.0913909450173378,
0.07190929353237152,
-0.006748401094228029,
-0.06881333887577057,
-0.0022698568645864725,
0.13859030604362488,
-0.10453063249588013,
0.1088864803314209,
0.12417273223400116,
-0.034439824521541595,
0.02093339152634144,
-0.08423852920532227,
-0.06471096724271774,
0.024591730907559395,
-0.11046013981103897,
-0.0028035365976393223,
-0.02370581030845642,
-0.10235404223203659,
-0.07096131891012192,
-0.003728361800312996,
-0.17452870309352875,
0.041247520595788956,
-0.10491348057985306,
-0.025282593443989754,
-0.08411908149719238,
0.14922566711902618,
-0.00017377943731844425,
0.001837921910919249,
0.020193153992295265,
0.09407888352870941,
0.03925147280097008,
0.05215199664235115,
-0.11990641057491302,
-0.03715139627456665
] |
null | null |
transformers
|
# beer_vs_wine
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### beer
#### wine
|
{"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]}
|
image-classification
|
filipafcastro/beer_vs_wine
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# beer_vs_wine
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### beer
!beer
#### wine
!wine
|
[
"# beer_vs_wine\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### beer\n\n!beer",
"#### wine\n\n!wine"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# beer_vs_wine\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### beer\n\n!beer",
"#### wine\n\n!wine"
] |
[
49,
45,
4,
6,
6
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n# beer_vs_wine\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.## Example Images#### beer\n\n!beer#### wine\n\n!wine"
] |
[
-0.10991454869508743,
0.1619662046432495,
-0.0016634310595691204,
0.07083617150783539,
0.09594986587762833,
0.0659630075097084,
0.1335122138261795,
0.05358761176466942,
0.21538904309272766,
0.018834993243217468,
0.13585367798805237,
0.11634305119514465,
-0.014162198640406132,
0.22972974181175232,
0.007837938144803047,
-0.22485972940921783,
-0.014270204119384289,
0.07413078844547272,
0.123846136033535,
0.08562196791172028,
0.07270117849111557,
-0.12536880373954773,
0.1024811714887619,
-0.031443510204553604,
-0.24900273978710175,
-0.03327563777565956,
0.0055869026109576225,
-0.052458617836236954,
0.08307554572820663,
-0.004460838623344898,
0.09300292283296585,
0.05851452052593231,
0.014781879261136055,
0.011759604327380657,
0.06125110387802124,
-0.032442521303892136,
-0.035019613802433014,
0.057475488632917404,
0.08672704547643661,
-0.029086589813232422,
0.14125825464725494,
0.1856766790151596,
0.005441606044769287,
0.08882443606853485,
-0.09373166412115097,
-0.01857544109225273,
-0.06295007467269897,
0.06589950621128082,
-0.014338493347167969,
0.02796631120145321,
-0.04736446589231491,
0.06541970372200012,
-0.14413334429264069,
0.029277397319674492,
0.2077057659626007,
-0.09170281887054443,
-0.08543086796998978,
0.14129063487052917,
0.03838222846388817,
-0.0675811916589737,
-0.07011642307043076,
0.06803461164236069,
0.02577279508113861,
0.045469824224710464,
-0.07984735816717148,
-0.0602748803794384,
-0.03735341504216194,
-0.05179242789745331,
-0.04307118058204651,
-0.04295443743467331,
0.09832829236984253,
0.0034828626085072756,
-0.062362682074308395,
-0.09452690184116364,
0.017364315688610077,
0.04941684380173683,
-0.085239477455616,
-0.027432365342974663,
-0.03944598510861397,
-0.04905830696225166,
-0.10058299452066422,
-0.04993000999093056,
-0.13648059964179993,
-0.04649421200156212,
-0.08195305615663528,
0.23779375851154327,
0.03223187476396561,
0.03195630759000778,
-0.05003117397427559,
0.09119979292154312,
-0.07302869856357574,
-0.05662466585636139,
-0.022964026778936386,
-0.08871788531541824,
-0.0719718337059021,
-0.05300658196210861,
0.0042786551639437675,
0.06617036461830139,
0.1133437529206276,
0.20105761289596558,
-0.052991658449172974,
0.03351392224431038,
0.06028466671705246,
0.015108512714505196,
0.0430421307682991,
0.27030447125434875,
0.10089265555143356,
0.004813346080482006,
0.00572437047958374,
-0.08335558325052261,
-0.05092084780335426,
0.04384797438979149,
-0.1876281499862671,
-0.058709315955638885,
-0.04236269369721413,
-0.015973201021552086,
0.010167629458010197,
0.047363586723804474,
-0.11496242880821228,
-0.12028241902589798,
0.18675599992275238,
0.02797636017203331,
0.07773102819919586,
0.021453168243169785,
-0.03240645304322243,
0.01682869903743267,
0.004471396561712027,
0.05308755859732628,
-0.041002027690410614,
0.07313288748264313,
-0.041654255241155624,
0.02595180831849575,
-0.08361907303333282,
0.029158886522054672,
0.019631190225481987,
-0.12164752185344696,
0.001996220089495182,
-0.11727266013622284,
-0.01716206595301628,
-0.012534638866782188,
0.10212237387895584,
-0.0449778251349926,
-0.09008660167455673,
-0.020192230120301247,
0.10782412439584732,
-0.08197112381458282,
-0.005386259872466326,
-0.1775369942188263,
0.008053719997406006,
0.015482996590435505,
-0.015389413572847843,
0.060411497950553894,
-0.16379395127296448,
0.070762500166893,
-0.1842087358236313,
-0.02200966514647007,
-0.30397966504096985,
0.07629513740539551,
-0.0831921175122261,
0.15584421157836914,
-0.11049342900514603,
-0.00374565739184618,
-0.004095883574336767,
0.016822995617985725,
-0.04860979691147804,
0.17432613670825958,
-0.11785419285297394,
-0.13175801932811737,
0.05087627097964287,
-0.09995236247777939,
-0.06420189142227173,
0.11634539067745209,
0.011738813482224941,
-0.02137506753206253,
0.1469365954399109,
0.1807536780834198,
0.08966902643442154,
-0.12498383969068527,
0.006455591414123774,
-0.046330466866493225,
-0.11329829692840576,
0.08427079766988754,
0.022424442693591118,
0.014240373857319355,
-0.04568318650126457,
0.019118737429380417,
0.030180469155311584,
0.035468097776174545,
-0.04237321391701698,
-0.05729344114661217,
-0.00817259680479765,
-0.012175587937235832,
0.11873113363981247,
0.0722503513097763,
0.006410015746951103,
-0.024604544043540955,
-0.03074491396546364,
-0.06155982241034508,
0.06526218354701996,
0.016114862635731697,
-0.06575237959623337,
-0.10681115090847015,
0.20555171370506287,
0.059695061296224594,
-0.02545403316617012,
-0.021367892622947693,
-0.0762350931763649,
0.0859619602560997,
-0.037386704236269,
0.19386082887649536,
0.0762644037604332,
0.051390908658504486,
0.03957829251885414,
0.030515097081661224,
-0.019812026992440224,
0.04318247735500336,
-0.03448478877544403,
0.0007928148843348026,
-0.10225947201251984,
0.0753583312034607,
-0.01848091185092926,
0.12007055431604385,
-0.12634402513504028,
-0.05049929767847061,
0.15530018508434296,
0.10337211936712265,
0.05082201212644577,
-0.0647943988442421,
-0.018914686515927315,
-0.058479275554418564,
-0.0281666312366724,
-0.03231809288263321,
0.10875944048166275,
-0.05629374459385872,
0.011885124258697033,
0.17936758697032928,
0.11512568593025208,
-0.011342006735503674,
0.170493945479393,
-0.068851038813591,
-0.0713333860039711,
-0.21802976727485657,
-0.0586511455476284,
0.06496090441942215,
0.019558852538466454,
0.06269582360982895,
0.08498656749725342,
-0.01335227396339178,
0.05921774357557297,
-0.022659115493297577,
-0.052928630262613297,
0.04152178391814232,
-0.009937492199242115,
-0.06566949188709259,
0.1171426773071289,
0.08523965626955032,
-0.11560802161693573,
0.06360916793346405,
0.12055698037147522,
0.06607646495103836,
0.0815662071108818,
0.07551603764295578,
0.022245366126298904,
0.0028210158925503492,
-0.028914421796798706,
-0.01033773273229599,
0.23024879395961761,
-0.1150931790471077,
-0.008090022020041943,
0.04947472736239433,
-0.13198226690292358,
-0.003927470650523901,
-0.12426047027111053,
0.01477067545056343,
-0.018561165779829025,
-0.01776941679418087,
0.13109904527664185,
0.061633285135030746,
0.009555164724588394,
0.08512686938047409,
0.04830178618431091,
-0.030480992048978806,
-0.006330879870802164,
0.06692471355199814,
-0.04932454973459244,
0.1251663714647293,
-0.04020720720291138,
-0.26688942313194275,
-0.051075320690870285,
-0.08434689044952393,
-0.04082315415143967,
0.11954410374164581,
0.06718333810567856,
-0.09985703229904175,
-0.026590658351778984,
0.05169326812028885,
0.15049782395362854,
0.07223936170339584,
-0.02045370824635029,
-0.15497025847434998,
-0.0008813684689812362,
-0.011192492209374905,
-0.046157315373420715,
-0.04541390389204025,
-0.03794463351368904,
-0.06967538595199585,
0.1777040660381317,
-0.10574911534786224,
0.09548414498567581,
0.07908741384744644,
-0.11125297844409943,
0.10310419648885727,
-0.003633267479017377,
0.21500082314014435,
-0.15336966514587402,
0.04760376736521721,
0.08824532479047775,
0.017328312620520592,
0.0077741509303450584,
0.06816821545362473,
0.04950937256217003,
-0.08192551136016846,
0.004315792117267847,
0.009104249067604542,
-0.09703178703784943,
-0.05295883119106293,
-0.15010885894298553,
-0.03180508315563202,
0.08874848484992981,
0.18765567243099213,
0.08654622733592987,
0.09229057282209396,
0.14645732939243317,
-0.059214286506175995,
0.03956454247236252,
-0.031889885663986206,
0.08120705932378769,
0.000504925730638206,
-0.04682512953877449,
0.0296760443598032,
-0.005304497200995684,
-0.12198159843683243,
0.17257821559906006,
-0.0005241199396550655,
0.19040992856025696,
0.006975805386900902,
0.036238010972738266,
0.07369869202375412,
0.053830794990062714,
0.07486312091350555,
-0.08237951993942261,
-0.02396884560585022,
-0.04144654795527458,
-0.021048283204436302,
-0.08196735382080078,
-0.005095649976283312,
0.05434947460889816,
0.07301250845193863,
-0.1038600504398346,
0.005534145049750805,
-0.1500236988067627,
0.07748166471719742,
0.09966042637825012,
0.03707915544509888,
-0.3002418875694275,
-0.04540332406759262,
0.0022704799193888903,
-0.04314602166414261,
-0.04516226798295975,
-0.0132997902110219,
-0.009479421190917492,
-0.09420835971832275,
0.057175327092409134,
-0.07714790850877762,
0.13551287353038788,
-0.059300683438777924,
0.011431388556957245,
0.052071377635002136,
0.06071772053837776,
0.022031312808394432,
0.06316222250461578,
-0.2591959834098816,
0.08713484555482864,
-0.011775918304920197,
-0.057221557945013046,
-0.1269558072090149,
-0.01072701346129179,
0.030120572075247765,
0.14078068733215332,
0.1019793376326561,
0.07507073879241943,
0.05244976654648781,
-0.12643709778785706,
-0.06900814920663834,
0.01914701610803604,
-0.05636053904891014,
-0.13148394227027893,
-0.030766066163778305,
0.02436528354883194,
-0.015514743514358997,
-0.04080573469400406,
-0.02384079061448574,
-0.15060573816299438,
-0.07765606045722961,
-0.010587096214294434,
-0.03135964646935463,
0.027978992089629173,
0.037635937333106995,
-0.030453210696578026,
-0.03296992555260658,
0.08704882115125656,
0.07498674094676971,
-0.01014093216508627,
-0.10366255044937134,
0.026014789938926697,
-0.0029248534701764584,
-0.0813831016421318,
0.030558492988348007,
-0.11369361728429794,
0.15698647499084473,
-0.1154063269495964,
-0.12908031046390533,
0.057644352316856384,
-0.09992799907922745,
-0.08880272507667542,
-0.07888023555278778,
0.04693228751420975,
0.09723905473947525,
0.012781267985701561,
0.12505190074443817,
0.08456115424633026,
-0.10481700301170349,
-0.10576271265745163,
0.01505113486200571,
-0.06318359076976776,
0.05779849365353584,
0.043137077242136,
0.01952858455479145,
-0.10138599574565887,
-0.022920789197087288,
0.06075236573815346,
0.01455638837069273,
0.0960211530327797,
-0.07998082041740417,
0.1399364024400711,
-0.024437446147203445,
-0.015771880745887756,
-0.2734529972076416,
-0.01327274739742279,
-0.02204311639070511,
-0.044697798788547516,
-0.016012204810976982,
-0.12841103971004486,
0.14838799834251404,
0.02168947085738182,
-0.059958651661872864,
0.14939367771148682,
-0.029553856700658798,
-0.13586574792861938,
0.15212751924991608,
0.0911957398056984,
0.10584753006696701,
-0.09903991222381592,
-0.08734606951475143,
-0.0977054312825203,
0.03647636994719505,
0.24370768666267395,
0.04331822693347931,
0.04571538418531418,
-0.05396779999136925,
0.10386893898248672,
0.055737197399139404,
0.007839251309633255,
0.06812763959169388,
0.011755592189729214,
-0.043521106243133545,
-0.1236628070473671,
-0.180536687374115,
-0.02946370653808117,
0.008645583875477314,
0.07211467623710632,
-0.0024222086649388075,
0.052031341940164566,
-0.13387317955493927,
0.010061283595860004,
-0.08195389807224274,
0.18411977589130402,
-0.016980063170194626,
-0.015504013746976852,
-0.12433718144893646,
0.09173673391342163,
-0.14821875095367432,
0.07475421577692032,
0.10459159314632416,
-0.02347961626946926,
0.030177537351846695,
0.012381626293063164,
0.0462736040353775,
0.009209503419697285,
-0.04983954504132271,
-0.006011765915900469,
-0.022735245525836945,
0.09992499649524689,
-0.09060196578502655,
0.051141075789928436,
0.09818952530622482,
0.015364640392363071,
0.08705281466245651,
-0.015646325424313545,
-0.028370708227157593,
-0.003109821118414402,
0.13087327778339386,
-0.1867392212152481,
-0.015493818558752537,
-0.1062433123588562,
0.006472592242062092,
0.10328204184770584,
0.04450191557407379,
0.08635757118463516,
-0.002467608777806163,
-0.09832138568162918,
0.025108356028795242,
-0.027003204450011253,
-0.022258667275309563,
0.07342810183763504,
0.0713053047657013,
-0.008996059186756611,
-0.15505608916282654,
-0.030080579221248627,
0.07161809504032135,
-0.09797149896621704,
-0.09523151069879532,
0.1258334219455719,
-0.027396729215979576,
-0.11479559540748596,
0.036087196320295334,
0.10579098761081696,
-0.1552567332983017,
0.02733469381928444,
-0.06976031512022018,
-0.071495421230793,
0.06300947815179825,
-0.06437124311923981,
0.12851904332637787,
0.07564256340265274,
0.08135808259248734,
0.03433220461010933,
-0.0250674057751894,
0.056230608373880386,
0.023690253496170044,
0.1166912168264389,
-0.15654127299785614,
-0.030430780723690987,
-0.04296377673745155,
0.08587243407964706,
-0.08674342930316925,
-0.06309224665164948,
-0.1569046676158905,
-0.04102066159248352,
-0.0862170159816742,
0.069647416472435,
-0.07496534287929535,
-0.0012894845567643642,
-0.039254263043403625,
-0.07150354236364365,
0.05045641213655472,
0.007486230228096247,
-0.1416323482990265,
-0.00510855158790946,
-0.03584880381822586,
0.11272832751274109,
-0.07971394062042236,
0.02113613300025463,
0.09539011865854263,
-0.01078773569315672,
0.16777081787586212,
0.05866100639104843,
-0.025261996313929558,
-0.0716480016708374,
-0.16191169619560242,
-0.0593138225376606,
0.08231016993522644,
0.00866275280714035,
0.06859611719846725,
0.0328897200524807,
0.019914688542485237,
0.022448336705565453,
0.058942969888448715,
0.007106638979166746,
0.1327928900718689,
-0.07734730839729309,
-0.030684135854244232,
0.0459708496928215,
-0.23606853187084198,
-0.0257852952927351,
0.056284572929143906,
0.0450730063021183,
-0.014910600148141384,
0.10272696614265442,
-0.06461013108491898,
0.036869440227746964,
-0.13686901330947876,
0.005833668168634176,
0.010612722486257553,
-0.09996400028467178,
-0.2029702067375183,
-0.036216944456100464,
-0.020786235108971596,
0.017742706462740898,
0.0863480195403099,
0.18102753162384033,
0.07096093893051147,
0.03138027340173721,
0.16975374519824982,
0.044027719646692276,
0.04841513931751251,
-0.00809305440634489,
-0.05361519753932953,
0.10308006405830383,
-0.1318253129720688,
0.06671930104494095,
-0.025956299155950546,
-0.1945124715566635,
-0.012513606809079647,
0.07266495376825333,
-0.05362194404006004,
-0.04471222311258316,
0.15564467012882233,
0.10234662890434265,
-0.16297203302383423,
0.16701537370681763,
-0.08216328918933868,
0.07594012469053268,
-0.04700048640370369,
0.06163378432393074,
0.018712924793362617,
-0.14230620861053467,
0.09115403890609741,
-0.016123807057738304,
-0.010841102339327335,
-0.01979973167181015,
-0.1924905627965927,
-0.12173517793416977,
-0.21790435910224915,
0.04965735226869583,
-0.02950875833630562,
0.010167624801397324,
-0.07614948600530624,
-0.013176430016756058,
-0.11162688583135605,
0.16631542146205902,
-0.023404965177178383,
-0.06215619295835495,
0.10273931175470352,
0.024881700053811073,
-0.08057496696710587,
0.024983102455735207,
-0.036964450031518936,
-0.040820877999067307,
0.1228371188044548,
0.03559169918298721,
-0.002141190692782402,
0.021283339709043503,
-0.034013211727142334,
-0.021110568195581436,
-0.022866617888212204,
0.015998676419258118,
-0.022622784599661827,
-0.019372576847672462,
-0.0204460509121418,
-0.045504964888095856,
0.028819503262639046,
0.01556889247149229,
0.11509735882282257,
-0.006428997498005629,
-0.02090308628976345,
-0.049753256142139435,
0.20563003420829773,
-0.0901736244559288,
0.06320745497941971,
-0.05501990020275116,
0.037890806794166565,
-0.01647050678730011,
0.18415263295173645,
0.21995431184768677,
-0.04067206010222435,
-0.01918359287083149,
-0.005031421314924955,
-0.02187112346291542,
0.062451284378767014,
0.10102546215057373,
-0.08732418715953827,
0.18374177813529968,
-0.06792690604925156,
-0.05744173750281334,
0.030180785804986954,
0.0111605329439044,
-0.027430467307567596,
-0.00968053750693798,
0.06657177209854126,
0.010992676019668579,
-0.11802135407924652,
0.14695580303668976,
-0.04784972593188286,
-0.05499223619699478,
0.07181447744369507,
-0.18452078104019165,
-0.07924666255712509,
0.0021297575440257788,
0.0860455334186554,
0.01789393089711666,
0.09003967046737671,
-0.04391556233167648,
0.011197114363312721,
-0.10599656403064728,
-0.04997789487242699,
-0.21936006844043732,
0.009433208033442497,
0.10769228637218475,
-0.2160867303609848,
0.24326570332050323,
-0.0672639012336731,
0.05216490849852562,
0.06832960247993469,
-0.021512949839234352,
-0.0730060487985611,
-0.11359076201915741,
-0.07149623334407806,
-0.17079582810401917,
0.010848901234567165,
0.10579602420330048,
-0.02291032299399376,
-0.04871064051985741,
0.051188111305236816,
-0.11382580548524857,
0.02532709576189518,
0.04691344499588013,
0.005190500058233738,
-0.06247501075267792,
0.2179573029279709,
-0.13150638341903687,
0.0779208168387413,
0.048239655792713165,
-0.003975937608629465,
0.0029905112460255623,
-0.08190721273422241,
0.08128198236227036,
0.02504282258450985,
-0.06895899027585983,
-0.02593303471803665,
-0.04037183150649071,
-0.01934860274195671,
-0.035914331674575806,
-0.0558430477976799,
-0.17735277116298676,
-0.01834711991250515,
-0.13270607590675354,
0.09331052750349045,
-0.05197659134864807,
0.1711418330669403,
0.108716681599617,
0.0004323666507843882,
0.044612959027290344,
-0.14190936088562012,
-0.001750780618749559,
0.04872918501496315,
-0.014095822349190712,
-0.06671372056007385
] |
null | null |
transformers
|
# Emotion Analysis in English
## bertweet-base-emotion-analysis
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with EmoEvent corpus for Emotion detection in English. Base model is [BerTweet](https://huggingface.co/vinai/bertweet-base).
## License
`pysentimiento` is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses.
1. [TASS Dataset license](http://tass.sepln.org/tass_data/download.php)
2. [SEMEval 2017 Dataset license]()
## Citation
If you use `pysentimiento` in your work, please cite [this paper](https://arxiv.org/abs/2106.09462)
```
@misc{perez2021pysentimiento,
title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks},
author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque},
year={2021},
eprint={2106.09462},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
and also the dataset related paper
```
@inproceedings{del2020emoevent,
title={EmoEvent: A multilingual emotion corpus based on different events},
author={del Arco, Flor Miriam Plaza and Strapparava, Carlo and Lopez, L Alfonso Urena and Mart{\'\i}n-Valdivia, M Teresa},
booktitle={Proceedings of the 12th Language Resources and Evaluation Conference},
pages={1492--1498},
year={2020}
}
```
Enjoy! 🤗
|
{"language": ["en"], "tags": ["emotion-analysis"]}
|
text-classification
|
finiteautomata/bertweet-base-emotion-analysis
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"emotion-analysis",
"en",
"arxiv:2106.09462",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2106.09462"
] |
[
"en"
] |
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #emotion-analysis #en #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Emotion Analysis in English
## bertweet-base-emotion-analysis
Repository: URL
Model trained with EmoEvent corpus for Emotion detection in English. Base model is BerTweet.
## License
'pysentimiento' is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses.
1. TASS Dataset license
2. [SEMEval 2017 Dataset license]()
If you use 'pysentimiento' in your work, please cite this paper
and also the dataset related paper
Enjoy!
|
[
"# Emotion Analysis in English",
"## bertweet-base-emotion-analysis\n\nRepository: URL\n\n\nModel trained with EmoEvent corpus for Emotion detection in English. Base model is BerTweet.",
"## License\n\n'pysentimiento' is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses. \n\n1. TASS Dataset license\n2. [SEMEval 2017 Dataset license]()\n\nIf you use 'pysentimiento' in your work, please cite this paper\n\n\n\nand also the dataset related paper\n\n\n\nEnjoy!"
] |
[
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #emotion-analysis #en #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Emotion Analysis in English",
"## bertweet-base-emotion-analysis\n\nRepository: URL\n\n\nModel trained with EmoEvent corpus for Emotion detection in English. Base model is BerTweet.",
"## License\n\n'pysentimiento' is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses. \n\n1. TASS Dataset license\n2. [SEMEval 2017 Dataset license]()\n\nIf you use 'pysentimiento' in your work, please cite this paper\n\n\n\nand also the dataset related paper\n\n\n\nEnjoy!"
] |
[
62,
7,
39,
95
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #emotion-analysis #en #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Emotion Analysis in English## bertweet-base-emotion-analysis\n\nRepository: URL\n\n\nModel trained with EmoEvent corpus for Emotion detection in English. Base model is BerTweet.## License\n\n'pysentimiento' is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses. \n\n1. TASS Dataset license\n2. [SEMEval 2017 Dataset license]()\n\nIf you use 'pysentimiento' in your work, please cite this paper\n\n\n\nand also the dataset related paper\n\n\n\nEnjoy!"
] |
[
-0.08491697907447815,
0.09979724138975143,
-0.0022016740404069424,
0.049972400069236755,
0.1286986619234085,
0.0656442865729332,
0.10621599107980728,
0.03482232987880707,
0.05932803079485893,
-0.04376578330993652,
0.03705483302474022,
0.13671697676181793,
0.015596735291182995,
0.06254466623067856,
-0.06792331486940384,
-0.30033329129219055,
-0.01018340140581131,
0.07474832236766815,
0.1988394856452942,
0.11551054567098618,
0.12922929227352142,
-0.0572594553232193,
0.12603072822093964,
0.05763412266969681,
-0.04766567423939705,
-0.02529011107981205,
0.014437887817621231,
-0.06409161537885666,
0.14857563376426697,
-0.002441061194986105,
0.034090638160705566,
0.07844976335763931,
0.06410954147577286,
-0.14756520092487335,
0.04755634814500809,
-0.01435660757124424,
-0.010811572894454002,
0.08279071003198624,
0.07878274470567703,
-0.13027016818523407,
0.2527633607387543,
-0.01453046128153801,
0.0835098996758461,
0.030812591314315796,
-0.04137662798166275,
-0.2113446444272995,
-0.040637873113155365,
0.0216075349599123,
0.0793500617146492,
0.091807521879673,
-0.035269759595394135,
0.25855064392089844,
-0.10533560067415237,
0.09289450943470001,
0.14427447319030762,
-0.13824261724948883,
-0.03817145153880119,
-0.018237542361021042,
0.05142040178179741,
-0.08209403604269028,
-0.0954783707857132,
0.0061720372177660465,
0.0593796968460083,
0.05140356719493866,
0.07335306704044342,
-0.09066400676965714,
-0.014554068446159363,
-0.033714886754751205,
-0.10235218703746796,
0.034226298332214355,
0.22423267364501953,
0.04797101765871048,
-0.09426215291023254,
-0.10559001564979553,
0.0006781889242120087,
0.11017466336488724,
0.020616337656974792,
-0.06995435059070587,
0.041659530252218246,
0.0008104618173092604,
-0.045226335525512695,
0.049946900457143784,
-0.1448662430047989,
0.030788827687501907,
-0.09517788887023926,
0.05857662856578827,
-0.0260580163449049,
0.04894399270415306,
0.028672516345977783,
0.04171207174658775,
-0.10453324019908905,
-0.08492043614387512,
-0.03823922947049141,
-0.14105261862277985,
0.07931464910507202,
-0.02472723089158535,
-0.13199235498905182,
-0.11355196684598923,
-0.020870206877589226,
0.05519348010420799,
-0.02760791778564453,
-0.035171229392290115,
0.02556219883263111,
-0.01967620849609375,
0.19025292992591858,
0.13981561362743378,
-0.07137876003980637,
0.032403599470853806,
0.0009105396457016468,
-0.04661170765757561,
0.025208594277501106,
0.031751591712236404,
-0.023633230477571487,
-0.015757061541080475,
0.027817897498607635,
-0.02672440931200981,
-0.11513392627239227,
0.0851292833685875,
-0.13105544447898865,
-0.019461506977677345,
0.040406517684459686,
-0.009045800194144249,
-0.00396792171522975,
0.02615986205637455,
0.001686116331256926,
0.148127481341362,
0.08392872661352158,
0.040925487875938416,
0.026796167716383934,
0.0253299567848444,
-0.025135576725006104,
-0.02475692890584469,
-0.05354638025164604,
-0.07109217345714569,
0.06963510066270828,
-0.08411438763141632,
0.10571780800819397,
-0.2115517258644104,
-0.27964845299720764,
-0.041737861931324005,
0.09599584341049194,
-0.016473140567541122,
0.025996224954724312,
-0.03226754441857338,
0.05015794560313225,
0.013257576152682304,
-0.04619127884507179,
-0.04817967116832733,
-0.034503985196352005,
-0.0013310558861121535,
-0.06015016883611679,
0.12991949915885925,
-0.10384071618318558,
0.01210419274866581,
-0.21771197021007538,
-0.022013269364833832,
0.007716453168541193,
0.07636578381061554,
-0.006957010831683874,
0.10927684605121613,
-0.012076645158231258,
-0.09668301790952682,
0.04923086613416672,
0.01897614262998104,
0.0034738299436867237,
0.16298836469650269,
-0.1687961220741272,
-0.06918804347515106,
0.08568930625915527,
-0.11421168595552444,
-0.09230523556470871,
0.1414162963628769,
-0.06613739579916,
0.18946239352226257,
0.11340128630399704,
0.17903999984264374,
-0.01582970656454563,
-0.052224334329366684,
-0.041790422052145004,
0.022699689492583275,
-0.10136272758245468,
-0.002343018539249897,
0.04787648469209671,
0.13076739013195038,
-0.04970772936940193,
0.04390130564570427,
-0.014342342503368855,
0.040866222232580185,
-0.07216386497020721,
-0.07261625677347183,
-0.004274265840649605,
-0.11300725489854813,
0.07515512406826019,
0.07133132219314575,
0.002477701287716627,
-0.10480222851037979,
-0.028322115540504456,
-0.03686510771512985,
0.07731893658638,
-0.03885600343346596,
0.004483255557715893,
-0.07787685096263885,
0.002122048055753112,
0.04961467906832695,
-0.013042773120105267,
-0.11959380656480789,
0.06760267168283463,
-0.044629741460084915,
0.1427774876356125,
0.013718625530600548,
0.024012554436922073,
0.043209828436374664,
0.0006744275451637805,
-0.029903218150138855,
-0.021155066788196564,
0.049029700458049774,
0.02039168030023575,
-0.003882257267832756,
-0.19552086293697357,
0.043604377657175064,
-0.07253539562225342,
0.11437930166721344,
-0.07125025242567062,
0.03757677599787712,
-0.04703887179493904,
0.00780390715226531,
-0.0034683351404964924,
0.02486204355955124,
0.005366014316678047,
-0.002429969608783722,
-0.05637475848197937,
0.020279226824641228,
0.0746721625328064,
0.02145165577530861,
-0.05646902695298195,
0.1638488620519638,
-0.026788029819726944,
0.09637634456157684,
0.1219337061047554,
-0.09337108582258224,
-0.06684117019176483,
-0.03112236224114895,
-0.047994621098041534,
0.06235533207654953,
0.006163396406918764,
0.0314297191798687,
0.18184639513492584,
-0.0003196676552761346,
0.03828521817922592,
-0.05744834616780281,
0.029459813609719276,
0.016511721536517143,
-0.15818825364112854,
-0.1107948049902916,
0.08890686184167862,
0.007947968319058418,
-0.11529023200273514,
0.056658077985048294,
0.12953971326351166,
-0.059600017964839935,
0.15313346683979034,
0.027259159833192825,
-0.01697939820587635,
-0.034881364554166794,
-0.16356228291988373,
-0.08136104792356491,
0.06300243735313416,
-0.16788828372955322,
-0.02300754189491272,
0.09011820703744888,
-0.07713833451271057,
-0.03163478523492813,
-0.10779962688684464,
-0.0796300545334816,
0.06154109165072441,
0.058619424700737,
-0.06633967906236649,
0.0717887207865715,
-0.012279637157917023,
0.08775125443935394,
-0.05247114226222038,
-0.02414427138864994,
-0.019416749477386475,
0.008474599570035934,
-0.1726381778717041,
0.161313995718956,
-0.09907815605401993,
-0.18157830834388733,
-0.06659505516290665,
-0.04407574608922005,
-0.014860270544886589,
0.030763907358050346,
0.09058509767055511,
-0.13908641040325165,
-0.06844045966863632,
-0.058371156454086304,
-0.02321006916463375,
0.087312251329422,
-0.02735085040330887,
0.0616411492228508,
-0.04896039888262749,
-0.03745552524924278,
-0.09791682660579681,
-0.05158696323633194,
-0.07848069816827774,
-0.05297762528061867,
0.13792717456817627,
-0.06276455521583557,
0.1018950566649437,
0.1375320851802826,
-0.021223457530140877,
-0.009472527541220188,
-0.08157698065042496,
0.19927437603473663,
-0.004847516771405935,
-0.038052354007959366,
0.1914190798997879,
-0.03208872675895691,
0.04651705175638199,
0.11430464684963226,
0.021514814347028732,
-0.08256819099187851,
0.05831162631511688,
0.009388546459376812,
-0.07357663661241531,
-0.20373500883579254,
-0.09144396334886551,
0.04498394578695297,
0.0818442553281784,
-0.0637933760881424,
-0.024966364726424217,
0.06939586251974106,
0.1436605304479599,
0.020170973613858223,
-0.15316225588321686,
-0.07271488010883331,
0.08081521838903427,
0.12705765664577484,
-0.05958528444170952,
0.0749988704919815,
0.007189474068582058,
-0.018976174294948578,
0.13479579985141754,
-0.10165481269359589,
0.10467562079429626,
-0.017002148553729057,
0.0004848797107115388,
0.07074917107820511,
0.09117301553487778,
-0.02051580883562565,
0.17622904479503632,
-0.06651609390974045,
-0.005233749281615019,
-0.08803553134202957,
-0.0024239991325885057,
-0.12213099747896194,
0.11001046746969223,
-0.06579994410276413,
0.08947072923183441,
-0.16682377457618713,
-0.02240975759923458,
0.07556095719337463,
0.190763458609581,
0.007175902370363474,
-0.33335989713668823,
-0.09952013194561005,
0.006914666388183832,
-0.02347728982567787,
0.007076943293213844,
-0.00397461699321866,
-0.0383027121424675,
-0.14445196092128754,
0.12063632160425186,
0.0010857583256438375,
0.028080826625227928,
-0.03950239345431328,
0.0653885006904602,
-0.12003511935472488,
-0.06417801231145859,
-0.01206553541123867,
0.07429894059896469,
-0.19098274409770966,
0.175845667719841,
-0.041388098150491714,
0.03551866114139557,
-0.07824063301086426,
-0.07305094599723816,
0.10065771639347076,
0.12262864410877228,
0.10269051045179367,
-0.015135031193494797,
0.1779005378484726,
-0.057256683707237244,
-0.05672454833984375,
0.08289157599210739,
-0.05370620638132095,
-0.02134132757782936,
0.05384126678109169,
0.0018241109792143106,
-0.0028954800218343735,
0.032422322779893875,
0.15869075059890747,
-0.1475115418434143,
-0.05371885746717453,
-0.023607676848769188,
0.04871053621172905,
0.03733618184924126,
0.024562397971749306,
-0.15660937130451202,
-0.0817742794752121,
0.07424019277095795,
0.15488359332084656,
-0.0723903700709343,
-0.09899318218231201,
-0.029578067362308502,
-0.0070450943894684315,
-0.060779958963394165,
-0.009059133008122444,
0.022587453946471214,
0.06305215507745743,
-0.016243021935224533,
-0.11450263857841492,
0.08098277449607849,
-0.12955544888973236,
-0.0998818427324295,
-0.04758960008621216,
0.0031089410185813904,
0.1102951392531395,
0.059610120952129364,
0.09462408721446991,
-0.04049697518348694,
-0.11199627816677094,
-0.059595052152872086,
0.0749579593539238,
0.16252370178699493,
0.029236070811748505,
-0.012867704033851624,
-0.04510439559817314,
-0.16709169745445251,
-0.0814991444349289,
-0.03640925884246826,
0.14326532185077667,
0.13612596690654755,
-0.04104913771152496,
0.08904367685317993,
0.17872613668441772,
-0.06776823103427887,
-0.18969862163066864,
0.016094764694571495,
0.04779521003365517,
0.020216286182403564,
0.09200379252433777,
-0.09725265204906464,
0.11508022993803024,
-0.018758967518806458,
-0.019807180389761925,
-0.14206844568252563,
0.013536309823393822,
-0.07566637545824051,
0.230010986328125,
0.11233334988355637,
0.2685476243495941,
-0.11831378191709518,
0.05832705274224281,
-0.07273437827825546,
-0.12259697914123535,
0.21818475425243378,
-0.10800342261791229,
0.03674943372607231,
-0.012772219255566597,
0.19616754353046417,
0.0226534865796566,
-0.00473403837531805,
0.08609946072101593,
0.017203686758875847,
0.06799066066741943,
-0.09441041946411133,
0.01162861380726099,
0.02835233137011528,
-0.022612834349274635,
0.17000959813594818,
0.01455169077962637,
-0.016813311725854874,
-0.07543737441301346,
-0.08765039592981339,
-0.12716200947761536,
0.06847183406352997,
0.03656157851219177,
-0.123665951192379,
-0.03820175305008888,
0.034512538462877274,
0.06340550631284714,
-0.04262223839759827,
-0.1615981012582779,
-0.03937187045812607,
-0.06845910102128983,
0.1226767748594284,
0.29789793491363525,
-0.06382621079683304,
0.04660462588071823,
-0.03337875381112099,
-0.08086605370044708,
0.038989368826150894,
-0.09242269396781921,
-0.025110328570008278,
0.09718252718448639,
-0.025280935689806938,
0.11707615107297897,
0.06414559483528137,
-0.08577647805213928,
0.05036840960383415,
0.09097360074520111,
-0.11331646889448166,
-0.024691957980394363,
-0.055701158940792084,
-0.0780634731054306,
-0.06162211298942566,
0.016075845807790756,
0.10283256322145462,
-0.09767504781484604,
-0.03538447618484497,
-0.06526228040456772,
0.03050430677831173,
0.0055617839097976685,
0.01754693314433098,
0.0141220111399889,
-0.06748257577419281,
-0.09606936573982239,
-0.010996365919709206,
-0.08653818070888519,
-0.11955489963293076,
0.09554342180490494,
-0.03891494870185852,
-0.06934688985347748,
-0.08432884514331818,
-0.03299310430884361,
0.19593645632266998,
-0.25485703349113464,
-0.0795934870839119,
-0.03187580034136772,
-0.14996249973773956,
0.013644921593368053,
0.14026163518428802,
0.10492884367704391,
-0.014579028822481632,
-0.09285124391317368,
0.02213846705853939,
-0.04583894833922386,
0.06732488423585892,
0.14886006712913513,
-0.018514906987547874,
-0.07635737955570221,
-0.00584992952644825,
-0.04285554960370064,
0.07976723462343216,
-0.0738397166132927,
-0.028470933437347412,
-0.04374629259109497,
-0.03960360214114189,
-0.21385902166366577,
-0.002394634298980236,
-0.10095969587564468,
0.022014565765857697,
0.03993891924619675,
-0.017726141959428787,
0.015161656774580479,
-0.015465503558516502,
-0.058599721640348434,
0.04687277227640152,
0.042399726808071136,
0.08543549478054047,
-0.10721589624881744,
-0.06964235752820969,
0.04130856320261955,
-0.0073721506632864475,
0.12138507515192032,
0.06868837773799896,
-0.045077670365571976,
0.07042548805475235,
-0.290177583694458,
0.010123400017619133,
0.13093487918376923,
-0.009228245355188847,
0.0186876580119133,
-0.0285926666110754,
-0.022007005289196968,
0.06450434029102325,
-0.007178622763603926,
0.01629600301384926,
0.1382271647453308,
-0.018379949033260345,
0.09125669300556183,
0.1450701355934143,
-0.09194212406873703,
-0.055571746081113815,
-0.0662769302725792,
0.005209858994930983,
0.06737277656793594,
0.20060481131076813,
-0.04911087825894356,
0.038277771323919296,
-0.1409635990858078,
0.015246044844388962,
-0.009468290023505688,
-0.023977428674697876,
-0.16709427535533905,
-0.06868212670087814,
0.013114293105900288,
-0.001384560251608491,
0.1840749979019165,
0.14310698211193085,
-0.045466501265764236,
-0.06482556462287903,
0.12665453553199768,
0.09680629521608353,
-0.031180068850517273,
0.13256990909576416,
0.012267816811800003,
-0.009080248884856701,
-0.06052791699767113,
0.1100773885846138,
0.08309853821992874,
-0.08613232523202896,
0.09133962541818619,
0.06176568940281868,
0.11285265535116196,
0.10300477594137192,
0.020145315676927567,
0.09830386936664581,
-0.0283278226852417,
-0.22724032402038574,
0.007391178049147129,
0.05310250073671341,
0.016433484852313995,
0.2187996208667755,
0.09891785681247711,
-0.06953757256269455,
0.0443587563931942,
-0.017644625157117844,
-0.022534627467393875,
-0.13824883103370667,
-0.1770983636379242,
-0.06204809620976448,
-0.11600177735090256,
0.006322178989648819,
-0.15830227732658386,
-0.06756513565778732,
-0.0035152246709913015,
0.03080815263092518,
-0.07438623905181885,
0.01630399189889431,
-0.09703432023525238,
-0.027606744319200516,
0.156091570854187,
-0.025150828063488007,
-0.08295242488384247,
-0.031149884685873985,
0.029161449521780014,
-0.04348476231098175,
0.07052510231733322,
0.049992192536592484,
0.004664294421672821,
-0.0704784169793129,
-0.01774982176721096,
-0.05076780915260315,
-0.08924311399459839,
-0.02926553785800934,
0.029317224398255348,
0.021452009677886963,
-0.009028129279613495,
0.0004967884160578251,
0.0025394835975021124,
0.04114818572998047,
0.12498073279857635,
-0.00217818352393806,
0.0749320462346077,
-0.1339370161294937,
0.17607344686985016,
-0.07356419414281845,
0.020710472017526627,
0.03404022008180618,
-0.06695779412984848,
0.009407803416252136,
0.207015261054039,
0.2185865044593811,
-0.05423450469970703,
-0.007359435316175222,
-0.09091074019670486,
0.0435430072247982,
-0.059292733669281006,
0.10012800246477127,
0.12618935108184814,
0.13733144104480743,
-0.11540262401103973,
0.11645068973302841,
-0.07603726536035538,
0.005949740763753653,
-0.003787872614338994,
-0.060511961579322815,
0.06334897875785828,
-0.03808623552322388,
-0.0397067628800869,
0.09487882256507874,
-0.09248718619346619,
0.011009748093783855,
0.010781511664390564,
-0.06800702959299088,
-0.04147974029183388,
-0.003136164043098688,
-0.04046420007944107,
0.10249637067317963,
0.10024404525756836,
-0.008442141115665436,
0.012124944478273392,
0.2240120768547058,
0.0013913161819800735,
-0.18772083520889282,
0.06324940919876099,
0.18011115491390228,
-0.10257833451032639,
0.15614967048168182,
-0.04783511906862259,
0.05316143110394478,
0.05978480726480484,
0.03882220759987831,
-0.027165254577994347,
0.08889428526163101,
0.011517155915498734,
-0.03649982810020447,
0.06702109426259995,
0.06571241468191147,
-0.004748558159917593,
0.025077486410737038,
0.02117672562599182,
-0.2306908220052719,
0.03856693208217621,
0.03407847136259079,
-0.04688962176442146,
-0.063006192445755,
0.15351568162441254,
-0.12702590227127075,
0.0713939294219017,
0.12805971503257751,
-0.035308804363012314,
-0.024473272264003754,
-0.037717320024967194,
0.038507211953401566,
0.017938092350959778,
-0.134146049618721,
0.03828287497162819,
-0.10817130655050278,
-0.018578751012682915,
0.13281363248825073,
0.02860674262046814,
-0.28708338737487793,
-0.04335387051105499,
-0.08677808195352554,
-0.027235927060246468,
-0.03900556638836861,
-0.01886274479329586,
0.024704499170184135,
0.03397964686155319,
0.005715557839721441,
-0.0068732211366295815,
0.022212665528059006,
0.13408437371253967,
-0.06538213789463043,
-0.06727378815412521
] |
null | null |
transformers
|
# Sentiment Analysis in English
## bertweet-sentiment-analysis
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with SemEval 2017 corpus (around ~40k tweets). Base model is [BERTweet](https://github.com/VinAIResearch/BERTweet), a RoBERTa model trained on English tweets.
Uses `POS`, `NEG`, `NEU` labels.
## License
`pysentimiento` is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses.
1. [TASS Dataset license](http://tass.sepln.org/tass_data/download.php)
2. [SEMEval 2017 Dataset license]()
## Citation
If you use `pysentimiento` in your work, please cite [this paper](https://arxiv.org/abs/2106.09462)
```
@misc{perez2021pysentimiento,
title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks},
author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque},
year={2021},
eprint={2106.09462},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Enjoy! 🤗
|
{"language": ["en"], "tags": ["sentiment-analysis"]}
|
text-classification
|
finiteautomata/bertweet-base-sentiment-analysis
|
[
"transformers",
"pytorch",
"tf",
"roberta",
"text-classification",
"sentiment-analysis",
"en",
"arxiv:2106.09462",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2106.09462"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #roberta #text-classification #sentiment-analysis #en #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Sentiment Analysis in English
## bertweet-sentiment-analysis
Repository: URL
Model trained with SemEval 2017 corpus (around ~40k tweets). Base model is BERTweet, a RoBERTa model trained on English tweets.
Uses 'POS', 'NEG', 'NEU' labels.
## License
'pysentimiento' is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses.
1. TASS Dataset license
2. [SEMEval 2017 Dataset license]()
If you use 'pysentimiento' in your work, please cite this paper
Enjoy!
|
[
"# Sentiment Analysis in English",
"## bertweet-sentiment-analysis\n\nRepository: URL\n\n\nModel trained with SemEval 2017 corpus (around ~40k tweets). Base model is BERTweet, a RoBERTa model trained on English tweets.\n\nUses 'POS', 'NEG', 'NEU' labels.",
"## License\n\n'pysentimiento' is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses. \n\n1. TASS Dataset license\n2. [SEMEval 2017 Dataset license]()\n\nIf you use 'pysentimiento' in your work, please cite this paper\n\n\nEnjoy!"
] |
[
"TAGS\n#transformers #pytorch #tf #roberta #text-classification #sentiment-analysis #en #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Sentiment Analysis in English",
"## bertweet-sentiment-analysis\n\nRepository: URL\n\n\nModel trained with SemEval 2017 corpus (around ~40k tweets). Base model is BERTweet, a RoBERTa model trained on English tweets.\n\nUses 'POS', 'NEG', 'NEU' labels.",
"## License\n\n'pysentimiento' is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses. \n\n1. TASS Dataset license\n2. [SEMEval 2017 Dataset license]()\n\nIf you use 'pysentimiento' in your work, please cite this paper\n\n\nEnjoy!"
] |
[
60,
7,
70,
88
] |
[
"passage: TAGS\n#transformers #pytorch #tf #roberta #text-classification #sentiment-analysis #en #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Sentiment Analysis in English## bertweet-sentiment-analysis\n\nRepository: URL\n\n\nModel trained with SemEval 2017 corpus (around ~40k tweets). Base model is BERTweet, a RoBERTa model trained on English tweets.\n\nUses 'POS', 'NEG', 'NEU' labels.## License\n\n'pysentimiento' is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses. \n\n1. TASS Dataset license\n2. [SEMEval 2017 Dataset license]()\n\nIf you use 'pysentimiento' in your work, please cite this paper\n\n\nEnjoy!"
] |
[
-0.04629984498023987,
0.13754847645759583,
-0.005528416950255632,
0.040350183844566345,
0.07794279605150223,
0.022000668570399284,
0.1224530041217804,
0.039848774671554565,
-0.009154165163636208,
-0.061743494123220444,
0.07931508868932724,
0.1579657942056656,
-0.01035155076533556,
0.04122380167245865,
-0.04054129496216774,
-0.2951887845993042,
0.01235952228307724,
0.052271533757448196,
0.09586969017982483,
0.13059879839420319,
0.1496945172548294,
-0.0700087696313858,
0.0973760187625885,
0.03942175954580307,
-0.018713053315877914,
-0.007316503673791885,
0.018281958997249603,
-0.09453245252370834,
0.15334905683994293,
0.011342767626047134,
0.07451409846544266,
0.10412608087062836,
0.015578528866171837,
-0.061712153255939484,
0.04808886721730232,
0.005165968090295792,
-0.05789482966065407,
0.08882109820842743,
0.07732614874839783,
-0.08965412527322769,
0.28207725286483765,
-0.0019036754965782166,
0.028680680319666862,
0.04322906211018562,
-0.1216927245259285,
-0.13947167992591858,
-0.07681312412023544,
0.07959693670272827,
0.04707852751016617,
0.06897322088479996,
-0.012204967439174652,
0.18828077614307404,
-0.10522183775901794,
0.07479684054851532,
0.17046020925045013,
-0.1514299362897873,
-0.04883814975619316,
0.0012705919798463583,
0.006921096704900265,
0.028121044859290123,
-0.014565171673893929,
0.013418804854154587,
0.04437069594860077,
-0.009778687730431557,
0.06861385703086853,
-0.07945046573877335,
0.0017718407325446606,
-0.006642279680818319,
-0.13248319923877716,
-0.03355448320508003,
0.2332942634820938,
0.02355008013546467,
-0.08105970174074173,
-0.012891938909888268,
-0.031123731285333633,
0.08590587228536606,
0.01827367953956127,
-0.09065653383731842,
0.04368079826235771,
-0.01573316380381584,
-0.024018535390496254,
-0.029316602274775505,
-0.13069848716259003,
0.008636769838631153,
-0.138229101896286,
0.08889520168304443,
0.02248704619705677,
0.05818294361233711,
-0.02160736732184887,
0.03906264156103134,
-0.12024429440498352,
-0.04786933958530426,
-0.036670196801424026,
-0.13020794093608856,
0.0610324926674366,
-0.04166160151362419,
-0.06676749885082245,
-0.1252521276473999,
0.012679096311330795,
0.1385383903980255,
-0.02572513371706009,
-0.05912915989756584,
0.07979471981525421,
0.011009439826011658,
0.12811411917209625,
-0.0026575468946248293,
-0.11167628318071365,
0.041789695620536804,
0.027177689597010612,
-0.11205437779426575,
-0.012037389911711216,
0.013283289968967438,
-0.04199668765068054,
-0.04617580026388168,
0.029973456636071205,
-0.020094724372029305,
-0.017594754695892334,
0.06495845317840576,
-0.026136208325624466,
-0.033684708178043365,
0.05895908176898956,
-0.008981018327176571,
-0.035266123712062836,
-0.0065537490881979465,
-0.039993759244680405,
0.14969445765018463,
0.057376470416784286,
0.05566182732582092,
-0.023065784946084023,
0.06344019621610641,
-0.07660922408103943,
-0.03090216964483261,
-0.057579368352890015,
-0.08735718578100204,
0.061330802738666534,
-0.13247150182724,
0.039048951119184494,
-0.20081116259098053,
-0.2672148048877716,
-0.01657254993915558,
0.07204929739236832,
0.009429704397916794,
-0.05051630362868309,
-0.010074282996356487,
0.028825977817177773,
-0.03796384483575821,
-0.03984614834189415,
-0.006657585501670837,
-0.04553795978426933,
0.07888051867485046,
-0.07005436718463898,
0.05862944945693016,
-0.16743364930152893,
0.027170900255441666,
-0.17117638885974884,
-0.016765525564551353,
0.02240375429391861,
0.0563637875020504,
-0.027959510684013367,
0.11114838719367981,
-0.08186571300029755,
-0.11760000884532928,
-0.020575575530529022,
0.015040709637105465,
0.010864678770303726,
0.14456813037395477,
-0.17044126987457275,
-0.0589236319065094,
0.060570623725652695,
-0.144718736410141,
-0.11127469688653946,
0.1461476981639862,
-0.06803864985704422,
0.19941450655460358,
0.14968430995941162,
0.1461746096611023,
0.09825973212718964,
-0.07289739698171616,
-0.012450188398361206,
-0.009226849302649498,
-0.09350138157606125,
-0.10158282518386841,
0.07281571626663208,
0.10451042652130127,
-0.07710429280996323,
0.03174393251538277,
-0.0900934487581253,
0.019100822508335114,
-0.06243313476443291,
-0.07396745681762695,
0.013068933971226215,
-0.0639348030090332,
0.09689453989267349,
0.025792447850108147,
0.04283788055181503,
-0.06097659468650818,
-0.025346627458930016,
0.07590194791555405,
0.05350113287568092,
0.0055007231421768665,
0.007771873380988836,
-0.06476790457963943,
0.07429428398609161,
0.0764947235584259,
-0.017577411606907845,
-0.09755190461874008,
0.0024131317622959614,
-0.07658123970031738,
0.10429619997739792,
0.11882216483354568,
0.10618148744106293,
0.00007698714034631848,
-0.015331191942095757,
-0.021632906049489975,
0.0446329340338707,
-0.02779271826148033,
0.01479057502001524,
0.01303019467741251,
-0.11482620984315872,
0.054893020540475845,
-0.018013890832662582,
0.03285262733697891,
-0.07193121314048767,
0.05487527325749397,
0.06120116636157036,
0.03261099383234978,
0.01734018139541149,
0.029938319697976112,
0.005733885802328587,
0.038860782980918884,
-0.01741519197821617,
0.059691887348890305,
0.07815664261579514,
0.0008185271290130913,
-0.06366845220327377,
0.1321835219860077,
0.021990222856402397,
0.09546894580125809,
0.10980508476495743,
-0.14820678532123566,
-0.06633178144693375,
-0.015793437138199806,
-0.0529707595705986,
0.033259402960538864,
-0.021299710497260094,
0.017123611643910408,
0.10538925230503082,
-0.00039056086097843945,
0.038831520825624466,
-0.10378313809633255,
-0.013744326308369637,
-0.0040635522454977036,
-0.1368883103132248,
-0.04506593942642212,
0.09934203326702118,
0.10243195295333862,
-0.12607428431510925,
0.10809265822172165,
0.09836871176958084,
-0.11301025748252869,
0.16070207953453064,
-0.006332512013614178,
-0.03464431315660477,
-0.02350154146552086,
-0.12259304523468018,
-0.03898441419005394,
0.036572229117155075,
-0.15400095283985138,
-0.00014308675599750131,
0.07325489819049835,
-0.048388831317424774,
-0.000722913013305515,
-0.09066091477870941,
-0.0786297395825386,
0.022896135225892067,
0.017971890047192574,
-0.11535187065601349,
0.09382113814353943,
-0.03139045834541321,
0.104844830930233,
-0.05840186029672623,
0.005476330406963825,
0.01714508607983589,
0.033096905797719955,
-0.12689551711082458,
0.13121311366558075,
-0.050280194729566574,
-0.14642773568630219,
-0.08956024795770645,
-0.01332220621407032,
-0.026845267042517662,
0.006445436272770166,
0.06973768770694733,
-0.034756287932395935,
-0.02925346978008747,
-0.025554001331329346,
-0.046961355954408646,
0.02918265573680401,
-0.017554279416799545,
-0.09011327475309372,
0.0036723490338772535,
0.009066044352948666,
-0.08009190112352371,
-0.026326468214392662,
-0.0625711977481842,
-0.07675248384475708,
0.10877878963947296,
-0.06594450026750565,
0.1316509246826172,
0.09139767289161682,
-0.03386571258306503,
-0.010774048045277596,
-0.07932230085134506,
0.24979281425476074,
-0.03172674402594566,
0.015112991444766521,
0.21118183434009552,
0.008920863270759583,
0.0687306597828865,
0.10025504976511002,
0.036888644099235535,
-0.03904525563120842,
0.04245223104953766,
0.016871090978384018,
-0.10953507572412491,
-0.24598918855190277,
-0.04375245049595833,
-0.016161417588591576,
0.0604548417031765,
-0.02075083926320076,
-0.01102744322270155,
0.08587516844272614,
0.15267431735992432,
0.01563630811870098,
-0.07229799032211304,
-0.03281319886445999,
0.09147725254297256,
0.21409201622009277,
-0.017773859202861786,
0.0925116166472435,
-0.05072098225355148,
-0.01458030380308628,
0.1383211761713028,
-0.04644133150577545,
0.1343264877796173,
-0.0022882153280079365,
0.09670112282037735,
0.10919082164764404,
0.11049474030733109,
0.021517032757401466,
0.08153274655342102,
-0.022455813363194466,
0.029754428192973137,
-0.06664460897445679,
-0.022506991401314735,
-0.10115037858486176,
0.06647619605064392,
-0.06232204660773277,
-0.022118352353572845,
-0.0727650374174118,
-0.04512455686926842,
0.08483832329511642,
0.18011480569839478,
0.002120108110830188,
-0.252611368894577,
-0.11166375875473022,
0.006487367674708366,
-0.06786512583494186,
0.04296677187085152,
-0.0002552214136812836,
0.0017602371517568827,
-0.10426244884729385,
0.1149865910410881,
-0.017860092222690582,
0.0724252313375473,
-0.02619670145213604,
0.04178202152252197,
-0.08655916154384613,
-0.033005282282829285,
-0.021253211423754692,
0.08993326127529144,
-0.2158697098493576,
0.28623849153518677,
-0.05981743335723877,
-0.004084559623152018,
-0.040970828384160995,
-0.09877247363328934,
0.033625416457653046,
0.13700918853282928,
0.11685829609632492,
0.011102868244051933,
0.10858266800642014,
0.038954999297857285,
-0.05705222114920616,
0.03489353880286217,
-0.05818869546055794,
-0.07127664983272552,
0.04823635518550873,
-0.014475901611149311,
0.034200139343738556,
0.0032901354134082794,
0.15132220089435577,
-0.10849449038505554,
-0.038809776306152344,
0.02736169844865799,
-0.031286828219890594,
0.023649360984563828,
0.0031264708377420902,
-0.12645743787288666,
-0.10516270250082016,
0.09029482305049896,
0.01653914712369442,
-0.10362803190946579,
-0.11025194823741913,
0.007746438030153513,
-0.024131564423441887,
-0.08062150329351425,
0.014243561774492264,
0.005665093194693327,
-0.012876749970018864,
-0.028726398944854736,
-0.09176390618085861,
0.030003687366843224,
-0.07540497183799744,
-0.09022662043571472,
-0.009860318154096603,
0.06760678440332413,
0.09329511225223541,
0.02401111274957657,
0.05611854046583176,
0.012086598202586174,
-0.07911476492881775,
-0.10202623158693314,
0.015467651188373566,
0.03590957075357437,
0.013006937690079212,
-0.03728918358683586,
-0.0030734853353351355,
-0.14592072367668152,
-0.06155572086572647,
-0.032046202570199966,
0.15190961956977844,
0.13385069370269775,
-0.06653130799531937,
0.13537955284118652,
0.15748286247253418,
-0.07708145678043365,
-0.20793598890304565,
-0.05260589346289635,
0.0650855302810669,
-0.04847528040409088,
0.05654938519001007,
-0.1608334630727768,
0.06003427132964134,
0.10589022189378738,
-0.03245269134640694,
-0.20320791006088257,
-0.16709551215171814,
-0.10083769261837006,
0.11426045000553131,
0.11956588923931122,
0.3052305281162262,
-0.12130388617515564,
-0.0010123316897079349,
-0.026806818321347237,
-0.09504424035549164,
0.25814762711524963,
-0.09977339953184128,
0.02041097730398178,
-0.015432550571858883,
0.20641961693763733,
0.021924536675214767,
-0.02197720855474472,
0.10793525725603104,
0.014834707602858543,
0.049140915274620056,
-0.09086741507053375,
-0.04314322769641876,
0.07467781752347946,
-0.014173174276947975,
0.1282859593629837,
0.06370781362056732,
0.03393709659576416,
-0.04374502971768379,
-0.06424900144338608,
-0.10423900187015533,
0.1098795160651207,
0.001120691536925733,
-0.11933664232492447,
-0.07267957925796509,
0.0533658005297184,
0.02690333127975464,
-0.05700630694627762,
0.013245548121631145,
-0.0651640072464943,
0.04376654326915741,
0.09365393221378326,
0.2607015073299408,
-0.037574414163827896,
0.020781559869647026,
-0.03582032397389412,
-0.07960517704486847,
0.03930080309510231,
-0.10548729449510574,
-0.02789120376110077,
0.1091526597738266,
0.02341650426387787,
0.09955085068941116,
0.026252441108226776,
-0.09492495656013489,
0.028701625764369965,
0.10759738087654114,
-0.1878906786441803,
-0.13261617720127106,
-0.05966362729668617,
-0.05843982845544815,
-0.038492243736982346,
0.03029428981244564,
0.14790800213813782,
-0.10167767107486725,
-0.036116693168878555,
-0.002570400945842266,
0.040662094950675964,
-0.01582212746143341,
0.008346169255673885,
0.07762617617845535,
-0.04776743799448013,
-0.07341092079877853,
-0.05833166837692261,
0.029350362718105316,
-0.04953231289982796,
0.009884770959615707,
0.03359398618340492,
-0.0652158260345459,
-0.10046160966157913,
-0.11220909655094147,
0.011342976242303848,
-0.25959381461143494,
-0.06744019687175751,
0.004339804872870445,
-0.08148711919784546,
0.02581910975277424,
0.21447314321994781,
0.07773469388484955,
0.03751040995121002,
-0.032632555812597275,
0.0151717783883214,
0.016470426693558693,
0.038400888442993164,
0.07539186626672745,
-0.01265803538262844,
-0.07139851152896881,
0.11920370161533356,
-0.028207048773765564,
0.059815414249897,
-0.06766320765018463,
-0.008548730984330177,
-0.08654354512691498,
-0.016634268686175346,
-0.11077971011400223,
0.03004617616534233,
-0.13662219047546387,
0.011180436238646507,
0.010940183885395527,
-0.04875140264630318,
-0.02529093064367771,
-0.022085953503847122,
-0.08211900293827057,
0.0847216472029686,
0.061114631593227386,
0.10655725747346878,
-0.12386862188577652,
-0.005042268428951502,
0.08277752995491028,
0.022433124482631683,
0.09416770935058594,
0.016699161380529404,
-0.055840738117694855,
0.07013989984989166,
-0.2652077376842499,
0.056579530239105225,
0.07114201039075851,
0.03172074258327484,
-0.008919257670640945,
-0.07318276911973953,
0.00018308940343558788,
0.07123824208974838,
0.007410087622702122,
0.032845646142959595,
0.059184327721595764,
-0.052669599652290344,
0.07486037164926529,
0.18897505104541779,
-0.09452980011701584,
-0.057778049260377884,
-0.044748999178409576,
0.014803745783865452,
0.058965787291526794,
0.20194408297538757,
-0.04555955156683922,
0.021302679553627968,
-0.08876768499612808,
0.026329852640628815,
-0.013580028899013996,
-0.06055813282728195,
-0.14661894738674164,
-0.10596572607755661,
-0.008048715069890022,
-0.0214370209723711,
0.19929079711437225,
0.1405559629201889,
-0.08492893725633621,
0.016616759821772575,
0.10815481096506119,
0.03045031428337097,
-0.021515415981411934,
0.14092133939266205,
0.008476254530251026,
0.016132088378071785,
-0.0631834864616394,
0.07330665737390518,
-0.05456004664301872,
-0.0980212464928627,
0.17899884283542633,
0.06696604937314987,
0.02257886342704296,
-0.009963967837393284,
0.02995983697474003,
0.11289636045694351,
0.023778660222887993,
-0.16787579655647278,
0.016236605122685432,
0.01852269470691681,
-0.04888208582997322,
0.16673144698143005,
0.1530134379863739,
-0.10917060822248459,
0.06830591708421707,
0.03730464354157448,
-0.032548148185014725,
-0.1534430831670761,
-0.24999722838401794,
-0.06709140539169312,
-0.0829407349228859,
-0.0037403651513159275,
-0.11954768747091293,
-0.04325546696782112,
-0.009279201738536358,
0.03707509487867355,
-0.06539221107959747,
-0.03732755035161972,
-0.07634491473436356,
-0.06736698746681213,
0.06489760428667068,
-0.007918322458863258,
-0.04861557111144066,
-0.12167652696371078,
0.021701468154788017,
-0.06477805227041245,
0.10581719130277634,
-0.017504293471574783,
-0.01404441799968481,
-0.01539597101509571,
-0.03341033309698105,
-0.11333020776510239,
-0.042423658072948456,
-0.03733902797102928,
0.046918414533138275,
0.03951326012611389,
0.029612364247441292,
0.024082697927951813,
-0.013434015214443207,
0.05426532402634621,
0.20899392664432526,
0.024936610832810402,
-0.030741484835743904,
-0.1481630802154541,
0.187872514128685,
-0.03996345028281212,
0.002176932990550995,
0.045361340045928955,
-0.04734048619866371,
0.06460200995206833,
0.2058684229850769,
0.27069324254989624,
-0.021218273788690567,
0.012159944511950016,
-0.09011519700288773,
0.0444255992770195,
0.06308703124523163,
0.08327741175889969,
0.042966827750205994,
0.2223873734474182,
-0.05862260237336159,
0.06893296539783478,
-0.0806821882724762,
0.06053061783313751,
-0.017174411565065384,
0.0432305671274662,
0.01602117158472538,
-0.09060971438884735,
-0.05792236328125,
0.15384869277477264,
-0.1254916787147522,
-0.11895427107810974,
-0.03318438306450844,
-0.050447143614292145,
-0.03762827813625336,
-0.0047444673255085945,
-0.05686163529753685,
0.061475690454244614,
0.04876004159450531,
-0.007163194008171558,
-0.023209495469927788,
0.1507159024477005,
0.06581275165081024,
-0.1569637805223465,
0.028001857921481133,
0.23698104918003082,
0.00842540804296732,
0.12232568860054016,
-0.02290327474474907,
0.06115110591053963,
0.06537915021181107,
0.014025735668838024,
-0.034428585320711136,
0.10366661846637726,
0.053466688841581345,
-0.029444599524140358,
0.012232544831931591,
-0.014521424658596516,
0.023648662492632866,
-0.030721021816134453,
0.05108434334397316,
-0.1816083788871765,
0.023122508078813553,
0.0582100972533226,
-0.046238161623477936,
-0.04962444305419922,
0.17123407125473022,
-0.1396077275276184,
0.06474429368972778,
0.13565367460250854,
-0.041533008217811584,
-0.013898107223212719,
-0.07842998951673508,
0.018306192010641098,
-0.004477123264223337,
-0.19665099680423737,
0.009912525303661823,
-0.07607952505350113,
-0.038631945848464966,
0.021204546093940735,
0.03298371285200119,
-0.1732020378112793,
0.009730466641485691,
-0.08074881881475449,
0.015609662979841232,
-0.06523585319519043,
0.019200216978788376,
0.05120183154940605,
-0.02153799682855606,
0.015006019733846188,
0.08355612307786942,
-0.014078835025429726,
0.08146790415048599,
-0.05773758515715599,
-0.044883254915475845
] |
null | null |
transformers
|
# Emotion Analysis in Spanish
## beto-emotion-analysis
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with TASS 2020 Task 2 corpus for Emotion detection in Spanish. Base model is [BETO](https://github.com/dccuchile/beto), a BERT model trained in Spanish.
## License
`pysentimiento` is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses.
1. [TASS Dataset license](http://tass.sepln.org/tass_data/download.php)
2. [SEMEval 2017 Dataset license]()
## Citation
If you use `pysentimiento` in your work, please cite [this paper](https://arxiv.org/abs/2106.09462)
```
@misc{perez2021pysentimiento,
title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks},
author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque},
year={2021},
eprint={2106.09462},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
and also the dataset related paper
```
@inproceedings{del2020emoevent,
title={EmoEvent: A multilingual emotion corpus based on different events},
author={del Arco, Flor Miriam Plaza and Strapparava, Carlo and Lopez, L Alfonso Urena and Mart{\'\i}n-Valdivia, M Teresa},
booktitle={Proceedings of the 12th Language Resources and Evaluation Conference},
pages={1492--1498},
year={2020}
}
```
Enjoy! 🤗
|
{"language": ["es"], "tags": ["emotion-analysis"]}
|
text-classification
|
finiteautomata/beto-emotion-analysis
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"emotion-analysis",
"es",
"arxiv:2106.09462",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2106.09462"
] |
[
"es"
] |
TAGS
#transformers #pytorch #safetensors #bert #text-classification #emotion-analysis #es #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Emotion Analysis in Spanish
## beto-emotion-analysis
Repository: URL
Model trained with TASS 2020 Task 2 corpus for Emotion detection in Spanish. Base model is BETO, a BERT model trained in Spanish.
## License
'pysentimiento' is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses.
1. TASS Dataset license
2. [SEMEval 2017 Dataset license]()
If you use 'pysentimiento' in your work, please cite this paper
and also the dataset related paper
Enjoy!
|
[
"# Emotion Analysis in Spanish",
"## beto-emotion-analysis\n\nRepository: URL\n\n\nModel trained with TASS 2020 Task 2 corpus for Emotion detection in Spanish. Base model is BETO, a BERT model trained in Spanish.",
"## License\n\n'pysentimiento' is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses. \n\n1. TASS Dataset license\n2. [SEMEval 2017 Dataset license]()\n\nIf you use 'pysentimiento' in your work, please cite this paper\n\n\n\nand also the dataset related paper\n\n\n\nEnjoy!"
] |
[
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #emotion-analysis #es #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Emotion Analysis in Spanish",
"## beto-emotion-analysis\n\nRepository: URL\n\n\nModel trained with TASS 2020 Task 2 corpus for Emotion detection in Spanish. Base model is BETO, a BERT model trained in Spanish.",
"## License\n\n'pysentimiento' is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses. \n\n1. TASS Dataset license\n2. [SEMEval 2017 Dataset license]()\n\nIf you use 'pysentimiento' in your work, please cite this paper\n\n\n\nand also the dataset related paper\n\n\n\nEnjoy!"
] |
[
61,
7,
48,
95
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bert #text-classification #emotion-analysis #es #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Emotion Analysis in Spanish## beto-emotion-analysis\n\nRepository: URL\n\n\nModel trained with TASS 2020 Task 2 corpus for Emotion detection in Spanish. Base model is BETO, a BERT model trained in Spanish.## License\n\n'pysentimiento' is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses. \n\n1. TASS Dataset license\n2. [SEMEval 2017 Dataset license]()\n\nIf you use 'pysentimiento' in your work, please cite this paper\n\n\n\nand also the dataset related paper\n\n\n\nEnjoy!"
] |
[
-0.1206742376089096,
0.10255607217550278,
-0.002829404780641198,
0.06331507116556168,
0.1407719999551773,
0.01742444932460785,
0.07722873240709305,
0.03661289066076279,
0.10082000494003296,
-0.0024800929240882397,
0.018952513113617897,
0.13243944942951202,
0.025582103058695793,
0.09126461297273636,
-0.1049397885799408,
-0.2968568205833435,
0.009207011200487614,
0.08551470935344696,
0.15580707788467407,
0.10263219475746155,
0.11329232156276703,
-0.08553504943847656,
0.11394652724266052,
0.04816747456789017,
-0.035316262394189835,
-0.00979787390679121,
0.003935842774808407,
-0.11433091014623642,
0.14778287708759308,
0.011072194203734398,
0.0791492611169815,
0.10657311975955963,
0.04129837825894356,
-0.08878591656684875,
0.04144306108355522,
-0.019153602421283722,
-0.020381463691592216,
0.08662977814674377,
0.13005681335926056,
-0.12251420319080353,
0.20204798877239227,
0.00027756180497817695,
0.06226227432489395,
0.0003794108924921602,
-0.08285439759492874,
-0.20346951484680176,
-0.05082007497549057,
-0.0047575184144079685,
0.09598071128129959,
0.09348790347576141,
-0.025521595031023026,
0.21769195795059204,
-0.07787184417247772,
0.06006445363163948,
0.20779936015605927,
-0.13865946233272552,
-0.06806516647338867,
-0.043440062552690506,
0.06197017431259155,
-0.050936032086610794,
-0.07604280114173889,
-0.005225679837167263,
0.07609810680150986,
0.03230622783303261,
0.09403902292251587,
-0.10173200815916061,
-0.010865496471524239,
-0.058590132743120193,
-0.11558029055595398,
0.011339791119098663,
0.1757187843322754,
0.028025534003973007,
-0.10389241576194763,
-0.0786965861916542,
-0.032743584364652634,
0.11063694953918457,
-0.0007232855423353612,
-0.09705835580825806,
0.060361720621585846,
-0.02018798515200615,
0.013298425823450089,
0.06448296457529068,
-0.1521596759557724,
0.02563527598977089,
-0.0848172977566719,
0.10287142544984818,
-0.02344169095158577,
0.0215305145829916,
0.0320388525724411,
0.05339318886399269,
-0.13299259543418884,
-0.07692822068929672,
-0.04175593703985214,
-0.10930398851633072,
0.0962679535150528,
-0.04058681055903435,
-0.08068197965621948,
-0.15682493150234222,
-0.024923430755734444,
-0.008833073079586029,
-0.0872197076678276,
-0.03455999121069908,
-0.02468934841454029,
-0.01489871833473444,
0.17295922338962555,
0.131700798869133,
-0.04882173240184784,
0.014693032950162888,
0.002290950156748295,
-0.0563557930290699,
0.007486396469175816,
0.034630581736564636,
-0.04905660077929497,
-0.04024792090058327,
0.027522966265678406,
-0.007727789226919413,
-0.0797608271241188,
0.07033273577690125,
-0.1139269694685936,
-0.019593803212046623,
0.06628050655126572,
-0.009527167305350304,
0.005669049918651581,
0.03779848292469978,
-0.011350814253091812,
0.11048229038715363,
0.05824580043554306,
0.03665686398744583,
-0.0010746910702437162,
0.04743191599845886,
-0.039160214364528656,
-0.027407357469201088,
-0.05738718807697296,
-0.08933711051940918,
0.08302149176597595,
-0.09258086234331131,
0.09500309824943542,
-0.22654473781585693,
-0.2502950131893158,
-0.04753641411662102,
0.07417972385883331,
-0.05400032550096512,
0.04165194556117058,
-0.051777433604002,
0.044119928032159805,
-0.01263174694031477,
-0.04796212539076805,
-0.011286851949989796,
-0.04301096126437187,
0.01752888411283493,
-0.012233692221343517,
0.11701171845197678,
-0.10008010268211365,
0.0014127464964985847,
-0.20290371775627136,
-0.005357733462005854,
-0.07729818671941757,
0.06979860365390778,
-0.011770426295697689,
0.09927015006542206,
-0.058902546763420105,
-0.10762986540794373,
0.021514760330319405,
0.026401493698358536,
0.016415420919656754,
0.20141135156154633,
-0.11497049033641815,
-0.0722619891166687,
0.13240356743335724,
-0.10468830913305283,
-0.07450807094573975,
0.1160842627286911,
-0.054561421275138855,
0.22467191517353058,
0.1359822005033493,
0.17571373283863068,
-0.03273976594209671,
-0.0920824408531189,
-0.027380524203181267,
-0.0018297195201739669,
-0.06701626628637314,
-0.05999860167503357,
0.08684582263231277,
0.09491416811943054,
-0.06588814407587051,
0.03685636445879936,
-0.07835263013839722,
0.01721002534031868,
-0.06670637428760529,
-0.051729828119277954,
0.02823372557759285,
-0.07308301329612732,
0.0708095133304596,
0.05782149359583855,
0.03762808069586754,
-0.07958000898361206,
-0.01837366446852684,
-0.0095442496240139,
0.033616919070482254,
-0.03052438609302044,
0.013892609626054764,
-0.08028726279735565,
0.07079793512821198,
-0.009003081358969212,
-0.017057664692401886,
-0.11314039677381516,
0.08278295397758484,
-0.0324828177690506,
0.15966396033763885,
-0.029333874583244324,
0.008281179703772068,
0.03302684426307678,
0.0027372348122298717,
-0.029914993792772293,
-0.02837769314646721,
0.051635611802339554,
0.013692731969058514,
-0.02970130555331707,
-0.1795254498720169,
0.03675984591245651,
-0.07480443269014359,
0.06650236248970032,
-0.10193382948637009,
0.03662736341357231,
-0.017015749588608742,
-0.013818980194628239,
-0.00026510388124734163,
0.02689173072576523,
-0.0040250807069242,
0.019740108400583267,
-0.04504141956567764,
0.002411584137007594,
0.09706060588359833,
0.03239437937736511,
-0.039389099925756454,
0.13292968273162842,
-0.021463895216584206,
0.09227439016103745,
0.10574121028184891,
-0.06104188412427902,
-0.06011928617954254,
-0.08184871077537537,
-0.05510512739419937,
0.07507865130901337,
-0.022017819806933403,
0.025407815352082253,
0.18589754402637482,
-0.0006568831740878522,
0.0735085979104042,
-0.11976958066225052,
0.04747910797595978,
0.039684172719717026,
-0.15400224924087524,
-0.0999126061797142,
0.10140570253133774,
-0.000048170233640121296,
-0.13714030385017395,
0.0743323415517807,
0.0870848074555397,
-0.03707678243517876,
0.14618514478206635,
0.05415715277194977,
-0.028738802298903465,
-0.026188157498836517,
-0.07827089726924896,
-0.04424451291561127,
0.0717785656452179,
-0.19319361448287964,
0.005121850874274969,
0.0730171948671341,
-0.06834462285041809,
-0.016222387552261353,
-0.11035475879907608,
-0.06683902442455292,
0.055316291749477386,
0.0564606674015522,
-0.10365965217351913,
0.05823652818799019,
-0.024394851177930832,
0.09606990218162537,
-0.010861745104193687,
-0.0542651042342186,
-0.00017575280799064785,
0.01476332452148199,
-0.16104143857955933,
0.18940776586532593,
-0.07494336366653442,
-0.20673488080501556,
-0.06694968044757843,
-0.05968543142080307,
0.01580650545656681,
0.0672277957201004,
0.09840405732393265,
-0.1035536378622055,
-0.058878008276224136,
-0.03953469544649124,
0.0007587253930978477,
0.08476252108812332,
-0.016631728038191795,
0.05897627770900726,
-0.029067248106002808,
-0.03157738968729973,
-0.07561007887125015,
-0.06085865572094917,
-0.07509676367044449,
-0.06196312978863716,
0.11053212732076645,
-0.08227353543043137,
0.12377427518367767,
0.11801210790872574,
0.0038439002819359303,
-0.019334161654114723,
-0.07957155257463455,
0.17745189368724823,
-0.02017923817038536,
-0.01956065557897091,
0.20268015563488007,
-0.02524426206946373,
0.03518233820796013,
0.13263995945453644,
0.010917054489254951,
-0.0782669261097908,
0.02601507119834423,
-0.004234503023326397,
-0.08516529947519302,
-0.2394474595785141,
-0.07199335098266602,
0.02382425218820572,
0.08945908397436142,
-0.032568152993917465,
-0.015985636040568352,
0.06921853125095367,
0.149385005235672,
0.017129136249423027,
-0.14135698974132538,
-0.06896740943193436,
0.10085896402597427,
0.0966954156756401,
-0.06042920798063278,
0.05375175550580025,
0.00966961495578289,
-0.023941829800605774,
0.11867660284042358,
-0.05617174506187439,
0.1430307924747467,
-0.0203934945166111,
0.008873943239450455,
0.06568948924541473,
0.09852336347103119,
-0.02769436687231064,
0.16768766939640045,
-0.06212114542722702,
-0.007946080528199673,
-0.10423657298088074,
-0.015890909358859062,
-0.10529422014951706,
0.08568060398101807,
-0.10049262642860413,
0.08072324842214584,
-0.20980150997638702,
-0.07772582769393921,
0.05988430604338646,
0.18377606570720673,
-0.033376678824424744,
-0.3336355686187744,
-0.09954134374856949,
0.018235398456454277,
-0.02684037759900093,
-0.03617940843105316,
-0.02282935380935669,
-0.006543205585330725,
-0.15920627117156982,
0.10353823751211166,
-0.019370783120393753,
0.03852479159832001,
-0.007669893093407154,
0.061790190637111664,
-0.13049788773059845,
-0.07297942787408829,
-0.028903061524033546,
0.07857656478881836,
-0.17435497045516968,
0.23980848491191864,
-0.04408154636621475,
0.0638057217001915,
-0.07271714508533478,
-0.06069665029644966,
0.03888721019029617,
0.15038013458251953,
0.15375332534313202,
-0.016763823106884956,
0.21495553851127625,
-0.07653865963220596,
-0.0535450242459774,
0.06591218709945679,
-0.08456585556268692,
-0.02739650569856167,
0.06957200914621353,
0.014011770486831665,
-0.006301757413893938,
0.03714161366224289,
0.14791442453861237,
-0.08833497762680054,
-0.04156672582030296,
-0.0034366208128631115,
-0.007003788370639086,
0.06608552485704422,
-0.002909119240939617,
-0.1471594125032425,
-0.07885310053825378,
0.06408311426639557,
0.12472175806760788,
-0.07937611639499664,
-0.11191511899232864,
0.0005349903949536383,
-0.021795282140374184,
-0.06634407490491867,
0.009123231284320354,
0.020699337124824524,
0.07860255241394043,
-0.016362689435482025,
-0.06995733827352524,
0.11644621193408966,
-0.1512131243944168,
-0.09865960478782654,
-0.045425351709127426,
0.042196761816740036,
0.10284067690372467,
0.04051646217703819,
0.11478254944086075,
-0.05645329877734184,
-0.06273758411407471,
-0.04587305709719658,
0.03337692841887474,
0.10894033312797546,
0.06344055384397507,
-0.05660638585686684,
-0.05999637767672539,
-0.187827929854393,
-0.07630471140146255,
-0.0350736528635025,
0.11707664281129837,
0.11310452222824097,
-0.02616303041577339,
0.11623257398605347,
0.1875820904970169,
-0.0970047265291214,
-0.17970022559165955,
0.04019854962825775,
0.09154654294252396,
0.005224036052823067,
0.06340068578720093,
-0.11489269882440567,
0.07952192425727844,
0.006158997304737568,
-0.02528255805373192,
-0.15746824443340302,
-0.09833252429962158,
-0.05643095448613167,
0.19337216019630432,
0.12444888800382614,
0.3297382593154907,
-0.11467506736516953,
0.023506497964262962,
-0.06908899545669556,
-0.09135925024747849,
0.2038436233997345,
-0.11073798686265945,
0.030193520709872246,
-0.0017260120948776603,
0.15746377408504486,
0.04733336716890335,
-0.013101826421916485,
0.1101750060915947,
-0.01459058653563261,
0.0406024232506752,
-0.08352290093898773,
-0.022538818418979645,
0.020955601707100868,
-0.03023337945342064,
0.11441148817539215,
0.08867049962282181,
-0.03076999820768833,
-0.07100280374288559,
-0.1170596033334732,
-0.13003504276275635,
0.09708248823881149,
0.034579262137413025,
-0.10100959986448288,
-0.00719728646799922,
0.03640540689229965,
0.08944517374038696,
-0.03055642358958721,
-0.10691668093204498,
-0.05816005542874336,
-0.053493306040763855,
0.1464037001132965,
0.2924632728099823,
-0.05617599934339523,
0.030586589127779007,
-0.00823255069553852,
-0.05573010817170143,
0.05820999667048454,
-0.07438381016254425,
-0.017452280968427658,
0.09982054680585861,
-0.03195200487971306,
0.08778607100248337,
0.0632472038269043,
-0.08510804921388626,
0.04350138083100319,
0.10109210014343262,
-0.09317955374717712,
-0.07100256532430649,
-0.053554195910692215,
-0.02261713147163391,
-0.0657273381948471,
0.024016940966248512,
0.11287959665060043,
-0.08343857526779175,
-0.03984316438436508,
-0.06618504971265793,
0.014338605105876923,
0.005984972231090069,
0.03779809549450874,
-0.023116225376725197,
-0.07655467092990875,
-0.08932454884052277,
-0.014205208979547024,
-0.04729101434350014,
-0.11473299562931061,
0.0768742635846138,
-0.041500482708215714,
-0.0655292347073555,
-0.07109713554382324,
0.006784176453948021,
0.19322596490383148,
-0.26012304425239563,
-0.09918563067913055,
-0.05950678512454033,
-0.14363987743854523,
0.020891224965453148,
0.17840538918972015,
0.0809028223156929,
-0.049960993230342865,
-0.06943727284669876,
0.04553021118044853,
-0.04533374309539795,
0.03914690762758255,
0.12208013236522675,
-0.003355659544467926,
-0.06613503396511078,
0.04464704543352127,
-0.056560903787612915,
0.049619778990745544,
-0.07476689666509628,
-0.016687698662281036,
-0.06442024558782578,
-0.02491540089249611,
-0.1747369021177292,
0.028739288449287415,
-0.10083464533090591,
-0.011071310378611088,
0.0025840564630925655,
-0.024918921291828156,
0.015329134650528431,
-0.01183301955461502,
-0.07135272026062012,
0.07771392166614532,
0.030135812237858772,
0.10745737701654434,
-0.08371268212795258,
-0.06370064616203308,
0.023060690611600876,
-0.010814129374921322,
0.1020715981721878,
0.06311561912298203,
-0.039816152304410934,
0.08533233404159546,
-0.3096538484096527,
0.04032163694500923,
0.13790355622768402,
-0.007134168408811092,
0.02909083478152752,
-0.03431790694594383,
-0.007011827081441879,
0.08471539616584778,
-0.04056870937347412,
-0.00020576758834067732,
0.12836939096450806,
-0.05840304121375084,
0.07345449179410934,
0.1668587327003479,
-0.10670103132724762,
-0.033179327845573425,
-0.024980900809168816,
0.03570103272795677,
0.050658926367759705,
0.1709156632423401,
-0.05011817440390587,
0.031255532056093216,
-0.11778345704078674,
0.009234774857759476,
-0.016590876504778862,
-0.024199984967708588,
-0.16127710044384003,
-0.06540500372648239,
0.020572427660226822,
0.009870870970189571,
0.14242644608020782,
0.16307583451271057,
-0.011643596924841404,
-0.055212151259183884,
0.06396238505840302,
0.11750715225934982,
-0.017685098573565483,
0.16159118711948395,
0.022378578782081604,
-0.003508706809952855,
-0.04066237062215805,
0.1208472028374672,
0.09033162891864777,
-0.04479934275150299,
0.11323581635951996,
0.10221333801746368,
0.14197589457035065,
0.1048276424407959,
0.012623230926692486,
0.07767920941114426,
-0.0644591897726059,
-0.22722302377223969,
-0.0009316565701738,
0.02844676375389099,
0.01603410206735134,
0.23512589931488037,
0.08702168613672256,
-0.10458798706531525,
0.05002473667263985,
-0.0061385720036923885,
-0.031133802607655525,
-0.13145698606967926,
-0.1868695616722107,
-0.05688382685184479,
-0.11403236538171768,
-0.0051499311812222,
-0.15996451675891876,
-0.03481551632285118,
-0.027051622048020363,
0.012245171703398228,
-0.07818612456321716,
0.06705094128847122,
-0.07953813672065735,
-0.07659497857093811,
0.15336491167545319,
0.000847591261845082,
-0.027706019580364227,
-0.06238880753517151,
0.02476724050939083,
-0.058761175721883774,
0.10556264221668243,
0.027503281831741333,
0.017479384317994118,
-0.036087505519390106,
-0.02650769054889679,
-0.02504325471818447,
-0.07109642773866653,
-0.023205474019050598,
0.03810515254735947,
0.0449281670153141,
0.04941478744149208,
-0.016400089487433434,
-0.011417921632528305,
0.04402002692222595,
0.19649101793766022,
-0.0056541478261351585,
0.1095941886305809,
-0.11907187849283218,
0.186361163854599,
-0.035341035574674606,
0.04080551490187645,
0.03952552750706673,
-0.06996314227581024,
0.01866382360458374,
0.2163849174976349,
0.1761484146118164,
-0.027588343247771263,
-0.01975092850625515,
-0.10030756145715714,
0.04447292163968086,
-0.013164295814931393,
0.1112276017665863,
0.09152247756719589,
0.20153649151325226,
-0.11323350667953491,
0.12119795382022858,
-0.051873140037059784,
0.05218657851219177,
-0.043755218386650085,
0.012090420350432396,
0.04593789950013161,
-0.03583497926592827,
-0.049708038568496704,
0.11040856689214706,
-0.07179897278547287,
-0.1295700967311859,
0.010761938989162445,
-0.06919272243976593,
-0.0551353357732296,
-0.02511155791580677,
-0.03632307052612305,
0.09347659349441528,
0.12456569075584412,
0.003566458821296692,
0.007112855091691017,
0.21691466867923737,
0.028860002756118774,
-0.126931831240654,
0.018123049288988113,
0.18915750086307526,
-0.09189930558204651,
0.2003263533115387,
-0.04337078332901001,
0.0249416995793581,
0.06583482027053833,
0.03245743364095688,
-0.053407661616802216,
0.05865827575325966,
0.023048264905810356,
-0.049056198447942734,
0.08901527523994446,
0.021057646721601486,
-0.019211038947105408,
0.007965675555169582,
0.019413325935602188,
-0.22993360459804535,
0.03374331071972847,
0.042898405343294144,
-0.04808579012751579,
-0.07519927620887756,
0.1544407308101654,
-0.15289396047592163,
0.055099643766880035,
0.1367356777191162,
-0.040034811943769455,
-0.017847761511802673,
-0.04609052464365959,
0.07374852895736694,
0.03137781471014023,
-0.07282581180334091,
0.043195877224206924,
-0.1724976748228073,
-0.017413560301065445,
0.11648108810186386,
0.03030470199882984,
-0.2866232991218567,
-0.012250042520463467,
-0.09419877827167511,
-0.017986910417675972,
-0.05176835507154465,
-0.052087441086769104,
-0.012695569545030594,
0.008733788505196571,
0.00442729564383626,
0.005728666204959154,
-0.009760506451129913,
0.14122921228408813,
-0.028269385918974876,
-0.04817444458603859
] |
null | null |
transformers
|
# Targeted Sentiment Analysis in News Headlines
BERT classifier fine-tuned in a news headlines dataset annotated for target polarity.
(details to be published)
## Examples
Input is as follows
`Headline [SEP] Target`
where headline is the news title and target is an entity present in the headline.
Try
`Alberto Fernández: "El gobierno de Macri fue un desastre" [SEP] Macri` (should be NEG)
and
`Alberto Fernández: "El gobierno de Macri fue un desastre" [SEP] Alberto Fernández` (POS or NEU)
|
{}
|
text-classification
|
finiteautomata/beto-headlines-sentiment-analysis
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Targeted Sentiment Analysis in News Headlines
BERT classifier fine-tuned in a news headlines dataset annotated for target polarity.
(details to be published)
## Examples
Input is as follows
'Headline [SEP] Target'
where headline is the news title and target is an entity present in the headline.
Try
'Alberto Fernández: "El gobierno de Macri fue un desastre" [SEP] Macri' (should be NEG)
and
'Alberto Fernández: "El gobierno de Macri fue un desastre" [SEP] Alberto Fernández' (POS or NEU)
|
[
"# Targeted Sentiment Analysis in News Headlines\n\nBERT classifier fine-tuned in a news headlines dataset annotated for target polarity.\n\n(details to be published)",
"## Examples\n\nInput is as follows\n\n'Headline [SEP] Target'\n\nwhere headline is the news title and target is an entity present in the headline.\n\nTry\n\n'Alberto Fernández: \"El gobierno de Macri fue un desastre\" [SEP] Macri' (should be NEG)\n\nand\n\n'Alberto Fernández: \"El gobierno de Macri fue un desastre\" [SEP] Alberto Fernández' (POS or NEU)"
] |
[
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Targeted Sentiment Analysis in News Headlines\n\nBERT classifier fine-tuned in a news headlines dataset annotated for target polarity.\n\n(details to be published)",
"## Examples\n\nInput is as follows\n\n'Headline [SEP] Target'\n\nwhere headline is the news title and target is an entity present in the headline.\n\nTry\n\n'Alberto Fernández: \"El gobierno de Macri fue un desastre\" [SEP] Macri' (should be NEG)\n\nand\n\n'Alberto Fernández: \"El gobierno de Macri fue un desastre\" [SEP] Alberto Fernández' (POS or NEU)"
] |
[
45,
42,
97
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Targeted Sentiment Analysis in News Headlines\n\nBERT classifier fine-tuned in a news headlines dataset annotated for target polarity.\n\n(details to be published)## Examples\n\nInput is as follows\n\n'Headline [SEP] Target'\n\nwhere headline is the news title and target is an entity present in the headline.\n\nTry\n\n'Alberto Fernández: \"El gobierno de Macri fue un desastre\" [SEP] Macri' (should be NEG)\n\nand\n\n'Alberto Fernández: \"El gobierno de Macri fue un desastre\" [SEP] Alberto Fernández' (POS or NEU)"
] |
[
-0.05583092197775841,
-0.09205784648656845,
-0.005246345419436693,
-0.011270178481936455,
0.10720119625329971,
-0.006759732495993376,
0.044351354241371155,
-0.03524785861372948,
0.034906014800071716,
0.1058664470911026,
0.08586011826992035,
0.1735573410987854,
0.0032372111454606056,
0.026930201798677444,
-0.09993860870599747,
-0.22360657155513763,
0.12736551463603973,
0.0005171757657080889,
0.14926642179489136,
0.11514700204133987,
0.07995188236236572,
-0.014744078740477562,
0.07639570534229279,
0.05208694189786911,
-0.05031810700893402,
0.06302649527788162,
0.04087319225072861,
-0.07500943541526794,
0.14673562347888947,
0.03900798410177231,
0.12749868631362915,
0.0700773075222969,
-0.06114371865987778,
-0.10020483285188675,
0.053991273045539856,
0.026323989033699036,
-0.03549792617559433,
0.016625450924038887,
0.11463046073913574,
-0.08344671130180359,
0.2053522914648056,
-0.21006622910499573,
0.013158505782485008,
0.00047209992771968246,
-0.16366660594940186,
-0.015846824273467064,
-0.03201242536306381,
-0.0015184191288426518,
0.07625647634267807,
0.0697181448340416,
-0.05449648201465607,
0.13368582725524902,
-0.15822593867778778,
0.007306552026420832,
0.10554422438144684,
-0.18361978232860565,
-0.07344323396682739,
-0.032635435461997986,
-0.00447468226775527,
0.03913965821266174,
-0.020929239690303802,
0.055981267243623734,
0.05356273055076599,
-0.05933220684528351,
-0.12285927683115005,
-0.03409634903073311,
-0.11268379539251328,
-0.017144545912742615,
-0.12689101696014404,
-0.06300825625658035,
0.10119625180959702,
0.060187023133039474,
-0.02221667394042015,
-0.1058574765920639,
-0.06790214776992798,
0.151030033826828,
0.0059052156284451485,
-0.054027725011110306,
-0.03101644106209278,
-0.015641381964087486,
0.10209629684686661,
0.07591164112091064,
-0.0848446935415268,
-0.016564860939979553,
-0.21091511845588684,
0.11848769336938858,
-0.017754292115569115,
0.01983506977558136,
0.02541530504822731,
-0.008463378064334393,
-0.026629582047462463,
-0.05884500592947006,
0.034732092171907425,
-0.07603570818901062,
-0.02688664011657238,
-0.008738450706005096,
-0.11450446397066116,
-0.08926978707313538,
0.0014903252013027668,
-0.05450403690338135,
-0.08925193548202515,
-0.04199019819498062,
-0.006499829236418009,
0.09190360456705093,
0.06937529891729355,
0.028154481202363968,
-0.09371905028820038,
-0.02356274239718914,
-0.03517201915383339,
-0.0944482684135437,
0.10194192826747894,
-0.01616373099386692,
-0.06758129596710205,
-0.024678273126482964,
-0.05783422663807869,
-0.03728355094790459,
0.034323569387197495,
0.09787464886903763,
-0.15545058250427246,
-0.030639365315437317,
0.013938583433628082,
-0.06062670052051544,
-0.01295492798089981,
0.002498408081009984,
0.01487846951931715,
0.12966783344745636,
-0.12237377464771271,
0.020355360582470894,
-0.006544965319335461,
0.08630257099866867,
-0.10500407218933105,
-0.027693819254636765,
-0.045812949538230896,
-0.07126805931329727,
0.08375559747219086,
-0.007481044624000788,
-0.03799687325954437,
-0.15974242985248566,
-0.15543432533740997,
-0.08451841026544571,
-0.024072697386145592,
-0.043230243027210236,
-0.05593528598546982,
-0.05907195061445236,
0.05235200747847557,
0.010105849243700504,
-0.03248677775263786,
-0.013567183166742325,
-0.08344709128141403,
0.08861014246940613,
0.0066427369602024555,
0.1219237819314003,
-0.02311684936285019,
-0.021441858261823654,
-0.15647658705711365,
0.017613409087061882,
-0.18647390604019165,
0.11804036796092987,
-0.0919288843870163,
0.12255574017763138,
-0.0363728292286396,
0.03391934558749199,
-0.11595743894577026,
0.06845701485872269,
-0.036789245903491974,
0.23606440424919128,
-0.18225105106830597,
-0.0834278091788292,
0.08210282027721405,
-0.13040632009506226,
0.0567014142870903,
0.10169245302677155,
-0.01774980127811432,
0.06546424329280853,
0.14241142570972443,
0.27577683329582214,
-0.14988838136196136,
-0.09334496408700943,
-0.021885881200432777,
-0.013684631325304508,
-0.08639363199472427,
0.04267170652747154,
0.07887265831232071,
0.047378167510032654,
-0.16319304704666138,
0.0161855760961771,
-0.09797253459692001,
0.03372473269701004,
-0.007171902805566788,
-0.013038912788033485,
0.07027772068977356,
0.06227146089076996,
0.0549597293138504,
0.029937980696558952,
0.004350792616605759,
-0.04418418928980827,
-0.06168077886104584,
0.05378088727593422,
-0.02138497866690159,
0.06677878648042679,
0.0035156046506017447,
-0.08357659727334976,
0.1279628425836563,
-0.0770765021443367,
0.014578619040548801,
-0.093065544962883,
-0.05042048543691635,
-0.08859732002019882,
0.03947809338569641,
0.056238505989313126,
-0.002483966061845422,
-0.030978310853242874,
-0.10191872715950012,
-0.008207142353057861,
0.04966031014919281,
0.022722328081727028,
0.0190338846296072,
0.05499802529811859,
-0.18121683597564697,
0.09422236680984497,
-0.03374561294913292,
0.12314280867576599,
-0.12609393894672394,
0.010892330668866634,
0.12513461709022522,
-0.010678711347281933,
-0.009069244377315044,
-0.015203500166535378,
0.010441936552524567,
0.09896423667669296,
-0.016834517940878868,
0.022939758375287056,
0.11220522224903107,
0.030583715066313744,
-0.0583130307495594,
0.18332740664482117,
-0.02783997356891632,
0.08899537473917007,
0.13120883703231812,
-0.21532589197158813,
-0.0666658952832222,
0.009143762290477753,
-0.038116395473480225,
0.03723873943090439,
-0.06608479470014572,
-0.01629956252872944,
0.041074369102716446,
-0.01832648552954197,
0.0257161483168602,
-0.12110321968793869,
-0.04030158370733261,
0.006886495277285576,
-0.014840834774076939,
-0.12335139513015747,
0.1241622194647789,
0.14437739551067352,
-0.09882787615060806,
0.1594986915588379,
0.11582627147436142,
-0.008375530131161213,
0.2000516653060913,
0.05081608146429062,
-0.03803465887904167,
0.0022593929897993803,
-0.00850349199026823,
0.017032377421855927,
0.03353293612599373,
-0.18979521095752716,
0.035752709954977036,
0.008688327856361866,
-0.06774961203336716,
0.05543875694274902,
-0.08423873037099838,
-0.10127776116132736,
0.013661307282745838,
0.010554064065217972,
-0.15127815306186676,
0.01659572124481201,
0.014636741019785404,
0.1216813325881958,
0.03868028521537781,
-0.1087384968996048,
0.010378055274486542,
-0.021479060873389244,
-0.09334319084882736,
0.09934377670288086,
-0.01115234475582838,
-0.3175118565559387,
-0.09680801630020142,
0.002818706212565303,
-0.05795270949602127,
0.0674506425857544,
-0.00405512610450387,
-0.10066664963960648,
0.01395380962640047,
-0.010883762501180172,
0.042865313589572906,
0.11094628274440765,
-0.03427287936210632,
0.06031860411167145,
-0.013663734309375286,
-0.001714328769594431,
0.0014120226260274649,
-0.035928286612033844,
-0.16363099217414856,
-0.07879328727722168,
0.014904587529599667,
-0.12424182146787643,
0.20298336446285248,
0.137267604470253,
0.028828127309679985,
-0.022095708176493645,
-0.04023095965385437,
0.22484616935253143,
-0.12433122843503952,
-0.04703587293624878,
0.06161941587924957,
-0.02168688364326954,
0.036727700382471085,
0.21241351962089539,
0.02121688611805439,
-0.08408719301223755,
-0.008898071013391018,
0.05520062893629074,
-0.009541457518935204,
-0.18069952726364136,
-0.1490379273891449,
-0.08003034442663193,
0.013097324408590794,
-0.01329341996461153,
-0.0027779422234743834,
-0.03850700333714485,
0.08269418776035309,
-0.030447645112872124,
-0.019636407494544983,
0.04351186007261276,
0.10081116855144501,
0.28952422738075256,
-0.02966250292956829,
0.059531763195991516,
-0.009691759012639523,
-0.14948144555091858,
0.10513091832399368,
-0.027123581618070602,
0.00024334336922038347,
0.06775157898664474,
0.09148187935352325,
0.06830671429634094,
0.025512859225273132,
0.06456394493579865,
0.027741510421037674,
-0.06644871085882187,
-0.07558400183916092,
-0.11592216789722443,
-0.035756584256887436,
-0.1142507866024971,
0.019233230501413345,
-0.078942209482193,
0.0015501162270084023,
-0.035821523517370224,
-0.20434235036373138,
0.1223926767706871,
0.059178028255701065,
0.022386059165000916,
-0.20338286459445953,
-0.0412350594997406,
0.06755442172288895,
-0.03467312082648277,
-0.015850065276026726,
0.008659024722874165,
0.07428620010614395,
-0.06010115146636963,
0.14342471957206726,
0.07185955345630646,
0.08582732826471329,
-0.020227765664458275,
0.08318903297185898,
-0.19629351794719696,
-0.16312512755393982,
-0.03941398486495018,
0.08897987008094788,
-0.13048668205738068,
0.2541341781616211,
0.0028646981809288263,
-0.022029763087630272,
-0.010491825640201569,
-0.07924050837755203,
-0.02972147986292839,
0.141309455037117,
0.12936818599700928,
0.04391126707196236,
-0.03662504628300667,
-0.06345828622579575,
-0.04654291272163391,
0.0333036333322525,
-0.029639776796102524,
-0.044585827738046646,
0.03034205362200737,
0.026333266869187355,
0.04408375546336174,
0.007826396264135838,
0.16358241438865662,
-0.1030072420835495,
-0.18444588780403137,
0.014495594426989555,
0.03736678883433342,
0.06671231985092163,
-0.017840173095464706,
-0.0965401902794838,
-0.05016081780195236,
0.18968810141086578,
-0.10049090534448624,
-0.0003033349639736116,
-0.10692647099494934,
0.02825476974248886,
-0.08653285354375839,
-0.05425326153635979,
-0.07228553295135498,
0.031467411667108536,
0.11765234172344208,
-0.05318965017795563,
-0.0663139745593071,
0.10718221217393875,
-0.07015694677829742,
-0.049144648015499115,
-0.10217712819576263,
0.059395723044872284,
0.05457989498972893,
0.0505094937980175,
0.07483050972223282,
0.04981439933180809,
0.0006409534835256636,
-0.08229628950357437,
0.0039874231442809105,
0.015376758761703968,
-0.007198150735348463,
0.07559061795473099,
-0.04616174101829529,
-0.28055405616760254,
-0.04006550833582878,
-0.05409710109233856,
0.14736728370189667,
0.17494690418243408,
-0.0065093375742435455,
0.0753643736243248,
0.21272853016853333,
-0.024405566975474358,
-0.1949760466814041,
-0.021371550858020782,
0.019215188920497894,
-0.040749091655015945,
0.031889114528894424,
-0.03649357706308365,
0.07698818296194077,
0.21159978210926056,
-0.0336625836789608,
-0.038801223039627075,
-0.2359839379787445,
-0.059503041207790375,
0.07371591776609421,
0.0008083099382929504,
0.43375885486602783,
-0.12607663869857788,
-0.08784196525812149,
-0.09795921295881271,
0.0410456657409668,
0.2651836574077606,
0.03510485216975212,
0.03400556370615959,
0.01607654243707657,
0.11423134058713913,
0.03717811405658722,
0.008005726151168346,
0.18576015532016754,
0.03966345638036728,
0.0014526871964335442,
-0.08616147935390472,
-0.1187446117401123,
0.07591960579156876,
0.017172327265143394,
-0.05999461188912392,
0.09578311443328857,
-0.07747482508420944,
-0.20721936225891113,
-0.10748177021741867,
-0.09690117090940475,
0.08733515441417694,
0.031091583892703056,
0.0182723980396986,
-0.029538000002503395,
0.05612128600478172,
0.03982119634747505,
-0.007127799559384584,
0.11147646605968475,
-0.1723370999097824,
0.16477684676647186,
-0.06463634222745895,
0.06813288480043411,
-0.031052405014634132,
0.035524219274520874,
-0.011829610913991928,
-0.04094464331865311,
0.09244835376739502,
0.019335070624947548,
-0.015412860549986362,
0.07900853455066681,
0.003406838746741414,
0.010920906439423561,
0.08189747482538223,
-0.0341632254421711,
0.06088370829820633,
0.12916557490825653,
-0.16501063108444214,
0.003298796946182847,
-0.010546986013650894,
-0.02878524176776409,
0.052756763994693756,
-0.03882361948490143,
0.04926028102636337,
0.03762879595160484,
-0.037189383059740067,
0.053304463624954224,
-0.021986600011587143,
0.04397595301270485,
0.039498113095760345,
-0.008162430487573147,
-0.016705481335520744,
-0.13145427405834198,
-0.018023326992988586,
0.10225648432970047,
-0.27755722403526306,
-0.035659149289131165,
0.03573460876941681,
-0.09319781512022018,
-0.049121372401714325,
-0.09119529277086258,
0.09289086610078812,
-0.1127045676112175,
-0.07993122190237045,
-0.05632922798395157,
-0.14364881813526154,
0.09731364250183105,
0.23596420884132385,
0.10730777680873871,
0.012050885707139969,
0.0015105551574379206,
-0.021649347618222237,
0.03058629296720028,
0.0027356743812561035,
0.07801680266857147,
0.021839968860149384,
-0.03349144756793976,
-0.05771142616868019,
0.024876629933714867,
0.030884364619851112,
-0.057180531322956085,
-0.025703100487589836,
-0.17307999730110168,
0.006868316791951656,
-0.012591167353093624,
0.08626396209001541,
-0.04238294064998627,
0.0003273001639172435,
-0.0025226890575140715,
-0.04095884785056114,
-0.06636718660593033,
-0.04172998666763306,
-0.06667627394199371,
0.06456644088029861,
0.016446147114038467,
0.08758074790239334,
-0.10698113590478897,
-0.014366324990987778,
0.11574789881706238,
-0.036923233419656754,
0.014395400881767273,
0.04245859012007713,
-0.013855001889169216,
0.04422513768076897,
-0.3936077654361725,
-0.013419527560472488,
0.07874486595392227,
-0.006826482713222504,
0.018113667145371437,
0.06686151027679443,
0.07482769340276718,
0.11627086251974106,
-0.03005382791161537,
0.04898381978273392,
0.06161174178123474,
-0.09958070516586304,
0.15357571840286255,
0.13237304985523224,
-0.2551132142543793,
0.007857231423258781,
-0.035968467593193054,
0.05627989023923874,
-0.014148332178592682,
0.15148088335990906,
-0.08430735021829605,
-0.021451514214277267,
-0.05654248222708702,
0.04529164731502533,
0.01942555047571659,
-0.08975143730640411,
-0.04523412510752678,
-0.03559180721640587,
0.03294559568166733,
0.03554368019104004,
0.26271361112594604,
0.08808310329914093,
0.03356129303574562,
0.021757254377007484,
0.12624739110469818,
0.019318578764796257,
-0.012987093068659306,
0.10455728322267532,
0.12502478063106537,
0.03909945487976074,
-0.004815340507775545,
-0.012009811587631702,
0.0549948550760746,
0.0887068659067154,
0.08186469227075577,
0.10623063892126083,
0.05200663208961487,
0.08799085766077042,
0.020314717665314674,
0.07485257834196091,
0.05337447673082352,
-0.06895451992750168,
-0.09287133812904358,
-0.010952295735478401,
0.015827329829335213,
0.18533803522586823,
0.1494041085243225,
-0.06728490442037582,
0.050032079219818115,
-0.0018929025391116738,
0.011127002537250519,
-0.11778199672698975,
-0.2744251787662506,
-0.07069189101457596,
-0.10919860750436783,
0.02615523524582386,
-0.056963000446558,
-0.03360572084784508,
0.14865662157535553,
0.02499503456056118,
-0.00368178216740489,
0.0824020653963089,
0.014686779119074345,
-0.14029791951179504,
0.10208616405725479,
-0.035954251885414124,
0.042473774403333664,
-0.11874706298112869,
-0.016390517354011536,
0.008343013934791088,
0.09608194977045059,
0.03484148159623146,
0.05669133737683296,
0.05664863809943199,
-0.06218670681118965,
-0.11854265630245209,
-0.05712571367621422,
-0.02276773378252983,
0.043592024594545364,
-0.025616396218538284,
0.0950452983379364,
0.02889023907482624,
0.03164001181721687,
0.029129616916179657,
0.1616438925266266,
0.05965343490242958,
-0.011931386776268482,
-0.020114650949835777,
0.12719778716564178,
-0.05164231359958649,
0.13180890679359436,
-0.04921978339552879,
-0.07295121252536774,
0.030956652015447617,
0.24819223582744598,
0.15733709931373596,
-0.09355364739894867,
-0.01422498095780611,
-0.04711444303393364,
0.04890310764312744,
0.09391216188669205,
0.03403981775045395,
0.03443940356373787,
0.39517176151275635,
-0.039799757301807404,
0.02569972164928913,
-0.03575167432427406,
0.06450583785772324,
-0.07042111456394196,
-0.004627536982297897,
0.07738383114337921,
-0.04837770760059357,
-0.10620268434286118,
0.20084114372730255,
-0.12375979870557785,
-0.10315373539924622,
0.055084358900785446,
-0.08404321223497391,
-0.09227786213159561,
-0.03792912885546684,
0.07957518100738525,
-0.006662904284894466,
0.0911356583237648,
0.012829119339585304,
-0.07978072762489319,
-0.12165111303329468,
0.04946017265319824,
-0.14934273064136505,
-0.13175715506076813,
0.10582446306943893,
0.06544098258018494,
0.2170286774635315,
-0.07260722666978836,
0.07830692082643509,
0.02728143334388733,
-0.0016245176084339619,
-0.027854815125465393,
0.10823197662830353,
0.013384458608925343,
-0.05642395094037056,
0.060380350798368454,
-0.036420468240976334,
0.021927926689386368,
0.03180255368351936,
0.13020525872707367,
-0.16439425945281982,
0.10358378291130066,
-0.0160398967564106,
-0.024628276005387306,
-0.06894020736217499,
0.17018349468708038,
-0.118108831346035,
0.02528894692659378,
0.10766800493001938,
0.03540850058197975,
0.04314642772078514,
-0.08454608172178268,
0.018118619918823242,
0.028304394334554672,
0.023916279897093773,
0.003979761153459549,
-0.12356109917163849,
-0.021699082106351852,
-0.01333814486861229,
0.03922701254487038,
-0.2035195529460907,
0.025777604430913925,
-0.11725662648677826,
0.01615195907652378,
-0.021773016080260277,
-0.0008905192371457815,
-0.025788143277168274,
-0.03194892033934593,
0.007213921751827002,
-0.271918922662735,
0.0829121321439743,
0.09990863502025604,
-0.02978852204978466,
-0.006264649797230959
] |
null | null |
transformers
|
# Sentiment Analysis in Spanish
## beto-sentiment-analysis
**NOTE: this model will be removed soon -- use [pysentimiento/robertuito-sentiment-analysis](https://huggingface.co/pysentimiento/robertuito-sentiment-analysis) instead**
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/pysentimiento/pysentimiento/)
Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is [BETO](https://github.com/dccuchile/beto), a BERT model trained in Spanish.
Uses `POS`, `NEG`, `NEU` labels.
## License
`pysentimiento` is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses.
1. [TASS Dataset license](http://tass.sepln.org/tass_data/download.php)
2. [SEMEval 2017 Dataset license]()
## Citation
If you use this model in your work, please cite the following papers:
```
@misc{perez2021pysentimiento,
title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks},
author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque},
year={2021},
eprint={2106.09462},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{canete2020spanish,
title={Spanish pre-trained bert model and evaluation data},
author={Ca{\~n}ete, Jos{\'e} and Chaperon, Gabriel and Fuentes, Rodrigo and Ho, Jou-Hui and Kang, Hojin and P{\'e}rez, Jorge},
journal={Pml4dc at iclr},
volume={2020},
number={2020},
pages={1--10},
year={2020}
}
```
Enjoy! 🤗
|
{"language": ["es"], "tags": ["sentiment-analysis"]}
|
text-classification
|
finiteautomata/beto-sentiment-analysis
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"sentiment-analysis",
"es",
"arxiv:2106.09462",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2106.09462"
] |
[
"es"
] |
TAGS
#transformers #pytorch #jax #bert #text-classification #sentiment-analysis #es #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Sentiment Analysis in Spanish
## beto-sentiment-analysis
NOTE: this model will be removed soon -- use pysentimiento/robertuito-sentiment-analysis instead
Repository: URL
Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is BETO, a BERT model trained in Spanish.
Uses 'POS', 'NEG', 'NEU' labels.
## License
'pysentimiento' is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses.
1. TASS Dataset license
2. [SEMEval 2017 Dataset license]()
If you use this model in your work, please cite the following papers:
Enjoy!
|
[
"# Sentiment Analysis in Spanish",
"## beto-sentiment-analysis\n\nNOTE: this model will be removed soon -- use pysentimiento/robertuito-sentiment-analysis instead\n\nRepository: URL\n\n\nModel trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is BETO, a BERT model trained in Spanish.\n\nUses 'POS', 'NEG', 'NEU' labels.",
"## License\n\n'pysentimiento' is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses. \n\n1. TASS Dataset license\n2. [SEMEval 2017 Dataset license]()\n\nIf you use this model in your work, please cite the following papers:\n\n\n\nEnjoy!"
] |
[
"TAGS\n#transformers #pytorch #jax #bert #text-classification #sentiment-analysis #es #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Sentiment Analysis in Spanish",
"## beto-sentiment-analysis\n\nNOTE: this model will be removed soon -- use pysentimiento/robertuito-sentiment-analysis instead\n\nRepository: URL\n\n\nModel trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is BETO, a BERT model trained in Spanish.\n\nUses 'POS', 'NEG', 'NEU' labels.",
"## License\n\n'pysentimiento' is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses. \n\n1. TASS Dataset license\n2. [SEMEval 2017 Dataset license]()\n\nIf you use this model in your work, please cite the following papers:\n\n\n\nEnjoy!"
] |
[
59,
7,
96,
88
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #sentiment-analysis #es #arxiv-2106.09462 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Sentiment Analysis in Spanish## beto-sentiment-analysis\n\nNOTE: this model will be removed soon -- use pysentimiento/robertuito-sentiment-analysis instead\n\nRepository: URL\n\n\nModel trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is BETO, a BERT model trained in Spanish.\n\nUses 'POS', 'NEG', 'NEU' labels.## License\n\n'pysentimiento' is an open-source library for non-commercial use and scientific research purposes only. Please be aware that models are trained with third-party datasets and are subject to their respective licenses. \n\n1. TASS Dataset license\n2. [SEMEval 2017 Dataset license]()\n\nIf you use this model in your work, please cite the following papers:\n\n\n\nEnjoy!"
] |
[
-0.0800396054983139,
0.14537687599658966,
-0.0031896333675831556,
0.04979890584945679,
0.07987964153289795,
0.01183101162314415,
0.15827630460262299,
0.04806627333164215,
0.05785142257809639,
-0.0007966099656186998,
0.04691259562969208,
0.04959157109260559,
0.04889379441738129,
0.13477547466754913,
-0.07011624425649643,
-0.2765890955924988,
0.03473780304193497,
0.0024609267711639404,
0.018851393833756447,
0.09064854681491852,
0.14010848104953766,
-0.04323481395840645,
0.10610128939151764,
0.06608785688877106,
-0.03957340493798256,
-0.008295108564198017,
0.059532929211854935,
-0.12777167558670044,
0.11415807157754898,
0.025231139734387398,
0.11888688057661057,
0.0941607803106308,
0.06535263359546661,
-0.08947563171386719,
0.023577403277158737,
-0.009205901995301247,
-0.06738051772117615,
0.10338196158409119,
0.14595738053321838,
-0.06520704925060272,
0.24173776805400848,
-0.01194817665964365,
0.07575021684169769,
0.05467655882239342,
-0.1321267932653427,
-0.13151641190052032,
-0.1311725676059723,
0.022607414051890373,
0.1313769519329071,
0.060175832360982895,
-0.015050368383526802,
0.11247839778661728,
-0.09760269522666931,
0.012768597342073917,
0.15706098079681396,
-0.17468014359474182,
-0.0516926646232605,
0.006279372610151768,
-0.0005991020007058978,
0.056546859443187714,
-0.007083260454237461,
0.01860368251800537,
0.07451598346233368,
0.0026696850545704365,
0.03935501351952553,
-0.05303700268268585,
-0.0031188172288239002,
-0.010344435460865498,
-0.1297224760055542,
-0.061207860708236694,
0.2224852740764618,
0.007512120995670557,
-0.08360348641872406,
-0.006049647927284241,
-0.0356808640062809,
0.012331539765000343,
0.0005394035251811147,
-0.08246741443872452,
0.04504785314202309,
0.007711159065365791,
0.02670898847281933,
-0.002511509694159031,
-0.14711514115333557,
0.009240638464689255,
-0.13550394773483276,
0.016277844086289406,
0.003942313604056835,
0.05226556211709976,
-0.005544679705053568,
0.028991317376494408,
-0.15405219793319702,
-0.06544174998998642,
-0.04312615469098091,
-0.108740895986557,
0.006799302063882351,
-0.04787152633070946,
-0.08409187197685242,
-0.06600287556648254,
-0.016615087166428566,
0.08525410294532776,
-0.10514980554580688,
-0.039693091064691544,
-0.008233385160565376,
0.004628445953130722,
0.11720412969589233,
0.02113092876970768,
-0.02780202217400074,
0.007971839979290962,
0.018294937908649445,
-0.09077289700508118,
0.008697779849171638,
0.00946568138897419,
-0.06906504184007645,
-0.05317242443561554,
-0.010539939627051353,
0.005113144405186176,
0.011476574465632439,
-0.0058986893855035305,
-0.09738560020923615,
-0.04870687425136566,
0.08654085546731949,
-0.010269424878060818,
0.007201986387372017,
0.018276114016771317,
-0.022298326715826988,
0.05141228437423706,
0.0556732714176178,
0.06317495554685593,
0.009804068133234978,
0.009696848690509796,
-0.09372574090957642,
-0.036969151347875595,
-0.06121540442109108,
-0.0939495638012886,
0.089415043592453,
-0.09831689298152924,
0.016200389713048935,
-0.16888493299484253,
-0.20682859420776367,
-0.014181463979184628,
0.0940103828907013,
-0.08722654730081558,
-0.04576224461197853,
-0.016718676313757896,
0.008924553170800209,
0.009990096092224121,
-0.037473488599061966,
0.047117311507463455,
-0.031005732715129852,
0.04266311973333359,
-0.06465344876050949,
0.08064226061105728,
-0.13793231546878815,
0.024012602865695953,
-0.12208977341651917,
0.014774888753890991,
-0.055278439074754715,
0.055467866361141205,
-0.06459707766771317,
0.05133995786309242,
-0.1525905877351761,
-0.09749606996774673,
-0.02255125716328621,
0.014181666076183319,
0.02900974452495575,
0.1643526703119278,
-0.2010938674211502,
-0.043516259640455246,
0.05664568021893501,
-0.1435983031988144,
-0.05366922914981842,
0.15049229562282562,
-0.05753867328166962,
0.1990535855293274,
0.09628305584192276,
0.18940003216266632,
0.045636579394340515,
-0.11019587516784668,
0.009844404645264149,
-0.03521667420864105,
0.005094696301966906,
-0.1180863082408905,
0.1255815327167511,
0.03885843977332115,
-0.04698280245065689,
0.0015273389872163534,
-0.13378554582595825,
-0.00023518444504588842,
-0.04360368102788925,
-0.10067315399646759,
0.040404047816991806,
-0.06159498542547226,
0.0795673057436943,
-0.009753554128110409,
0.0584971085190773,
-0.06397378444671631,
-0.03419843316078186,
0.051564477384090424,
0.028529908508062363,
0.01541319489479065,
0.025395836681127548,
-0.05721238628029823,
0.08086594939231873,
-0.02666000835597515,
-0.038939595222473145,
-0.11059843003749847,
-0.014142236672341824,
-0.01966933347284794,
0.04899878054857254,
0.06511764228343964,
0.10498451441526413,
-0.0025410386733710766,
-0.0014970556367188692,
-0.03781794756650925,
0.03802536427974701,
-0.00022322469158098102,
0.020826764404773712,
-0.05554429814219475,
-0.17383268475532532,
0.028331413865089417,
-0.05013810470700264,
0.07733923941850662,
-0.10719269514083862,
0.02392958663403988,
0.039233509451150894,
-0.014552981592714787,
0.00458989292383194,
0.008046778850257397,
-0.03343798220157623,
0.03638661652803421,
-0.04996059834957123,
0.014255077578127384,
0.0606950968503952,
0.015111985616385937,
0.010781272314488888,
0.1234169751405716,
-0.0394287034869194,
0.011171749792993069,
0.10250451415777206,
0.01502606924623251,
-0.05710798501968384,
-0.04100034758448601,
-0.051086511462926865,
0.05175289139151573,
-0.06297332793474197,
0.03770161420106888,
0.07407333701848984,
-0.008648024871945381,
0.04820859432220459,
-0.13102319836616516,
0.006542055401951075,
0.03329799696803093,
-0.1396208256483078,
-0.11195894330739975,
0.05805283412337303,
0.08688974380493164,
-0.2133965641260147,
0.13738983869552612,
0.09797288477420807,
-0.06631709635257721,
0.13445433974266052,
0.01226819772273302,
-0.06068025901913643,
-0.05423307046294212,
-0.05549658462405205,
0.00815780833363533,
0.105980284512043,
-0.06547044217586517,
0.02146979793906212,
0.07210862636566162,
-0.006559310480952263,
0.010982065461575985,
-0.1229052022099495,
-0.05203434079885483,
0.023029616102576256,
-0.012245229445397854,
-0.15509416162967682,
0.03558695688843727,
-0.06256578117609024,
0.11209002137184143,
-0.013273621909320354,
-0.03735848888754845,
0.01734023727476597,
0.014950078912079334,
-0.16035719215869904,
0.13750627636909485,
-0.10143986344337463,
-0.11695562303066254,
-0.12990789115428925,
-0.03982096537947655,
-0.017635216936469078,
0.06420733779668808,
0.09069803357124329,
-0.06739597022533417,
-0.06693841516971588,
-0.04723784327507019,
-0.07477562874555588,
0.009874319657683372,
-0.017192959785461426,
0.0045424592681229115,
0.024828413501381874,
0.006528541445732117,
-0.07330842316150665,
-0.04291694983839989,
-0.04977312684059143,
-0.03643009066581726,
0.05053739249706268,
-0.08448685705661774,
0.10199419409036636,
0.09016255289316177,
0.005400143098086119,
-0.02013685740530491,
-0.061026979237794876,
0.2191653847694397,
-0.013191074132919312,
0.018194783478975296,
0.2031937539577484,
0.023645954206585884,
0.045940838754177094,
0.18363948166370392,
0.009916868060827255,
-0.04869041591882706,
0.006838549859821796,
-0.000931568443775177,
-0.10837550461292267,
-0.30558961629867554,
-0.07300785183906555,
-0.04614721238613129,
-0.002208467572927475,
0.028129348531365395,
-0.0024115503765642643,
0.10446984320878983,
0.14928722381591797,
0.0011626558844000101,
-0.048302896320819855,
-0.02410939149558544,
0.1021348237991333,
0.16413529217243195,
-0.005404171533882618,
0.08114749193191528,
-0.06241032853722572,
0.02346855029463768,
0.16374069452285767,
0.02102416753768921,
0.21833470463752747,
0.033659208565950394,
0.10541529953479767,
0.08872426301240921,
0.14730392396450043,
0.017731962725520134,
0.07919324934482574,
0.007557796314358711,
0.03468277305364609,
-0.1056441143155098,
-0.015840090811252594,
-0.07826065272092819,
0.12941700220108032,
-0.03799295425415039,
-0.04366694763302803,
-0.08662641048431396,
-0.048340748995542526,
0.004426433239132166,
0.15866170823574066,
0.039104003459215164,
-0.29822835326194763,
-0.09893851727247238,
0.06070563569664955,
-0.0393308587372303,
-0.021892927587032318,
-0.019970301538705826,
0.05806274712085724,
-0.08504383265972137,
0.11242423951625824,
-0.012919462285935879,
0.055709294974803925,
0.07921852171421051,
0.028099507093429565,
-0.06178716570138931,
-0.06304419785737991,
-0.028559546917676926,
0.10207490622997284,
-0.2383722960948944,
0.31030142307281494,
-0.040809955447912216,
0.029213588684797287,
-0.03830798342823982,
-0.03469625115394592,
-0.007655840367078781,
0.10923565924167633,
0.13347414135932922,
0.01941957324743271,
-0.06533076614141464,
0.011674420908093452,
-0.020629357546567917,
0.02533339522778988,
-0.07899819314479828,
-0.05385313183069229,
0.05924086272716522,
-0.0121354004368186,
0.025893906131386757,
0.03900374099612236,
0.17218469083309174,
-0.09710951149463654,
-0.0901106670498848,
0.04068592190742493,
-0.07064381241798401,
0.02535894326865673,
-0.03651629388332367,
-0.11905009299516678,
-0.050397615879774094,
0.07886196672916412,
0.016060559079051018,
-0.0948638916015625,
-0.13845805823802948,
0.003969703335314989,
0.045537009835243225,
-0.08877475559711456,
0.015197692438960075,
0.033572059124708176,
0.06435719132423401,
-0.033521655946969986,
-0.10425901412963867,
0.08296790719032288,
-0.133310005068779,
-0.11426126956939697,
-0.05439860373735428,
0.053654659539461136,
0.0842074453830719,
0.07411068677902222,
0.0821903795003891,
0.017817500978708267,
-0.05399744212627411,
-0.07332224398851395,
0.012192514725029469,
0.08341805636882782,
0.06306394934654236,
-0.08740919083356857,
-0.048676490783691406,
-0.19026482105255127,
-0.06125456094741821,
-0.03788625821471214,
0.1051270067691803,
0.1980210244655609,
-0.06466980278491974,
0.20385536551475525,
0.15262144804000854,
-0.10266800969839096,
-0.15964412689208984,
-0.03125954791903496,
0.13735058903694153,
-0.032812777906656265,
0.09940314292907715,
-0.15247413516044617,
-0.010795336216688156,
0.07826917618513107,
-0.059783369302749634,
-0.22858116030693054,
-0.16862191259860992,
-0.09346316754817963,
0.09432144463062286,
0.09317681938409805,
0.3188059329986572,
-0.12739646434783936,
-0.02341289632022381,
-0.03643970191478729,
-0.062206048518419266,
0.3084406852722168,
-0.04587689787149429,
0.022283175960183144,
0.04556967690587044,
0.18927763402462006,
0.016746360808610916,
-0.011913437396287918,
0.1868811547756195,
-0.014614365063607693,
0.016475319862365723,
-0.07324985414743423,
-0.026480797678232193,
0.12056317925453186,
-0.041768256574869156,
0.08115822076797485,
0.11518020182847977,
-0.007881797850131989,
-0.07207353413105011,
-0.06552240252494812,
-0.11384259909391403,
0.09182200580835342,
-0.020853886380791664,
-0.10173363983631134,
-0.021702099591493607,
0.05710963159799576,
0.08188194781541824,
-0.06588000059127808,
0.02872942015528679,
-0.008762400597333908,
0.05194234848022461,
0.15907251834869385,
0.22932374477386475,
0.0588836707174778,
0.049095842987298965,
-0.0007654284127056599,
-0.07995663583278656,
0.045287735760211945,
-0.17145907878875732,
-0.021084336563944817,
0.06581254303455353,
-0.02151830494403839,
0.08049096167087555,
0.027300216257572174,
-0.09531722217798233,
0.048950836062431335,
0.11677447706460953,
-0.0694194957613945,
-0.14906726777553558,
-0.04024169594049454,
0.01910099759697914,
-0.10841552913188934,
-0.02611210197210312,
0.14298154413700104,
-0.07648018002510071,
-0.03931594640016556,
-0.02730334922671318,
0.06763485074043274,
-0.03332006186246872,
0.08657437562942505,
0.02780398167669773,
-0.04160192236304283,
-0.08742215484380722,
-0.016574949026107788,
0.06305557489395142,
-0.06493537127971649,
0.06029585376381874,
-0.000013657729141414165,
-0.06021249294281006,
-0.08014975488185883,
-0.06505522131919861,
0.04293743520975113,
-0.28773024678230286,
-0.07822543382644653,
-0.028393376618623734,
-0.10184087604284286,
0.021511996164917946,
0.16245077550411224,
0.04610585793852806,
-0.023027585819363594,
-0.0552392415702343,
0.0021890406496822834,
-0.00618590647354722,
0.03926893323659897,
0.07104028016328812,
0.037359509617090225,
-0.06987933069467545,
0.06956952065229416,
-0.06484098732471466,
0.026531251147389412,
-0.053936220705509186,
0.014090551994740963,
-0.08727812021970749,
-0.023813003674149513,
-0.15483814477920532,
0.05361286550760269,
-0.1339312046766281,
0.0038416306488215923,
-0.0002413329784758389,
-0.10510450601577759,
-0.05709875375032425,
0.010519122704863548,
-0.09883935749530792,
0.07660917192697525,
-0.0263670701533556,
0.1366853266954422,
-0.08750565350055695,
-0.0017247250070795417,
0.048986613750457764,
-0.005789863411337137,
0.0685935840010643,
0.03547615930438042,
-0.023689066991209984,
0.06579950451850891,
-0.23083749413490295,
0.05169356241822243,
0.07179505378007889,
0.019267937168478966,
0.027075733989477158,
-0.1419452279806137,
-0.008498452603816986,
0.09370215237140656,
-0.025053298100829124,
0.015795057639479637,
0.07632669061422348,
-0.060273926705121994,
0.03532577306032181,
0.11465355008840561,
-0.12548469007015228,
-0.038162961602211,
-0.001226633321493864,
0.08348428457975388,
0.04801972955465317,
0.18284747004508972,
-0.018633872270584106,
-0.0077234841883182526,
-0.11227637529373169,
0.0021667361725121737,
-0.028675708919763565,
0.011534481309354305,
-0.14882375299930573,
-0.09246856719255447,
0.011616528034210205,
0.026058252900838852,
0.139411062002182,
0.12829090654850006,
-0.024488506838679314,
0.007859887555241585,
0.10223669558763504,
0.06342773884534836,
0.020208116620779037,
0.20075175166130066,
0.02723490260541439,
0.025556471198797226,
-0.042394693940877914,
0.03464318811893463,
0.011421422474086285,
-0.023918263614177704,
0.1679229885339737,
0.09812098741531372,
0.04149555414915085,
0.04017120227217674,
0.027810072526335716,
-0.005016481503844261,
-0.032927997410297394,
-0.052373167127370834,
-0.021730434149503708,
-0.011558614671230316,
-0.024219250306487083,
0.11340044438838959,
0.14549188315868378,
-0.20446611940860748,
0.04198285937309265,
0.014087524265050888,
-0.03486225754022598,
-0.15392884612083435,
-0.25263354182243347,
-0.05853179469704628,
-0.10579322278499603,
-0.005263029597699642,
-0.1363784372806549,
0.03457077592611313,
0.06302236765623093,
0.0091848848387599,
-0.048503436148166656,
-0.05520714074373245,
-0.09800200164318085,
-0.10491542518138885,
0.02256612479686737,
0.02773132361471653,
0.019923914223909378,
-0.08676537871360779,
0.0021344239357858896,
-0.04426136612892151,
0.10508953034877777,
0.014943379908800125,
0.02875995635986328,
0.04278895631432533,
-0.003874624613672495,
-0.046059079468250275,
-0.052321016788482666,
-0.040500715374946594,
0.023910511285066605,
0.03948717564344406,
0.11355796456336975,
0.01962074264883995,
0.005354136228561401,
0.08063556998968124,
0.23167254030704498,
-0.006536706816405058,
-0.011699254624545574,
-0.17561575770378113,
0.20965984463691711,
-0.02660355158150196,
0.029889121651649475,
0.04960763081908226,
-0.07445205748081207,
0.03576560690999031,
0.1569330394268036,
0.1962049901485443,
0.004117471165955067,
-0.03261440247297287,
-0.08540701866149902,
0.01951017417013645,
0.04367584362626076,
0.11315002292394638,
-0.010062871500849724,
0.24700160324573517,
-0.09952062368392944,
0.09730701893568039,
-0.03530561178922653,
0.060923703014850616,
-0.09469236433506012,
0.1068475991487503,
-0.020540520548820496,
-0.09858650714159012,
-0.03298374265432358,
0.15281562507152557,
-0.029779206961393356,
-0.12366265803575516,
-0.003908541519194841,
-0.06431341171264648,
-0.08221862465143204,
0.009924685582518578,
0.04542350396513939,
0.03712272271513939,
0.08008787035942078,
-0.02739736996591091,
0.003748722840100527,
0.17736192047595978,
0.03638317063450813,
-0.12329261004924774,
0.010774010792374611,
0.22361643612384796,
-0.014351989142596722,
0.15187419950962067,
0.0003120007459074259,
0.029045483097434044,
0.07644251734018326,
0.018282802775502205,
-0.11287765949964523,
0.034927837550640106,
0.0648547112941742,
-0.023138895630836487,
0.06893356144428253,
0.00885028950870037,
-0.03219928592443466,
0.0031402301974594593,
0.06738530099391937,
-0.16597416996955872,
0.02802005596458912,
0.024710670113563538,
-0.020755479112267494,
-0.048767510801553726,
0.1316850334405899,
-0.16177614033222198,
0.10599686205387115,
0.07454397529363632,
-0.05881363898515701,
-0.0015691877342760563,
-0.0703735202550888,
0.04017747938632965,
0.03563553839921951,
-0.009584572166204453,
0.04917134717106819,
-0.16525262594223022,
-0.03615546599030495,
0.04747088626027107,
0.053040556609630585,
-0.20587198436260223,
0.014239221811294556,
-0.07703675329685211,
0.016286250203847885,
-0.07772722840309143,
0.028910940513014793,
0.05475882440805435,
-0.041455719619989395,
-0.00609146011993289,
-0.01063811406493187,
-0.015468122437596321,
0.0838070884346962,
-0.0013285161694511771,
-0.017167070880532265
] |
null | null |
transformers
|
# llama_or_what
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### alpaca
#### guanaco
#### llama
#### vicuna
|
{"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]}
|
image-classification
|
firebolt/llama_or_what
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# llama_or_what
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### alpaca
!alpaca
#### guanaco
!guanaco
#### llama
!llama
#### vicuna
!vicuna
|
[
"# llama_or_what\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### alpaca\n\n!alpaca",
"#### guanaco\n\n!guanaco",
"#### llama\n\n!llama",
"#### vicuna\n\n!vicuna"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# llama_or_what\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### alpaca\n\n!alpaca",
"#### guanaco\n\n!guanaco",
"#### llama\n\n!llama",
"#### vicuna\n\n!vicuna"
] |
[
49,
44,
4,
8,
9,
6,
8
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n# llama_or_what\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.## Example Images#### alpaca\n\n!alpaca#### guanaco\n\n!guanaco#### llama\n\n!llama#### vicuna\n\n!vicuna"
] |
[
-0.08607829362154007,
0.22989317774772644,
-0.0036510638892650604,
0.1193341314792633,
0.21562734246253967,
0.0309975054115057,
0.11858244240283966,
0.1251250058412552,
0.03641373664140701,
0.03825967013835907,
0.12134289741516113,
0.22656948864459991,
0.0362720787525177,
0.08317583799362183,
0.05752447620034218,
-0.29540732502937317,
-0.013753339648246765,
0.03833711892366409,
-0.04965915530920029,
0.08199050277471542,
0.02565723843872547,
-0.10480911284685135,
0.14047430455684662,
-0.005105537828058004,
-0.2003289759159088,
-0.017241913825273514,
-0.04754731431603432,
-0.08036848902702332,
0.08840594440698624,
0.041346460580825806,
0.06779938191175461,
0.0006194390589371324,
0.010713103227317333,
-0.07272420823574066,
0.044828347861766815,
-0.008610311895608902,
-0.0350886806845665,
0.0670265182852745,
0.10125264525413513,
0.0007481859647668898,
0.156455397605896,
0.08555453270673752,
-0.038236718624830246,
0.031993038952350616,
-0.1036849245429039,
-0.09632287174463272,
-0.00762735353782773,
0.02316814288496971,
0.09682343900203705,
0.023344330489635468,
0.004489879589527845,
0.10583493858575821,
-0.033944033086299896,
0.06995240598917007,
0.19026131927967072,
-0.11084578186273575,
-0.11056622117757797,
0.05992875248193741,
0.04871615394949913,
-0.0039175148122012615,
-0.03651345893740654,
0.11302623152732849,
0.05936024710536003,
-0.015462842769920826,
-0.023342585191130638,
-0.08096927404403687,
-0.1508478969335556,
-0.05316375941038132,
-0.09888412803411484,
0.02507297694683075,
0.06362368911504745,
-0.002293097088113427,
0.0058409045450389385,
-0.015537855215370655,
-0.0265281330794096,
0.008381342515349388,
-0.05487397685647011,
0.07319622486829758,
0.03627948835492134,
-0.026437649503350258,
-0.03616844862699509,
-0.059755466878414154,
-0.10574217140674591,
-0.006625224370509386,
-0.05974093824625015,
0.0711771696805954,
0.04823245480656624,
0.039386916905641556,
-0.09617782384157181,
0.05446117743849754,
0.056807778775691986,
-0.0895548164844513,
0.022132229059934616,
-0.025082604959607124,
0.015800902619957924,
-0.013553057797253132,
0.04789288341999054,
-0.039194125682115555,
0.1634233593940735,
0.020237701013684273,
0.05944390594959259,
0.0679238811135292,
0.021490037441253662,
0.05252928286790848,
0.05847081542015076,
0.147389754652977,
-0.11416182667016983,
-0.009580934420228004,
0.06765224784612656,
0.008618134073913097,
0.014390743337571621,
-0.03845745697617531,
-0.1582336127758026,
-0.052002664655447006,
0.015103182755410671,
0.07086184620857239,
0.1060807928442955,
0.04646393284201622,
-0.029468538239598274,
-0.06816205382347107,
-0.033097755163908005,
-0.01635335199534893,
0.023465650156140327,
-0.05867171287536621,
-0.04304232448339462,
0.05098257213830948,
0.07048581540584564,
0.01096775010228157,
-0.0028522927314043045,
-0.0014976103557273746,
-0.07674026489257812,
-0.005405825562775135,
-0.049145884811878204,
0.045416347682476044,
0.03845227137207985,
-0.1460147649049759,
0.009200477041304111,
-0.19530479609966278,
0.013327467255294323,
-0.027009326964616776,
0.08704397827386856,
-0.04973432421684265,
-0.08596517890691757,
-0.005347889382392168,
0.07198972254991531,
-0.06754809617996216,
0.003956390079110861,
-0.0017240806482732296,
-0.033991459757089615,
0.07369855791330338,
0.04898764193058014,
0.13374528288841248,
-0.04991404339671135,
0.02786223217844963,
-0.0755288302898407,
0.051284462213516235,
-0.13194386661052704,
0.06883925944566727,
-0.05016707256436348,
0.2505413889884949,
-0.08664967864751816,
0.012651178054511547,
0.019759275019168854,
0.003240088699385524,
0.0041809589602053165,
0.17184916138648987,
-0.0958101823925972,
-0.0999121442437172,
0.11855129152536392,
-0.07260191440582275,
-0.11525145918130875,
0.10279212892055511,
0.020832248032093048,
0.08372899144887924,
0.0978604406118393,
0.13002163171768188,
0.09426135569810867,
-0.14468199014663696,
0.08064120262861252,
-0.058270394802093506,
-0.07398230582475662,
-0.029011694714426994,
0.010028252378106117,
0.05727311596274376,
-0.11982208490371704,
0.03782451152801514,
-0.037507422268390656,
0.1513238102197647,
-0.06916312128305435,
-0.07741193473339081,
0.008685531094670296,
-0.10903249680995941,
-0.0345093309879303,
0.09793106466531754,
0.030475787818431854,
0.03364715352654457,
-0.043154772371053696,
-0.11820316314697266,
0.04399098455905914,
-0.016100570559501648,
-0.04896954074501991,
-0.07949749380350113,
0.2556858956813812,
-0.04834165424108505,
-0.008810498751699924,
-0.05439649522304535,
-0.07717123627662659,
-0.003402889706194401,
-0.018062269315123558,
0.11170052736997604,
-0.03611128404736519,
0.0419306755065918,
0.013116275891661644,
-0.0030875420197844505,
0.045333683490753174,
0.10569356381893158,
-0.03130197152495384,
-0.0629204735159874,
-0.1067037582397461,
0.09334696829319,
-0.021649088710546494,
0.14099788665771484,
-0.11121922731399536,
-0.024382328614592552,
0.007995729334652424,
0.0769684836268425,
0.02998497523367405,
-0.030318360775709152,
0.06761511415243149,
-0.040101271122694016,
-0.054870348423719406,
-0.053699709475040436,
0.1348726451396942,
-0.025845764204859734,
-0.03615674003958702,
0.09058918803930283,
-0.06996813416481018,
0.028033729642629623,
0.14149877429008484,
-0.1745731383562088,
-0.08422684669494629,
-0.15399488806724548,
-0.0028649833984673023,
0.05335310846567154,
-0.00518807815387845,
0.061686839908361435,
0.04021211713552475,
-0.015454833395779133,
0.12175983190536499,
-0.04182218760251999,
0.03406374156475067,
0.029597574844956398,
-0.08241341263055801,
-0.06620758026838303,
0.0962405577301979,
0.17992208898067474,
-0.06610333174467087,
0.08382578194141388,
0.10079888999462128,
-0.04985059052705765,
0.11600914597511292,
0.06023877486586571,
-0.002130703069269657,
-0.00010497039329493418,
-0.015336696058511734,
0.06294553726911545,
0.09011662006378174,
-0.1399816870689392,
-0.029216919094324112,
0.045754458755254745,
-0.14599676430225372,
0.03632936626672745,
-0.1363695114850998,
-0.03911403939127922,
-0.002830038545653224,
0.010944630019366741,
0.10457546263933182,
0.05896271765232086,
-0.061871759593486786,
0.08942538499832153,
0.009544470347464085,
-0.013625540770590305,
-0.03275945782661438,
0.04864602908492088,
-0.0090695321559906,
0.15707740187644958,
-0.029865803197026253,
-0.3438994884490967,
-0.1019362285733223,
-0.15170766413211823,
-0.0053888531401753426,
0.09347229450941086,
0.04349331557750702,
-0.17983384430408478,
-0.03850045055150986,
0.04792254790663719,
0.05640218034386635,
0.07131913304328918,
0.0399557426571846,
-0.0780758485198021,
0.043635111302137375,
-0.007218852173537016,
-0.03856772184371948,
0.009426175616681576,
-0.019673198461532593,
-0.051674384623765945,
0.11965823918581009,
-0.08647795766592026,
0.123293437063694,
0.14371784031391144,
0.00184567307587713,
0.03331964462995529,
0.02879101224243641,
0.22377508878707886,
-0.13047830760478973,
0.03388053923845291,
0.19429759681224823,
0.0005809638532809913,
0.05812230706214905,
0.12927255034446716,
0.013922512531280518,
-0.10358394682407379,
0.003464390290901065,
0.05905114486813545,
-0.16573375463485718,
-0.09152921289205551,
-0.03361804410815239,
-0.036402732133865356,
0.05325184389948845,
0.14863221347332,
0.05723268538713455,
0.07364776730537415,
0.24164308607578278,
0.034740813076496124,
0.006711873225867748,
-0.05123916640877724,
0.039751145988702774,
0.032164379954338074,
-0.030319662764668465,
0.053246259689331055,
-0.016380826011300087,
-0.12291780859231949,
0.09017123281955719,
0.09393422305583954,
0.14988082647323608,
0.016969438642263412,
-0.013585946522653103,
0.034104373306035995,
0.08281616121530533,
0.03470393270254135,
-0.030015312135219574,
-0.03488851711153984,
-0.017457744106650352,
-0.03267105668783188,
-0.06387723237276077,
-0.058339837938547134,
-0.0047950721345841885,
0.09600675851106644,
-0.11497548967599869,
-0.021691031754016876,
-0.03463420644402504,
0.017078038305044174,
0.13217397034168243,
0.026133138686418533,
-0.3031248450279236,
0.06508985906839371,
-0.0018950230441987514,
0.01940912939608097,
-0.07298395782709122,
-0.011891086585819721,
0.005045966245234013,
-0.08905820548534393,
0.10851005464792252,
-0.08624115586280823,
0.10173990577459335,
-0.12937702238559723,
0.00292117684148252,
0.022541454061865807,
0.0374237559735775,
-0.0003546212974470109,
0.058103982359170914,
-0.07871103286743164,
0.14796313643455505,
-0.013873700983822346,
-0.08412813395261765,
-0.06233123689889908,
-0.009020067751407623,
0.0892828181385994,
0.12919625639915466,
0.12202320247888565,
-0.003879339201375842,
0.09854846447706223,
-0.11929170042276382,
-0.08284024149179459,
0.018989067524671555,
-0.029073452576994896,
-0.07481813430786133,
0.027133721858263016,
-0.000734133820515126,
-0.03942141681909561,
-0.05228571966290474,
0.005688858684152365,
-0.0705842450261116,
-0.14311617612838745,
0.03504185751080513,
0.042204465717077255,
0.01874229498207569,
0.020323017612099648,
-0.049768224358558655,
-0.17578081786632538,
0.07492165267467499,
0.0962047353386879,
-0.06396808475255966,
-0.08424616605043411,
0.05306222289800644,
0.058095403015613556,
-0.0810658410191536,
0.07246691733598709,
-0.13782985508441925,
0.10517790168523788,
0.012597215361893177,
-0.11339353024959564,
0.07858067005872726,
-0.07840641587972641,
-0.037332113832235336,
-0.07180705666542053,
0.02636248990893364,
0.018977971747517586,
-0.028255414217710495,
0.06573919951915741,
-0.005458870902657509,
-0.07398561388254166,
-0.05972256138920784,
0.025737211108207703,
0.02123316377401352,
-0.02852794900536537,
0.014589992351830006,
-0.07099606096744537,
-0.02647307887673378,
-0.10530098527669907,
-0.013466711156070232,
0.1770966351032257,
0.1237863078713417,
-0.05640138313174248,
0.061512529850006104,
0.07340898364782333,
-0.04875332862138748,
-0.2869563400745392,
-0.06508515775203705,
0.059432003647089005,
-0.0252714641392231,
0.0583830326795578,
-0.16730403900146484,
0.1281404346227646,
0.0880376324057579,
-0.04602392390370369,
0.18340060114860535,
-0.1990109533071518,
-0.08709418773651123,
0.07459785789251328,
0.14335505664348602,
0.09933164715766907,
-0.23961932957172394,
-0.06261789053678513,
-0.0810733512043953,
0.006329640280455351,
0.20328308641910553,
-0.0051455432549119,
0.09830600023269653,
-0.027604220435023308,
0.10424888879060745,
0.08485838770866394,
0.002163226017728448,
0.15987184643745422,
-0.06328319758176804,
-0.062272775918245316,
-0.14237111806869507,
-0.1422891616821289,
-0.10354828089475632,
-0.05453231558203697,
-0.03258414566516876,
-0.04920397698879242,
-0.0029744417406618595,
-0.13745565712451935,
-0.019689839333295822,
-0.05032994598150253,
0.11057185381650925,
0.02186892181634903,
-0.024496469646692276,
-0.07827615737915039,
0.045348480343818665,
-0.02871219627559185,
0.07167588174343109,
0.13288824260234833,
-0.10361040383577347,
0.06462009996175766,
0.12326692044734955,
0.025911126285791397,
-0.02498486079275608,
-0.04221285879611969,
-0.0850277990102768,
-0.00880335457623005,
0.08374262601137161,
-0.1540704071521759,
0.05310250446200371,
0.07622963190078735,
-0.007748044561594725,
0.009704538621008396,
0.0007181223481893539,
-0.003154017496854067,
0.07089519500732422,
0.1866828203201294,
-0.040833406150341034,
-0.016651468351483345,
-0.03155875205993652,
0.021480165421962738,
0.014404220506548882,
-0.0030328910797834396,
0.09783975780010223,
0.04912100359797478,
-0.057673778384923935,
0.03837183117866516,
0.00009570377005729824,
0.0009663602686487138,
0.09442833065986633,
0.1285596787929535,
-0.03257840871810913,
-0.10431203246116638,
0.011690348386764526,
0.0653921514749527,
-0.048009905964136124,
-0.053917296230793,
0.09499506652355194,
-0.03043331392109394,
-0.10832826048135757,
0.04207204654812813,
0.08800465613603592,
-0.2452598512172699,
0.00033164830529130995,
-0.012455727905035019,
-0.05200080946087837,
-0.00867954920977354,
-0.016825107857584953,
0.059871915727853775,
-0.040222279727458954,
0.06027675420045853,
0.01982877030968666,
-0.06970266997814178,
0.024763768538832664,
0.047236815094947815,
0.09955296665430069,
-0.2402559518814087,
-0.0052398149855434895,
0.023857736960053444,
0.12411639839410782,
-0.0966419130563736,
-0.062724769115448,
-0.17892692983150482,
0.001975245540961623,
-0.047246094793081284,
0.07970661669969559,
-0.12419509887695312,
0.006877650041133165,
0.0037833438254892826,
-0.055324699729681015,
-0.037906575947999954,
-0.05357833579182625,
-0.12979230284690857,
-0.004663667641580105,
-0.006074720993638039,
0.04393287003040314,
0.0033501037396490574,
-0.06708721071481705,
0.09161321818828583,
-0.020983321592211723,
0.12235891073942184,
-0.0417943112552166,
-0.006777440197765827,
0.005223051179200411,
-0.2878039479255676,
-0.008832428604364395,
0.09889867901802063,
-0.006777741946280003,
0.0181865431368351,
-0.010156982578337193,
0.08556883782148361,
-0.05518881604075432,
0.017503531649708748,
0.014101570472121239,
0.1470973789691925,
-0.12741051614284515,
-0.032814353704452515,
-0.017595365643501282,
-0.15052524209022522,
0.016799114644527435,
0.06728468090295792,
0.04569157585501671,
-0.03462238982319832,
0.08300736546516418,
-0.0716954693198204,
0.0668916255235672,
-0.09123526513576508,
0.033446427434682846,
-0.04269556701183319,
-0.0760418027639389,
-0.03353060409426689,
-0.08606290072202682,
0.008739731274545193,
0.008730879053473473,
0.03996884077787399,
0.135044127702713,
0.06690707802772522,
-0.030887259170413017,
0.12665380537509918,
0.12268853932619095,
0.006125189363956451,
0.10570146888494492,
0.03524286299943924,
0.06620580703020096,
-0.05237139016389847,
0.08010270446538925,
0.07828864455223083,
-0.09922464191913605,
-0.03347776457667351,
-0.006474402733147144,
-0.09688621759414673,
0.04305604472756386,
0.10332651436328888,
0.02789911814033985,
-0.013589203357696533,
0.06538575887680054,
-0.07557171583175659,
0.11021299660205841,
-0.011073275469243526,
0.04784443974494934,
0.11722121387720108,
-0.03173871710896492,
0.024619044736027718,
0.012292634695768356,
-0.0251595638692379,
-0.08690140396356583,
-0.15884844958782196,
-0.06640944629907608,
-0.15028153359889984,
0.048256609588861465,
0.01049214880913496,
-0.052137941122055054,
0.007061521522700787,
-0.009587152861058712,
-0.07561074942350388,
0.1228126585483551,
0.04385479539632797,
-0.10142236948013306,
0.0700240507721901,
0.058583252131938934,
-0.07340990006923676,
0.005967097822576761,
-0.045190583914518356,
-0.07908684760332108,
0.12458418309688568,
-0.04353941231966019,
-0.0013450713595375419,
-0.010555888526141644,
0.0589599609375,
-0.0710364431142807,
-0.046612925827503204,
-0.017711203545331955,
-0.013378011994063854,
0.028353143483400345,
0.004920995328575373,
-0.0721142515540123,
-0.011780780740082264,
-0.010189289227128029,
0.13538382947444916,
-0.0030615907162427902,
0.11907428503036499,
-0.08759775012731552,
0.05503760650753975,
-0.15939520299434662,
0.03217015042901039,
-0.11448778212070465,
-0.00001926403638208285,
-0.021690456196665764,
0.39396458864212036,
0.15724582970142365,
-0.12027449905872345,
-0.03384329378604889,
-0.03311555087566376,
0.018517468124628067,
0.01275045145303011,
0.13937117159366608,
0.02484198659658432,
0.1435777246952057,
-0.04442379251122475,
-0.008345991373062134,
-0.032705288380384445,
0.017874224111437798,
-0.13688893616199493,
-0.03921409696340561,
0.0514850877225399,
0.02106895111501217,
-0.07793276757001877,
0.14035987854003906,
-0.15219788253307343,
-0.03720487654209137,
0.1530701369047165,
-0.14449456334114075,
-0.13573873043060303,
-0.04765550419688225,
0.043685074895620346,
0.04992751404643059,
0.07749248296022415,
-0.05265412852168083,
-0.005815597251057625,
-0.02156323753297329,
0.003698913846164942,
-0.18143180012702942,
-0.10482829064130783,
0.035260118544101715,
-0.04939250275492668,
0.27561381459236145,
-0.06838494539260864,
-0.067241370677948,
0.0829392746090889,
-0.03472865745425224,
-0.08223102241754532,
-0.04860978573560715,
0.008984164334833622,
-0.13628630340099335,
0.08490625768899918,
0.010356010869145393,
-0.006932308431714773,
-0.08010877668857574,
0.04123019427061081,
0.10352973639965057,
-0.0010039469925686717,
-0.015598473139107227,
0.0995025783777237,
-0.05346140265464783,
0.1543680876493454,
-0.14010374248027802,
0.09501372277736664,
0.05880855768918991,
-0.027152985334396362,
-0.05110403150320053,
-0.08152042329311371,
0.0494498535990715,
0.03998911753296852,
-0.060979798436164856,
-0.08878584951162338,
-0.09174561500549316,
-0.0786530151963234,
-0.054835811257362366,
-0.03471779450774193,
-0.13990208506584167,
-0.003143402049317956,
-0.13410785794258118,
-0.0016076372703537345,
-0.04671088233590126,
0.11950536072254181,
0.11307686567306519,
-0.002523354021832347,
0.03187544271349907,
-0.0427335724234581,
-0.005056421738117933,
0.059661708772182465,
-0.09132775664329529,
-0.06120537593960762
] |
null | null |
transformers
|
# llama_or_what2
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### alpaca
#### guanaco
#### llama
#### vicuna
|
{"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]}
|
image-classification
|
firebolt/llama_or_what2
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# llama_or_what2
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### alpaca
!alpaca
#### guanaco
!guanaco
#### llama
!llama
#### vicuna
!vicuna
|
[
"# llama_or_what2\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### alpaca\n\n!alpaca",
"#### guanaco\n\n!guanaco",
"#### llama\n\n!llama",
"#### vicuna\n\n!vicuna"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# llama_or_what2\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### alpaca\n\n!alpaca",
"#### guanaco\n\n!guanaco",
"#### llama\n\n!llama",
"#### vicuna\n\n!vicuna"
] |
[
49,
45,
4,
8,
9,
6,
8
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n# llama_or_what2\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.## Example Images#### alpaca\n\n!alpaca#### guanaco\n\n!guanaco#### llama\n\n!llama#### vicuna\n\n!vicuna"
] |
[
-0.08044975250959396,
0.22313964366912842,
-0.004392124246805906,
0.11824846267700195,
0.2103700488805771,
0.03592701628804207,
0.11266171932220459,
0.12655696272850037,
0.05994877219200134,
0.037109654396772385,
0.11186423152685165,
0.21641650795936584,
0.035604093223810196,
0.05546464025974274,
0.05892955884337425,
-0.30407580733299255,
-0.017179135233163834,
0.031975071877241135,
-0.06134132295846939,
0.08228619396686554,
0.023282941430807114,
-0.10156381130218506,
0.14308743178844452,
-0.0034285569563508034,
-0.18118490278720856,
-0.013137291185557842,
-0.051069118082523346,
-0.08721674978733063,
0.09383584558963776,
0.047058939933776855,
0.07837475836277008,
-0.0028022839687764645,
-0.0012288427678868175,
-0.08752146363258362,
0.044381387531757355,
-0.01067108940333128,
-0.04656554386019707,
0.062173131853342056,
0.10499128699302673,
-0.02546684630215168,
0.14521674811840057,
0.08920592814683914,
-0.04260437935590744,
0.032378438860177994,
-0.11585249751806259,
-0.10436496883630753,
-0.001532172318547964,
0.03520423173904419,
0.08936392515897751,
0.016558904200792313,
0.010192464105784893,
0.10639096051454544,
-0.05082761496305466,
0.07017266005277634,
0.1962691992521286,
-0.11569203436374664,
-0.11538516730070114,
0.06441465765237808,
0.06030222028493881,
0.0021691464353352785,
-0.026020597666502,
0.11286312341690063,
0.05518356338143349,
-0.015725987032055855,
-0.040027737617492676,
-0.08509034663438797,
-0.13112616539001465,
-0.04285039007663727,
-0.10195862501859665,
0.01804916001856327,
0.0665646493434906,
-0.0045705088414251804,
0.01480037160217762,
-0.000060243393818382174,
-0.029778091236948967,
0.004171417560428381,
-0.05273076146841049,
0.07718835026025772,
0.03680959716439247,
-0.014560159295797348,
-0.02391567826271057,
-0.05690934881567955,
-0.11212782561779022,
-0.01546280737966299,
-0.059219568967819214,
0.08580757677555084,
0.04819740355014801,
0.04039374738931656,
-0.10785919427871704,
0.04495582729578018,
0.058875564485788345,
-0.08857329189777374,
0.02630266733467579,
-0.02913597784936428,
0.011442887596786022,
-0.009306310676038265,
0.047662511467933655,
-0.04100560024380684,
0.16833457350730896,
0.008975902572274208,
0.04708044230937958,
0.06587807834148407,
0.024319617077708244,
0.06245824322104454,
0.06283003091812134,
0.14254097640514374,
-0.10657593607902527,
0.0006042748573236167,
0.04092811793088913,
0.007664746139198542,
0.016168296337127686,
-0.03818583860993385,
-0.15992504358291626,
-0.056739047169685364,
0.011329594068229198,
0.08208534121513367,
0.10339051485061646,
0.0426546186208725,
-0.031160147860646248,
-0.06993497163057327,
-0.03209665045142174,
-0.012179202400147915,
0.030079076066613197,
-0.05156450346112251,
-0.03689444065093994,
0.04761531949043274,
0.06429741531610489,
0.01001528836786747,
0.0014982179272919893,
-0.002824730472639203,
-0.06950867176055908,
-0.0017208732897415757,
-0.05594079941511154,
0.03949934244155884,
0.036531053483486176,
-0.13409239053726196,
0.0049192761071026325,
-0.20732074975967407,
0.018258672207593918,
-0.019423557445406914,
0.0889548510313034,
-0.04743093252182007,
-0.07081805914640427,
0.002891669049859047,
0.06318061798810959,
-0.07152755558490753,
0.006993982475250959,
-0.005762504413723946,
-0.042628686875104904,
0.07310807704925537,
0.049401357769966125,
0.14275063574314117,
-0.057561345398426056,
0.025491617619991302,
-0.07690802961587906,
0.06200358271598816,
-0.13137568533420563,
0.07108370214700699,
-0.0575936920940876,
0.2450004369020462,
-0.08667222410440445,
0.014702245593070984,
0.016428066417574883,
0.008315534330904484,
0.004738461691886187,
0.1646702140569687,
-0.1114848330616951,
-0.10073301941156387,
0.13135235011577606,
-0.08045246452093124,
-0.11623701453208923,
0.09641740471124649,
0.026872878894209862,
0.08386257290840149,
0.0990385040640831,
0.15266206860542297,
0.09752411395311356,
-0.1455998569726944,
0.079595647752285,
-0.056168247014284134,
-0.0783914104104042,
-0.024165187031030655,
0.013315466232597828,
0.05330515280365944,
-0.13047060370445251,
0.033705469220876694,
-0.04043488949537277,
0.14860932528972626,
-0.06712625175714493,
-0.08086929470300674,
0.014031791128218174,
-0.10451453924179077,
-0.028780043125152588,
0.09579658508300781,
0.02059469185769558,
0.03419269621372223,
-0.046540744602680206,
-0.11533986032009125,
0.04127197712659836,
-0.01611361838877201,
-0.05028758943080902,
-0.08379040658473969,
0.2725360095500946,
-0.0408385694026947,
-0.00866848323494196,
-0.0568796768784523,
-0.06645658612251282,
-0.0018482549348846078,
-0.018743950873613358,
0.09858078509569168,
-0.030792368575930595,
0.04192810878157616,
0.010144932195544243,
-0.0006649653078056872,
0.037267692387104034,
0.0956319123506546,
-0.03218754380941391,
-0.055815376341342926,
-0.1004064604640007,
0.08895571529865265,
-0.0173500943928957,
0.13434535264968872,
-0.11178730428218842,
-0.02981407381594181,
0.027837852016091347,
0.07737458497285843,
0.03176931291818619,
-0.020742928609251976,
0.06481263041496277,
-0.03820257633924484,
-0.04865865036845207,
-0.049300361424684525,
0.1373792588710785,
-0.02639385312795639,
-0.033868156373500824,
0.10354028642177582,
-0.0717325285077095,
0.008846849203109741,
0.1386192888021469,
-0.17446132004261017,
-0.08196798712015152,
-0.14962899684906006,
-0.004395944532006979,
0.05541316419839859,
0.00691151712089777,
0.06369328498840332,
0.041581060737371445,
-0.016471706330776215,
0.11791063100099564,
-0.043043337762355804,
0.03936456888914108,
0.027153365314006805,
-0.08356320858001709,
-0.06561461836099625,
0.10163815319538116,
0.1764182150363922,
-0.06630587577819824,
0.07498903572559357,
0.10107738524675369,
-0.048811957240104675,
0.12726238369941711,
0.06347492337226868,
0.0025191614404320717,
-0.004717062693089247,
-0.007223142776638269,
0.06726005673408508,
0.09293688088655472,
-0.14905786514282227,
-0.028571685776114464,
0.03932027891278267,
-0.14728589355945587,
0.03839631751179695,
-0.13816893100738525,
-0.04149734601378441,
-0.0027120616286993027,
0.007935856468975544,
0.08802482485771179,
0.05893987417221069,
-0.06303231418132782,
0.08752647787332535,
0.005350156687200069,
-0.020527562126517296,
-0.03592958301305771,
0.042212024331092834,
-0.0005434805061668158,
0.15694347023963928,
-0.025793472304940224,
-0.34973984956741333,
-0.1033451184630394,
-0.15121476352214813,
0.006089644972234964,
0.09244570881128311,
0.04898251220583916,
-0.17801713943481445,
-0.03687449172139168,
0.050666894763708115,
0.06280957907438278,
0.0509055070579052,
0.0278791356831789,
-0.08089221268892288,
0.04957203567028046,
-0.013614678755402565,
-0.03681810572743416,
0.007931621745228767,
-0.02288636937737465,
-0.04897109791636467,
0.12802357971668243,
-0.09680931270122528,
0.12896636128425598,
0.13853693008422852,
0.004979731980711222,
0.03624139726161957,
0.02869189903140068,
0.21805550158023834,
-0.13179850578308105,
0.032707810401916504,
0.1803228259086609,
-0.0039028446190059185,
0.05763848125934601,
0.13495151698589325,
0.01663355901837349,
-0.10100757330656052,
0.0014196282718330622,
0.05984719842672348,
-0.16155819594860077,
-0.10469170659780502,
-0.03145499899983406,
-0.033607203513383865,
0.0527258925139904,
0.14763015508651733,
0.058030810207128525,
0.08136630058288574,
0.24350810050964355,
0.03645675629377365,
0.00851260032504797,
-0.06118907406926155,
0.0355423241853714,
0.03974360227584839,
-0.024225285276770592,
0.044997118413448334,
-0.011919202283024788,
-0.12318430095911026,
0.0883796215057373,
0.1013820692896843,
0.1475275307893753,
0.019160570576786995,
-0.008874503895640373,
0.0407208576798439,
0.07327236235141754,
0.050190068781375885,
-0.044494759291410446,
-0.02495804987847805,
-0.011936505325138569,
-0.03030356392264366,
-0.06795233488082886,
-0.053378619253635406,
-0.0042921253480017185,
0.11302295327186584,
-0.11166339367628098,
-0.016321947798132896,
-0.030482498928904533,
0.016776399686932564,
0.15872542560100555,
0.018351277336478233,
-0.2980118989944458,
0.06336304545402527,
-0.00016787943604867905,
0.0197165347635746,
-0.07132577151060104,
-0.005122065544128418,
0.013760112226009369,
-0.08254863321781158,
0.11361251026391983,
-0.07211825251579285,
0.10220798850059509,
-0.1293308287858963,
0.0048152972012758255,
0.01733517274260521,
0.021481169387698174,
0.0029534422792494297,
0.05862312391400337,
-0.07146705687046051,
0.1472529172897339,
-0.009094209410250187,
-0.08941767364740372,
-0.0529000461101532,
-0.007495031226426363,
0.08533893525600433,
0.14097429811954498,
0.121519073843956,
0.0065420251339674,
0.10294760763645172,
-0.10296045243740082,
-0.07929359376430511,
0.01454161573201418,
-0.030419381335377693,
-0.08762969821691513,
0.023207401856780052,
0.0009893105598166585,
-0.046357039362192154,
-0.057875603437423706,
0.0026043029502034187,
-0.08218741416931152,
-0.1292913854122162,
0.033274292945861816,
0.0519920289516449,
0.020636506378650665,
0.01300935447216034,
-0.0469425693154335,
-0.17026464641094208,
0.0802379846572876,
0.09932783246040344,
-0.05980348587036133,
-0.07608732581138611,
0.04817016422748566,
0.05937360227108002,
-0.08225809037685394,
0.06324828416109085,
-0.136432945728302,
0.0975022166967392,
0.009493077173829079,
-0.10914032906293869,
0.08818379044532776,
-0.07763266563415527,
-0.03713126853108406,
-0.07207141071557999,
0.03475373238325119,
0.016755923628807068,
-0.02342098578810692,
0.06479954719543457,
-0.004600170999765396,
-0.07755530625581741,
-0.05840504541993141,
0.028873177245259285,
0.027627618983387947,
-0.04072234407067299,
0.02272069826722145,
-0.06629788875579834,
-0.03438403457403183,
-0.10218881815671921,
-0.006271903403103352,
0.18219026923179626,
0.12786217033863068,
-0.05910655856132507,
0.05448194593191147,
0.0793829932808876,
-0.05125412344932556,
-0.28650546073913574,
-0.07094048708677292,
0.06516051292419434,
-0.029559031128883362,
0.05856984481215477,
-0.1686679720878601,
0.12181494385004044,
0.09180264174938202,
-0.04927418380975723,
0.1874408721923828,
-0.21730680763721466,
-0.07716305553913116,
0.06052762269973755,
0.141061931848526,
0.09943240135908127,
-0.24649354815483093,
-0.07340661436319351,
-0.08279518783092499,
-0.011128470301628113,
0.2018115222454071,
0.00018756403005681932,
0.09575799852609634,
-0.03777778148651123,
0.09536837786436081,
0.0790666714310646,
-0.00015009561320766807,
0.16397200524806976,
-0.05841650441288948,
-0.06301537156105042,
-0.14168930053710938,
-0.1532842367887497,
-0.08886607736349106,
-0.05236589536070824,
-0.04649203643202782,
-0.05085201561450958,
-0.0034836414270102978,
-0.13014018535614014,
-0.017396559938788414,
-0.056720469146966934,
0.11313646286725998,
0.02174249291419983,
-0.018139054998755455,
-0.07610557228326797,
0.04474074766039848,
-0.03402111306786537,
0.08389464765787125,
0.12968778610229492,
-0.11613747477531433,
0.06683985143899918,
0.11473428457975388,
0.01722601056098938,
-0.026149464771151543,
-0.053321100771427155,
-0.08249274641275406,
-0.009436841122806072,
0.09558367729187012,
-0.15021947026252747,
0.05433650314807892,
0.06904496997594833,
-0.004410644061863422,
0.011098816059529781,
0.004783090204000473,
-0.014753730967640877,
0.07658156007528305,
0.1844143122434616,
-0.04488286003470421,
-0.030957795679569244,
-0.042161211371421814,
0.03155824914574623,
0.024473782628774643,
-0.0077907913364470005,
0.09919638931751251,
0.05927528813481331,
-0.059100233018398285,
0.03399445116519928,
-0.007275515701621771,
0.0029629499185830355,
0.07727602124214172,
0.13429705798625946,
-0.034695133566856384,
-0.09816762804985046,
0.009842772968113422,
0.07307054847478867,
-0.04811771214008331,
-0.06131701543927193,
0.10053888708353043,
-0.03203801065683365,
-0.12121250480413437,
0.027513455599546432,
0.08724360167980194,
-0.233650803565979,
-0.013281455263495445,
-0.0038706548511981964,
-0.04693296551704407,
-0.01234036311507225,
-0.004278805572539568,
0.06446149200201035,
-0.04481986537575722,
0.06166640296578407,
0.019701657816767693,
-0.05654911696910858,
0.018497729673981667,
0.03157660365104675,
0.10877187550067902,
-0.23043259978294373,
-0.021318435668945312,
0.02500571683049202,
0.11907113343477249,
-0.09093128144741058,
-0.07295281440019608,
-0.18319061398506165,
0.008530382998287678,
-0.053294580429792404,
0.09229203313589096,
-0.12690585851669312,
0.0063690971583127975,
0.003988923970609903,
-0.054688189178705215,
-0.04187610745429993,
-0.049717873334884644,
-0.12998302280902863,
-0.0064499154686927795,
-0.006793259643018246,
0.041736356914043427,
-0.009448078460991383,
-0.06847986578941345,
0.09189985692501068,
-0.020930519327521324,
0.12153267860412598,
-0.03880568593740463,
-0.0037008316721767187,
0.009420689195394516,
-0.2897805869579315,
-0.013610034249722958,
0.10313942283391953,
-0.005057601723819971,
0.024143848568201065,
-0.0030895890668034554,
0.08753057569265366,
-0.05313015729188919,
0.011092147789895535,
0.013483628630638123,
0.15095141530036926,
-0.12586702406406403,
-0.03203761577606201,
-0.008743172511458397,
-0.16176779568195343,
0.019554782658815384,
0.07180920988321304,
0.04317733272910118,
-0.03433654084801674,
0.07537928968667984,
-0.06764459609985352,
0.06331776082515717,
-0.08286213874816895,
0.03365086391568184,
-0.04467712342739105,
-0.06762160360813141,
-0.025770245119929314,
-0.08044569194316864,
0.004620599560439587,
-0.004070297349244356,
0.04162561148405075,
0.12709681689739227,
0.07584375888109207,
-0.039459098130464554,
0.11854499578475952,
0.12088808417320251,
0.008377713151276112,
0.10202695429325104,
0.03610832244157791,
0.06490147858858109,
-0.04960709437727928,
0.06911836564540863,
0.08390601724386215,
-0.0918952226638794,
-0.03434852883219719,
-0.0033213209826499224,
-0.09171464294195175,
0.03914621099829674,
0.11236854642629623,
0.03791477903723717,
-0.014348894357681274,
0.06181223690509796,
-0.07981472462415695,
0.10855092853307724,
-0.011047963984310627,
0.03240417316555977,
0.11628364771604538,
-0.02652025781571865,
0.028805647045373917,
0.0034512104466557503,
-0.02324846386909485,
-0.08649179339408875,
-0.15867121517658234,
-0.06747089326381683,
-0.14913925528526306,
0.04778233543038368,
0.01493767648935318,
-0.05718864127993584,
0.0072492193430662155,
-0.01619400829076767,
-0.07232002168893814,
0.12512925267219543,
0.06114453077316284,
-0.10633186995983124,
0.06729539483785629,
0.05201200768351555,
-0.06684373319149017,
0.0046233213506639,
-0.03800636902451515,
-0.07542101293802261,
0.1206568107008934,
-0.05093562230467796,
0.008644131943583488,
-0.013511479832231998,
0.054357994347810745,
-0.06943058967590332,
-0.0440802238881588,
-0.016805360093712807,
-0.011799999512732029,
0.003910065162926912,
-0.012070228345692158,
-0.05612834170460701,
-0.0021405210718512535,
-0.0061248173005878925,
0.13729625940322876,
-0.001974119571968913,
0.11329244822263718,
-0.08579441159963608,
0.06295548379421234,
-0.16392755508422852,
0.03797326236963272,
-0.11096245050430298,
-0.00876533892005682,
-0.02259160950779915,
0.38034382462501526,
0.16144323348999023,
-0.11846670508384705,
-0.026906756684184074,
-0.021463852375745773,
0.01825227588415146,
0.01751558855175972,
0.1284511834383011,
0.024139270186424255,
0.14029370248317719,
-0.04568132013082504,
-0.006626914255321026,
-0.04207875207066536,
0.021911481395363808,
-0.13542960584163666,
-0.03972311690449715,
0.04852930083870888,
0.012556418776512146,
-0.07850178331136703,
0.1481258124113083,
-0.15113285183906555,
-0.028399677947163582,
0.1587011069059372,
-0.1510428786277771,
-0.13027715682983398,
-0.047250885516405106,
0.05369994789361954,
0.05084940791130066,
0.08484590798616409,
-0.061610691249370575,
-0.023723343387246132,
-0.025898588821291924,
0.010636295191943645,
-0.19040827453136444,
-0.11342280358076096,
0.03729451820254326,
-0.050890564918518066,
0.2715279161930084,
-0.06864859908819199,
-0.062106117606163025,
0.08147413283586502,
-0.025070270523428917,
-0.06697487086057663,
-0.04609159752726555,
0.019746366888284683,
-0.12841716408729553,
0.0729086697101593,
0.005410668905824423,
-0.008421155624091625,
-0.07188984751701355,
0.04974998161196709,
0.11076881736516953,
0.002822455484420061,
0.0004798147128894925,
0.10988302528858185,
-0.05991147831082344,
0.1446991115808487,
-0.14442358911037445,
0.08431362360715866,
0.054884664714336395,
-0.021728476509451866,
-0.050707124173641205,
-0.0774928405880928,
0.04696725308895111,
0.03650043532252312,
-0.06114514172077179,
-0.09707331657409668,
-0.07203581184148788,
-0.08224158734083176,
-0.051147639751434326,
-0.012386703863739967,
-0.13341024518013,
-0.0009594457224011421,
-0.1328509896993637,
0.0021622423082590103,
-0.042972736060619354,
0.10969655215740204,
0.1148260161280632,
0.0013443415518850088,
0.029878811910748482,
-0.03683354705572128,
-0.0016487514367327094,
0.05987459048628807,
-0.09798038750886917,
-0.06466756016016006
] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 310939
## Validation Metrics
- Loss: 0.027471264824271202
- Accuracy: 0.9931118314424635
- Precision: 0.946236559139785
- Recall: 0.88
- AUC: 0.9952871621621622
- F1: 0.911917098445596
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/fjarrett/autonlp-giveaway_detection_05-310939
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("fjarrett/autonlp-giveaway_detection_05-310939", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("fjarrett/autonlp-giveaway_detection_05-310939", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "en", "tags": ["autonlp"], "datasets": ["fjarrett/autonlp-data-giveaway_detection_05"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]}
|
text-classification
|
popsmash-admin/autonlp-giveaway_detection_05-310939
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:fjarrett/autonlp-data-giveaway_detection_05",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-fjarrett/autonlp-data-giveaway_detection_05 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 310939
## Validation Metrics
- Loss: 0.027471264824271202
- Accuracy: 0.9931118314424635
- Precision: 0.946236559139785
- Recall: 0.88
- AUC: 0.9952871621621622
- F1: 0.911917098445596
## Usage
You can use cURL to access this model:
Or Python API:
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 310939",
"## Validation Metrics\n\n- Loss: 0.027471264824271202\n- Accuracy: 0.9931118314424635\n- Precision: 0.946236559139785\n- Recall: 0.88\n- AUC: 0.9952871621621622\n- F1: 0.911917098445596",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-fjarrett/autonlp-data-giveaway_detection_05 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 310939",
"## Validation Metrics\n\n- Loss: 0.027471264824271202\n- Accuracy: 0.9931118314424635\n- Precision: 0.946236559139785\n- Recall: 0.88\n- AUC: 0.9952871621621622\n- F1: 0.911917098445596",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
65,
24,
74,
17
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #text-classification #autonlp #en #dataset-fjarrett/autonlp-data-giveaway_detection_05 #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 310939## Validation Metrics\n\n- Loss: 0.027471264824271202\n- Accuracy: 0.9931118314424635\n- Precision: 0.946236559139785\n- Recall: 0.88\n- AUC: 0.9952871621621622\n- F1: 0.911917098445596## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
-0.1388847976922989,
0.18178927898406982,
-0.000622284016571939,
0.10893894731998444,
0.1403890699148178,
0.019678594544529915,
0.026009878143668175,
0.09965254366397858,
0.023998456075787544,
0.06030210480093956,
0.16557928919792175,
0.17861880362033844,
0.03890397772192955,
0.16984885931015015,
-0.16938842833042145,
-0.14821012318134308,
0.053231578320264816,
0.050351254642009735,
0.12893332540988922,
0.11684218049049377,
0.07760190218687057,
-0.07838145643472672,
0.12588605284690857,
0.020510803908109665,
-0.14907720685005188,
-0.009317158721387386,
0.06480486690998077,
-0.12482049316167831,
0.0940590649843216,
0.07432742416858673,
0.11745259910821915,
-0.0047470503486692905,
0.08480551838874817,
-0.0846962034702301,
-0.027209866791963577,
-0.011057224124670029,
-0.021547608077526093,
0.0692470371723175,
0.026953378692269325,
-0.0729808434844017,
0.03687983378767967,
0.010178538970649242,
0.08662049472332001,
0.03880957514047623,
-0.11094605922698975,
-0.12500721216201782,
-0.05515699461102486,
0.08373788744211197,
0.11400561034679413,
0.10806184262037277,
-0.01054596621543169,
0.23330210149288177,
-0.031123679131269455,
0.0933600515127182,
-0.02776186726987362,
-0.28086695075035095,
-0.022075971588492393,
0.08362807333469391,
-0.07397201657295227,
-0.0900057703256607,
-0.014274189248681068,
-0.006261687725782394,
0.08652178198099136,
0.045169513672590256,
0.028000518679618835,
-0.03689916059374809,
-0.08430095762014389,
-0.021674280986189842,
-0.12006519734859467,
-0.0749807208776474,
0.24251601099967957,
0.01477656327188015,
-0.0948542132973671,
-0.019466601312160492,
-0.062449220567941666,
-0.08506230264902115,
-0.06663230806589127,
-0.015659309923648834,
-0.06507790833711624,
-0.04030036926269531,
-0.029215192422270775,
0.09136145561933517,
-0.07001347839832306,
-0.06788919121026993,
-0.1744959056377411,
0.10249005258083344,
0.005444551818072796,
0.09231817722320557,
-0.034511808305978775,
0.07351141422986984,
-0.08229387551546097,
-0.07319698482751846,
-0.018397323787212372,
-0.018698114901781082,
-0.06112128496170044,
-0.09374812245368958,
-0.050388168543577194,
0.02710323967039585,
-0.0037639697548002005,
0.19663113355636597,
0.044002220034599304,
0.05214095488190651,
-0.02104957215487957,
-0.014963344670832157,
0.007669159211218357,
0.15009979903697968,
-0.11886487156152725,
0.03723277151584625,
0.034799449145793915,
-0.03690043091773987,
0.017405131831765175,
-0.0418652705848217,
-0.08261432498693466,
-0.09614215046167374,
0.17449241876602173,
0.020105836912989616,
0.016270151361823082,
0.0794149711728096,
-0.06366738677024841,
-0.06179459020495415,
0.0590890608727932,
-0.07470102608203888,
0.024546369910240173,
0.0363963358104229,
-0.11269672960042953,
0.03234838321805,
0.07674036920070648,
0.035839635878801346,
-0.08288215100765228,
0.037442341446876526,
-0.0943218320608139,
0.020171934738755226,
-0.008366182446479797,
-0.12123093754053116,
0.03137553855776787,
-0.01960156112909317,
0.02575710043311119,
-0.19083340466022491,
-0.22218218445777893,
-0.024283090606331825,
0.006524980068206787,
-0.0711078867316246,
-0.0343339741230011,
-0.05659733712673187,
-0.03127768263220787,
0.051649633795022964,
-0.0037593699526041746,
-0.011659389361739159,
-0.04281577095389366,
0.04398983716964722,
0.06108831986784935,
0.0790930762887001,
-0.1349959522485733,
0.04470202326774597,
-0.09147270768880844,
0.009633507579565048,
-0.06978312134742737,
0.08020161092281342,
-0.037245359271764755,
0.08540547639131546,
-0.14720626175403595,
-0.050869207829236984,
0.1049928143620491,
-0.036376480013132095,
0.04047292098402977,
0.16422004997730255,
-0.08095653355121613,
-0.0007476135506294668,
0.07515130192041397,
-0.0525863915681839,
-0.09307749569416046,
0.10421207547187805,
-0.060749735683202744,
-0.012610253877937794,
0.08858354389667511,
0.02342177741229534,
0.12448520958423615,
-0.047815918922424316,
-0.036198899149894714,
0.02249123901128769,
-0.06720373779535294,
-0.12246545404195786,
0.07036244124174118,
0.0316670797765255,
-0.24652284383773804,
0.04138987138867378,
0.019192993640899658,
0.023795265704393387,
-0.05665597692131996,
-0.10293105244636536,
-0.036371584981679916,
-0.0750388354063034,
0.05509472265839577,
0.018496667966246605,
0.1041075810790062,
-0.011848343536257744,
-0.03034437820315361,
-0.01609146222472191,
0.12930288910865784,
0.007368183694779873,
-0.05434722453355789,
-0.16855014860630035,
0.13803111016750336,
-0.14046427607536316,
-0.032722391188144684,
-0.1986168473958969,
0.01589629240334034,
-0.014885648153722286,
0.0730457529425621,
-0.04021497070789337,
-0.09204287827014923,
0.0712311863899231,
0.017880398780107498,
0.03642282634973526,
0.008700148202478886,
0.1699357032775879,
-0.0022884169593453407,
-0.10956181585788727,
-0.027149874716997147,
-0.019132765009999275,
-0.02524416148662567,
0.24153219163417816,
-0.17991654574871063,
-0.015360378660261631,
0.00026536660152487457,
0.0805370956659317,
-0.013325369916856289,
0.011558158323168755,
-0.004589456599205732,
0.04520877078175545,
-0.03175940737128258,
-0.00946655310690403,
0.04100004956126213,
-0.01372985728085041,
-0.10350292176008224,
0.0010484566446393728,
-0.1663590520620346,
0.16991186141967773,
0.14787447452545166,
-0.057827796787023544,
-0.09532412886619568,
0.030548032373189926,
0.019735174253582954,
0.013354557566344738,
-0.0017125728772953153,
0.02754763886332512,
0.11894816905260086,
0.014690164476633072,
0.15654216706752777,
-0.02938172034919262,
-0.005674812011420727,
0.05837158113718033,
-0.06943602859973907,
-0.0661061629652977,
0.1565953642129898,
0.007345663383603096,
-0.20439936220645905,
0.09376310557126999,
0.008932393975555897,
-0.10369890183210373,
0.012379449792206287,
0.017534999176859856,
-0.029398906975984573,
-0.025314735248684883,
-0.007349981926381588,
0.04647005721926689,
0.034736502915620804,
-0.026874667033553123,
0.021363357082009315,
0.08945411443710327,
-0.023464476689696312,
-0.00874734204262495,
-0.09423233568668365,
-0.000040577302570454776,
0.025964662432670593,
0.018232915550470352,
-0.07216884195804596,
-0.012650486081838608,
0.025322994217276573,
0.12952947616577148,
0.051919348537921906,
-0.13902418315410614,
0.07396449148654938,
0.022429225966334343,
-0.1379786729812622,
0.2440234124660492,
-0.09058703482151031,
-0.21211951971054077,
-0.16165468096733093,
-0.16414384543895721,
-0.057562243193387985,
0.020798157900571823,
-0.013664411380887032,
-0.04546324536204338,
-0.07793235033750534,
-0.05336059629917145,
-0.09406376630067825,
-0.031363360583782196,
-0.002853354439139366,
0.012639518827199936,
-0.032949015498161316,
0.09267041087150574,
-0.046634916216135025,
-0.0482851006090641,
-0.04483865574002266,
-0.0530351921916008,
0.1381162852048874,
-0.07351693511009216,
0.09213444590568542,
0.17201071977615356,
-0.034261852502822876,
0.041721899062395096,
0.015809152275323868,
0.2287328988313675,
-0.002854175865650177,
-0.06865110993385315,
0.14258810877799988,
-0.026702888309955597,
0.01897217519581318,
0.14573347568511963,
0.009584752842783928,
-0.09208644181489944,
0.012399284169077873,
-0.019877981394529343,
-0.03770811855792999,
-0.12995275855064392,
-0.19064438343048096,
0.004652226343750954,
0.03181818872690201,
0.17896893620491028,
0.025349076837301254,
0.10323766618967056,
0.13239368796348572,
0.04630037397146225,
0.11762349307537079,
-0.057605527341365814,
0.07472653687000275,
0.19060128927230835,
0.023895788937807083,
0.14962713420391083,
-0.044669259339571,
-0.08627276122570038,
0.0670812577009201,
-0.032715220004320145,
0.09635446220636368,
0.027983851730823517,
-0.06007478013634682,
-0.0366232730448246,
0.13321352005004883,
0.06058638170361519,
0.12017742544412613,
0.04526948556303978,
-0.032083190977573395,
0.02303726226091385,
-0.02690044604241848,
-0.11393003910779953,
0.023479994386434555,
0.02201463095843792,
0.05284493416547775,
-0.13985513150691986,
-0.03790343180298805,
-0.015354637056589127,
0.08356377482414246,
0.19075387716293335,
-0.5062791109085083,
-0.1007205918431282,
0.015908818691968918,
-0.026437869295477867,
-0.13472279906272888,
-0.006239691283553839,
-0.11088796705007553,
-0.1398041695356369,
0.041399918496608734,
-0.04158283397555351,
0.10908947139978409,
-0.1096590906381607,
0.0059156822971999645,
-0.07446663081645966,
-0.03345083072781563,
-0.02565491758286953,
0.06822095811367035,
-0.2449377477169037,
0.22670961916446686,
0.040501195937395096,
0.05588298290967941,
-0.092629574239254,
-0.014401095919311047,
0.05071742460131645,
0.10002028197050095,
0.14461542665958405,
-0.009004287421703339,
0.006698363460600376,
-0.3096696734428406,
-0.12441196292638779,
0.02998950332403183,
-0.06445889174938202,
0.01061679795384407,
0.07877055555582047,
-0.0009019547724165022,
0.004189844708889723,
-0.009636546485126019,
-0.017507493495941162,
-0.07498496025800705,
-0.047123756259679794,
0.00824756734073162,
0.10644739121198654,
-0.003635712433606386,
0.014260717667639256,
-0.06100507825613022,
-0.013325692154467106,
0.04739174246788025,
-0.016075635328888893,
-0.0680767223238945,
-0.14469611644744873,
0.07117082923650742,
0.08534758538007736,
-0.10334864258766174,
0.04952741786837578,
-0.046704746782779694,
0.03580065071582794,
0.019987843930721283,
-0.1488039642572403,
0.08481809496879578,
-0.07417012006044388,
-0.025642627850174904,
0.018596835434436798,
0.03129536658525467,
0.037759069353342056,
0.02567930705845356,
0.07529959827661514,
0.0567588284611702,
-0.06010757386684418,
-0.08555300533771515,
0.03839423879981041,
0.03932543843984604,
0.1025984063744545,
0.11628657579421997,
0.03645583242177963,
-0.2007770985364914,
-0.08157426863908768,
0.0997743085026741,
0.1906900256872177,
0.15017074346542358,
-0.07762759923934937,
-0.010884767398238182,
0.0684339851140976,
0.014055524952709675,
-0.24670915305614471,
-0.024518294259905815,
-0.004692325368523598,
0.0529494434595108,
-0.09945329278707504,
-0.07726819068193436,
0.10606493055820465,
0.059828680008649826,
-0.059787094593048096,
0.010223674587905407,
-0.19468307495117188,
-0.082284115254879,
0.32547634840011597,
0.05297934636473656,
0.21040301024913788,
-0.08033113926649094,
-0.048749715089797974,
-0.1113208681344986,
-0.2100934088230133,
0.14684246480464935,
0.019994741305708885,
0.06525162607431412,
-0.06647688150405884,
0.1381651908159256,
0.062128424644470215,
-0.05178334191441536,
0.1252489984035492,
0.030256113037467003,
0.04538845270872116,
-0.07571851462125778,
-0.07490033656358719,
-0.08427837491035461,
-0.0641198605298996,
0.1537846177816391,
0.04395593702793121,
0.08427440375089645,
-0.2427312582731247,
-0.04573163762688637,
-0.03945348411798477,
0.10143675655126572,
0.015192201361060143,
-0.02696768008172512,
-0.0011248530354350805,
-0.002739453222602606,
-0.005056194961071014,
-0.03788270056247711,
-0.016498522832989693,
0.00017049122834578156,
0.06963518261909485,
0.1725769191980362,
0.10251287370920181,
-0.01199502032250166,
0.04079406335949898,
0.030548961833119392,
-0.06741481274366379,
0.10556434094905853,
-0.1415320634841919,
0.09471695125102997,
0.12360823154449463,
-0.0018142522312700748,
0.12448757886886597,
0.07307818531990051,
-0.02903958596289158,
0.0012817843817174435,
0.05158177763223648,
-0.14939862489700317,
0.033915337175130844,
-0.01014312356710434,
-0.008754190057516098,
-0.033679667860269547,
0.03774530813097954,
0.13592952489852905,
-0.06742841750383377,
-0.03313027322292328,
-0.010758965276181698,
0.010104609653353691,
-0.0359371080994606,
0.18780969083309174,
0.06316134333610535,
0.04697791114449501,
-0.1404116302728653,
0.05043430253863335,
0.06795240938663483,
-0.02356232888996601,
0.0732019916176796,
-0.041889604181051254,
-0.13926227390766144,
-0.10146185010671616,
-0.049323536455631256,
0.15375137329101562,
-0.2135242074728012,
-0.06897448003292084,
-0.022657601162791252,
-0.09757159650325775,
0.08821436017751694,
0.16293485462665558,
0.11138414591550827,
0.047731511294841766,
-0.03744984045624733,
-0.11242866516113281,
-0.1271516978740692,
0.044748712331056595,
0.1095256581902504,
0.0018963583279401064,
-0.16205507516860962,
0.12112115323543549,
-0.056185368448495865,
0.05863061174750328,
-0.04615714028477669,
-0.0332036130130291,
-0.1677335649728775,
0.019378185272216797,
-0.18349534273147583,
0.04065476730465889,
-0.08209966123104095,
0.034864816814661026,
0.033967260271310806,
-0.003291562432423234,
-0.05185236409306526,
0.013556672260165215,
-0.08109288662672043,
-0.019888071343302727,
0.02819470688700676,
0.024346400052309036,
-0.08128157258033752,
-0.07085460424423218,
0.048105619847774506,
-0.005122918635606766,
0.07301612198352814,
0.15759456157684326,
0.006464530248194933,
0.047517627477645874,
-0.05894887074828148,
-0.05341299623250961,
0.07981174439191818,
0.030822496861219406,
0.10052558034658432,
-0.1228392943739891,
0.05461005121469498,
0.06808623671531677,
-0.005984064191579819,
0.08121362328529358,
0.1590067595243454,
-0.08430812507867813,
-0.0590764619410038,
-0.059529468417167664,
-0.045631106942892075,
-0.10146719217300415,
0.05204518139362335,
0.10744937509298325,
0.06120268628001213,
0.12436079233884811,
-0.08372439444065094,
0.03922949731349945,
-0.11693034321069717,
-0.013223010115325451,
-0.06791482120752335,
-0.08284811675548553,
-0.09872335940599442,
-0.018834546208381653,
0.07249915599822998,
-0.00461895577609539,
0.07874554395675659,
-0.014997655525803566,
0.04538683220744133,
-0.0030059916898608208,
0.09283356368541718,
0.037097103893756866,
-0.009038131684064865,
0.2134917825460434,
0.10418493300676346,
-0.04952121153473854,
0.05906863510608673,
0.09456643462181091,
0.06677725911140442,
0.04291326180100441,
-0.029467254877090454,
0.05238410830497742,
-0.07569652795791626,
0.08647329360246658,
0.02426033467054367,
-0.05670861154794693,
-0.012038166634738445,
-0.042390260845422745,
-0.16281254589557648,
0.06819360703229904,
0.019875477999448776,
0.03046472556889057,
0.08333931863307953,
-0.05067339912056923,
-0.02163686975836754,
-0.08071301132440567,
-0.08713621646165848,
-0.18062111735343933,
-0.12188227474689484,
-0.14138449728488922,
-0.09092055261135101,
0.0011016250355169177,
-0.07163843512535095,
-0.06096461042761803,
0.12593728303909302,
0.03573323413729668,
-0.026768308132886887,
0.05913596972823143,
-0.10498746484518051,
0.004725135862827301,
0.0523347407579422,
-0.03008529730141163,
-0.04548506438732147,
-0.013444495387375355,
0.014507859945297241,
0.021507347002625465,
0.011008142493665218,
0.0681149885058403,
-0.02098391391336918,
0.018354138359427452,
0.10735628753900528,
-0.027417752891778946,
-0.1046605035662651,
-0.015395356342196465,
0.04308689385652542,
0.03597159683704376,
0.07279125601053238,
0.04604174941778183,
0.040075261145830154,
-0.016424262896180153,
0.2320215404033661,
-0.08033934235572815,
0.008803464472293854,
-0.18171627819538116,
0.36553335189819336,
0.003383197356015444,
0.05746571347117424,
0.01496636588126421,
-0.05165933817625046,
0.021872010082006454,
0.1936279833316803,
0.11808314174413681,
-0.05543598532676697,
-0.009444264695048332,
-0.02215546742081642,
-0.01592792570590973,
0.013045433908700943,
0.054387737065553665,
0.061971910297870636,
0.14994969964027405,
-0.08097488433122635,
-0.003217778168618679,
-0.01842927187681198,
-0.019320877268910408,
-0.02685093693435192,
0.04124192148447037,
0.008700734935700893,
-0.0008019452216103673,
-0.05459720268845558,
0.11114024370908737,
-0.11901038140058517,
0.11753297597169876,
0.028228038921952248,
-0.0750596895813942,
-0.1266428679227829,
-0.00005934475484536961,
-0.09820003807544708,
0.03131755441427231,
0.09361887723207474,
-0.07906023412942886,
0.003041115589439869,
0.033681124448776245,
-0.007882256992161274,
-0.15733776986598969,
-0.06925080716609955,
0.05269784480333328,
0.15730704367160797,
0.1870989203453064,
0.014545583166182041,
0.1370580941438675,
0.13483691215515137,
0.015643145889043808,
-0.11667587608098984,
0.0713382437825203,
-0.016169512644410133,
-0.010549354366958141,
0.10819321125745773,
0.04747769609093666,
0.010043461807072163,
0.0465095229446888,
0.04780450835824013,
-0.18148089945316315,
-0.010907748714089394,
-0.08445615321397781,
0.044585905969142914,
-0.08963204175233841,
0.00705873966217041,
-0.05358980968594551,
0.10949382185935974,
0.15692941844463348,
-0.06874130666255951,
-0.00771793769672513,
-0.03942091017961502,
0.04724102467298508,
-0.002465910278260708,
-0.08322898298501968,
-0.02965802326798439,
-0.1323944628238678,
0.03992665559053421,
-0.08576119691133499,
-0.0042330180294811726,
-0.25823667645454407,
-0.0034731875639408827,
-0.05937361344695091,
-0.0747063010931015,
-0.008390719071030617,
0.09005814045667648,
0.056535881012678146,
0.029140867292881012,
-0.06440148502588272,
0.005699203349649906,
-0.007395932916551828,
0.11556467413902283,
-0.04172822833061218,
-0.13552416861057281
] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2157
- Accuracy: 0.9173
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1125 | 1.0 | 13 | 0.2066 | 0.9165 |
| 0.0186 | 2.0 | 26 | 0.2157 | 0.9173 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-bne-finetuned-amazon_reviews_multi", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_reviews_multi", "type": "amazon_reviews_multi", "args": "es"}, "metrics": [{"type": "accuracy", "value": 0.91725, "name": "Accuracy"}]}]}]}
|
text-classification
|
fjluque/roberta-base-bne-finetuned-amazon_reviews_multi
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
roberta-base-bne-finetuned-amazon\_reviews\_multi
=================================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2157
* Accuracy: 0.9173
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
71,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
-0.08748505264520645,
0.10135051608085632,
-0.002402752870693803,
0.12601561844348907,
0.1706485152244568,
0.03687635064125061,
0.1622617095708847,
0.11569225788116455,
-0.08599758893251419,
-0.005865089595317841,
0.11542874574661255,
0.1548892855644226,
0.02126530557870865,
0.12217552959918976,
-0.05617023631930351,
-0.24667197465896606,
-0.0027746891137212515,
0.031354084610939026,
-0.04765567183494568,
0.14976318180561066,
0.10777784883975983,
-0.12571990489959717,
0.10260860621929169,
0.006307870149612427,
-0.16979162395000458,
-0.014389348216354847,
0.024533210322260857,
-0.0696016252040863,
0.13328847289085388,
0.02715180069208145,
0.12315018475055695,
-0.001376794883981347,
0.07599072903394699,
-0.19718699157238007,
0.018568968400359154,
0.04323352873325348,
0.007994817569851875,
0.09346025437116623,
0.02965611405670643,
-0.017832037061452866,
0.11385732144117355,
-0.05450711026787758,
0.07709876447916031,
0.017052244395017624,
-0.12038940191268921,
-0.23559437692165375,
-0.09075293689966202,
0.04976227506995201,
0.05859314277768135,
0.09819751977920532,
-0.005986036732792854,
0.15179461240768433,
-0.08127760142087936,
0.08983530849218369,
0.23404285311698914,
-0.28485214710235596,
-0.0688856989145279,
0.021996930241584778,
0.030939679592847824,
0.08619936555624008,
-0.09751075506210327,
-0.033980995416641235,
0.04781979322433472,
0.05363927781581879,
0.12734320759773254,
-0.0339665561914444,
-0.09865715354681015,
0.014300784096121788,
-0.14419035613536835,
-0.03703422099351883,
0.2110384702682495,
0.05224103480577469,
-0.04936090111732483,
-0.033593520522117615,
-0.033585041761398315,
-0.13613587617874146,
-0.043771665543317795,
0.00799341220408678,
0.06177709624171257,
-0.047818057239055634,
-0.08494832366704941,
-0.01645578444004059,
-0.1082940623164177,
-0.04342750832438469,
-0.06920549273490906,
0.12287094444036484,
0.039220601320266724,
0.02422584593296051,
-0.04213975742459297,
0.0993783175945282,
0.016896851360797882,
-0.11286630481481552,
0.016236115247011185,
0.007824072614312172,
-0.005469383206218481,
-0.04410327225923538,
-0.055700261145830154,
-0.07245267927646637,
0.01179128885269165,
0.1487048864364624,
-0.03140841796994209,
0.029435962438583374,
0.03786361217498779,
0.058199744671583176,
-0.08600983023643494,
0.2011789083480835,
-0.03808872774243355,
-0.019710825756192207,
0.0025984770618379116,
0.06378598511219025,
0.0366097018122673,
-0.01270147506147623,
-0.1338830590248108,
0.021989855915308,
0.08388182520866394,
0.012604576535522938,
-0.05425940081477165,
0.05256333574652672,
-0.07947677373886108,
-0.051174573600292206,
-0.00332809635438025,
-0.07323624938726425,
0.01931113190948963,
-0.012507437728345394,
-0.06862229853868484,
-0.023805459961295128,
0.019980812445282936,
0.038462378084659576,
-0.004955208860337734,
0.12952286005020142,
-0.08990225940942764,
0.027578171342611313,
-0.08297199010848999,
-0.09065180271863937,
0.030825134366750717,
-0.08147244155406952,
0.04217829927802086,
-0.10889053344726562,
-0.18562471866607666,
-0.022130921483039856,
0.06331305205821991,
-0.01936176046729088,
-0.08627636730670929,
-0.031135743483901024,
-0.06069953739643097,
0.007166009396314621,
-0.012592478655278683,
0.14891761541366577,
-0.07661006599664688,
0.10016986727714539,
0.018237467855215073,
0.05291328206658363,
-0.04553815349936485,
0.04563181847333908,
-0.10529284179210663,
-0.0009554275893606246,
-0.14402805268764496,
0.03092854842543602,
-0.038881681859493256,
0.07289519160985947,
-0.08744402229785919,
-0.10114515572786331,
0.01812109909951687,
0.0007858672179281712,
0.043990358710289,
0.08458218723535538,
-0.16604909300804138,
-0.0736570879817009,
0.1392926573753357,
-0.05869176238775253,
-0.13591888546943665,
0.12868312001228333,
-0.07782956212759018,
0.054878707975149155,
0.0784146636724472,
0.16067740321159363,
0.06189722940325737,
-0.06682360172271729,
0.03472161293029785,
-0.00491720624268055,
0.042379315942525864,
-0.06637439131736755,
0.09732348471879959,
0.008913968689739704,
-0.017364542931318283,
0.027644045650959015,
-0.05057794228196144,
0.04496327042579651,
-0.08496037125587463,
-0.09797971695661545,
-0.03988826647400856,
-0.10726158320903778,
0.06420964747667313,
0.06216416135430336,
0.06328140944242477,
-0.10556590557098389,
-0.07531622052192688,
0.05133743956685066,
0.09141959249973297,
-0.03835410252213478,
0.019969282671809196,
-0.054281849414110184,
0.07662713527679443,
-0.04022379219532013,
-0.0186300165951252,
-0.17294596135616302,
-0.020581988617777824,
0.014201127924025059,
-0.021982483565807343,
0.04035462439060211,
0.01553034782409668,
0.052458200603723526,
0.034605711698532104,
-0.06715890020132065,
-0.0013398109003901482,
-0.05249806120991707,
-0.004969918634742498,
-0.11456017941236496,
-0.21327568590641022,
-0.022570785135030746,
-0.015485681593418121,
0.15145157277584076,
-0.20356561243534088,
0.03070143051445484,
-0.05047479271888733,
0.07912503182888031,
0.038957469165325165,
-0.008191968314349651,
-0.023654483258724213,
0.06938077509403229,
-0.029641080647706985,
-0.041676975786685944,
0.07319847494363785,
0.015923120081424713,
-0.11104981601238251,
-0.004017536528408527,
-0.07411255687475204,
0.18232916295528412,
0.12914419174194336,
-0.09189614653587341,
-0.07028195261955261,
0.02698177844285965,
-0.04606953263282776,
-0.033419687300920486,
-0.07677464187145233,
0.04015912488102913,
0.1786910742521286,
0.0123989786952734,
0.1393493115901947,
-0.09585434198379517,
-0.046626247465610504,
0.026534689590334892,
-0.04766058921813965,
0.029939081519842148,
0.1388590931892395,
0.11947977542877197,
-0.08978938311338425,
0.1408824771642685,
0.1801275759935379,
-0.08513115346431732,
0.13522493839263916,
-0.04518233612179756,
-0.057722315192222595,
-0.027640100568532944,
-0.046595100313425064,
-0.014140930958092213,
0.10802013427019119,
-0.12211117148399353,
0.008783064782619476,
0.038905952125787735,
0.01179005578160286,
0.007891636341810226,
-0.220276340842247,
-0.04456881061196327,
0.03768514469265938,
-0.03834724798798561,
-0.016397174447774887,
-0.0017061536200344563,
0.01423916406929493,
0.1071462482213974,
0.013389743864536285,
-0.07625076174736023,
0.04752439633011818,
0.007178463041782379,
-0.08612769842147827,
0.21150490641593933,
-0.07518668472766876,
-0.1776520013809204,
-0.13706344366073608,
-0.0545949712395668,
-0.05295511707663536,
-0.003149912226945162,
0.0614364892244339,
-0.07413402199745178,
-0.03180314600467682,
-0.07279524952173233,
-0.0024537749122828245,
-0.0038665372412651777,
0.008661623112857342,
-0.011396964080631733,
0.014962404035031796,
0.05599657818675041,
-0.09647130221128464,
-0.012222180142998695,
-0.04970064014196396,
-0.02174299955368042,
0.039092689752578735,
0.040774259716272354,
0.10311493277549744,
0.15333402156829834,
-0.014502475969493389,
-0.008167930878698826,
-0.02158825471997261,
0.23083464801311493,
-0.08114238828420639,
-0.043072085827589035,
0.14223170280456543,
-0.01715271733701229,
0.0431506372988224,
0.13593509793281555,
0.07226701825857162,
-0.08730296790599823,
0.015815844759345055,
0.02846592850983143,
-0.03927796334028244,
-0.268171101808548,
-0.033646851778030396,
-0.0507233627140522,
-0.007956546731293201,
0.0783345103263855,
0.019632715731859207,
0.003466376569122076,
0.06746618449687958,
0.036469560116529465,
0.07697193324565887,
-0.024515310302376747,
0.0725059062242508,
0.12220457196235657,
0.047124069184064865,
0.13204236328601837,
-0.051729895174503326,
-0.05593999847769737,
0.06652961671352386,
-0.005725420080125332,
0.2295539677143097,
0.018175311386585236,
0.14277383685112,
0.07377737015485764,
0.1386025846004486,
0.008008481003344059,
0.04504062235355377,
0.017918335273861885,
-0.019810272380709648,
-0.03349725902080536,
-0.024137189611792564,
-0.03777434304356575,
0.026318218559026718,
-0.04787589609622955,
0.05510532483458519,
-0.1216469258069992,
-0.011227552779018879,
0.05953656882047653,
0.26264089345932007,
0.02338678576052189,
-0.31383731961250305,
-0.10186455398797989,
0.016122763976454735,
-0.05420726165175438,
-0.0018863070290535688,
0.028928078711032867,
0.06222778186202049,
-0.13198141753673553,
0.041389141231775284,
-0.07491769641637802,
0.10316328704357147,
-0.08767863363027573,
0.04334351792931557,
0.0641695037484169,
0.07428254187107086,
0.0013433737913146615,
0.08148038387298584,
-0.29981619119644165,
0.27460289001464844,
-0.008198552764952183,
0.048941873013973236,
-0.06314488500356674,
-0.027548687532544136,
0.035845763981342316,
0.054587073624134064,
0.05386405438184738,
0.0038666280452162027,
-0.0410676933825016,
-0.15771129727363586,
-0.040473781526088715,
0.02706184983253479,
0.06241348013281822,
-0.02723764255642891,
0.08145689219236374,
-0.03775765746831894,
0.004442242439836264,
0.05066261440515518,
-0.000723798293620348,
-0.05325597897171974,
-0.09176760166883469,
-0.0010839176829904318,
0.02408144436776638,
-0.048524193465709686,
-0.06867502629756927,
-0.1267763376235962,
-0.07732057571411133,
0.11763031780719757,
-0.018511952832341194,
-0.04903564229607582,
-0.09150055795907974,
0.06172432377934456,
0.08089186996221542,
-0.08031509816646576,
0.03645249828696251,
-0.002867340575903654,
0.09249304234981537,
0.028154658153653145,
-0.04747135564684868,
0.09116265922784805,
-0.0564410425722599,
-0.19131174683570862,
-0.06531649827957153,
0.1091921254992485,
0.02420763298869133,
0.06942029297351837,
-0.02535104751586914,
0.010509409941732883,
-0.06197625771164894,
-0.08357580006122589,
0.01257217675447464,
0.00821640994399786,
0.07106112688779831,
0.04573948308825493,
-0.036858171224594116,
0.012068760581314564,
-0.08304876834154129,
-0.06253399699926376,
0.18849623203277588,
0.2214498221874237,
-0.08738681674003601,
0.04031470790505409,
0.036602821201086044,
-0.07414983212947845,
-0.15455880761146545,
0.011297655291855335,
0.05963510647416115,
0.0006671959417872131,
0.06797772645950317,
-0.14696437120437622,
0.11902517825365067,
0.09333305060863495,
-0.02068314328789711,
0.11209964007139206,
-0.3217496871948242,
-0.13395251333713531,
0.10134933888912201,
0.1372978538274765,
0.13894343376159668,
-0.13172198832035065,
-0.02082870528101921,
-0.04572247341275215,
-0.146324023604393,
0.1378229707479477,
-0.10358641296625137,
0.12806963920593262,
-0.03092869371175766,
0.1106255054473877,
0.0037294209469109774,
-0.047299932688474655,
0.12950772047042847,
0.019552117213606834,
0.09933143854141235,
-0.057583749294281006,
-0.028160307556390762,
0.03231542930006981,
-0.04104400798678398,
0.016753770411014557,
-0.09342203289270401,
0.0307865459471941,
-0.09329255670309067,
-0.030915401875972748,
-0.07106336951255798,
0.023760778829455376,
-0.040977418422698975,
-0.048031218349933624,
-0.04165596887469292,
0.03756199777126312,
0.034384556114673615,
-0.012077606283128262,
0.1650484949350357,
0.024534137919545174,
0.13603870570659637,
0.07593537122011185,
0.09001367539167404,
-0.062306176871061325,
-0.09832610934972763,
-0.04641509801149368,
-0.030273811891674995,
0.048144929111003876,
-0.17123320698738098,
0.02887239120900631,
0.13160167634487152,
0.011140162125229836,
0.15611597895622253,
0.06286188215017319,
-0.03973785787820816,
0.007328100968152285,
0.060210712254047394,
-0.1473669558763504,
-0.09190437942743301,
-0.007199518382549286,
-0.055258866399526596,
-0.13529729843139648,
0.03206604719161987,
0.12203475832939148,
-0.06728495657444,
-0.03177840635180473,
-0.006757003255188465,
0.017898136749863625,
-0.05075324699282646,
0.17833282053470612,
0.07896671444177628,
0.047494325786828995,
-0.10676582902669907,
0.1078370213508606,
0.07093336433172226,
-0.0688825398683548,
0.004336613230407238,
0.05840630084276199,
-0.09213374555110931,
-0.056954748928546906,
0.0604483038187027,
0.1810775101184845,
-0.08093660324811935,
-0.05632931366562843,
-0.14352649450302124,
-0.12352213263511658,
0.08281289786100388,
0.1308985948562622,
0.11646679043769836,
0.011451567523181438,
-0.03895536810159683,
-0.02378356270492077,
-0.08104820549488068,
0.10755844414234161,
0.07420925796031952,
0.06614638864994049,
-0.1541292667388916,
0.07461540400981903,
0.02957092598080635,
0.05183961242437363,
-0.01501353271305561,
0.037376768887043,
-0.10405530780553818,
0.014517159201204777,
-0.14131805300712585,
0.0029443365056067705,
-0.01665409840643406,
0.023352298885583878,
-0.0036167106591165066,
-0.06881476938724518,
-0.07280328869819641,
0.013769056648015976,
-0.12243233621120453,
-0.023349132388830185,
0.037886541336774826,
0.07895298302173615,
-0.09635411202907562,
-0.03891822323203087,
0.04241982474923134,
-0.04902099817991257,
0.07320401817560196,
0.04164178669452667,
0.014727015048265457,
0.06638604402542114,
-0.12203842401504517,
0.031979624181985855,
0.03355097770690918,
0.020873717963695526,
0.047238755971193314,
-0.131159245967865,
-0.0029366749804466963,
0.0008752434514462948,
0.06524120271205902,
0.023719217628240585,
0.07833414524793625,
-0.15566109120845795,
-0.011630808934569359,
0.008436531759798527,
-0.08374901115894318,
-0.04931286349892616,
0.013993371278047562,
0.0644172802567482,
0.03146012872457504,
0.2301100641489029,
-0.06705774366855621,
0.03818197548389435,
-0.19638380408287048,
0.011850639246404171,
-0.02603587694466114,
-0.12201353162527084,
-0.14749230444431305,
-0.07459408044815063,
0.04430370405316353,
-0.06755319982767105,
0.16799087822437286,
0.03966196998953819,
0.07443663477897644,
0.029320290312170982,
0.019833926111459732,
-0.0012724585831165314,
0.01750226877629757,
0.15605953335762024,
0.017472129315137863,
-0.0385797917842865,
0.0553094781935215,
0.029253395274281502,
0.10055885463953018,
0.09190146625041962,
0.1844479888677597,
0.17639435827732086,
0.029132237657904625,
0.08518511056900024,
0.03540175408124924,
-0.019055966287851334,
-0.1243986189365387,
0.04176418483257294,
-0.01574995554983616,
0.11440835893154144,
-0.013763918541371822,
0.21950466930866241,
0.07746719568967819,
-0.16513484716415405,
0.046616800129413605,
-0.06316568702459335,
-0.0753798633813858,
-0.1084527000784874,
-0.0666743814945221,
-0.09666218608617783,
-0.150067538022995,
0.0005522034480236471,
-0.11988969147205353,
0.0009473717072978616,
0.11587756127119064,
0.0018456995021551847,
-0.043480828404426575,
0.09077279269695282,
0.009991518221795559,
0.0034860013984143734,
0.08070904016494751,
0.009907604195177555,
-0.04668661952018738,
-0.08920586854219437,
-0.06421113014221191,
-0.021299738436937332,
-0.012637236155569553,
0.021962594240903854,
-0.05690223351120949,
-0.05890936031937599,
0.0226567555218935,
-0.028140811249613762,
-0.10631848871707916,
0.021962102502584457,
0.009738780558109283,
0.07327093183994293,
0.032759103924036026,
0.01137351430952549,
0.026509786024689674,
0.007413798477500677,
0.26563242077827454,
-0.06026383861899376,
-0.07014074176549911,
-0.12706468999385834,
0.22582225501537323,
0.02476755902171135,
-0.04121285676956177,
0.03648244962096214,
-0.06837393343448639,
0.0009176467428915203,
0.24895620346069336,
0.2247450053691864,
-0.09457942843437195,
-0.01954338513314724,
0.011463322676718235,
-0.007764757145196199,
-0.013498657383024693,
0.10854029655456543,
0.10903426259756088,
0.006476501002907753,
-0.08121499419212341,
-0.014552807435393333,
-0.05886773392558098,
-0.0011637939605861902,
-0.016025392338633537,
0.06969863176345825,
0.03950197994709015,
-0.0072053768672049046,
-0.04185018688440323,
0.07962869107723236,
-0.0905042514204979,
-0.11997628957033157,
0.032596368342638016,
-0.20977969467639923,
-0.18324455618858337,
-0.02352200448513031,
0.09226783365011215,
0.017664752900600433,
0.058241747319698334,
-0.01702686958014965,
-0.012324018403887749,
0.07180946320295334,
-0.016307998448610306,
-0.10270724445581436,
-0.10217396169900894,
0.1081206202507019,
-0.09479250013828278,
0.20147524774074554,
-0.04637618735432625,
0.05569562315940857,
0.12050214409828186,
0.06346482783555984,
-0.07389548420906067,
0.05974552035331726,
0.044635169208049774,
-0.024630477651953697,
0.04403224587440491,
0.0851675420999527,
-0.025177715346217155,
0.06843596696853638,
0.06200204789638519,
-0.10778521746397018,
0.0037345902528613806,
-0.06963280588388443,
-0.04284678027033806,
-0.05494605004787445,
-0.01213314663618803,
-0.07727371901273727,
0.1269443780183792,
0.22313793003559113,
-0.05046144500374794,
-0.014237524941563606,
-0.06106938049197197,
0.02333267591893673,
0.06654650717973709,
0.01524131279438734,
-0.05319321155548096,
-0.2105739861726761,
0.00934837106615305,
0.06471182405948639,
-0.008181453682482243,
-0.26207032799720764,
-0.07192134112119675,
-0.0029018190689384937,
-0.06448803097009659,
-0.06932234764099121,
0.08557683229446411,
0.07956942170858383,
0.03840881958603859,
-0.06028938665986061,
-0.04749587923288345,
-0.07239669561386108,
0.15115003287792206,
-0.15023668110370636,
-0.09064730256795883
] |
null | null |
transformers
|
this is my model card
|
{}
|
automatic-speech-recognition
|
fkHug/modelFromWav2vec
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
|
this is my model card
|
[] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
[
37
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
[
-0.0721188485622406,
-0.028641358017921448,
-0.008131618611514568,
-0.06536999344825745,
0.10369497537612915,
-0.029162423685193062,
0.05449262633919716,
0.09215618669986725,
0.09012462943792343,
0.0024160705506801605,
0.09899672865867615,
0.19874386489391327,
0.0032586746383458376,
-0.005444767884910107,
-0.07112546265125275,
-0.21837934851646423,
0.09600707143545151,
0.055372726172208786,
0.09617701917886734,
0.10875947028398514,
0.0784798189997673,
-0.07249201834201813,
0.032563697546720505,
0.0496663972735405,
-0.13367274403572083,
0.037079449743032455,
0.058643877506256104,
-0.1467711329460144,
0.11741682142019272,
0.03624606877565384,
0.09462464600801468,
0.015703529119491577,
-0.025202931836247444,
-0.2087007611989975,
0.0028133990708738565,
-0.010769401676952839,
-0.015249740332365036,
-0.01163998618721962,
0.05165225267410278,
-0.06630266457796097,
0.009819267317652702,
0.04223077744245529,
0.007102530915290117,
0.07885675877332687,
-0.07011023163795471,
-0.16595390439033508,
0.027373118326067924,
0.04979840666055679,
0.06278723478317261,
0.09646070003509521,
-0.015308019705116749,
0.15824733674526215,
-0.10311099886894226,
0.11664288491010666,
0.11729957163333893,
-0.34086450934410095,
0.025533635169267654,
-0.018387693911790848,
0.06145255267620087,
-0.005980021320283413,
-0.023320090025663376,
0.08407841622829437,
-0.009680325165390968,
0.02594558708369732,
-0.06523970514535904,
-0.053816139698028564,
-0.17229726910591125,
0.02152976207435131,
-0.10039443522691727,
-0.05630653724074364,
0.14903028309345245,
-0.02459312416613102,
0.050798237323760986,
-0.070815309882164,
-0.06357286870479584,
-0.017195679247379303,
-0.024608483538031578,
-0.05171274393796921,
-0.05008924379944801,
0.05738884210586548,
-0.0357147753238678,
-0.03484039008617401,
-0.11432810872793198,
-0.056904442608356476,
-0.20911253988742828,
0.30520865321159363,
0.01152665913105011,
0.09113361686468124,
-0.19182562828063965,
0.018077926710247993,
-0.016760041937232018,
-0.04602903127670288,
-0.009070603176951408,
-0.03480092063546181,
-0.005194336175918579,
0.03196761757135391,
-0.09672658145427704,
-0.005024821497499943,
0.09619411826133728,
0.05404296889901161,
0.048924271017313004,
0.05579700693488121,
-0.05309535190463066,
0.08241341263055801,
-0.028520308434963226,
0.12802933156490326,
-0.022204747423529625,
0.005049536935985088,
0.019602568820118904,
-0.15963000059127808,
0.021364036947488785,
-0.046107299625873566,
-0.1059512197971344,
-0.09832640737295151,
0.05150148272514343,
0.10308951884508133,
0.007624712772667408,
0.02769755758345127,
-0.02915896475315094,
-0.005024684127420187,
-0.0010898011969402432,
-0.08071205765008926,
-0.0027757075149565935,
0.06134745106101036,
0.054733775556087494,
0.2042790800333023,
0.01401777658611536,
0.027865497395396233,
-0.13477809727191925,
0.03430946171283722,
0.029110778123140335,
0.03076641820371151,
0.06137247383594513,
-0.027149634435772896,
0.016688477247953415,
-0.10584334284067154,
0.02761022001504898,
-0.2108437865972519,
-0.038738854229450226,
0.019536681473255157,
-0.04570518061518669,
0.011210971511900425,
-0.005862658843398094,
-0.07294338941574097,
-0.02412441000342369,
0.026644060388207436,
-0.0848965272307396,
-0.02483426034450531,
-0.04335634037852287,
0.09084957093000412,
0.035603780299425125,
0.1144782155752182,
-0.13740399479866028,
0.06781121343374252,
-0.041382916271686554,
-0.024146918207406998,
0.006467015482485294,
0.0786295235157013,
-0.017164042219519615,
0.09126786887645721,
-0.07609090209007263,
-0.0363796167075634,
-0.11339505016803741,
0.061230018734931946,
-0.03372715786099434,
0.1294335424900055,
-0.10931240767240524,
-0.12374426424503326,
0.2250494360923767,
-0.0921396017074585,
-0.09642865508794785,
0.09675160050392151,
0.047799888998270035,
-0.023683663457632065,
0.09046079218387604,
0.26092445850372314,
-0.006438442971557379,
-0.1344638615846634,
0.06955351680517197,
0.11876583099365234,
-0.1670559197664261,
-0.10538151115179062,
0.01964217610657215,
-0.07074971497058868,
-0.09296547621488571,
0.021963927894830704,
0.01981767639517784,
0.060468241572380066,
-0.05301972106099129,
-0.0665108859539032,
-0.03185911104083061,
-0.06936094164848328,
0.04747062921524048,
-0.03376079350709915,
0.07992979139089584,
-0.04860096052289009,
0.00022167984570842236,
-0.040190186351537704,
0.026694048196077347,
-0.03910767287015915,
0.08643262833356857,
-0.152516707777977,
0.10339003056287766,
-0.03455405682325363,
0.02255146950483322,
-0.18151403963565826,
0.1193070039153099,
-0.021366599947214127,
0.08709456026554108,
0.044252097606658936,
0.053591787815093994,
0.10817640274763107,
-0.07318807393312454,
0.037599824368953705,
-0.03522860258817673,
0.1588101089000702,
0.05225297436118126,
-0.018174700438976288,
-0.05587159842252731,
0.034731682389974594,
-0.06348273903131485,
-0.047311894595623016,
0.009232322685420513,
-0.02524193562567234,
0.10357552021741867,
0.11902469396591187,
-0.002044686349108815,
0.023700617253780365,
-0.015857627615332603,
0.033494967967271805,
-0.003626496996730566,
0.031247051432728767,
0.08235985040664673,
-0.02272169478237629,
-0.0769941657781601,
0.25266093015670776,
-0.14026528596878052,
0.23503975570201874,
0.24047064781188965,
-0.29727834463119507,
0.048978712409734726,
0.07219430804252625,
0.01735152304172516,
0.0021673247683793306,
0.0925217941403389,
-0.05285301432013512,
0.20381318032741547,
-0.01743427850306034,
0.1389402598142624,
-0.03674538433551788,
-0.0029116040095686913,
0.03353562951087952,
-0.03337210416793823,
-0.05041591078042984,
0.04755973070859909,
0.0015957624418660998,
-0.08388978242874146,
0.0828937366604805,
0.15193380415439606,
-0.0368693470954895,
0.09280958771705627,
-0.005733860656619072,
-0.016735907644033432,
0.06162073463201523,
0.012068409472703934,
-0.032136328518390656,
-0.02572881057858467,
-0.3251122832298279,
-0.08921423554420471,
0.06767655909061432,
-0.004273096099495888,
0.1147976741194725,
-0.1401272714138031,
0.006002949085086584,
0.006024875678122044,
-0.059807486832141876,
-0.07236415892839432,
0.07770498842000961,
0.020530637353658676,
0.07419945299625397,
-0.02522803470492363,
-0.11176551133394241,
0.07386600971221924,
-0.03610837087035179,
-0.10568254441022873,
0.07786476612091064,
-0.11866384744644165,
-0.2900512218475342,
-0.14687477052211761,
-0.14070986211299896,
0.02008247748017311,
0.06637454032897949,
0.119004026055336,
-0.11709826439619064,
-0.004356969613581896,
0.037115056067705154,
0.03698491305112839,
-0.08836003392934799,
0.06865092366933823,
0.029483821243047714,
0.030205586925148964,
-0.02406148426234722,
-0.09716613590717316,
-0.03349224105477333,
-0.07106846570968628,
-0.01235450804233551,
0.08198442310094833,
-0.06806303560733795,
0.07756998389959335,
0.1869477778673172,
0.04971100017428398,
0.08263817429542542,
-0.0121439378708601,
0.12353795021772385,
-0.05943383276462555,
-0.10815918445587158,
0.17252440750598907,
-0.07307032495737076,
0.02297825925052166,
0.14187809824943542,
0.000404434947995469,
-0.07981004565954208,
-0.044941119849681854,
-0.09086789190769196,
-0.09349201619625092,
-0.19090873003005981,
-0.12945573031902313,
-0.08469346910715103,
-0.027750767767429352,
0.003936579450964928,
0.03929224982857704,
0.10053049772977829,
-0.015164372511208057,
0.0318857803940773,
-0.06527750939130783,
0.037118468433618546,
0.04925777390599251,
0.22011712193489075,
-0.035632967948913574,
0.11447834223508835,
-0.06257307529449463,
-0.11675461381673813,
0.042986106127500534,
0.060705941170454025,
0.10117717832326889,
0.15401828289031982,
0.027867771685123444,
0.005864634178578854,
0.11680784821510315,
0.1787092089653015,
0.12349317967891693,
0.057697635143995285,
-0.014458432793617249,
0.04202825948596001,
-0.031030582264065742,
-0.07545241713523865,
0.06351669877767563,
0.24637570977210999,
-0.10907962918281555,
-0.04299181327223778,
-0.17554126679897308,
0.06269893795251846,
0.17280790209770203,
0.06886234879493713,
-0.19033943116664886,
0.009430297650396824,
0.06161557883024216,
-0.08718495815992355,
-0.039426445960998535,
0.12834890186786652,
0.03255130723118782,
-0.08927353471517563,
0.08593298494815826,
0.023056067526340485,
0.06641831994056702,
-0.06849268823862076,
0.08725766092538834,
-0.10478068888187408,
-0.15477952361106873,
0.06414807587862015,
0.04578937590122223,
-0.24747368693351746,
0.2281670868396759,
-0.015506764873862267,
0.026676084846258163,
-0.07251138240098953,
-0.014630771242082119,
0.005118122790008783,
0.07661780714988708,
0.14594794809818268,
-0.005033341236412525,
-0.04946841299533844,
-0.12404008209705353,
-0.007966546341776848,
0.0572483129799366,
0.17199034988880157,
0.05051308125257492,
-0.031220808625221252,
-0.007178888190537691,
-0.06425842642784119,
-0.0023619099520146847,
-0.057564083486795425,
-0.04774671047925949,
-0.10623916983604431,
0.013821293599903584,
0.2107272744178772,
0.11030402034521103,
0.008771294727921486,
-0.014119311235845089,
-0.14217756688594818,
0.13927903771400452,
-0.20314770936965942,
-0.012101659551262856,
-0.06178198382258415,
-0.17580774426460266,
0.09098359942436218,
-0.047833316028118134,
0.06665368378162384,
-0.0246900487691164,
0.007919765077531338,
-0.05927768722176552,
-0.15633289515972137,
0.1300991028547287,
-0.11683275550603867,
-0.019420908764004707,
-0.02266664244234562,
0.2542378008365631,
-0.02868989109992981,
-0.00022344836906995624,
0.06588499993085861,
0.012667160481214523,
-0.07948637753725052,
-0.045111484825611115,
0.08735814690589905,
0.14315460622310638,
-0.08427585661411285,
0.053661324083805084,
0.03975791856646538,
-0.17768527567386627,
-0.07372710108757019,
0.034207575023174286,
0.2839866280555725,
0.07747307419776917,
-0.057250987738370895,
0.18260008096694946,
0.20878978073596954,
-0.010367152281105518,
-0.28379762172698975,
-0.15502293407917023,
-0.07944010198116302,
0.0009141949703916907,
-0.11721421778202057,
-0.09218011796474457,
0.09241478890180588,
-0.06837643682956696,
-0.05737648904323578,
0.07152894884347916,
-0.19546766579151154,
-0.0981190875172615,
0.219879612326622,
-0.034856200218200684,
0.4114883840084076,
-0.06148048862814903,
-0.1459461748600006,
-0.0555725172162056,
-0.18084096908569336,
0.0894971713423729,
-0.026558540761470795,
0.0820658728480339,
0.01966485008597374,
0.09183495491743088,
0.05033127963542938,
-0.04245033487677574,
0.10872094333171844,
0.0760914608836174,
-0.057044703513383865,
-0.05393856763839722,
-0.05787365511059761,
-0.03484024107456207,
0.016244076192378998,
0.03970678150653839,
0.029991568997502327,
0.023477301001548767,
-0.08885850012302399,
-0.06251884996891022,
-0.12129434943199158,
0.0884556919336319,
0.0841657966375351,
-0.0031620634254068136,
0.051357369869947433,
-0.14773449301719666,
-0.021893896162509918,
0.04363230988383293,
0.14964716136455536,
-0.09176138788461685,
0.07405997067689896,
0.17426884174346924,
0.14886562526226044,
-0.14664708077907562,
0.003058107104152441,
-0.05776814743876457,
-0.1011233702301979,
0.12968605756759644,
0.024770202115178108,
0.06956075131893158,
0.08249596506357193,
0.019914090633392334,
0.00811604131013155,
0.09039079397916794,
-0.020434709265828133,
0.0070288218557834625,
0.09483665227890015,
-0.15127912163734436,
-0.07125047594308853,
-0.015156523324549198,
0.017886854708194733,
0.1545657217502594,
0.14007161557674408,
0.16529449820518494,
0.02486141212284565,
-0.012096241116523743,
-0.04985383525490761,
-0.005592867266386747,
-0.14501993358135223,
0.09596604108810425,
0.056048765778541565,
0.03177822381258011,
-0.1689673811197281,
0.053186409175395966,
-0.04085535928606987,
-0.19084806740283966,
0.013390779495239258,
0.017495164647698402,
-0.11244485527276993,
-0.11561312526464462,
-0.10894180089235306,
0.02844228409230709,
-0.06521294265985489,
-0.13255275785923004,
0.043576110154390335,
-0.16649563610553741,
0.08408192545175552,
0.2183782309293747,
0.05991727486252785,
0.10767434537410736,
-0.08789479732513428,
-0.03133482486009598,
0.005640141665935516,
-0.058524247258901596,
-0.03673648089170456,
-0.011027595959603786,
-0.10944608598947525,
0.0506475605070591,
0.013274303637444973,
0.13179078698158264,
-0.09587356448173523,
-0.10142890363931656,
-0.11364098638296127,
0.09812068939208984,
-0.14064590632915497,
-0.030631106346845627,
-0.1098189726471901,
-0.03166437894105911,
0.06403834372758865,
-0.0851232185959816,
-0.03693908452987671,
0.017473148182034492,
-0.0980556383728981,
0.05528227239847183,
-0.004201426636427641,
0.001913837855681777,
-0.10883459448814392,
0.002962311264127493,
0.062244582921266556,
-0.040038544684648514,
0.12362077832221985,
0.249018132686615,
-0.14891332387924194,
0.1465185135602951,
-0.18008393049240112,
-0.19724424183368683,
0.1500670164823532,
0.01500400435179472,
0.009510787203907967,
0.02629696950316429,
0.012114698998630047,
0.12700627744197845,
0.040209777653217316,
0.028408609330654144,
0.17248576879501343,
-0.06685706973075867,
0.03851442039012909,
-0.0759325698018074,
-0.10440655797719955,
-0.034970253705978394,
-0.07160848379135132,
0.1400478631258011,
0.06202966347336769,
0.09748422354459763,
-0.03615143150091171,
0.06252597272396088,
0.02196015976369381,
0.035767797380685806,
-0.06036775931715965,
-0.10780566930770874,
-0.04740653187036514,
-0.03826800733804703,
0.042743176221847534,
-0.031614698469638824,
0.2334204912185669,
-0.10858950763940811,
0.038850028067827225,
0.011987561360001564,
-0.008462372235953808,
-0.09795255213975906,
0.03197858855128288,
0.2952418327331543,
0.1218862533569336,
-0.03296373039484024,
-0.066999152302742,
0.013009646907448769,
0.01815200038254261,
0.030443403869867325,
0.001572250621393323,
0.14926548302173615,
0.023670630529522896,
0.17915105819702148,
0.10188481956720352,
0.02828742191195488,
-0.12838032841682434,
-0.1490498036146164,
-0.09192974120378494,
0.03111591562628746,
-0.03810209035873413,
0.11369074881076813,
0.14607982337474823,
0.016350792720913887,
0.01697431318461895,
-0.004843822680413723,
-0.022071609273552895,
-0.1752617508172989,
-0.10440753400325775,
-0.08142578601837158,
-0.11838482320308685,
0.032670944929122925,
-0.014440135098993778,
0.03735579550266266,
0.05140050873160362,
0.05859667435288429,
-0.02119131200015545,
0.09585927426815033,
0.009440023452043533,
-0.06592240929603577,
0.10088648647069931,
-0.04326128214597702,
0.013741651549935341,
0.016331855207681656,
-0.03154413402080536,
-0.010452311486005783,
-0.019497450441122055,
-0.005802567582577467,
0.046686772257089615,
-0.13079345226287842,
0.010648160241544247,
-0.12401501834392548,
-0.09085725992918015,
-0.041131582111120224,
0.029086057096719742,
-0.03246871381998062,
0.11770851910114288,
0.07890281081199646,
-0.06317076832056046,
0.028930896893143654,
0.13956138491630554,
-0.11462242156267166,
-0.15169039368629456,
-0.012151491828262806,
0.19187034666538239,
0.049830734729766846,
0.14869624376296997,
-0.039655085653066635,
-0.011490479111671448,
-0.1268738955259323,
0.3134293258190155,
0.21894627809524536,
-0.029566623270511627,
0.06968390941619873,
0.012242328375577927,
0.04945865646004677,
0.05147209390997887,
0.037134476006031036,
0.14212264120578766,
0.3142167627811432,
-0.005606405436992645,
-0.0678129643201828,
-0.029410207644104958,
-0.040482621639966965,
-0.08318427950143814,
0.060821712017059326,
-0.06858836114406586,
-0.13105294108390808,
-0.042223330587148666,
0.09486715495586395,
-0.21290788054466248,
0.08625967800617218,
-0.014330721460282803,
-0.16493283212184906,
-0.025777185335755348,
0.025004224851727486,
0.12373810261487961,
0.08473566174507141,
0.059048641473054886,
-0.032053492963314056,
-0.10001970827579498,
0.0649167001247406,
0.03845933824777603,
-0.2532598376274109,
0.04978358373045921,
0.01770191080868244,
-0.07009257376194,
-0.05270304158329964,
-0.0012992133852094412,
0.13695812225341797,
0.01977551355957985,
0.1564396768808365,
0.00699559086933732,
0.1385391801595688,
-0.011141940020024776,
-0.10966970771551132,
0.0020350429695099592,
0.13195984065532684,
-0.00764200184494257,
-0.012492666952311993,
0.04399271681904793,
-0.17780189216136932,
0.0534556619822979,
-0.018362876027822495,
-0.0200018472969532,
-0.0611613392829895,
-0.025413908064365387,
-0.040704384446144104,
0.02814488857984543,
-0.029701540246605873,
-0.01489523146301508,
-0.018930602818727493,
0.009367123246192932,
-0.0017991254571825266,
0.015221191570162773,
-0.11187604814767838,
-0.1212301030755043,
-0.15346519649028778,
-0.07677175104618073,
-0.032612718641757965,
0.027920229360461235,
-0.09698854386806488,
-0.016907071694731712,
-0.0763961598277092,
0.026637034490704536,
-0.07471530884504318,
0.02430429309606552,
0.08550451695919037,
0.001998303458094597,
0.014178152196109295,
-0.058580655604600906,
0.12117161601781845,
0.1384691596031189,
-0.1377907544374466,
-0.12039846181869507
] |
null | null |
flair
|
## English Chunking in Flair (fast model)
This is the fast phrase chunking model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **96,22** (CoNLL-2000)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| ADJP | adjectival |
| ADVP | adverbial |
| CONJP | conjunction |
| INTJ | interjection |
| LST | list marker |
| NP | noun phrase |
| PP | prepositional |
| PRT | particle |
| SBAR | subordinate clause |
| VP | verb phrase |
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/chunk-english-fast")
# make example sentence
sentence = Sentence("The happy man has been eating at the diner")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('np'):
print(entity)
```
This yields the following output:
```
Span [1,2,3]: "The happy man" [− Labels: NP (0.9958)]
Span [4,5,6]: "has been eating" [− Labels: VP (0.8759)]
Span [7]: "at" [− Labels: PP (1.0)]
Span [8,9]: "the diner" [− Labels: NP (0.9991)]
```
So, the spans "*The happy man*" and "*the diner*" are labeled as **noun phrases** (NP) and "*has been eating*" is labeled as a **verb phrase** (VP) in the sentence "*The happy man has been eating at the diner*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import CONLL_2000
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. get the corpus
corpus: Corpus = CONLL_2000()
# 2. what tag do we want to predict?
tag_type = 'np'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
# contextual string embeddings, forward
FlairEmbeddings('news-forward-fast'),
# contextual string embeddings, backward
FlairEmbeddings('news-backward-fast'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/chunk-english-fast',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following paper when using this model.
```
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
{"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2000"], "widget": [{"text": "The happy man has been eating at the diner"}]}
|
token-classification
|
flair/chunk-english-fast
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:conll2000",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2000 #region-us
|
English Chunking in Flair (fast model)
--------------------------------------
This is the fast phrase chunking model for English that ships with Flair.
F1-Score: 96,22 (CoNLL-2000)
Predicts 4 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the spans "*The happy man*" and "*the diner*" are labeled as noun phrases (NP) and "*has been eating*" is labeled as a verb phrase (VP) in the sentence "*The happy man has been eating at the diner*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following paper when using this model.
---
### Issues?
The Flair issue tracker is available here.
|
[
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the spans \"*The happy man*\" and \"*the diner*\" are labeled as noun phrases (NP) and \"*has been eating*\" is labeled as a verb phrase (VP) in the sentence \"*The happy man has been eating at the diner*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2000 #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the spans \"*The happy man*\" and \"*the diner*\" are labeled as noun phrases (NP) and \"*has been eating*\" is labeled as a verb phrase (VP) in the sentence \"*The happy man has been eating at the diner*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
37,
100,
22,
15,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2000 #region-us \n### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the spans \"*The happy man*\" and \"*the diner*\" are labeled as noun phrases (NP) and \"*has been eating*\" is labeled as a verb phrase (VP) in the sentence \"*The happy man has been eating at the diner*\".\n\n\n\n\n---### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
-0.05668017640709877,
0.06476648151874542,
0.0006157592870295048,
0.059531453996896744,
0.08117757737636566,
0.058503005653619766,
-0.05634255334734917,
0.1276300996541977,
0.038955967873334885,
0.11561989784240723,
0.13684166967868805,
0.17606821656227112,
0.09489888697862625,
0.08880829066038132,
0.06130628660321236,
-0.22107616066932678,
0.03142988309264183,
0.02574889548122883,
0.015712006017565727,
0.22811242938041687,
0.12130684405565262,
-0.0002687641535885632,
0.061824291944503784,
-0.031639114022254944,
-0.10111887753009796,
-0.03649694472551346,
0.050087541341781616,
-0.024468040093779564,
0.16020382940769196,
-0.02349018305540085,
0.1257496327161789,
0.056155044585466385,
-0.004596293438225985,
-0.20209433138370514,
0.027282055467367172,
0.05636316165328026,
0.044610071927309036,
0.07025883346796036,
-0.06321623921394348,
0.07138686627149582,
0.2989605963230133,
-0.0645839273929596,
0.011206330731511116,
0.09110283851623535,
-0.1625920683145523,
-0.22020049393177032,
-0.165207639336586,
0.0420253686606884,
-0.02524079754948616,
0.09821128845214844,
-0.05825298652052879,
0.13078512251377106,
-0.18368175625801086,
0.00936988927423954,
0.1410243809223175,
-0.21879032254219055,
-0.04102535918354988,
-0.0044290716759860516,
0.13306628167629242,
-0.001995994243770838,
-0.09132218360900879,
0.045634958893060684,
0.02948825992643833,
0.055985063314437866,
-0.038244493305683136,
-0.0030405682045966387,
0.029603062197566032,
0.07549285143613815,
-0.10329005867242813,
-0.08968445658683777,
0.2784474790096283,
0.0754084661602974,
-0.05489986762404442,
-0.077884741127491,
-0.04873689264059067,
-0.07506085187196732,
0.029601601883769035,
-0.16296590864658356,
-0.019057752564549446,
0.009146575815975666,
0.12912926077842712,
-0.08275297284126282,
-0.08591334521770477,
-0.04152865335345268,
0.10658146440982819,
0.09896735101938248,
-0.03458802402019501,
0.006509811617434025,
-0.06105492264032364,
0.04459867998957634,
0.02418772503733635,
-0.05034228786826134,
0.029435396194458008,
-0.05180229991674423,
-0.0012936159037053585,
0.03318510577082634,
-0.02576836198568344,
0.003943101968616247,
0.030969318002462387,
0.03138843551278114,
0.06672628968954086,
-0.02205756865441799,
-0.03713911026716232,
0.043021488934755325,
-0.0016972838202491403,
0.11400008201599121,
-0.0835285410284996,
-0.09939125180244446,
-0.04028020054101944,
0.07334404438734055,
0.0782356932759285,
-0.0568951815366745,
-0.067285917699337,
0.042358849197626114,
-0.030358824878931046,
0.017337258905172348,
-0.0006839215639047325,
0.06302262097597122,
-0.044926468282938004,
0.025222640484571457,
0.13928531110286713,
-0.01172239612787962,
-0.031072773039340973,
0.09078193455934525,
-0.050863854587078094,
0.13641305267810822,
0.010015297681093216,
0.019056718796491623,
0.0027276179753243923,
0.005949483253061771,
-0.06298565864562988,
0.01723170094192028,
-0.09329544752836227,
-0.10403873026371002,
0.018418248742818832,
-0.041592538356781006,
-0.04746468365192413,
-0.03537526726722717,
-0.1353594809770584,
-0.052207596600055695,
-0.008901873603463173,
0.001685276161879301,
-0.046500708907842636,
-0.08882095664739609,
0.018633782863616943,
-0.03855454921722412,
0.032988373190164566,
0.07673229277133942,
-0.048448819667100906,
0.13171176612377167,
0.025911567732691765,
0.06264998018741608,
-0.047013066709041595,
0.031372010707855225,
-0.0912238359451294,
0.020156942307949066,
-0.0916898250579834,
-0.02485555410385132,
0.023645097389817238,
-0.05705420672893524,
-0.1016509085893631,
-0.0976966992020607,
-0.02576359733939171,
0.015456944704055786,
-0.011318834498524666,
0.2103787213563919,
-0.293266624212265,
0.002606804948300123,
0.15888729691505432,
-0.10949978977441788,
-0.09975872188806534,
0.13209965825080872,
-0.027196401730179787,
0.21279937028884888,
0.08865106850862503,
0.03873295336961746,
-0.009538468904793262,
-0.22821398079395294,
-0.0032109012827277184,
0.02164909802377224,
-0.10888191312551498,
0.18214328587055206,
0.07053015381097794,
-0.07112209498882294,
-0.17297802865505219,
0.004903972148895264,
-0.10805381089448929,
0.059086333960294724,
-0.01693500019609928,
-0.07253856211900711,
0.010129458270967007,
-0.0017651336966082454,
0.13017328083515167,
-0.014178076758980751,
-0.04295821487903595,
-0.043569229543209076,
-0.14132995903491974,
-0.27612411975860596,
0.03610258176922798,
0.030440611764788628,
0.050331443548202515,
-0.03496576473116875,
0.15701830387115479,
0.0835365355014801,
-0.0495290644466877,
-0.12150654196739197,
0.02807142771780491,
-0.06219227612018585,
0.0584859699010849,
-0.030397234484553337,
0.06643449515104294,
-0.01595781184732914,
0.012133914977312088,
0.045195113867521286,
-0.01092141680419445,
-0.07435327768325806,
0.04279867932200432,
-0.02317745052278042,
-0.028540488332509995,
-0.050778359174728394,
-0.07433360069990158,
0.22101259231567383,
-0.13812118768692017,
0.03249093145132065,
0.00692357262596488,
0.011107472702860832,
-0.012609618715941906,
-0.019076265394687653,
0.034291885793209076,
0.053029756993055344,
-0.0246259942650795,
-0.004890916403383017,
0.1381601095199585,
0.02234466001391411,
-0.09033982455730438,
0.021843474358320236,
-0.06580257415771484,
-0.28396958112716675,
0.06864358484745026,
-0.09392712265253067,
-0.1772526055574417,
-0.0695720762014389,
-0.06442081183195114,
-0.005583062767982483,
-0.04357106238603592,
-0.07681261003017426,
0.08148984611034393,
0.060759954154491425,
0.046038918197155,
0.01971437782049179,
0.049949534237384796,
0.012965232133865356,
-0.03989405930042267,
-0.034819819033145905,
0.07555527240037918,
0.10513807833194733,
-0.03471197932958603,
0.08139097690582275,
0.1121964082121849,
0.08284907042980194,
0.05577896907925606,
0.05368558317422867,
-0.007867288775742054,
-0.09639353305101395,
0.0930478423833847,
-0.03257722035050392,
0.12967827916145325,
-0.20355023443698883,
0.05322418361902237,
0.01473867055028677,
-0.02797752618789673,
0.02349521405994892,
-0.14399710297584534,
-0.08201875537633896,
0.032270029187202454,
-0.05090401694178581,
-0.07426794618368149,
0.09812739491462708,
-0.060310784727334976,
0.09503598511219025,
0.009913953021168709,
-0.04771719500422478,
0.07231200486421585,
0.009989634156227112,
-0.10136407613754272,
0.10198807716369629,
-0.08990195393562317,
-0.28252941370010376,
-0.0831940546631813,
-0.07592492550611496,
0.07222338020801544,
0.0029893878381699324,
0.044001881033182144,
-0.08893812447786331,
0.0185752734541893,
0.047302693128585815,
0.07144618779420853,
0.024986106902360916,
-0.10081813484430313,
-0.12400297820568085,
-0.06287536770105362,
0.006994825787842274,
-0.08162805438041687,
0.005664683412760496,
-0.02484760619699955,
0.060786984860897064,
-0.003952206578105688,
-0.0761927142739296,
0.13738802075386047,
0.1321752816438675,
0.09756377339363098,
0.00804863404482603,
-0.03966658189892769,
0.2599010765552521,
-0.06826469302177429,
0.02464035525918007,
0.11614120006561279,
0.03318364918231964,
0.04711725935339928,
0.0848730280995369,
0.03549911454319954,
-0.07272583991289139,
-0.015414469875395298,
-0.021250132471323013,
-0.047338902950286865,
-0.08888857811689377,
-0.06511171907186508,
-0.09126510471105576,
-0.06555680930614471,
-0.04516053572297096,
0.012887227348983288,
0.044631581753492355,
0.01373412273824215,
-0.01634453423321247,
-0.08090966939926147,
-0.09501238167285919,
-0.011018941178917885,
0.1171543300151825,
-0.1105676144361496,
-0.04331391304731369,
0.019147206097841263,
-0.044697947800159454,
0.030819756910204887,
0.03589902073144913,
-0.016867579892277718,
0.08893182873725891,
0.1616266667842865,
0.1508983075618744,
0.09052980691194534,
-0.02500222809612751,
0.04805850610136986,
-0.06023462861776352,
-0.008988162502646446,
-0.07012541592121124,
-0.0857991874217987,
-0.07210510969161987,
0.038845859467983246,
0.04277869313955307,
0.04898081719875336,
-0.012742485851049423,
-0.057665593922138214,
0.06352342665195465,
0.045322809368371964,
0.12549331784248352,
-0.09953184425830841,
0.025542467832565308,
0.01023099385201931,
0.14579525589942932,
-0.062448881566524506,
0.010887705720961094,
0.0023847161792218685,
-0.10145575553178787,
0.005433309823274612,
-0.06406160444021225,
0.06564631313085556,
0.09687108546495438,
0.057033851742744446,
0.015579795464873314,
0.04296676069498062,
0.009841668419539928,
0.13524016737937927,
-0.24298517405986786,
0.26995712518692017,
-0.011942231096327305,
0.02974935807287693,
-0.034840650856494904,
-0.00007908532279543579,
0.024028439074754715,
0.07117526978254318,
0.2708522081375122,
0.03660259395837784,
-0.008516131900250912,
-0.05629435181617737,
-0.09904783964157104,
0.04323387145996094,
0.018105408176779747,
-0.03697172552347183,
0.05017341300845146,
0.041140105575323105,
0.03503856435418129,
0.0357559472322464,
0.17429913580417633,
-0.15752926468849182,
-0.09775198251008987,
0.02003845013678074,
-0.018025949597358704,
0.02667335793375969,
0.01165898609906435,
-0.04265438765287399,
-0.10592951625585556,
0.06625976413488388,
-0.07309107482433319,
0.01757298782467842,
-0.08713909238576889,
0.17192599177360535,
-0.07512212544679642,
-0.034320928156375885,
-0.12137501686811447,
-0.030272336676716805,
0.023711666464805603,
-0.06781444698572159,
0.0734771117568016,
0.03301885351538658,
0.04858072102069855,
-0.07550303637981415,
-0.11018716543912888,
0.1087334081530571,
0.05467798188328743,
0.05501765385270119,
-0.03164117410778999,
0.05155060440301895,
-0.00886154267936945,
-0.12741811573505402,
0.05713535100221634,
-0.048192098736763,
-0.012004213407635689,
0.04821410030126572,
-0.08728489279747009,
0.013556452468037605,
-0.16711005568504333,
0.011637909337878227,
0.27434253692626953,
0.2583998143672943,
-0.1100812777876854,
0.1435347944498062,
0.05308089032769203,
-0.09735394269227982,
-0.10161885619163513,
-0.0818614512681961,
-0.04540402814745903,
0.03344359248876572,
-0.020529475063085556,
-0.1426728516817093,
0.0424610897898674,
0.03400539979338646,
-0.01994084194302559,
0.03456775099039078,
-0.3718850910663605,
-0.006367322523146868,
0.04963307082653046,
0.09428281337022781,
0.04706771299242973,
-0.0003009601787198335,
-0.045527949929237366,
0.020557355135679245,
-0.1557890772819519,
0.0447796955704689,
-0.022825920954346657,
0.05820567533373833,
-0.013171553611755371,
0.05489561706781387,
-0.010758727788925171,
-0.06248844042420387,
0.14795280992984772,
-0.017201203852891922,
-0.01215592585504055,
-0.0029697397258132696,
-0.21770042181015015,
0.032386552542448044,
0.019354522228240967,
0.060480352491140366,
-0.05670279636979103,
-0.0482633039355278,
-0.20550277829170227,
-0.013361898250877857,
-0.16787917912006378,
0.1132020652294159,
-0.13809040188789368,
-0.06132431700825691,
-0.05290711671113968,
0.05572625622153282,
0.05657687410712242,
-0.03967352211475372,
-0.1612573117017746,
-0.10311098396778107,
0.1687256395816803,
-0.02622818388044834,
-0.054560381919145584,
0.0911613330245018,
-0.32500916719436646,
-0.003788706613704562,
0.021330516785383224,
0.07608254998922348,
-0.011286011897027493,
-0.0824902281165123,
0.06110406666994095,
0.049930889159440994,
0.2008952498435974,
0.05639352276921272,
-0.07577580213546753,
0.07188033312559128,
0.01632622815668583,
-0.16650724411010742,
-0.05070860683917999,
-0.030623391270637512,
-0.08145952969789505,
-0.08387352526187897,
-0.2265099436044693,
0.060212697833776474,
-0.04382411390542984,
0.0026053376495838165,
-0.0051263910718262196,
0.05052638798952103,
-0.09944641590118408,
-0.022237049415707588,
0.1444551795721054,
0.04727005213499069,
-0.031042149290442467,
-0.1097232922911644,
0.054947737604379654,
-0.1593017429113388,
-0.03276403620839119,
0.11005964875221252,
-0.0803576186299324,
-0.04140246659517288,
0.02287168987095356,
0.26972246170043945,
0.016330167651176453,
-0.06738750636577606,
-0.025521038100123405,
-0.06283081322908401,
-0.012147866189479828,
0.038618072867393494,
0.07339005172252655,
0.06717536598443985,
-0.13603587448596954,
-0.027054427191615105,
-0.05456018075346947,
0.014381454326212406,
-0.04730387404561043,
-0.03423643484711647,
-0.018980959430336952,
0.13430315256118774,
0.056611567735672,
0.09926542639732361,
-0.028712905943393707,
-0.08944825828075409,
-0.06752952933311462,
0.0054443408735096455,
0.0034028843510895967,
0.046113356947898865,
-0.05337964743375778,
-0.0009363969438709319,
-0.0486137680709362,
0.03467382490634918,
-0.009280392900109291,
-0.04359431937336922,
-0.041734836995601654,
0.012796668335795403,
0.02678428404033184,
0.06948784738779068,
-0.1523163616657257,
0.024765970185399055,
0.06716082245111465,
-0.061715301126241684,
0.009279651567339897,
-0.010624261572957039,
-0.051638007164001465,
0.005672645755112171,
-0.24507364630699158,
0.017544709146022797,
0.037874650210142136,
0.01405318547040224,
0.04169345647096634,
-0.16016985476016998,
-0.025109397247433662,
-0.0506138913333416,
0.0008167570340447128,
0.026038872078061104,
0.17821277678012848,
-0.08333150297403336,
0.06534266471862793,
0.11719267070293427,
-0.0468134880065918,
-0.010262003168463707,
-0.008296391926705837,
0.01983012817800045,
0.021708885207772255,
0.16183722019195557,
-0.03872816264629364,
0.1555314064025879,
-0.10726770758628845,
-0.019078776240348816,
-0.02147519960999489,
0.032728131860494614,
-0.059817757457494736,
0.018380334600806236,
0.05708026513457298,
-0.008927270770072937,
0.09276005625724792,
0.04066716134548187,
0.11161684989929199,
0.02746455743908882,
0.05384554713964462,
-0.05787612870335579,
-0.029305854812264442,
0.05881490185856819,
0.013959558680653572,
0.01589314267039299,
0.0900646299123764,
0.030734237283468246,
-0.0347483828663826,
0.1357869952917099,
0.24106630682945251,
0.08097276091575623,
0.16065365076065063,
0.07483967393636703,
-0.04484095424413681,
0.019903896376490593,
-0.11955723911523819,
-0.02757277712225914,
0.043720792979002,
0.041095878928899765,
-0.02292514592409134,
0.06846880912780762,
-0.009984582662582397,
-0.13356126844882965,
0.018894338980317116,
-0.06497824937105179,
-0.11013422161340714,
-0.07219959795475006,
-0.060644637793302536,
0.012825296260416508,
-0.02268943563103676,
0.010861150920391083,
-0.0274977907538414,
0.009064375422894955,
0.03886577859520912,
-0.05900534614920616,
-0.023913005366921425,
0.06573987007141113,
-0.024448465555906296,
-0.03298410773277283,
0.030436933040618896,
-0.007678155321627855,
0.021114934235811234,
-0.2276824414730072,
0.07432445883750916,
0.0027066601905971766,
-0.01072230376303196,
0.07107583433389664,
0.012808158062398434,
0.017282869666814804,
-0.029564881697297096,
-0.13976268470287323,
-0.05532212555408478,
-0.013499956578016281,
0.042458876967430115,
0.051601625978946686,
0.13653002679347992,
0.0634240135550499,
0.04496990144252777,
-0.02369973063468933,
0.07207433134317398,
0.01440942008048296,
-0.1326027661561966,
-0.16269227862358093,
0.248417466878891,
-0.0903858169913292,
-0.02838514931499958,
-0.037223588675260544,
-0.011769634671509266,
0.04243329167366028,
0.2998742461204529,
0.2312701791524887,
-0.06920517235994339,
0.0024759008083492517,
-0.050937071442604065,
0.03610583022236824,
0.08076157420873642,
0.0968809500336647,
0.09109971672296524,
0.0944400280714035,
-0.12484100461006165,
-0.0045005520805716515,
-0.18940377235412598,
-0.060713693499565125,
0.007153904996812344,
0.03831895440816879,
0.11239109188318253,
-0.07984934002161026,
-0.07729075103998184,
0.1756945550441742,
-0.07764194905757904,
-0.14234980940818787,
0.04892714321613312,
-0.09392537921667099,
-0.04319251701235771,
-0.036505185067653656,
0.12844593822956085,
0.026208629831671715,
0.07979225367307663,
-0.03802237659692764,
-0.09815970063209534,
0.17062696814537048,
-0.0323999747633934,
-0.13613609969615936,
-0.029323071241378784,
0.07410681992769241,
-0.23086431622505188,
0.2153642624616623,
0.02096957340836525,
0.07362234592437744,
0.09165871143341064,
0.05206446349620819,
-0.039010342210531235,
0.08746485412120819,
0.04835072532296181,
0.12965267896652222,
-0.06268932670354843,
-0.006441777106374502,
0.0092672910541296,
-0.05641661211848259,
-0.010515823028981686,
-0.186118021607399,
0.12554946541786194,
0.13627614080905914,
-0.04113762080669403,
-0.09433139860630035,
0.09809216856956482,
-0.056756023317575455,
0.1415283977985382,
0.1722344607114792,
-0.03135617449879646,
0.01659109629690647,
-0.03337908908724785,
-0.029108796268701553,
0.011435631662607193,
0.04700940102338791,
-0.03965423256158829,
-0.08655600994825363,
-0.03257846459746361,
0.023795707151293755,
-0.10096555203199387,
-0.189686581492424,
-0.09699226915836334,
-0.04714656621217728,
-0.032280780375003815,
0.08806698769330978,
0.05967772752046585,
-0.0428137332201004,
0.05500294640660286,
-0.03139147162437439,
0.056736595928668976,
-0.020151743665337563,
0.16129842400550842,
-0.07579304277896881,
-0.06055609509348869
] |
null | null |
flair
|
## English Chunking in Flair (default model)
This is the standard phrase chunking model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **96,48** (CoNLL-2000)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| ADJP | adjectival |
| ADVP | adverbial |
| CONJP | conjunction |
| INTJ | interjection |
| LST | list marker |
| NP | noun phrase |
| PP | prepositional |
| PRT | particle |
| SBAR | subordinate clause |
| VP | verb phrase |
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/chunk-english")
# make example sentence
sentence = Sentence("The happy man has been eating at the diner")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('np'):
print(entity)
```
This yields the following output:
```
Span [1,2,3]: "The happy man" [− Labels: NP (0.9958)]
Span [4,5,6]: "has been eating" [− Labels: VP (0.8759)]
Span [7]: "at" [− Labels: PP (1.0)]
Span [8,9]: "the diner" [− Labels: NP (0.9991)]
```
So, the spans "*The happy man*" and "*the diner*" are labeled as **noun phrases** (NP) and "*has been eating*" is labeled as a **verb phrase** (VP) in the sentence "*The happy man has been eating at the diner*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import CONLL_2000
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. get the corpus
corpus: Corpus = CONLL_2000()
# 2. what tag do we want to predict?
tag_type = 'np'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
# contextual string embeddings, forward
FlairEmbeddings('news-forward'),
# contextual string embeddings, backward
FlairEmbeddings('news-backward'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/chunk-english',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following paper when using this model.
```
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
{"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2000"], "widget": [{"text": "The happy man has been eating at the diner"}]}
|
token-classification
|
flair/chunk-english
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:conll2000",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2000 #has_space #region-us
|
English Chunking in Flair (default model)
-----------------------------------------
This is the standard phrase chunking model for English that ships with Flair.
F1-Score: 96,48 (CoNLL-2000)
Predicts 4 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the spans "*The happy man*" and "*the diner*" are labeled as noun phrases (NP) and "*has been eating*" is labeled as a verb phrase (VP) in the sentence "*The happy man has been eating at the diner*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following paper when using this model.
---
### Issues?
The Flair issue tracker is available here.
|
[
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the spans \"*The happy man*\" and \"*the diner*\" are labeled as noun phrases (NP) and \"*has been eating*\" is labeled as a verb phrase (VP) in the sentence \"*The happy man has been eating at the diner*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2000 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the spans \"*The happy man*\" and \"*the diner*\" are labeled as noun phrases (NP) and \"*has been eating*\" is labeled as a verb phrase (VP) in the sentence \"*The happy man has been eating at the diner*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
41,
100,
22,
15,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2000 #has_space #region-us \n### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the spans \"*The happy man*\" and \"*the diner*\" are labeled as noun phrases (NP) and \"*has been eating*\" is labeled as a verb phrase (VP) in the sentence \"*The happy man has been eating at the diner*\".\n\n\n\n\n---### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
-0.0473257452249527,
0.0743308737874031,
0.0014010508311912417,
0.04951975494623184,
0.07674144208431244,
0.04207857325673103,
-0.06453297287225723,
0.1404692828655243,
0.029467768967151642,
0.1331094652414322,
0.13546864688396454,
0.16073337197303772,
0.0753994956612587,
0.09677869081497192,
0.06962139904499054,
-0.21758559346199036,
0.044647157192230225,
0.01850949414074421,
0.006987979635596275,
0.21782489120960236,
0.13423825800418854,
-0.01782548800110817,
0.058412786573171616,
-0.03199716657400131,
-0.10831629484891891,
-0.02504653111100197,
0.03508459031581879,
-0.016088740900158882,
0.14982008934020996,
-0.024158233776688576,
0.12660576403141022,
0.05794721469283104,
-0.015587728470563889,
-0.18794085085391998,
0.02620437741279602,
0.06473728269338608,
0.041157033294439316,
0.07468190789222717,
-0.07464335113763809,
0.06727716326713562,
0.2895132303237915,
-0.09141675382852554,
0.0034003350883722305,
0.08091356605291367,
-0.16256342828273773,
-0.2501840591430664,
-0.16094771027565002,
0.04430953785777092,
-0.03189872205257416,
0.09377912431955338,
-0.07024902105331421,
0.13781993091106415,
-0.18857742846012115,
0.010061794891953468,
0.15719124674797058,
-0.21690936386585236,
-0.03583897650241852,
0.02437002584338188,
0.16135597229003906,
0.017165927216410637,
-0.09659963101148605,
0.05215923488140106,
0.029331356287002563,
0.05252401530742645,
-0.017288850620388985,
0.0007604331476613879,
0.020392274484038353,
0.09303729236125946,
-0.11699704825878143,
-0.09011727571487427,
0.28918394446372986,
0.0535331666469574,
-0.04111132025718689,
-0.09070109575986862,
-0.04872278869152069,
-0.09624864906072617,
0.03171872720122337,
-0.144407257437706,
-0.011365951038897038,
0.005850926972925663,
0.1233329102396965,
-0.0994696095585823,
-0.1076837033033371,
-0.025174686685204506,
0.08752749860286713,
0.1062978059053421,
-0.04617302864789963,
0.001613517990335822,
-0.05393875762820244,
0.05313682556152344,
0.010804317891597748,
-0.04452824965119362,
0.034827183932065964,
-0.05968524515628815,
-0.009911165572702885,
0.03845816105604172,
-0.05357365310192108,
-0.0061858599074184895,
0.026211801916360855,
0.0331687331199646,
0.03097580559551716,
-0.030465692281723022,
-0.02345937304198742,
0.05601769685745239,
0.017131894826889038,
0.12183499336242676,
-0.10044827312231064,
-0.09682894498109818,
-0.050193075090646744,
0.07273293286561966,
0.08329487591981888,
-0.05800027772784233,
-0.07901832461357117,
0.04273999109864235,
-0.010001259855926037,
0.008891768753528595,
0.02167857065796852,
0.07080428302288055,
-0.027516432106494904,
0.03269612789154053,
0.13581901788711548,
-0.015161790885031223,
-0.009779173880815506,
0.08245791494846344,
-0.03644488751888275,
0.10476993024349213,
0.008487295359373093,
0.015082163736224174,
0.013607575558125973,
0.008116348646581173,
-0.08024713397026062,
0.015175536274909973,
-0.09870897978544235,
-0.12232181429862976,
0.012828962877392769,
-0.0391874685883522,
-0.05436348170042038,
-0.048676639795303345,
-0.11344888806343079,
-0.05740097537636757,
-0.014033997431397438,
0.003641539253294468,
-0.04957420378923416,
-0.055407773703336716,
0.012837447226047516,
-0.0176205113530159,
0.015750471502542496,
0.09116477519273758,
-0.046672672033309937,
0.1316901296377182,
0.021710220724344254,
0.07637396454811096,
-0.06812456250190735,
0.03642832487821579,
-0.09573278576135635,
0.028875619173049927,
-0.11155854165554047,
-0.02955552563071251,
0.007548970635980368,
-0.038610100746154785,
-0.09131345897912979,
-0.09144463390111923,
-0.04652479663491249,
0.031078943982720375,
-0.012646637856960297,
0.18381917476654053,
-0.2969454228878021,
0.0029588215984404087,
0.14380766451358795,
-0.10898679494857788,
-0.09107908606529236,
0.12904244661331177,
-0.02550741285085678,
0.20591293275356293,
0.07126262784004211,
0.08038684725761414,
-0.006776237394660711,
-0.2250826507806778,
-0.045442428439855576,
0.02335619367659092,
-0.10401374101638794,
0.1758813112974167,
0.07401587069034576,
-0.06846553832292557,
-0.1384643316268921,
0.0016475562006235123,
-0.09839357435703278,
0.05926972255110741,
-0.006484514102339745,
-0.06644707173109055,
-0.007547424174845219,
-0.0025475185830146074,
0.14726276695728302,
-0.005779948551207781,
-0.03094206564128399,
-0.06168828532099724,
-0.15265104174613953,
-0.2153901755809784,
0.03704874590039253,
0.04693327844142914,
0.05667205527424812,
-0.019065964967012405,
0.17888781428337097,
0.057306136935949326,
-0.05559467896819115,
-0.13077092170715332,
0.00811659637838602,
-0.06833222508430481,
0.05857209116220474,
-0.021324481815099716,
0.09506794810295105,
-0.009972862899303436,
-0.004359120037406683,
0.03672615438699722,
-0.0032358402386307716,
-0.06258270144462585,
0.05182814225554466,
-0.02452753484249115,
-0.013724458403885365,
-0.05942890793085098,
-0.08269309997558594,
0.19896218180656433,
-0.12740254402160645,
0.033853210508823395,
0.033395711332559586,
0.03389018028974533,
-0.008517795242369175,
-0.0219951830804348,
0.0373450368642807,
0.051597487181425095,
-0.025812359526753426,
-0.010124219581484795,
0.12832467257976532,
0.02400651015341282,
-0.07628349959850311,
0.06297535449266434,
-0.09915342926979065,
-0.2726720869541168,
0.08351682126522064,
-0.07662186771631241,
-0.16593551635742188,
-0.049499284476041794,
-0.06659462302923203,
0.0033178357407450676,
-0.04180373251438141,
-0.11401882022619247,
0.04137074574828148,
0.0374239981174469,
0.042817577719688416,
0.013478548265993595,
0.038969963788986206,
0.004129013977944851,
-0.04182649031281471,
-0.04871128126978874,
0.07556802779436111,
0.07978510111570358,
-0.021486233919858932,
0.1174587532877922,
0.1464904248714447,
0.06107884272933006,
0.08984996378421783,
0.04671679437160492,
-0.009121956303715706,
-0.10564368218183517,
0.08108921349048615,
-0.04224146902561188,
0.1325857937335968,
-0.200179785490036,
0.041308771818876266,
0.017582690343260765,
-0.012102380394935608,
0.03863222897052765,
-0.14213073253631592,
-0.07457020878791809,
0.03836767375469208,
-0.03843044862151146,
-0.04544144868850708,
0.10845855623483658,
-0.05082171782851219,
0.09070932120084763,
0.018646609038114548,
-0.026044344529509544,
0.05999840423464775,
0.010833989828824997,
-0.08749419450759888,
0.08202354609966278,
-0.09883932769298553,
-0.2880699038505554,
-0.08631367236375809,
-0.060612257570028305,
0.0712682381272316,
0.0076489499770104885,
0.0575006827712059,
-0.09025102853775024,
0.024298395961523056,
0.022674107924103737,
0.07046595215797424,
0.0028945421800017357,
-0.09471136331558228,
-0.13520729541778564,
-0.057656869292259216,
0.011705702170729637,
-0.08832503110170364,
0.0021369175519794226,
-0.012528619728982449,
0.06767208874225616,
0.012147331610321999,
-0.04976695403456688,
0.1374277025461197,
0.1340639889240265,
0.08380088955163956,
0.008756592869758606,
-0.03938215225934982,
0.2668544352054596,
-0.081475168466568,
0.025512224063277245,
0.11088906228542328,
0.04609887674450874,
0.05598814785480499,
0.10333597660064697,
0.03181559592485428,
-0.06444486975669861,
-0.00770224817097187,
-0.009633015841245651,
-0.05527711659669876,
-0.06662189960479736,
-0.079401895403862,
-0.10527008771896362,
-0.06996532529592514,
-0.04666787385940552,
0.025168897584080696,
0.04406530410051346,
-0.003262938931584358,
-0.015260111540555954,
-0.08194364607334137,
-0.11830370128154755,
-0.014163468033075333,
0.1214803010225296,
-0.12504397332668304,
-0.02424941025674343,
0.008552506566047668,
-0.05119394510984421,
0.0475170724093914,
0.04405755549669266,
-0.002083386993035674,
0.07977482676506042,
0.14590394496917725,
0.1508980393409729,
0.09435345232486725,
-0.018268024548888206,
0.03099692054092884,
-0.06129140406847,
-0.014656873419880867,
-0.07441902160644531,
-0.08989053964614868,
-0.049875255674123764,
0.0554625429213047,
0.08827045559883118,
0.021460695192217827,
-0.000059607034927466884,
-0.10834269225597382,
0.07399985194206238,
0.044045645743608475,
0.1517515629529953,
-0.09691397845745087,
0.01852918416261673,
0.02465127967298031,
0.12978962063789368,
-0.055748600512742996,
0.0007171411416493356,
0.04192021116614342,
-0.0798882320523262,
-0.00757489213719964,
-0.05264197662472725,
0.06170278787612915,
0.10625553876161575,
0.059516265988349915,
-0.008762228302657604,
0.015619704499840736,
0.00453135697171092,
0.12412034720182419,
-0.22437684237957,
0.26749542355537415,
-0.00856925267726183,
-0.0025909659452736378,
-0.032008834183216095,
-0.0017505120486021042,
0.03678419440984726,
0.06242175027728081,
0.2509567141532898,
0.04489349201321602,
-0.07044301182031631,
-0.05054697394371033,
-0.08128786832094193,
0.029125995934009552,
0.026919446885585785,
-0.046450547873973846,
0.0473884716629982,
0.038946427404880524,
0.04216323420405388,
0.04416702315211296,
0.2311193346977234,
-0.15020158886909485,
-0.10861522704362869,
0.028926219791173935,
0.009395965375006199,
0.025278199464082718,
0.0016157303471118212,
-0.0442265123128891,
-0.13455837965011597,
0.07030480355024338,
-0.04382868856191635,
0.027191270142793655,
-0.0974779799580574,
0.1534871906042099,
-0.051577311009168625,
-0.023006070405244827,
-0.11197483539581299,
-0.03382837027311325,
0.030134206637740135,
-0.06544364988803864,
0.06899938732385635,
0.03862762078642845,
0.05045200139284134,
-0.06415905803442001,
-0.11730115115642548,
0.10008642077445984,
0.026641378179192543,
0.05637073516845703,
-0.04197218641638756,
0.07201582938432693,
-0.0385684110224247,
-0.1292727142572403,
0.07487568259239197,
-0.05745726078748703,
0.004939576610922813,
0.0543597936630249,
-0.0816759541630745,
0.011978459544479847,
-0.1371677666902542,
-0.000756489229388535,
0.287784606218338,
0.26667726039886475,
-0.13237343728542328,
0.1473158895969391,
0.02740369364619255,
-0.08200088888406754,
-0.14625735580921173,
-0.07678737491369247,
-0.050009988248348236,
0.044080961495637894,
0.0026476045604795218,
-0.11833243817090988,
0.01657848432660103,
0.031228311359882355,
-0.03152668848633766,
0.02913145162165165,
-0.3516121208667755,
-0.02197604812681675,
0.03627707436680794,
0.06120169162750244,
0.03658014535903931,
-0.028448449447751045,
-0.04934989660978317,
0.026849979534745216,
-0.13997052609920502,
0.0759303867816925,
-0.054477088153362274,
0.07104263454675674,
-0.002445341320708394,
0.06587519496679306,
-0.010917508974671364,
-0.06491907685995102,
0.1621270775794983,
-0.03967801108956337,
-0.02320484071969986,
-0.013118468225002289,
-0.21550393104553223,
0.03790238872170448,
-0.004432241432368755,
0.060141436755657196,
-0.0657334104180336,
-0.043853793293237686,
-0.23431290686130524,
-0.0008275090367533267,
-0.17699939012527466,
0.11641567945480347,
-0.13212765753269196,
-0.06791715323925018,
-0.07991833239793777,
0.06968710571527481,
0.060403887182474136,
-0.039653580635786057,
-0.1379513442516327,
-0.11423208564519882,
0.18471375107765198,
0.018748993054032326,
-0.08480416983366013,
0.07553154975175858,
-0.32775723934173584,
0.021711356937885284,
0.019235646352171898,
0.07580438256263733,
-0.05461888387799263,
-0.06544151902198792,
0.05319669470191002,
0.043253496289253235,
0.19298291206359863,
0.050711337476968765,
-0.07842203974723816,
0.07522512227296829,
0.04176561161875725,
-0.1645011156797409,
-0.07397954910993576,
-0.029916226863861084,
-0.08290942758321762,
-0.0764903798699379,
-0.2275887131690979,
0.06872185319662094,
-0.04530804976820946,
0.00852157175540924,
-0.002786807483062148,
0.042964644730091095,
-0.1000494435429573,
-0.03589106723666191,
0.15162676572799683,
0.050988540053367615,
-0.023319300264120102,
-0.10985223203897476,
0.07383447885513306,
-0.15948274731636047,
-0.022753294557332993,
0.11144284904003143,
-0.06257800757884979,
-0.05859913304448128,
0.004933629650622606,
0.23190906643867493,
0.019184036180377007,
-0.06410875916481018,
-0.006456300616264343,
-0.055343400686979294,
-0.013429180718958378,
0.016891799867153168,
0.07868554443120956,
0.07014241069555283,
-0.11383558064699173,
-0.024356186389923096,
-0.039220791310071945,
0.015970243141055107,
-0.05369936674833298,
-0.01782367192208767,
-0.023039842024445534,
0.1418907195329666,
0.04316176101565361,
0.09738253802061081,
-0.036541156470775604,
-0.07980390638113022,
-0.08162585645914078,
-0.0063788616098463535,
-0.0037413006648421288,
0.022312071174383163,
-0.04800868779420853,
-0.017155934125185013,
-0.033355094492435455,
0.02009984478354454,
-0.024883346632122993,
-0.045588698238134384,
-0.055954575538635254,
0.0050120106898248196,
0.014341904781758785,
0.07886234670877457,
-0.1542583405971527,
0.02296103909611702,
0.07686672359704971,
-0.04945319890975952,
0.009675275534391403,
-0.024117309600114822,
-0.04364404454827309,
-0.007974064908921719,
-0.22588267922401428,
-0.001542417798191309,
0.03521902859210968,
0.006791123189032078,
0.047994162887334824,
-0.14988751709461212,
-0.03144630789756775,
-0.047707654535770416,
0.013524140231311321,
0.027959076687693596,
0.16358382999897003,
-0.08595238626003265,
0.07348879426717758,
0.10458248853683472,
-0.04300151392817497,
-0.003515147604048252,
-0.008985655382275581,
0.01737578958272934,
0.03228253498673439,
0.15222564339637756,
-0.039358481764793396,
0.134845569729805,
-0.11039740592241287,
-0.012094363570213318,
-0.03172341361641884,
0.017625227570533752,
-0.03481585532426834,
0.0235892403870821,
0.052560947835445404,
0.002089728368446231,
0.11470942199230194,
0.057649094611406326,
0.08885910362005234,
0.039533235132694244,
0.057462628930807114,
-0.06090877950191498,
-0.028349686414003372,
0.061154380440711975,
0.009996090084314346,
0.007675149943679571,
0.07361768186092377,
0.03850456327199936,
-0.034454040229320526,
0.14875811338424683,
0.2302151322364807,
0.10145828127861023,
0.17000547051429749,
0.08075099438428879,
-0.04487341642379761,
0.0067367637529969215,
-0.150257408618927,
-0.014095441438257694,
0.03255562484264374,
0.04321930184960365,
-0.05834325775504112,
0.05970802158117294,
0.011106514371931553,
-0.13615339994430542,
0.034305471926927567,
-0.077582947909832,
-0.08661907911300659,
-0.05913937836885452,
-0.04771379753947258,
0.00921299122273922,
-0.04994368925690651,
-0.000926235516089946,
-0.020731469616293907,
0.026159310713410378,
0.05412697046995163,
-0.057595085352659225,
-0.020808875560760498,
0.07095001637935638,
-0.031804732978343964,
-0.03483148291707039,
0.011787009425461292,
-0.011468767188489437,
0.03485618159174919,
-0.2183186262845993,
0.07562119513750076,
0.007278288248926401,
-0.008050198666751385,
0.07109075784683228,
0.022715989500284195,
-0.0058436463586986065,
-0.03215774521231651,
-0.13716663420200348,
-0.05392563343048096,
-0.02969408594071865,
0.04075963422656059,
0.04452434554696083,
0.1518251895904541,
0.056644875556230545,
0.04352512210607529,
-0.03411182016134262,
0.10329976677894592,
-0.006303016096353531,
-0.11249856650829315,
-0.1612558513879776,
0.22758162021636963,
-0.09651333838701248,
-0.023813527077436447,
-0.05887649953365326,
-0.04010462388396263,
0.014059590175747871,
0.28822046518325806,
0.2409929633140564,
-0.06822741031646729,
0.017230967059731483,
-0.047909338027238846,
0.03792339563369751,
0.06344583630561829,
0.08567105233669281,
0.08886539936065674,
0.10994437336921692,
-0.12500116229057312,
0.005947295110672712,
-0.18358159065246582,
-0.05921454355120659,
-0.008821465075016022,
0.03938121721148491,
0.09567549079656601,
-0.08274263143539429,
-0.07509729266166687,
0.15902836620807648,
-0.06770000606775284,
-0.1600266844034195,
0.06091011315584183,
-0.11710424721240997,
-0.045136261731386185,
-0.014564245007932186,
0.16406653821468353,
0.0009827043395489454,
0.08382569998502731,
-0.03636203333735466,
-0.08935510367155075,
0.143207848072052,
-0.043243158608675,
-0.12032503634691238,
-0.025421466678380966,
0.07131022214889526,
-0.2032228410243988,
0.214152529835701,
0.009521563537418842,
0.0457536019384861,
0.10507321357727051,
0.04635150730609894,
-0.058870382606983185,
0.09797114133834839,
0.04825625568628311,
0.09516647458076477,
-0.07081577181816101,
0.012880814261734486,
0.0038472518790513277,
-0.04808958247303963,
0.0013068424304947257,
-0.1995999962091446,
0.12556247413158417,
0.13720393180847168,
-0.019980883225798607,
-0.09712301194667816,
0.07472239434719086,
-0.0555955208837986,
0.1374599039554596,
0.1642749011516571,
-0.020988211035728455,
0.010878097265958786,
-0.03274740278720856,
-0.037282951176166534,
0.0041172378696501255,
0.05553806945681572,
-0.057190340012311935,
-0.08750670403242111,
-0.017010798677802086,
0.04534505680203438,
-0.09379979968070984,
-0.1897108405828476,
-0.09662328660488129,
-0.030613521113991737,
-0.01993028074502945,
0.09370076656341553,
0.07144740968942642,
-0.015164011158049107,
0.0430399551987648,
-0.03385941684246063,
0.03808362036943436,
-0.0197310633957386,
0.14905646443367004,
-0.0791303813457489,
-0.053371965885162354
] |
null | null |
flair
|
## English Verb Disambiguation in Flair (fast model)
This is the fast verb disambiguation model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **88,27** (Ontonotes) - predicts [Proposition Bank verb frames](http://verbs.colorado.edu/propbank/framesets-english-aliases/).
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/frame-english-fast")
# make example sentence
sentence = Sentence("George returned to Berlin to return his hat.")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following frame tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('frame'):
print(entity)
```
This yields the following output:
```
Span [2]: "returned" [− Labels: return.01 (0.9867)]
Span [6]: "return" [− Labels: return.02 (0.4741)]
```
So, the word "*returned*" is labeled as **return.01** (as in *go back somewhere*) while "*return*" is labeled as **return.02** (as in *give back something*) in the sentence "*George returned to Berlin to return his hat*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import ColumnCorpus
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. load the corpus (Ontonotes does not ship with Flair, you need to download and reformat into a column format yourself)
corpus = ColumnCorpus(
"resources/tasks/srl", column_format={1: "text", 11: "frame"}
)
# 2. what tag do we want to predict?
tag_type = 'frame'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
BytePairEmbeddings("en"),
FlairEmbeddings("news-forward-fast"),
FlairEmbeddings("news-backward-fast"),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/frame-english-fast',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following paper when using this model.
```
@inproceedings{akbik2019flair,
title={FLAIR: An easy-to-use framework for state-of-the-art NLP},
author={Akbik, Alan and Bergmann, Tanja and Blythe, Duncan and Rasul, Kashif and Schweter, Stefan and Vollgraf, Roland},
booktitle={{NAACL} 2019, 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)},
pages={54--59},
year={2019}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
{"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["ontonotes"], "widget": [{"text": "George returned to Berlin to return his hat."}]}
|
token-classification
|
flair/frame-english-fast
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:ontonotes",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #region-us
|
## English Verb Disambiguation in Flair (fast model)
This is the fast verb disambiguation model for English that ships with Flair.
F1-Score: 88,27 (Ontonotes) - predicts Proposition Bank verb frames.
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the word "*returned*" is labeled as return.01 (as in *go back somewhere*) while "*return*" is labeled as return.02 (as in *give back something*) in the sentence "*George returned to Berlin to return his hat*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following paper when using this model.
---
### Issues?
The Flair issue tracker is available here.
|
[
"## English Verb Disambiguation in Flair (fast model)\n\nThis is the fast verb disambiguation model for English that ships with Flair.\n\nF1-Score: 88,27 (Ontonotes) - predicts Proposition Bank verb frames.\n\nBased on Flair embeddings and LSTM-CRF.\n\n---",
"### Demo: How to use in Flair\n\nRequires: Flair ('pip install flair')\n\n\n\nThis yields the following output:\n\n\nSo, the word \"*returned*\" is labeled as return.01 (as in *go back somewhere*) while \"*return*\" is labeled as return.02 (as in *give back something*) in the sentence \"*George returned to Berlin to return his hat*\". \n\n\n---",
"### Training: Script to train this model\n\nThe following Flair script was used to train this model: \n\n\n\n\n\n---",
"### Cite\n\nPlease cite the following paper when using this model.\n\n\n\n---",
"### Issues?\n\nThe Flair issue tracker is available here."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #region-us \n",
"## English Verb Disambiguation in Flair (fast model)\n\nThis is the fast verb disambiguation model for English that ships with Flair.\n\nF1-Score: 88,27 (Ontonotes) - predicts Proposition Bank verb frames.\n\nBased on Flair embeddings and LSTM-CRF.\n\n---",
"### Demo: How to use in Flair\n\nRequires: Flair ('pip install flair')\n\n\n\nThis yields the following output:\n\n\nSo, the word \"*returned*\" is labeled as return.01 (as in *go back somewhere*) while \"*return*\" is labeled as return.02 (as in *give back something*) in the sentence \"*George returned to Berlin to return his hat*\". \n\n\n---",
"### Training: Script to train this model\n\nThe following Flair script was used to train this model: \n\n\n\n\n\n---",
"### Cite\n\nPlease cite the following paper when using this model.\n\n\n\n---",
"### Issues?\n\nThe Flair issue tracker is available here."
] |
[
37,
75,
96,
22,
15,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #region-us \n## English Verb Disambiguation in Flair (fast model)\n\nThis is the fast verb disambiguation model for English that ships with Flair.\n\nF1-Score: 88,27 (Ontonotes) - predicts Proposition Bank verb frames.\n\nBased on Flair embeddings and LSTM-CRF.\n\n---### Demo: How to use in Flair\n\nRequires: Flair ('pip install flair')\n\n\n\nThis yields the following output:\n\n\nSo, the word \"*returned*\" is labeled as return.01 (as in *go back somewhere*) while \"*return*\" is labeled as return.02 (as in *give back something*) in the sentence \"*George returned to Berlin to return his hat*\". \n\n\n---### Training: Script to train this model\n\nThe following Flair script was used to train this model: \n\n\n\n\n\n---### Cite\n\nPlease cite the following paper when using this model.\n\n\n\n---### Issues?\n\nThe Flair issue tracker is available here."
] |
[
-0.09385324269533157,
-0.02568841353058815,
-0.004020667634904385,
0.10809233039617538,
0.1158280298113823,
0.010329817421734333,
0.11594208329916,
0.1255909949541092,
0.054593220353126526,
0.10196743160486221,
0.10772020369768143,
0.061722636222839355,
0.12431774288415909,
0.11711468547582626,
0.06590162962675095,
-0.2562481462955475,
-0.0015663305530324578,
-0.057269137352705,
-0.01707419753074646,
0.13609348237514496,
0.13937672972679138,
-0.016598397865891457,
0.049767278134822845,
0.0010227991733700037,
-0.07043877243995667,
-0.017419835552573204,
0.014633546583354473,
-0.029973501339554787,
0.13581116497516632,
0.04505351558327675,
0.11827907711267471,
-0.0033312588930130005,
0.10324323177337646,
-0.1475701481103897,
0.0325641855597496,
0.047908611595630646,
-0.014955413527786732,
0.05221021920442581,
0.011215743608772755,
-0.03377417102456093,
0.19941619038581848,
-0.03456609323620796,
-0.000835781916975975,
0.06102123111486435,
-0.1891322284936905,
-0.17463800311088562,
-0.1197403222322464,
0.05379695072770119,
0.0850113183259964,
0.08446383476257324,
-0.02925841696560383,
0.027872858569025993,
-0.05423371121287346,
0.056635819375514984,
0.158364936709404,
-0.1973106563091278,
-0.0347905233502388,
0.10402475297451019,
0.0947214812040329,
0.013159047812223434,
-0.10755627602338791,
0.018825529143214226,
-0.03885611146688461,
0.05654003471136093,
-0.006247590761631727,
-0.011436275206506252,
0.037094272673130035,
0.047555845230817795,
-0.16345791518688202,
-0.08938369899988174,
0.2118028700351715,
0.02132784202694893,
-0.117616206407547,
-0.11229832470417023,
-0.06347762793302536,
0.004081189166754484,
-0.02374468557536602,
-0.08375769108533859,
0.0003853818343486637,
0.03889740630984306,
0.1557399481534958,
-0.10345261543989182,
-0.08331870287656784,
-0.05838815122842789,
0.017798908054828644,
0.08556191623210907,
-0.044326912611722946,
0.06335248798131943,
-0.11733891069889069,
0.13245832920074463,
-0.04893765226006508,
-0.10969121754169464,
-0.009606901556253433,
-0.037643853574991226,
0.005729163531213999,
-0.00995863787829876,
-0.00502242473885417,
-0.08867241442203522,
-0.0057615539990365505,
0.14792020618915558,
0.11777327954769135,
-0.01222995575517416,
-0.022039130330085754,
0.0633532926440239,
0.013931060209870338,
0.1897507607936859,
-0.11448562890291214,
-0.03228263556957245,
0.06263123452663422,
0.05202896147966385,
-0.02179861068725586,
-0.05792873352766037,
-0.12182549387216568,
0.007807777263224125,
0.0020245537161827087,
0.024430861696600914,
0.019382059574127197,
0.014858104288578033,
-0.07345020771026611,
-0.015812378376722336,
0.15053899586200714,
-0.11209611594676971,
0.007989687845110893,
0.05587070807814598,
-0.12284044921398163,
0.09791865199804306,
0.0959930345416069,
-0.01432872749865055,
-0.01790216937661171,
0.05254768580198288,
-0.07289616763591766,
0.03834189847111702,
-0.08125078678131104,
-0.1263333559036255,
0.00572217395529151,
-0.006398748606443405,
-0.031442802399396896,
-0.05614858493208885,
-0.17210158705711365,
-0.05347369983792305,
0.03009849041700363,
-0.027968134731054306,
0.025580940768122673,
-0.09677345305681229,
-0.08678427338600159,
0.011425281874835491,
0.03659094497561455,
0.030582081526517868,
-0.031214872375130653,
0.0724254846572876,
-0.053719762712717056,
0.047944702208042145,
0.006264633499085903,
0.011709794402122498,
-0.109951913356781,
-0.013300122693181038,
-0.21165777742862701,
0.061897557228803635,
-0.017257576808333397,
-0.025637583807110786,
-0.10895666480064392,
-0.07334477454423904,
-0.03127969056367874,
0.025489872321486473,
0.05226637050509453,
0.23312662541866302,
-0.3768836259841919,
0.009988621808588505,
0.2558037042617798,
-0.07900182902812958,
-0.01609455980360508,
0.13760381937026978,
-0.08597934991121292,
0.1876741349697113,
0.03581484034657478,
0.06104137748479843,
0.04470210522413254,
-0.23225745558738708,
-0.0004901646170765162,
-0.007218386512249708,
-0.16243194043636322,
0.08590894192457199,
0.09013208001852036,
-0.023399535566568375,
-0.05700160935521126,
0.012586284428834915,
-0.09600085020065308,
0.03134668618440628,
-0.03543788194656372,
-0.03575466200709343,
0.002004712587222457,
0.07053175568580627,
0.0005246754153631628,
-0.038725219666957855,
-0.08993837237358093,
0.0120035819709301,
-0.15943516790866852,
-0.09737586975097656,
0.01769685186445713,
0.01386314444243908,
0.020594600588083267,
-0.05354193225502968,
0.13256050646305084,
0.055046770721673965,
0.01789890043437481,
-0.1316736936569214,
-0.021917294710874557,
0.0011010045418515801,
-0.023854494094848633,
0.02619829960167408,
0.06525677442550659,
0.03297238424420357,
0.061355315148830414,
0.019600629806518555,
0.013657307252287865,
-0.008198718540370464,
-0.033991578966379166,
-0.04404887557029724,
-0.09488499164581299,
-0.05198889225721359,
-0.06964205205440521,
0.14217692613601685,
-0.17995195090770721,
0.06513458490371704,
0.08614381402730942,
0.03927944600582123,
-0.04357661306858063,
-0.04180782288312912,
0.01972150430083275,
-0.01651167683303356,
-0.022042876109480858,
-0.07342804223299026,
0.06339973956346512,
0.02453184500336647,
-0.06852235645055771,
-0.019926847890019417,
-0.17815512418746948,
-0.24845317006111145,
0.04933594539761543,
0.041017405688762665,
-0.13821014761924744,
-0.06934879720211029,
-0.04745203256607056,
0.0004194916400592774,
-0.046402670443058014,
-0.03949707746505737,
0.16563181579113007,
0.06684236973524094,
0.08045558631420135,
-0.08025524020195007,
0.005274168215692043,
0.025331567972898483,
-0.07180163264274597,
0.009264216758310795,
0.07439233362674713,
0.03285592794418335,
-0.03784165903925896,
0.03156270086765289,
0.03558895364403725,
0.039797741919755936,
0.10290398448705673,
0.0685592070221901,
-0.02324868179857731,
-0.1271761655807495,
0.04795030876994133,
0.02086011692881584,
0.1261897087097168,
0.0031962725333869457,
0.032386861741542816,
0.04017386958003044,
0.010893141850829124,
-0.011107442900538445,
-0.1379508376121521,
-0.02218513749539852,
0.025080544874072075,
-0.06999385356903076,
-0.0379486083984375,
0.0838465690612793,
-0.03480676934123039,
0.10386689007282257,
-0.013295114040374756,
-0.07989552617073059,
0.019084779545664787,
-0.023538198322057724,
-0.10213477164506912,
0.18243391811847687,
-0.06092078611254692,
-0.1827743798494339,
-0.19128428399562836,
-0.0897156298160553,
-0.0032232808880507946,
0.01406973134726286,
0.02161015197634697,
-0.035975564271211624,
-0.005206716246902943,
-0.05103597044944763,
0.06637763231992722,
0.02545437030494213,
-0.09104353189468384,
-0.15502049028873444,
-0.031055457890033722,
-0.0035535285715013742,
-0.12516556680202484,
-0.05967925116419792,
-0.023513633757829666,
-0.02184871770441532,
0.027420058846473694,
0.014259498566389084,
0.05795479193329811,
0.13066492974758148,
-0.00904193613678217,
0.009033299051225185,
-0.03704357147216797,
0.2566414773464203,
-0.06195121258497238,
0.13284951448440552,
0.1478857398033142,
-0.023198308423161507,
0.08461237698793411,
0.14896756410598755,
0.06137854978442192,
-0.08788768202066422,
-0.025685830041766167,
-0.03423658758401871,
-0.088934026658535,
-0.14551275968551636,
-0.044846951961517334,
-0.05030335858464241,
-0.01440283190459013,
-0.007652269676327705,
0.038900092244148254,
0.08718167990446091,
0.007730958983302116,
-0.00825370755046606,
-0.0541946180164814,
0.07303248345851898,
0.06884602457284927,
0.005065689329057932,
-0.028975648805499077,
0.058866750448942184,
0.019733209162950516,
0.008669528178870678,
0.0658697634935379,
0.014288478530943394,
0.08242983371019363,
0.05954432114958763,
0.012774964794516563,
0.09571295976638794,
0.054641999304294586,
0.015042576007544994,
0.10865072160959244,
-0.004936265759170055,
-0.006682616658508778,
-0.05537905544042587,
-0.0841631144285202,
-0.026344189420342445,
0.054731983691453934,
0.004569509532302618,
0.05575340986251831,
-0.060054898262023926,
0.009404529817402363,
0.04630914703011513,
0.04332125559449196,
0.07467392832040787,
-0.18769966065883636,
-0.05182608217000961,
0.015879929065704346,
0.05164666846394539,
-0.09473321586847305,
0.007133043836802244,
0.025917332619428635,
-0.16789832711219788,
-0.02400554157793522,
-0.08603561669588089,
0.08811481297016144,
0.03987734392285347,
0.01699405163526535,
0.002139135729521513,
0.12021199613809586,
-0.04227658733725548,
0.09910152107477188,
-0.20694133639335632,
0.22487206757068634,
0.00799148716032505,
0.06981556117534637,
-0.06292635947465897,
0.03932783007621765,
0.03967896103858948,
0.019650427624583244,
0.2767234444618225,
0.007482410408556461,
-0.07868459075689316,
-0.07894395291805267,
-0.11152003705501556,
-0.012801640667021275,
0.06537278741598129,
-0.1061321422457695,
0.10350614041090012,
0.020703811198472977,
0.009967530146241188,
-0.011118011549115181,
0.06905218213796616,
-0.039843931794166565,
-0.0935836210846901,
0.02804650366306305,
-0.06915555894374847,
-0.013516778126358986,
-0.022702431306242943,
-0.02165161445736885,
-0.03848479688167572,
0.019727984443306923,
-0.15012739598751068,
-0.014144282788038254,
-0.12239443510770798,
0.13238847255706787,
0.04463038221001625,
-0.06313073635101318,
-0.04922020062804222,
-0.04104751721024513,
0.05530126020312309,
-0.05143314227461815,
-0.003963596187531948,
0.04894936457276344,
-0.05741352215409279,
-0.04798787087202072,
-0.08501583337783813,
0.1303076148033142,
0.08181290328502655,
0.02549014240503311,
-0.024343622848391533,
0.06347604840993881,
-0.04216159135103226,
-0.1386289745569229,
0.05687252804636955,
0.0048006740398705006,
0.0006259646033868194,
0.01811045967042446,
-0.11498071998357773,
-0.014521033503115177,
-0.14519375562667847,
0.06928938627243042,
0.11901164799928665,
0.260001003742218,
-0.09925992786884308,
0.09934879839420319,
0.07188157737255096,
-0.132808655500412,
-0.228252112865448,
-0.05998162925243378,
0.016460103914141655,
0.06786293536424637,
0.046561598777770996,
-0.1569828987121582,
0.030604401603341103,
0.03989465534687042,
-0.004285633098334074,
0.05433136969804764,
-0.2851276695728302,
-0.09119361639022827,
0.11368866264820099,
-0.022462323307991028,
0.035710450261831284,
-0.0589514821767807,
-0.03292490169405937,
0.02955061011016369,
-0.06620927155017853,
0.0037272979971021414,
0.06168109551072121,
0.06539306044578552,
0.0107288658618927,
0.061727266758680344,
0.0185388270765543,
-0.05499570071697235,
0.13719041645526886,
0.043525103479623795,
0.0640479028224945,
-0.04546056315302849,
-0.16388040781021118,
0.09909062832593918,
-0.06626638770103455,
0.1301860213279724,
0.03266679868102074,
0.029184512794017792,
-0.20621508359909058,
-0.025169208645820618,
-0.0939134806394577,
0.12446597218513489,
-0.07489810138940811,
-0.00758480466902256,
-0.042946308851242065,
0.06401078402996063,
0.019290385767817497,
-0.023252246901392937,
-0.0768510103225708,
-0.08842781186103821,
0.02183401584625244,
0.1040034145116806,
0.03801048547029495,
0.07617495954036713,
-0.12321627885103226,
0.03188109025359154,
0.03717333450913429,
0.048007555305957794,
-0.12432663142681122,
-0.027125753462314606,
0.09819899499416351,
0.024527819827198982,
0.15702448785305023,
0.07782090455293655,
-0.0906836986541748,
-0.010573090985417366,
0.026416882872581482,
-0.14772149920463562,
-0.13190250098705292,
-0.029147455468773842,
0.023606829345226288,
-0.09942982345819473,
-0.11621938645839691,
0.08810477703809738,
-0.05503698065876961,
0.00035277780261822045,
0.007979749701917171,
0.06330087035894394,
-0.0752313882112503,
0.021604303270578384,
0.06028958037495613,
0.0689014419913292,
-0.03727953881025314,
0.03095296397805214,
0.08131859451532364,
-0.06411242485046387,
0.020229779183864594,
0.05229414626955986,
-0.0802277997136116,
-0.04063941538333893,
-0.048984769731760025,
0.2561149001121521,
-0.02602475881576538,
0.01636474020779133,
-0.03997909650206566,
-0.03820062056183815,
-0.00694811949506402,
0.14223739504814148,
0.05108019337058067,
0.00007471998105756938,
-0.1435001641511917,
-0.0005784433451481164,
-0.1090431809425354,
0.09224526584148407,
0.09564090520143509,
-0.058816246688365936,
0.00838343147188425,
0.1070636659860611,
0.02405361458659172,
0.05176163837313652,
-0.0295170359313488,
-0.053397782146930695,
-0.07780785113573074,
0.005655557382851839,
0.02541702426970005,
-0.018967481330037117,
-0.0014839645009487867,
-0.0014512983616441488,
-0.016487514600157738,
-0.01776927337050438,
0.01250844169408083,
-0.01993165910243988,
-0.02844879776239395,
-0.0007511776057071984,
-0.022237954661250114,
0.08201051503419876,
-0.10995794832706451,
-0.0648115947842598,
0.05109906569123268,
-0.08501525223255157,
0.03029438480734825,
0.13116127252578735,
-0.03708634525537491,
0.0024148973170667887,
-0.11208003014326096,
0.04216895252466202,
-0.020753439515829086,
0.007297297939658165,
0.015572586096823215,
-0.154456228017807,
0.04359164461493492,
-0.0583723783493042,
-0.030193163082003593,
-0.024141909554600716,
-0.01061486266553402,
-0.07128315418958664,
0.08614727109670639,
0.09837057441473007,
-0.027343252673745155,
-0.06585335731506348,
0.05858086049556732,
0.07764904946088791,
0.08939603716135025,
0.12025708705186844,
-0.04303354769945145,
0.14314205944538116,
-0.1655639261007309,
-0.02393854223191738,
-0.008781139738857746,
-0.015284442342817783,
-0.06900212168693542,
-0.05549311265349388,
0.0766112208366394,
-0.018682662397623062,
0.06305544823408127,
0.08309878408908844,
0.056886665523052216,
0.01410868763923645,
0.09618566185235977,
-0.0026187212206423283,
-0.005438885185867548,
-0.007277090102434158,
0.0026422985829412937,
-0.006238813977688551,
0.036706458777189255,
0.043999310582876205,
-0.02211723104119301,
0.024635514244437218,
0.18863731622695923,
0.0775362029671669,
0.057069603353738785,
0.08949098736047745,
0.018997816368937492,
-0.017758026719093323,
-0.05901457741856575,
-0.0013787888456135988,
0.04989270120859146,
0.006861453875899315,
-0.07100513577461243,
0.09470619261264801,
0.10824621468782425,
-0.11490864306688309,
0.12052266299724579,
0.005193926859647036,
-0.11558393388986588,
-0.118948794901371,
-0.20020924508571625,
0.007438960485160351,
0.0016208201413974166,
-0.009824627079069614,
-0.06508298218250275,
0.06265448778867722,
0.09584559500217438,
-0.011931425891816616,
-0.015528183430433273,
0.12580056488513947,
-0.09291177988052368,
-0.03885479271411896,
0.044767268002033234,
-0.018797487020492554,
0.052809420973062515,
-0.06011494621634483,
0.05549236014485359,
0.03746791183948517,
-0.04434169456362724,
0.056975144892930984,
0.05460574850440025,
0.13845667243003845,
-0.00014453484618570656,
-0.14973732829093933,
-0.09311045706272125,
-0.021017540246248245,
0.014139347709715366,
-0.0581592433154583,
0.11393527686595917,
0.060964930802583694,
-0.0005838591605424881,
0.0032776386942714453,
0.12440552562475204,
-0.0022345653269439936,
-0.11669252812862396,
-0.12977619469165802,
0.25513938069343567,
-0.005832596682012081,
0.025476964190602303,
-0.0926230251789093,
-0.06290047615766525,
0.038133617490530014,
0.1862751543521881,
0.13840077817440033,
-0.0457988977432251,
-0.044701315462589264,
0.04534114897251129,
0.024029552936553955,
0.09838700294494629,
0.03447095304727554,
0.03774695470929146,
0.17943817377090454,
-0.09189240634441376,
0.054487574845552444,
-0.10566315054893494,
-0.055058807134628296,
-0.005288227926939726,
0.11117778718471527,
0.015705375000834465,
-0.008530205115675926,
-0.06118088588118553,
0.13282042741775513,
-0.05725930258631706,
-0.1567264199256897,
0.0023877497296780348,
-0.051383018493652344,
-0.057499051094055176,
-0.018344776704907417,
0.0153805585578084,
0.029464973136782646,
0.11218414455652237,
-0.037233248353004456,
-0.06346001476049423,
0.25903695821762085,
-0.01670440100133419,
-0.060037318617105484,
-0.03269064053893089,
0.006406740751117468,
-0.19981762766838074,
0.1805897206068039,
-0.002847264287993312,
0.0924624651670456,
0.09714513272047043,
0.03985299915075302,
-0.08480078727006912,
0.03303806483745575,
0.01147837657481432,
0.03334128484129906,
-0.02797711454331875,
0.04694738611578941,
0.0008046340080909431,
-0.07546401023864746,
-0.0029646530747413635,
-0.1303092986345291,
0.09326409548521042,
-0.03481149300932884,
-0.07921796292066574,
-0.06839260458946228,
0.10177725553512573,
-0.027292119339108467,
0.12053027004003525,
0.14579059183597565,
-0.020048050209879875,
0.012025200761854649,
-0.061195988208055496,
-0.010230745188891888,
0.08281263709068298,
0.09269339591264725,
-0.0060235559940338135,
-0.11254573613405228,
0.0266620721668005,
-0.05056128278374672,
-0.017942748963832855,
-0.20369605720043182,
-0.08747562021017075,
0.02997121587395668,
-0.02709183655679226,
0.02583225443959236,
0.0886966735124588,
-0.00041047559352591634,
0.08196008950471878,
-0.0412043035030365,
0.03704022616147995,
-0.01065395213663578,
0.17118312418460846,
-0.10153798758983612,
-0.0759037509560585
] |
null | null |
flair
|
## English Verb Disambiguation in Flair (default model)
This is the standard verb disambiguation model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **89,34** (Ontonotes) - predicts [Proposition Bank verb frames](http://verbs.colorado.edu/propbank/framesets-english-aliases/).
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/frame-english")
# make example sentence
sentence = Sentence("George returned to Berlin to return his hat.")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following frame tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('frame'):
print(entity)
```
This yields the following output:
```
Span [2]: "returned" [− Labels: return.01 (0.9951)]
Span [6]: "return" [− Labels: return.02 (0.6361)]
```
So, the word "*returned*" is labeled as **return.01** (as in *go back somewhere*) while "*return*" is labeled as **return.02** (as in *give back something*) in the sentence "*George returned to Berlin to return his hat*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import ColumnCorpus
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. load the corpus (Ontonotes does not ship with Flair, you need to download and reformat into a column format yourself)
corpus = ColumnCorpus(
"resources/tasks/srl", column_format={1: "text", 11: "frame"}
)
# 2. what tag do we want to predict?
tag_type = 'frame'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
BytePairEmbeddings("en"),
FlairEmbeddings("news-forward"),
FlairEmbeddings("news-backward"),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/frame-english',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following paper when using this model.
```
@inproceedings{akbik2019flair,
title={FLAIR: An easy-to-use framework for state-of-the-art NLP},
author={Akbik, Alan and Bergmann, Tanja and Blythe, Duncan and Rasul, Kashif and Schweter, Stefan and Vollgraf, Roland},
booktitle={{NAACL} 2019, 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)},
pages={54--59},
year={2019}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
{"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["ontonotes"], "widget": [{"text": "George returned to Berlin to return his hat."}]}
|
token-classification
|
flair/frame-english
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:ontonotes",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #region-us
|
## English Verb Disambiguation in Flair (default model)
This is the standard verb disambiguation model for English that ships with Flair.
F1-Score: 89,34 (Ontonotes) - predicts Proposition Bank verb frames.
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the word "*returned*" is labeled as return.01 (as in *go back somewhere*) while "*return*" is labeled as return.02 (as in *give back something*) in the sentence "*George returned to Berlin to return his hat*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following paper when using this model.
---
### Issues?
The Flair issue tracker is available here.
|
[
"## English Verb Disambiguation in Flair (default model)\n\nThis is the standard verb disambiguation model for English that ships with Flair.\n\nF1-Score: 89,34 (Ontonotes) - predicts Proposition Bank verb frames.\n\nBased on Flair embeddings and LSTM-CRF.\n\n---",
"### Demo: How to use in Flair\n\nRequires: Flair ('pip install flair')\n\n\n\nThis yields the following output:\n\n\nSo, the word \"*returned*\" is labeled as return.01 (as in *go back somewhere*) while \"*return*\" is labeled as return.02 (as in *give back something*) in the sentence \"*George returned to Berlin to return his hat*\". \n\n\n---",
"### Training: Script to train this model\n\nThe following Flair script was used to train this model: \n\n\n\n\n\n---",
"### Cite\n\nPlease cite the following paper when using this model.\n\n\n\n---",
"### Issues?\n\nThe Flair issue tracker is available here."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #region-us \n",
"## English Verb Disambiguation in Flair (default model)\n\nThis is the standard verb disambiguation model for English that ships with Flair.\n\nF1-Score: 89,34 (Ontonotes) - predicts Proposition Bank verb frames.\n\nBased on Flair embeddings and LSTM-CRF.\n\n---",
"### Demo: How to use in Flair\n\nRequires: Flair ('pip install flair')\n\n\n\nThis yields the following output:\n\n\nSo, the word \"*returned*\" is labeled as return.01 (as in *go back somewhere*) while \"*return*\" is labeled as return.02 (as in *give back something*) in the sentence \"*George returned to Berlin to return his hat*\". \n\n\n---",
"### Training: Script to train this model\n\nThe following Flair script was used to train this model: \n\n\n\n\n\n---",
"### Cite\n\nPlease cite the following paper when using this model.\n\n\n\n---",
"### Issues?\n\nThe Flair issue tracker is available here."
] |
[
37,
75,
96,
22,
15,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #region-us \n## English Verb Disambiguation in Flair (default model)\n\nThis is the standard verb disambiguation model for English that ships with Flair.\n\nF1-Score: 89,34 (Ontonotes) - predicts Proposition Bank verb frames.\n\nBased on Flair embeddings and LSTM-CRF.\n\n---### Demo: How to use in Flair\n\nRequires: Flair ('pip install flair')\n\n\n\nThis yields the following output:\n\n\nSo, the word \"*returned*\" is labeled as return.01 (as in *go back somewhere*) while \"*return*\" is labeled as return.02 (as in *give back something*) in the sentence \"*George returned to Berlin to return his hat*\". \n\n\n---### Training: Script to train this model\n\nThe following Flair script was used to train this model: \n\n\n\n\n\n---### Cite\n\nPlease cite the following paper when using this model.\n\n\n\n---### Issues?\n\nThe Flair issue tracker is available here."
] |
[
-0.09481070190668106,
-0.006538239773362875,
-0.004002320114523172,
0.10720296949148178,
0.11911100149154663,
0.012423484586179256,
0.12272780388593674,
0.12201778590679169,
0.06094275787472725,
0.10303807258605957,
0.1122765988111496,
0.05828210338950157,
0.12299235910177231,
0.1151522770524025,
0.060061000287532806,
-0.25913599133491516,
-0.0033996854908764362,
-0.05461311340332031,
-0.01845511980354786,
0.1335974782705307,
0.13550248742103577,
-0.013423279859125614,
0.04969935864210129,
0.0015294880140572786,
-0.08174311369657516,
-0.00821553636342287,
0.02156534232199192,
-0.02955000475049019,
0.1327802836894989,
0.04267599806189537,
0.12419445067644119,
-0.005059915594756603,
0.11326359212398529,
-0.15310052037239075,
0.03419141843914986,
0.04702107980847359,
-0.022162266075611115,
0.05311690270900726,
0.015230925753712654,
-0.048735715448856354,
0.210543692111969,
-0.020356250926852226,
0.00531109469011426,
0.0640222504734993,
-0.18837419152259827,
-0.17316634953022003,
-0.1222086176276207,
0.08392222225666046,
0.08217384666204453,
0.07685533910989761,
-0.027491288259625435,
0.023859193548560143,
-0.03384105861186981,
0.06085624545812607,
0.14341746270656586,
-0.1752893477678299,
-0.03062494657933712,
0.12223231047391891,
0.08295504748821259,
0.008701597340404987,
-0.10628378391265869,
0.015007137320935726,
-0.032440464943647385,
0.05762150138616562,
-0.01385795883834362,
-0.013977942988276482,
0.04294547066092491,
0.041383787989616394,
-0.15436320006847382,
-0.1033320426940918,
0.23833903670310974,
0.02377450279891491,
-0.12182141840457916,
-0.10117358714342117,
-0.06341461837291718,
0.008775800466537476,
-0.009005180560052395,
-0.07268354296684265,
-0.012664501555263996,
0.04068424552679062,
0.16706697642803192,
-0.09951398521661758,
-0.08181418478488922,
-0.05679548531770706,
0.018537649884819984,
0.08255179971456528,
-0.04394121468067169,
0.062050070613622665,
-0.12091370671987534,
0.1366664469242096,
-0.058520782738924026,
-0.11209489405155182,
-0.0010387221118435264,
-0.03894801065325737,
0.004284009337425232,
-0.0025702915154397488,
-0.003875491674989462,
-0.09739576280117035,
-0.01256980374455452,
0.16618481278419495,
0.11726297438144684,
-0.009766093455255032,
-0.02754802815616131,
0.06553542613983154,
0.02569851279258728,
0.18883202970027924,
-0.1126101166009903,
-0.024469483643770218,
0.06405845284461975,
0.04934617131948471,
-0.02483765408396721,
-0.05957280844449997,
-0.11999402940273285,
0.008496522903442383,
0.003140165703371167,
0.015371312387287617,
0.026303663849830627,
0.018219485878944397,
-0.06456523388624191,
-0.01585257798433304,
0.1606212705373764,
-0.11964351683855057,
0.007411657366901636,
0.05429920554161072,
-0.11036276817321777,
0.0960010290145874,
0.1143985390663147,
-0.01805691607296467,
-0.014242776669561863,
0.05531115457415581,
-0.07467366755008698,
0.03554295003414154,
-0.0805426836013794,
-0.12268449366092682,
0.006368414964526892,
-0.01136834267526865,
-0.028314273804426193,
-0.05906492471694946,
-0.16138049960136414,
-0.05332617834210396,
0.032750874757766724,
-0.01654387265443802,
0.026201069355010986,
-0.0918787494301796,
-0.08284611254930496,
0.004409319721162319,
0.03425285592675209,
0.02192848175764084,
-0.02355123497545719,
0.0650014653801918,
-0.0522819384932518,
0.040861837565898895,
-0.007573000155389309,
0.010398303158581257,
-0.09917990118265152,
-0.0029571724589914083,
-0.2103002816438675,
0.051951099187135696,
-0.016353633254766464,
-0.027198027819395065,
-0.1035727709531784,
-0.07143305242061615,
-0.017285790294408798,
0.020130960270762444,
0.05571464076638222,
0.22449615597724915,
-0.37093624472618103,
0.008289456367492676,
0.2610163688659668,
-0.08815216273069382,
-0.012534881010651588,
0.12388085573911667,
-0.08861877024173737,
0.185184046626091,
0.0344221293926239,
0.06972751766443253,
0.040678463876247406,
-0.23085488379001617,
-0.0032462109811604023,
-0.022460730746388435,
-0.14987260103225708,
0.08575782924890518,
0.08320355415344238,
-0.03139401599764824,
-0.06321558356285095,
0.01696951873600483,
-0.0990614965558052,
0.02373342402279377,
-0.03203216567635536,
-0.03499075025320053,
-0.007225077599287033,
0.07617871463298798,
-0.0036843023262917995,
-0.034599050879478455,
-0.09612831473350525,
0.008953262120485306,
-0.1562204360961914,
-0.09383458644151688,
0.013939659111201763,
0.0012875142274424434,
0.024955950677394867,
-0.051865916699171066,
0.13158917427062988,
0.05390775948762894,
0.018050678074359894,
-0.14006493985652924,
-0.0114723090082407,
-0.00483638746663928,
-0.024402214214205742,
0.035663433372974396,
0.06822893768548965,
0.027104433625936508,
0.06680092960596085,
0.015779836103320122,
0.012271649204194546,
-0.013717801310122013,
-0.03030645102262497,
-0.031169982627034187,
-0.09669199585914612,
-0.0585833303630352,
-0.07640378177165985,
0.12598098814487457,
-0.17675264179706573,
0.0708109512925148,
0.07589517533779144,
0.045998815447092056,
-0.04620863124728203,
-0.049649737775325775,
0.016332974657416344,
-0.010418672114610672,
-0.026680411770939827,
-0.07244081795215607,
0.059950023889541626,
0.024903077632188797,
-0.06861797720193863,
-0.03771882504224777,
-0.18778008222579956,
-0.26498281955718994,
0.0497773252427578,
0.03972873091697693,
-0.13965106010437012,
-0.071828693151474,
-0.04908926784992218,
0.0053781731985509396,
-0.04100741818547249,
-0.04459217190742493,
0.19093802571296692,
0.06609765440225601,
0.07408245652914047,
-0.07376941293478012,
0.005061757750809193,
0.027124041691422462,
-0.07746719568967819,
-0.0003416560939513147,
0.062149062752723694,
0.029508167877793312,
-0.03656286746263504,
0.032770123332738876,
0.019915657117962837,
0.03878489509224892,
0.09490571916103363,
0.06613235920667648,
-0.012980345636606216,
-0.12748806178569794,
0.04305747151374817,
0.020598307251930237,
0.1394282430410385,
-0.0064336215145885944,
0.032601289451122284,
0.0393427275121212,
0.01638489030301571,
-0.011131592094898224,
-0.13600079715251923,
-0.020124973729252815,
0.02256765030324459,
-0.0675782635807991,
-0.06523247808218002,
0.07995124906301498,
-0.03948325291275978,
0.1017443835735321,
-0.02207356132566929,
-0.0649600476026535,
0.014070002362132072,
-0.023756179958581924,
-0.10734307765960693,
0.17798978090286255,
-0.07008089125156403,
-0.17489582300186157,
-0.19207477569580078,
-0.08635453879833221,
-0.01742694340646267,
0.020242951810359955,
0.02168693020939827,
-0.03570162132382393,
-0.010218924842774868,
-0.04634920880198479,
0.07143165916204453,
0.02612489089369774,
-0.09161502867937088,
-0.15260812640190125,
-0.03414604067802429,
0.00468903174623847,
-0.12765924632549286,
-0.05975928530097008,
-0.017842842265963554,
-0.019959449768066406,
0.029854869470000267,
0.01499760802835226,
0.058957789093256,
0.13706067204475403,
-0.015146433375775814,
0.017580315470695496,
-0.032767754048109055,
0.253644734621048,
-0.05306177958846092,
0.1355142444372177,
0.14737044274806976,
-0.03542337194085121,
0.07950066030025482,
0.15875355899333954,
0.05670374631881714,
-0.09073349833488464,
-0.025489680469036102,
-0.03805944323539734,
-0.09675606340169907,
-0.1505683958530426,
-0.04493440315127373,
-0.04985865205526352,
-0.018825886771082878,
0.005797514691948891,
0.0377013199031353,
0.09602469205856323,
0.01294146291911602,
-0.013721847906708717,
-0.059094130992889404,
0.07492976635694504,
0.07313717156648636,
0.0036149374209344387,
-0.030252112075686455,
0.057483166456222534,
0.02206932008266449,
0.016163034364581108,
0.058947738260030746,
0.029864683747291565,
0.08526743203401566,
0.0647522434592247,
-0.0006667072884738445,
0.1049647256731987,
0.050290390849113464,
0.009779896587133408,
0.12127257138490677,
-0.007968428544700146,
0.004235658794641495,
-0.05092580243945122,
-0.07631639391183853,
-0.029552560299634933,
0.0470285564661026,
0.003982696682214737,
0.056055184453725815,
-0.06501504778862,
-0.018743107095360756,
0.039260342717170715,
0.026342982426285744,
0.07114801555871964,
-0.1824018806219101,
-0.04998182877898216,
0.02014070749282837,
0.055756501853466034,
-0.0955868735909462,
0.0025541894137859344,
0.029323671013116837,
-0.16819033026695251,
-0.007071704138070345,
-0.083734430372715,
0.09101691097021103,
0.04000573977828026,
0.009491926990449429,
-0.005308755673468113,
0.10900399833917618,
-0.04597317799925804,
0.09506982564926147,
-0.2004929482936859,
0.23581765592098236,
0.007116701453924179,
0.06649520248174667,
-0.05912826582789421,
0.04563464596867561,
0.032374702394008636,
0.034830667078495026,
0.2713085412979126,
0.008783280849456787,
-0.09721209108829498,
-0.06947607547044754,
-0.09933169186115265,
-0.010781694203615189,
0.05058610439300537,
-0.11703622341156006,
0.10301104933023453,
0.026386819779872894,
0.010813364759087563,
-0.01191198080778122,
0.0715857520699501,
-0.05652712658047676,
-0.09751792252063751,
0.023747257888317108,
-0.07127712666988373,
-0.011302287690341473,
-0.022637730464339256,
-0.0211105328053236,
-0.03567805886268616,
0.015063567087054253,
-0.1519065499305725,
-0.01509019173681736,
-0.12453289330005646,
0.13410647213459015,
0.04307227581739426,
-0.06253334879875183,
-0.05305013060569763,
-0.04490044713020325,
0.05802324041724205,
-0.061893682926893234,
-0.009525319561362267,
0.038213495165109634,
-0.05456450581550598,
-0.0425582118332386,
-0.08037643134593964,
0.12877559661865234,
0.08968540281057358,
0.02119857631623745,
-0.01982862502336502,
0.066788449883461,
-0.03615680709481239,
-0.142562136054039,
0.057867877185344696,
0.01184538472443819,
0.027637260034680367,
0.020139649510383606,
-0.11627397686243057,
-0.0309432465583086,
-0.14412935078144073,
0.07685983180999756,
0.10197000950574875,
0.25091734528541565,
-0.09911826997995377,
0.09147636592388153,
0.06386279314756393,
-0.13943465054035187,
-0.21655626595020294,
-0.05377625301480293,
0.01143866591155529,
0.06827227771282196,
0.045740753412246704,
-0.15467169880867004,
0.016117138788104057,
0.024793829768896103,
-0.0013372452231124043,
0.05334991589188576,
-0.2678241431713104,
-0.09316269308328629,
0.11729525774717331,
-0.011178570799529552,
0.024475738406181335,
-0.049680445343256,
-0.027430733665823936,
0.0272702444344759,
-0.06455167382955551,
0.0035644671879708767,
0.07680907100439072,
0.05651983246207237,
0.013270051218569279,
0.07130186259746552,
0.018837515264749527,
-0.053092602640390396,
0.14157767593860626,
0.05825382471084595,
0.06235026568174362,
-0.04905045032501221,
-0.16438034176826477,
0.10553836822509766,
-0.06449240446090698,
0.12797364592552185,
0.027291471138596535,
0.029573334380984306,
-0.19758272171020508,
-0.02688363939523697,
-0.09044836461544037,
0.1262870579957962,
-0.07634686678647995,
-0.010089418850839138,
-0.04019094258546829,
0.055707186460494995,
0.01912001706659794,
-0.021206030622124672,
-0.07144097238779068,
-0.09795175492763519,
0.0375676155090332,
0.11870142817497253,
0.036319609731435776,
0.09110426902770996,
-0.13166168332099915,
0.036571208387613297,
0.035109587013721466,
0.04585158824920654,
-0.13321807980537415,
-0.028864996507763863,
0.09409314393997192,
0.02484283410012722,
0.15782609581947327,
0.07824862003326416,
-0.09374508261680603,
-0.013269541785120964,
0.028291871771216393,
-0.15096528828144073,
-0.1174246072769165,
-0.02064768597483635,
0.021141575649380684,
-0.09728790074586868,
-0.11290523409843445,
0.08510275185108185,
-0.05184752866625786,
-0.000982368248514831,
0.005421028006821871,
0.059254445135593414,
-0.08146350830793381,
0.016577936708927155,
0.06285125017166138,
0.06544443219900131,
-0.03379061818122864,
0.02345864847302437,
0.07757985591888428,
-0.06608114391565323,
0.02109374664723873,
0.033991437405347824,
-0.07769742608070374,
-0.03959747031331062,
-0.04992375522851944,
0.26584476232528687,
-0.028858480975031853,
0.010667224414646626,
-0.03418634831905365,
-0.0372655913233757,
-0.012945860624313354,
0.1344270259141922,
0.051904283463954926,
-0.0042352378368377686,
-0.1456395983695984,
-0.004880446009337902,
-0.1133827492594719,
0.09053324162960052,
0.09914755821228027,
-0.05518673360347748,
0.0027245343662798405,
0.09880085289478302,
0.028867637738585472,
0.048908352851867676,
-0.02580060251057148,
-0.05351024493575096,
-0.07412847876548767,
0.004068822134286165,
0.030765684321522713,
-0.01712021976709366,
-0.005583804566413164,
0.0006945890490897,
-0.013242233544588089,
-0.026157738640904427,
0.012069598771631718,
-0.017745913937687874,
-0.02913564443588257,
-0.000920241407584399,
-0.02189200185239315,
0.08293856680393219,
-0.10443763434886932,
-0.06116928160190582,
0.05545461177825928,
-0.08762048929929733,
0.027523431926965714,
0.13152973353862762,
-0.04445072263479233,
0.008212119340896606,
-0.10932108014822006,
0.05321180820465088,
-0.019904758781194687,
0.011196990497410297,
0.011180538684129715,
-0.1585407853126526,
0.04749570041894913,
-0.05738174542784691,
-0.03094867616891861,
-0.024180926382541656,
-0.0056052361615002155,
-0.07146759331226349,
0.07898188382387161,
0.10662782937288284,
-0.03589852899312973,
-0.060733407735824585,
0.05465315282344818,
0.08215805888175964,
0.09426943957805634,
0.11248264461755753,
-0.041016820818185806,
0.14761275053024292,
-0.1729886829853058,
-0.025053177028894424,
-0.003072163788601756,
-0.009489213116466999,
-0.060249943286180496,
-0.055030107498168945,
0.07192302495241165,
-0.01357747707515955,
0.06503073871135712,
0.0803431048989296,
0.05436862260103226,
0.012550074607133865,
0.10380520671606064,
0.011674195528030396,
-0.0037889406085014343,
0.006853272207081318,
-0.014331720769405365,
-0.0063131144270300865,
0.03605860471725464,
0.04596933722496033,
-0.032163601368665695,
-0.002226844197139144,
0.19240263104438782,
0.06658520549535751,
0.04494696110486984,
0.07335606217384338,
0.016248682513833046,
-0.024889083579182625,
-0.04568914324045181,
-0.01183994673192501,
0.05807669833302498,
0.0015799354296177626,
-0.06943889707326889,
0.07890769094228745,
0.11019463837146759,
-0.12770481407642365,
0.12933441996574402,
0.01077191811054945,
-0.11769573390483856,
-0.11533188819885254,
-0.21953703463077545,
0.011659127660095692,
-0.0042879641987383366,
-0.0057821995578706264,
-0.06335467845201492,
0.0511808767914772,
0.08740010857582092,
-0.020772388204932213,
-0.01866946741938591,
0.11767233908176422,
-0.08918550610542297,
-0.03922007977962494,
0.028773782774806023,
-0.011191547848284245,
0.050028443336486816,
-0.06331382691860199,
0.06026477366685867,
0.042043138295412064,
-0.04306434094905853,
0.05667098984122276,
0.06142804026603699,
0.1461000144481659,
-0.003115307306870818,
-0.14620481431484222,
-0.08334993571043015,
-0.021678537130355835,
0.011116601526737213,
-0.056368499994277954,
0.13575203716754913,
0.05761963129043579,
0.0031155303586274385,
0.0034012661781162024,
0.11245056986808777,
0.002351070288568735,
-0.10690509527921677,
-0.1374436467885971,
0.24824006855487823,
-0.008077802136540413,
0.02338113635778427,
-0.0810338631272316,
-0.06056315451860428,
0.03428289294242859,
0.17040927708148956,
0.14140227437019348,
-0.021038079634308815,
-0.04591246694326401,
0.03375864401459694,
0.023203181102871895,
0.10284680128097534,
0.03489498794078827,
0.029411418363451958,
0.17392267286777496,
-0.08332888036966324,
0.05409379303455353,
-0.10674998909235,
-0.052362799644470215,
0.004989802371710539,
0.1032162457704544,
0.010340378619730473,
-0.023723969236016273,
-0.056180018931627274,
0.14057238399982452,
-0.056466784328222275,
-0.15972082316875458,
0.020833555608987808,
-0.05429431423544884,
-0.0509454570710659,
-0.012645222246646881,
0.030295338481664658,
0.024077896028757095,
0.1109570637345314,
-0.04245184734463692,
-0.06468437612056732,
0.27729716897010803,
-0.016084903851151466,
-0.05538197234272957,
-0.0432211235165596,
-0.0015780521789565682,
-0.20728103816509247,
0.18600931763648987,
-0.005352707579731941,
0.09805930405855179,
0.09442251920700073,
0.04109473526477814,
-0.07911024987697601,
0.030011076480150223,
0.009770955890417099,
0.04982079938054085,
-0.03209443390369415,
0.05248972028493881,
-0.0030743295792490244,
-0.08394269645214081,
-0.013006387278437614,
-0.12991644442081451,
0.09085901081562042,
-0.043360572308301926,
-0.07538893818855286,
-0.06625042855739594,
0.10983426868915558,
-0.038484957069158554,
0.12370796501636505,
0.13204814493656158,
-0.025981657207012177,
0.01410618145018816,
-0.05656428635120392,
-0.006401095539331436,
0.08517035841941833,
0.09453325718641281,
-0.001187568181194365,
-0.11198665201663971,
0.022185994312167168,
-0.05297593027353287,
-0.022161543369293213,
-0.20921888947486877,
-0.08531484007835388,
0.03549116477370262,
-0.03207015246152878,
0.024468177929520607,
0.08814643323421478,
-0.014687343500554562,
0.08202623575925827,
-0.04524468630552292,
0.03450670465826988,
-0.013846160843968391,
0.17127715051174164,
-0.10044123977422714,
-0.07604389637708664
] |
null | null |
flair
|
# Danish NER in Flair (default model)
This is the standard 4-class NER model for Danish that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **81.78** (DaNER)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| MISC | other name |
Based on Transformer embeddings and LSTM-CRF.
---
# Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-danish")
# make example sentence
sentence = Sentence("Jens Peter Hansen kommer fra Danmark")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [1,2,3]: "Jens Peter Hansen" [− Labels: PER (0.9961)]
Span [6]: "Danmark" [− Labels: LOC (0.9816)]
```
So, the entities "*Jens Peter Hansen*" (labeled as a **person**) and "*Danmark*" (labeled as a **location**) are found in the sentence "*Jens Peter Hansen kommer fra Danmark*".
---
### Training: Script to train this model
The model was trained by the [DaNLP project](https://github.com/alexandrainst/danlp) using the [DaNE corpus](https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#danish-dependency-treebank-dane-dane). Check their repo for more information.
The following Flair script may be used to train such a model:
```python
from flair.data import Corpus
from flair.datasets import DANE
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. get the corpus
corpus: Corpus = DANE()
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
# GloVe embeddings
WordEmbeddings('da'),
# contextual string embeddings, forward
FlairEmbeddings('da-forward'),
# contextual string embeddings, backward
FlairEmbeddings('da-backward'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/ner-danish',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following papers when using this model.
```
@inproceedings{akbik-etal-2019-flair,
title = "{FLAIR}: An Easy-to-Use Framework for State-of-the-Art {NLP}",
author = "Akbik, Alan and
Bergmann, Tanja and
Blythe, Duncan and
Rasul, Kashif and
Schweter, Stefan and
Vollgraf, Roland",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
year = "2019",
url = "https://www.aclweb.org/anthology/N19-4010",
pages = "54--59",
}
```
And check the [DaNLP project](https://github.com/alexandrainst/danlp) for more information.
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
{"language": "da", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["DaNE"], "widget": [{"text": "Jens Peter Hansen kommer fra Danmark"}]}
|
token-classification
|
flair/ner-danish
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"da",
"dataset:DaNE",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"da"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #da #dataset-DaNE #region-us
|
Danish NER in Flair (default model)
===================================
This is the standard 4-class NER model for Danish that ships with Flair.
F1-Score: 81.78 (DaNER)
Predicts 4 tags:
Based on Transformer embeddings and LSTM-CRF.
---
Demo: How to use in Flair
=========================
Requires: Flair ('pip install flair')
This yields the following output:
So, the entities "*Jens Peter Hansen*" (labeled as a person) and "*Danmark*" (labeled as a location) are found in the sentence "*Jens Peter Hansen kommer fra Danmark*".
---
### Training: Script to train this model
The model was trained by the DaNLP project using the DaNE corpus. Check their repo for more information.
The following Flair script may be used to train such a model:
---
### Cite
Please cite the following papers when using this model.
And check the DaNLP project for more information.
---
### Issues?
The Flair issue tracker is available here.
|
[
"### Training: Script to train this model\n\n\nThe model was trained by the DaNLP project using the DaNE corpus. Check their repo for more information.\n\n\nThe following Flair script may be used to train such a model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following papers when using this model.\n\n\nAnd check the DaNLP project for more information.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #da #dataset-DaNE #region-us \n",
"### Training: Script to train this model\n\n\nThe model was trained by the DaNLP project using the DaNE corpus. Check their repo for more information.\n\n\nThe following Flair script may be used to train such a model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following papers when using this model.\n\n\nAnd check the DaNLP project for more information.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
36,
48,
27,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #da #dataset-DaNE #region-us \n### Training: Script to train this model\n\n\nThe model was trained by the DaNLP project using the DaNE corpus. Check their repo for more information.\n\n\nThe following Flair script may be used to train such a model:\n\n\n\n\n---### Cite\n\n\nPlease cite the following papers when using this model.\n\n\nAnd check the DaNLP project for more information.\n\n\n\n\n---### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
-0.09072000533342361,
0.14029118418693542,
-0.0039824326522648335,
0.07798761129379272,
0.053663093596696854,
0.04264770448207855,
0.1595097780227661,
0.07726062834262848,
-0.012525537051260471,
-0.016284186393022537,
0.1566057801246643,
0.11703546345233917,
0.023049192503094673,
0.16111260652542114,
0.07812467962503433,
-0.297065794467926,
-0.0027458490803837776,
0.010004966519773006,
-0.06759779155254364,
0.11675965785980225,
0.09028486162424088,
-0.045194488018751144,
0.04971249774098396,
0.004290544893592596,
-0.06884029507637024,
0.015732867643237114,
-0.05147416889667511,
-0.030310766771435738,
0.15368735790252686,
-0.05879472196102142,
0.16690737009048462,
-0.019957486540079117,
0.0734485536813736,
-0.1439029723405838,
0.05424056574702263,
0.015063912607729435,
-0.00018005806487053633,
0.09762615710496902,
0.03768689185380936,
0.010860390029847622,
0.20509065687656403,
0.0264787245541811,
0.0377560630440712,
-0.014207984320819378,
-0.13276629149913788,
-0.12184293568134308,
-0.10398124903440475,
0.08181797713041306,
0.1305955946445465,
0.10793416947126389,
-0.0341874435544014,
0.12332678586244583,
-0.10909654200077057,
0.030440952628850937,
-0.049505628645420074,
-0.10321695357561111,
-0.03311920166015625,
0.21483173966407776,
-0.0026235529221594334,
-0.027368644252419472,
-0.042293399572372437,
0.08420176804065704,
0.046969231218099594,
0.06316179782152176,
0.006678012665361166,
-0.03743235394358635,
0.016143931075930595,
0.003268798114731908,
-0.12307905405759811,
-0.06979697197675705,
0.23674747347831726,
-0.009645555168390274,
-0.06279982626438141,
-0.07444087415933609,
0.00973169133067131,
0.07402703911066055,
0.011420739814639091,
-0.1393827497959137,
-0.02482917532324791,
0.042327333241701126,
0.15950825810432434,
-0.08554460853338242,
-0.07563120871782303,
-0.10066992789506912,
0.007419982925057411,
0.062140002846717834,
0.030497174710035324,
0.008846063166856766,
-0.20756304264068604,
0.12278380244970322,
0.014459250494837761,
-0.061968281865119934,
0.056425027549266815,
-0.050529301166534424,
0.015996010974049568,
0.006763074547052383,
-0.029369503259658813,
-0.0555901825428009,
0.0709807351231575,
0.16673804819583893,
0.17728127539157867,
0.00788918323814869,
-0.037563472986221313,
0.04742693901062012,
0.0032111634500324726,
0.08761797845363617,
-0.044418223202228546,
0.00197167182341218,
0.07639860361814499,
0.04649609699845314,
0.057754144072532654,
-0.018319496884942055,
-0.13463470339775085,
-0.021730169653892517,
-0.02128538489341736,
0.08124120533466339,
-0.011782009154558182,
0.021952839568257332,
-0.03944065421819687,
-0.026698216795921326,
0.1269591748714447,
-0.05715872347354889,
-0.013502961955964565,
0.010302400216460228,
-0.08163612335920334,
0.0568724162876606,
0.048799701035022736,
0.02034319005906582,
-0.017169423401355743,
0.0545567125082016,
-0.060664787888526917,
-0.03667404130101204,
-0.08984974026679993,
-0.1050354540348053,
0.037675876170396805,
-0.12025836110115051,
0.06088532507419586,
-0.07061141729354858,
-0.176393061876297,
-0.0051056318916380405,
0.07010450959205627,
-0.007896101102232933,
-0.02707768604159355,
-0.0831838995218277,
-0.06918301433324814,
-0.05879083648324013,
0.030257245525717735,
0.11075035482645035,
-0.00020537279488053173,
0.02836119383573532,
0.015487100929021835,
0.05860825628042221,
-0.035687144845724106,
0.019323598593473434,
-0.04969092458486557,
0.0385671891272068,
-0.09559635072946548,
0.0691484659910202,
-0.028514113277196884,
-0.01362968422472477,
-0.07769537717103958,
-0.058285340666770935,
-0.0836091861128807,
0.031220775097608566,
0.053438592702150345,
0.249281644821167,
-0.2833634316921234,
0.011275816708803177,
0.11506548523902893,
-0.11767573654651642,
-0.07866957783699036,
0.10820099711418152,
-0.09118212759494781,
0.11317738890647888,
0.036593541502952576,
0.2007531374692917,
0.1670876443386078,
-0.22884154319763184,
0.12778449058532715,
0.07600783556699753,
-0.059846244752407074,
-0.010106926783919334,
0.08502911031246185,
-0.02681821770966053,
-0.1140810027718544,
0.0149079579859972,
-0.1143481433391571,
0.07190696895122528,
-0.00750327855348587,
-0.08485129475593567,
0.04286419600248337,
-0.032627008855342865,
0.009893485344946384,
0.016119467094540596,
0.03905590623617172,
-0.0025115772150456905,
-0.042362235486507416,
-0.079073965549469,
0.1308470219373703,
-0.01110035553574562,
-0.02112346887588501,
-0.07308809459209442,
0.021222200244665146,
-0.032471708953380585,
0.00209564040414989,
-0.14925964176654816,
-0.03757694736123085,
-0.027896296232938766,
-0.02089804969727993,
0.015182921662926674,
0.05371588468551636,
0.035156745463609695,
0.054498493671417236,
0.024740178138017654,
0.02524266391992569,
-0.023180222138762474,
-0.01879303716123104,
-0.052664466202259064,
-0.06702950596809387,
-0.057028841227293015,
-0.11559441685676575,
0.21062766015529633,
-0.21022635698318481,
0.04324344918131828,
-0.0716010183095932,
0.06766411662101746,
-0.0032375119626522064,
-0.013428747653961182,
0.09742465615272522,
0.03814835101366043,
-0.03283340483903885,
-0.05215846374630928,
0.0830543115735054,
-0.07550918310880661,
-0.08126938343048096,
-0.13523781299591064,
0.005648133810609579,
-0.019650572910904884,
0.09493808448314667,
-0.02711588144302368,
-0.03603056073188782,
-0.04528171569108963,
-0.02914690412580967,
-0.011591782793402672,
-0.03398214280605316,
0.014699460007250309,
0.14389480650424957,
0.045052286237478256,
0.06511996686458588,
-0.020331986248493195,
0.028692984953522682,
-0.02639784663915634,
0.00855906493961811,
-0.06243910640478134,
0.11707443743944168,
0.11786232143640518,
0.06493133306503296,
0.042768556624650955,
0.0013787713833153248,
-0.0052154106087982655,
0.026327142491936684,
-0.005324894096702337,
-0.02476087026298046,
-0.03833642601966858,
-0.04882308840751648,
0.015890780836343765,
0.15231095254421234,
-0.07363653182983398,
0.011012366972863674,
0.039704542607069016,
0.0004825679352506995,
0.077334463596344,
-0.19571810960769653,
-0.11443530768156052,
-0.009442143142223358,
-0.053939253091812134,
-0.0828329399228096,
0.12570157647132874,
-0.09681428223848343,
0.05811469256877899,
-0.03821299225091934,
-0.1023644506931305,
0.03080763854086399,
-0.017583198845386505,
-0.08812738955020905,
0.18643531203269958,
-0.06245005503296852,
-0.243647038936615,
-0.1259341835975647,
0.11786730587482452,
-0.011524707078933716,
0.008239868097007275,
-0.005164256319403648,
-0.12784147262573242,
0.014415833167731762,
-0.02734982594847679,
0.0303820613771677,
-0.09971684217453003,
-0.05811392888426781,
-0.07750015705823898,
-0.03185022994875908,
-0.04807431250810623,
-0.1331729292869568,
-0.004217653069645166,
-0.028126878663897514,
0.107723169028759,
-0.014689883217215538,
-0.07091815024614334,
0.12183564901351929,
0.1806531697511673,
0.027911517769098282,
0.04361478239297867,
-0.024896785616874695,
0.22694842517375946,
-0.11282049119472504,
0.05513069033622742,
0.17998956143856049,
-0.05173863470554352,
-0.010327917523682117,
0.04409312829375267,
0.02922842651605606,
-0.10912606865167618,
-0.002069476991891861,
0.005487632472068071,
-0.10261613875627518,
-0.1669328510761261,
-0.07133466005325317,
-0.05705390125513077,
-0.013417521491646767,
0.04830549284815788,
0.043003834784030914,
0.023073282092809677,
0.08100177347660065,
0.0682549998164177,
-0.02454637549817562,
-0.02468239516019821,
0.012258336879312992,
0.08591826260089874,
-0.0601654127240181,
-0.000025511137209832668,
-0.054658956825733185,
-0.08436066657304764,
0.011061912402510643,
0.09036462008953094,
0.07522720098495483,
0.07901957631111145,
0.04672315716743469,
0.08940528333187103,
0.1216864138841629,
0.011975321918725967,
0.09559516608715057,
-0.026784392073750496,
-0.00013897637836635113,
-0.03328470140695572,
-0.06120467558503151,
-0.04583268240094185,
0.03045656345784664,
-0.0369659923017025,
-0.0056653814390301704,
-0.056336693465709686,
0.024791093543171883,
0.019418274983763695,
-0.10176367312669754,
0.07378215342760086,
-0.23789873719215393,
-0.04466859996318817,
0.02734503522515297,
0.10921034961938858,
-0.011054238304495811,
0.08590669184923172,
0.0238677728921175,
-0.08093472570180893,
-0.017023839056491852,
-0.09194546192884445,
0.11127214878797531,
-0.04897388815879822,
-0.002652461640536785,
-0.08562387526035309,
0.11583756655454636,
-0.004452289082109928,
0.13297589123249054,
-0.2944411635398865,
0.2103298157453537,
-0.03268040716648102,
0.09883730858564377,
-0.07854011654853821,
-0.010120346210896969,
0.06236582249403,
0.15980635583400726,
0.163919597864151,
0.005817483179271221,
0.006521329283714294,
-0.04590611904859543,
-0.170862078666687,
0.05195247381925583,
-0.024477049708366394,
-0.07262586057186127,
0.01940261945128441,
0.06671951711177826,
0.03946331515908241,
0.005888180807232857,
0.08299422264099121,
-0.19350673258304596,
-0.06378407031297684,
0.017445888370275497,
-0.017169006168842316,
0.00345575250685215,
-0.011189348064363003,
-0.08835490792989731,
0.00007835403084754944,
-0.021266590803861618,
-0.054955363273620605,
-0.05025079473853111,
-0.06710534542798996,
0.05705220252275467,
0.08547322452068329,
-0.05044666677713394,
-0.05827975645661354,
0.01535043865442276,
0.02304857224225998,
-0.0762140303850174,
-0.060773640871047974,
-0.014264803379774094,
-0.09358065575361252,
0.021453555673360825,
-0.054694779217243195,
0.10266368836164474,
0.0639331117272377,
0.03127322345972061,
0.05652058497071266,
-0.009378338232636452,
0.005519500933587551,
-0.1167675107717514,
0.0927533432841301,
-0.017382632941007614,
0.031915731728076935,
-0.007086864206939936,
-0.07128270715475082,
0.09026876091957092,
-0.11829877644777298,
0.02335249073803425,
0.16044719517230988,
0.18951933085918427,
-0.05942769721150398,
0.0618491992354393,
0.10985154658555984,
-0.15219146013259888,
-0.155483677983284,
0.0331399068236351,
-0.03954038396477699,
0.04046951234340668,
0.008123967796564102,
-0.2910405397415161,
0.05856528505682945,
0.03706579655408859,
-0.00662774546071887,
0.08264954388141632,
-0.4074520468711853,
-0.09114490449428558,
0.25988221168518066,
0.0804070234298706,
0.056925803422927856,
-0.09987372905015945,
-0.01907958649098873,
0.022956252098083496,
-0.09711864590644836,
0.11917062103748322,
-0.013980196788907051,
0.03350858762860298,
-0.02677755057811737,
0.08171200752258301,
0.030073875561356544,
-0.06368144601583481,
0.1891152709722519,
0.07176732271909714,
0.11262857913970947,
-0.02955174818634987,
-0.20596514642238617,
0.0649343952536583,
-0.00979970209300518,
0.14450481534004211,
0.06367330998182297,
0.03775539994239807,
-0.23951949179172516,
-0.03675135597586632,
-0.0731644555926323,
0.059053100645542145,
-0.05184129253029823,
-0.09640835970640182,
-0.03937267139554024,
0.06055836006999016,
-0.027908822521567345,
0.0017078490927815437,
0.05488539859652519,
0.043795838952064514,
0.04259418696165085,
-0.032132118940353394,
0.11204928159713745,
0.1555178463459015,
-0.05646786466240883,
-0.004267785232514143,
-0.027035655453801155,
0.082229845225811,
-0.08644656091928482,
-0.08735744655132294,
0.12579824030399323,
0.03829463943839073,
0.13194096088409424,
0.055590562522411346,
-0.08958610892295837,
0.03521886467933655,
0.05852089449763298,
-0.16349034011363983,
-0.09744907915592194,
-0.03246919438242912,
-0.20052564144134521,
0.04336157441139221,
-0.01416255347430706,
0.07247447967529297,
-0.12329458445310593,
-0.008500049822032452,
0.01759905181825161,
0.01823950558900833,
-0.09530054777860641,
0.12464911490678787,
0.14133255183696747,
0.04730629548430443,
-0.057189714163541794,
0.08196766674518585,
0.03538893163204193,
-0.10268007218837738,
0.024335794150829315,
0.03893343731760979,
-0.09962096065282822,
-0.08253154158592224,
-0.06523106247186661,
0.2787803113460541,
-0.004497435875236988,
-0.07221664488315582,
-0.040838491171598434,
-0.02417912892997265,
0.00037797557888552547,
-0.022752320393919945,
0.09869823604822159,
-0.00403258390724659,
-0.14553533494472504,
0.00003177090547978878,
-0.1415943205356598,
0.04751354455947876,
0.030203109607100487,
-0.05199204757809639,
-0.12101063877344131,
0.16717618703842163,
0.026224901899695396,
0.051466166973114014,
-0.04835472255945206,
-0.0843704417347908,
-0.11602865904569626,
0.04321043938398361,
-0.1534184366464615,
0.015321054495871067,
-0.03753414750099182,
0.027405396103858948,
-0.002601839369162917,
0.002499526832252741,
0.005714440252631903,
-0.00045455514919012785,
-0.07408884167671204,
0.02251390926539898,
0.007954521104693413,
0.050220049917697906,
-0.07294761389493942,
-0.04121775180101395,
0.023698125034570694,
-0.00888099242001772,
0.04830782487988472,
0.0684233233332634,
-0.014756344258785248,
0.007829749956727028,
-0.11957546323537827,
0.05649564787745476,
0.07336277514696121,
0.0189538411796093,
-0.0010055905440822244,
-0.15906542539596558,
-0.016546759754419327,
-0.03858599811792374,
-0.03490312024950981,
0.02119944803416729,
0.1271723061800003,
-0.08356645703315735,
-0.05999288335442543,
0.06239625811576843,
-0.03243287280201912,
-0.06317956000566483,
0.02060306817293167,
0.06958343833684921,
0.0765557512640953,
0.14445006847381592,
-0.0025303279981017113,
0.1370425969362259,
-0.08536764979362488,
-0.04552598297595978,
-0.06269191950559616,
-0.03292175009846687,
-0.038704223930835724,
-0.03853427246212959,
0.053525160998106,
-0.04534542188048363,
0.16948090493679047,
0.06710019707679749,
0.020045435056090355,
-0.035117007791996,
0.03758448362350464,
0.006795878056436777,
-0.010434611700475216,
0.1576654613018036,
0.0479549802839756,
0.003920309711247683,
0.013079707510769367,
0.1015315055847168,
0.03136849030852318,
0.1364382803440094,
0.13526055216789246,
0.057478055357933044,
0.03145746886730194,
0.06376250088214874,
0.0358804427087307,
0.010980775579810143,
-0.08449466526508331,
-0.09416688233613968,
0.11693204194307327,
0.0535828061401844,
-0.003499708604067564,
-0.0085569703951478,
0.08289295434951782,
-0.12814725935459137,
0.0680476650595665,
-0.010166140273213387,
-0.0827898383140564,
-0.09918496757745743,
-0.2217724621295929,
0.02319110743701458,
0.023789474740624428,
-0.007966503500938416,
-0.12231815606355667,
-0.10571984201669693,
0.11618323624134064,
0.012337188236415386,
-0.06243959069252014,
0.14390908181667328,
-0.051423653960227966,
-0.035375744104385376,
0.035008128732442856,
-0.007288837339729071,
-0.017668593674898148,
-0.08905309438705444,
0.052985504269599915,
-0.011096857488155365,
-0.07986395061016083,
-0.004910141695290804,
-0.036161262542009354,
0.017136935144662857,
0.0005825036205351353,
-0.09817883372306824,
-0.029943302273750305,
-0.03266462683677673,
0.014183077961206436,
0.0837460309267044,
0.12877847254276276,
0.07311391830444336,
-0.07599925249814987,
0.02603200078010559,
0.09129374474287033,
0.046491365879774094,
-0.08183637261390686,
-0.0830116868019104,
0.2778306305408478,
-0.08382444828748703,
0.03265178203582764,
-0.010836554691195488,
-0.008118364959955215,
0.0023732115514576435,
0.3245772123336792,
0.31628847122192383,
-0.068686343729496,
-0.05312825366854668,
0.03666186332702637,
0.019768157973885536,
0.024894684553146362,
0.13677912950515747,
0.04338635131716728,
0.1623270958662033,
-0.07106940448284149,
0.024511702358722687,
-0.13934952020645142,
-0.0642101839184761,
-0.022466089576482773,
0.05684405937790871,
0.10279826819896698,
-0.06451021134853363,
-0.04855210706591606,
0.17985351383686066,
-0.142696350812912,
-0.12992271780967712,
0.022574707865715027,
-0.07076343148946762,
-0.1088024228811264,
-0.04810408875346184,
-0.038966141641139984,
0.01962948963046074,
0.08126682788133621,
-0.09240259230136871,
-0.007901694625616074,
0.09026602655649185,
-0.00574627285823226,
-0.07976428419351578,
-0.06752412766218185,
0.11057405173778534,
-0.0033086296170949936,
0.10036929696798325,
-0.013749460689723492,
0.1286378800868988,
0.07483880966901779,
0.009282451122999191,
-0.08350704610347748,
0.011517549864947796,
0.05102650448679924,
0.1337829828262329,
-0.01737852767109871,
-0.01481407880783081,
-0.02530660480260849,
-0.09155473113059998,
0.020290441811084747,
-0.05966871604323387,
0.03325837105512619,
-0.025040246546268463,
-0.05404888466000557,
-0.07160048186779022,
0.10848303139209747,
-0.08118869364261627,
0.1267838031053543,
0.1331886202096939,
-0.0212918221950531,
-0.008072083815932274,
-0.041532862931489944,
0.08986521512269974,
0.046199437230825424,
-0.039449289441108704,
-0.04659552127122879,
-0.025156959891319275,
-0.04340264946222305,
-0.01111968420445919,
-0.11092069000005722,
-0.19076938927173615,
-0.039914797991514206,
-0.07249230891466141,
-0.05856060981750488,
0.03532027825713158,
0.009495276026427746,
0.07222577929496765,
0.05737200379371643,
-0.044093403965234756,
-0.046547919511795044,
-0.019374622032046318,
0.06474018096923828,
-0.1252041757106781,
-0.07313240319490433
] |
null | null |
flair
|
## Dutch NER in Flair (large model)
This is the large 4-class NER model for Dutch that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **95,25** (CoNLL-03 Dutch)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| MISC | other name |
Based on document-level XLM-R embeddings and [FLERT](https://arxiv.org/pdf/2011.06993v1.pdf/).
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-dutch-large")
# make example sentence
sentence = Sentence("George Washington ging naar Washington")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [1,2]: "George Washington" [− Labels: PER (1.0)]
Span [5]: "Washington" [− Labels: LOC (1.0)]
```
So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging naar Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
import torch
# 1. get the corpus
from flair.datasets import CONLL_03_DUTCH
corpus = CONLL_03_DUTCH()
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize fine-tuneable transformer embeddings WITH document context
from flair.embeddings import TransformerWordEmbeddings
embeddings = TransformerWordEmbeddings(
model='xlm-roberta-large',
layers="-1",
subtoken_pooling="first",
fine_tune=True,
use_context=True,
)
# 5. initialize bare-bones sequence tagger (no CRF, no RNN, no reprojection)
from flair.models import SequenceTagger
tagger = SequenceTagger(
hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type='ner',
use_crf=False,
use_rnn=False,
reproject_embeddings=False,
)
# 6. initialize trainer with AdamW optimizer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus, optimizer=torch.optim.AdamW)
# 7. run training with XLM parameters (20 epochs, small LR)
from torch.optim.lr_scheduler import OneCycleLR
trainer.train('resources/taggers/ner-dutch-large',
learning_rate=5.0e-6,
mini_batch_size=4,
mini_batch_chunk_size=1,
max_epochs=20,
scheduler=OneCycleLR,
embeddings_storage_mode='none',
weight_decay=0.,
)
)
```
---
### Cite
Please cite the following paper when using this model.
```
@misc{schweter2020flert,
title={FLERT: Document-Level Features for Named Entity Recognition},
author={Stefan Schweter and Alan Akbik},
year={2020},
eprint={2011.06993},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
{"language": "nl", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington ging naar Washington"}]}
|
token-classification
|
flair/ner-dutch-large
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"nl",
"dataset:conll2003",
"arxiv:2011.06993",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2011.06993"
] |
[
"nl"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #nl #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us
|
Dutch NER in Flair (large model)
--------------------------------
This is the large 4-class NER model for Dutch that ships with Flair.
F1-Score: 95,25 (CoNLL-03 Dutch)
Predicts 4 tags:
Based on document-level XLM-R embeddings and FLERT.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the entities "*George Washington*" (labeled as a person) and "*Washington*" (labeled as a location) are found in the sentence "*George Washington ging naar Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following paper when using this model.
---
### Issues?
The Flair issue tracker is available here.
|
[
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging naar Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #nl #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging naar Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
50,
81,
22,
15,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #nl #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us \n### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging naar Washington*\".\n\n\n\n\n---### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
-0.10438837856054306,
0.173822283744812,
-0.0007876380695961416,
0.07675667107105255,
0.07141805440187454,
0.051035042852163315,
0.11999650299549103,
0.05233011394739151,
0.019593175500631332,
0.046542104333639145,
0.18495997786521912,
0.06109042093157768,
0.10083875805139542,
0.08094385266304016,
0.08018185943365097,
-0.25706976652145386,
0.02541494369506836,
-0.015112056396901608,
-0.07099733501672745,
0.16527630388736725,
0.08179426938295364,
-0.010208122432231903,
0.020228520035743713,
0.028512373566627502,
-0.10984868556261063,
-0.009605166502296925,
-0.03649190813302994,
-0.0983009859919548,
0.17743974924087524,
-0.04422029107809067,
0.20630082488059998,
0.04676204174757004,
0.12080205231904984,
-0.1486566960811615,
0.029735179618000984,
0.03966578096151352,
0.014981482177972794,
0.11052369326353073,
0.05194440111517906,
0.0022212862968444824,
0.32368525862693787,
-0.02953742817044258,
-0.005568918772041798,
0.027647262439131737,
-0.18579860031604767,
-0.25792744755744934,
-0.1059526801109314,
0.07157231122255325,
0.05406097695231438,
0.1148751899600029,
-0.027289099991321564,
0.11540587246417999,
-0.05595497414469719,
0.030423462390899658,
0.17609836161136627,
-0.2218950092792511,
-0.03928796201944351,
0.184976726770401,
0.01708800159394741,
-0.03212438151240349,
-0.047580402344465256,
0.047424476593732834,
0.0035161988344043493,
0.07383407652378082,
-0.028805984184145927,
-0.013388409279286861,
0.0034200488589704037,
0.11482501775026321,
-0.15653716027736664,
-0.09804195165634155,
0.3349704146385193,
0.03248550742864609,
-0.06017781421542168,
0.014864994212985039,
0.002626199973747134,
-0.017003390938043594,
0.047483108937740326,
-0.10020923614501953,
-0.015451634302735329,
-0.004437532741576433,
0.15009354054927826,
-0.1150149255990982,
-0.10716293007135391,
-0.09780693054199219,
-0.009056366048753262,
0.043194934725761414,
-0.08470630645751953,
0.09162720292806625,
-0.1690112054347992,
0.12072417140007019,
0.0553293451666832,
-0.06114400923252106,
0.04302244260907173,
-0.07710916548967361,
-0.03260530158877373,
0.02118215709924698,
-0.04151581600308418,
0.12429492175579071,
0.0309140682220459,
0.021540483459830284,
0.170062854886055,
-0.034456100314855576,
0.12093932926654816,
0.10462699830532074,
-0.00024057568225543946,
0.1720682978630066,
-0.12643063068389893,
-0.04692329466342926,
0.04166025295853615,
-0.006950772367417812,
-0.0037457456346601248,
-0.05488640442490578,
-0.1549147367477417,
-0.032708752900362015,
-0.060950350016355515,
0.02379673533141613,
0.04165919870138168,
-0.021508781239390373,
-0.08332628011703491,
0.022548893466591835,
0.09443918615579605,
-0.0639413371682167,
-0.03200889378786087,
-0.031202813610434532,
-0.0590745247900486,
0.08309679478406906,
0.10158612579107285,
0.03035479038953781,
0.017750125378370285,
0.10691364854574203,
-0.08485042303800583,
0.048777129501104355,
-0.04329535737633705,
-0.10078024119138718,
-0.011872725561261177,
-0.07370206713676453,
0.017485199496150017,
-0.067792147397995,
-0.15742242336273193,
-0.022669952362775803,
0.06455063819885254,
0.003767335554584861,
-0.06241951137781143,
-0.05231590196490288,
-0.04124492034316063,
-0.036975689232349396,
0.0360797755420208,
0.06970353424549103,
-0.055135395377874374,
0.06294387578964233,
-0.02295091561973095,
0.10789492726325989,
-0.06307824701070786,
0.008298338390886784,
-0.09657984226942062,
0.010866488330066204,
-0.08622391521930695,
-0.001011848566122353,
-0.017020389437675476,
0.02944152243435383,
-0.12610958516597748,
-0.10138358920812607,
-0.032290149480104446,
0.004620992112904787,
0.03811328485608101,
0.1958799958229065,
-0.28278520703315735,
0.006071863695979118,
0.09479911625385284,
-0.09441915899515152,
-0.05475388467311859,
0.050672437995672226,
-0.03461999073624611,
0.20408138632774353,
0.02745087817311287,
0.1397751420736313,
-0.029311513528227806,
-0.32921531796455383,
0.09410770982503891,
0.06341038644313812,
-0.16980059444904327,
0.0022778778802603483,
0.07554972916841507,
-0.03556041419506073,
-0.17574693262577057,
-0.001602650503627956,
-0.12662868201732635,
0.030920809134840965,
-0.03771653026342392,
-0.05674419552087784,
0.0024857916869223118,
-0.06059146299958229,
0.03417271748185158,
-0.038445599377155304,
-0.02798398770391941,
-0.0037451894022524357,
-0.14299936592578888,
-0.15310285985469818,
0.05007481202483177,
0.09581691026687622,
-0.03147866949439049,
0.013865653425455093,
0.041424740105867386,
0.02465405873954296,
-0.048002563416957855,
-0.10837231576442719,
0.021593526005744934,
-0.08912799507379532,
0.11822384595870972,
0.02218107506632805,
0.15941710770130157,
-0.018815692514181137,
0.035210851579904556,
0.06830435991287231,
0.030542239546775818,
-0.02998964488506317,
0.050751663744449615,
-0.007790904957801104,
0.009163527749478817,
-0.10872986912727356,
-0.09593147039413452,
0.11933053284883499,
-0.18108955025672913,
0.0470481812953949,
0.011733392253518105,
-0.015337163582444191,
-0.03485434874892235,
-0.02119128406047821,
0.10986582934856415,
0.02607942372560501,
0.0027987316716462374,
-0.06671048700809479,
0.0694718137383461,
0.0017254832200706005,
-0.07101976871490479,
-0.09392691403627396,
-0.06241748481988907,
-0.032489895820617676,
0.09067154675722122,
-0.007249254733324051,
-0.10139187425374985,
-0.07026632875204086,
-0.04185016080737114,
0.00998456496745348,
-0.03130995109677315,
-0.02397146262228489,
0.17363305389881134,
0.037908442318439484,
0.009782165288925171,
-0.027160830795764923,
0.03252605348825455,
-0.03227556124329567,
-0.03787580877542496,
-0.07096605002880096,
0.05449916422367096,
0.09135504066944122,
0.03199123963713646,
0.020262403413653374,
0.16133010387420654,
-0.01848071999847889,
0.008725154213607311,
0.059381287544965744,
-0.03994350507855415,
-0.09710165858268738,
-0.0890340730547905,
-0.021234098821878433,
0.17856864631175995,
-0.041710346937179565,
0.040950316935777664,
0.045544516295194626,
-0.007969554513692856,
0.044462140649557114,
-0.20286253094673157,
-0.13699676096439362,
0.050306834280490875,
-0.026836197823286057,
-0.20360267162322998,
0.06736460328102112,
-0.06476815044879913,
0.10258263349533081,
-0.031392842531204224,
-0.08940564095973969,
0.03962254896759987,
-0.002799789886921644,
-0.06900221109390259,
0.12182252109050751,
-0.05421282351016998,
-0.24587571620941162,
-0.10935753583908081,
-0.03525649383664131,
0.10255829989910126,
-0.0019291171338409185,
0.023677704855799675,
-0.11098510026931763,
0.018817508593201637,
-0.025008751079440117,
0.056848447769880295,
-0.08685193955898285,
-0.09430855512619019,
-0.0534387044608593,
0.008827747777104378,
-0.007820545695722103,
-0.12289505451917648,
-0.02242196351289749,
-0.04464748129248619,
0.06586513668298721,
0.031051985919475555,
0.02109035663306713,
0.1069163829088211,
0.1231783777475357,
0.017094194889068604,
0.06727257370948792,
-0.027769319713115692,
0.355813592672348,
-0.0760280042886734,
0.09370274841785431,
0.1312445104122162,
0.03137766197323799,
0.04555756226181984,
0.13810837268829346,
0.0680735856294632,
-0.09099292010068893,
-0.039552949368953705,
-0.08207885921001434,
-0.08591170608997345,
-0.1220749020576477,
-0.06724391877651215,
-0.05180371180176735,
-0.08148253709077835,
0.015674924477934837,
0.03196714073419571,
-0.0840541198849678,
0.05214755982160568,
0.07920318096876144,
-0.031639911234378815,
-0.06738444417715073,
-0.003686504438519478,
0.01330585964024067,
-0.017855817452073097,
0.002741991775110364,
-0.03604057431221008,
-0.0477411188185215,
0.024256952106952667,
0.10113861411809921,
0.10437463968992233,
0.08293246477842331,
-0.005687769968062639,
0.06311678141355515,
0.12298662960529327,
0.07602648437023163,
0.17756181955337524,
0.07270602136850357,
-0.01741793192923069,
-0.025174174457788467,
-0.06401286274194717,
0.0036403462290763855,
0.04395177215337753,
-0.02109287865459919,
-0.06367772817611694,
0.019493399187922478,
-0.08016186207532883,
-0.05349062383174896,
-0.023232338950037956,
0.12224970757961273,
-0.21582584083080292,
-0.01890048198401928,
0.0028449082747101784,
0.10926230251789093,
-0.042069293558597565,
0.05767866224050522,
0.041736770421266556,
-0.11323287338018417,
0.013501706533133984,
-0.02707730233669281,
0.08940637856721878,
0.03564991429448128,
0.027562523260712624,
-0.0924261212348938,
0.054845355451107025,
-0.040797844529151917,
0.12925608456134796,
-0.17104771733283997,
0.34951165318489075,
-0.046743083745241165,
0.024083556607365608,
-0.04857882857322693,
-0.0009044621256180108,
0.07571191340684891,
0.04821023344993591,
0.2483363300561905,
0.01528252474963665,
-0.13127736747264862,
-0.1044253334403038,
-0.11989500373601913,
0.050927650183439255,
-0.03812246769666672,
-0.050921350717544556,
0.05184153839945793,
0.06850825995206833,
0.0038946899585425854,
-0.0180240198969841,
0.08153429627418518,
-0.20115646719932556,
-0.11232566088438034,
0.014856526628136635,
-0.02062138356268406,
0.03564222902059555,
-0.015104776248335838,
-0.05172416940331459,
-0.09986672550439835,
-0.02412259578704834,
0.0312834270298481,
-0.023567212745547295,
-0.08742602914571762,
0.10488550364971161,
0.08567207306623459,
-0.0149886729195714,
-0.037037886679172516,
0.00550207681953907,
0.009439561516046524,
-0.09099870920181274,
0.014582538977265358,
0.01945745386183262,
-0.06746374815702438,
0.031876374036073685,
-0.0684133917093277,
0.08206359297037125,
0.0635075569152832,
0.048309870064258575,
0.028821758925914764,
0.06630760431289673,
-0.11771097034215927,
-0.1272440105676651,
0.17928801476955414,
-0.12764181196689606,
-0.0164978988468647,
0.02959502302110195,
0.034150153398513794,
0.024511344730854034,
-0.12871944904327393,
0.02243318036198616,
0.22640949487686157,
0.2539958357810974,
-0.13252763450145721,
0.1259494572877884,
-0.010252406820654869,
-0.10969900339841843,
-0.16495634615421295,
-0.02277766913175583,
-0.06058483570814133,
0.04932401329278946,
0.11614320427179337,
-0.17323951423168182,
0.054915666580200195,
0.11448931694030762,
-0.04517487809062004,
0.16431759297847748,
-0.2501848042011261,
-0.03610925003886223,
0.2055739164352417,
0.04824046045541763,
0.06869112700223923,
-0.05067914351820946,
-0.015544652938842773,
-0.0006037966813892126,
-0.1446850746870041,
0.07130677998065948,
0.0955837219953537,
0.03926468268036842,
-0.02519506961107254,
0.08359486609697342,
0.00819349568337202,
-0.07877545058727264,
0.20958183705806732,
0.006994493771344423,
0.030597923323512077,
-0.04732515662908554,
-0.2305196225643158,
0.13137857615947723,
-0.02356170117855072,
0.11173637956380844,
0.050580184906721115,
0.028227752074599266,
-0.16161251068115234,
-0.013535666279494762,
-0.1253809779882431,
0.09020831435918808,
-0.07051140069961548,
-0.04779006168246269,
-0.05787369981408119,
0.017626972869038582,
-0.08368181437253952,
-0.03936838358640671,
-0.13751284778118134,
-0.05745983496308327,
0.07221020013093948,
0.029118960723280907,
0.010381568223237991,
0.06604491174221039,
-0.16210684180259705,
0.059475526213645935,
-0.007199326995760202,
0.06029362231492996,
-0.10875434428453445,
-0.11807979643344879,
0.10437135398387909,
0.03796930983662605,
0.07553194463253021,
0.06803234666585922,
-0.06660863757133484,
0.013028991408646107,
0.056880902498960495,
-0.17335918545722961,
-0.06866797804832458,
-0.06176965311169624,
0.012445532716810703,
-0.011402624659240246,
-0.08934461325407028,
0.0296760406345129,
-0.05530340597033501,
-0.024192657321691513,
-0.007340812124311924,
0.0013052875874564052,
-0.13507725298404694,
0.02719719521701336,
0.13515491783618927,
0.0991327315568924,
-0.052855461835861206,
0.01339820958673954,
0.09421255439519882,
-0.05852704122662544,
-0.009846736676990986,
0.04800030589103699,
-0.0510360486805439,
-0.06569907814264297,
-0.0605192668735981,
0.16429901123046875,
0.041741106659173965,
-0.011789998039603233,
-0.00045978763955645263,
-0.08481713384389877,
-0.0013057456817477942,
0.05003746598958969,
0.08759855479001999,
-0.000678167853038758,
-0.11757130920886993,
-0.0930621549487114,
-0.06429121643304825,
0.007882188074290752,
0.030315661802887917,
-0.04292784631252289,
-0.13175536692142487,
0.09045788645744324,
0.005011130589991808,
0.11317142099142075,
-0.039067257195711136,
-0.08107495307922363,
-0.09531188011169434,
0.052241288125514984,
-0.021373769268393517,
0.009987394325435162,
-0.02911665104329586,
0.009130218997597694,
-0.007266107946634293,
-0.03784704953432083,
-0.006333496421575546,
-0.006641661282628775,
-0.1112493947148323,
0.047004830092191696,
-0.00911508034914732,
0.04172885790467262,
-0.03368998318910599,
-0.0348762609064579,
0.0913812592625618,
-0.03173571825027466,
0.05707055702805519,
0.06076987832784653,
-0.07425489276647568,
-0.03918490186333656,
-0.23448546230793,
0.021453941240906715,
0.027092289179563522,
-0.010406548157334328,
0.01473161019384861,
-0.18820764124393463,
-0.01194676198065281,
-0.05244399607181549,
-0.009521091356873512,
-0.022417234256863594,
0.05466952547430992,
-0.07284921407699585,
-0.05408719927072525,
0.10853025317192078,
-0.057845477014780045,
-0.026576964184641838,
-0.07766168564558029,
0.17524246871471405,
-0.008459572680294514,
0.10822698473930359,
0.020615097135305405,
0.1572405844926834,
-0.13674551248550415,
-0.026070065796375275,
-0.05672997608780861,
-0.03531177341938019,
-0.05533716455101967,
-0.05871152877807617,
0.09218945354223251,
0.010275745764374733,
0.20265284180641174,
-0.001998885767534375,
-0.0271876472979784,
-0.0258152037858963,
0.17149633169174194,
0.016562240198254585,
-0.03809937834739685,
0.10894697904586792,
0.02774077095091343,
0.006767281796783209,
0.0543573722243309,
0.054370589554309845,
-0.019074272364377975,
0.16571858525276184,
0.21967776119709015,
-0.01241504866629839,
0.12051640450954437,
0.08816318213939667,
-0.026286596432328224,
0.015087700448930264,
-0.006728417240083218,
-0.11321237683296204,
0.12029989063739777,
0.0001840277254814282,
-0.04213789105415344,
-0.052353158593177795,
0.09943164139986038,
-0.07294260710477829,
0.05248429998755455,
-0.04776635766029358,
-0.09151182323694229,
-0.10086652636528015,
-0.2145003378391266,
0.0389757975935936,
0.0031727177556604147,
-0.014567810110747814,
-0.04713980481028557,
-0.01257416047155857,
0.170181542634964,
-0.048739220947027206,
0.008350789546966553,
0.09395790100097656,
0.023138713091611862,
-0.05258287489414215,
0.020042013376951218,
-0.028984932228922844,
0.003777221078053117,
-0.14139381051063538,
0.07281607389450073,
-0.00561657827347517,
-0.0805925577878952,
0.03621536120772362,
-0.00219589751213789,
-0.014934522099792957,
-0.027359120547771454,
-0.11972994357347488,
-0.06700080633163452,
-0.04719623923301697,
0.06630716472864151,
-0.004591356962919235,
0.09343928843736649,
0.05861721187829971,
0.035031069070100784,
-0.001027261489070952,
0.14039357006549835,
-0.011265109293162823,
-0.13495463132858276,
-0.050437528640031815,
0.18758176267147064,
-0.1439335197210312,
-0.015329072251915932,
-0.06710736453533173,
-0.018714800477027893,
0.05170656740665436,
0.31528714299201965,
0.30836233496665955,
-0.04132890701293945,
-0.03785702586174011,
-0.041971106082201004,
0.022381722927093506,
0.06840955466032028,
0.05088338628411293,
0.10640621185302734,
0.16934852302074432,
-0.19053125381469727,
-0.033613380044698715,
-0.18068012595176697,
-0.0046836258843541145,
-0.02599259838461876,
0.01646093651652336,
0.10802748054265976,
-0.07320389151573181,
-0.033190298825502396,
0.20710855722427368,
-0.16805733740329742,
-0.05845308676362038,
0.01922418363392353,
-0.06197065860033035,
-0.11848663538694382,
0.021388595923781395,
0.06415462493896484,
0.005410886835306883,
0.11346019804477692,
-0.11619977653026581,
-0.08156085014343262,
0.16377127170562744,
-0.03171643987298012,
-0.13333052396774292,
-0.10274846851825714,
0.04906267300248146,
-0.1287725865840912,
0.13982447981834412,
0.0079369330778718,
0.1244664192199707,
0.05936890468001366,
0.07694745808839798,
-0.12680841982364655,
0.1139453575015068,
0.014233192428946495,
0.08769230544567108,
-0.03731098771095276,
-0.05191400647163391,
0.0024090693332254887,
-0.058341074734926224,
-0.0020277684088796377,
-0.14953899383544922,
0.06722840666770935,
0.1092722937464714,
0.018588421866297722,
-0.0018730005249381065,
0.04917405918240547,
-0.05106120556592941,
0.11600670963525772,
0.0943564623594284,
-0.07124700397253036,
0.0018552803667262197,
-0.009039674885571003,
0.010916011407971382,
0.0645829290151596,
-0.05368301272392273,
-0.03612503409385681,
-0.025540709495544434,
0.00870045181363821,
-0.007323314901441336,
-0.08060098439455032,
-0.0543677844107151,
-0.06746874004602432,
-0.05707486718893051,
-0.06170440837740898,
0.06348185241222382,
0.026612287387251854,
0.03450746461749077,
0.06742050498723984,
-0.019866878166794777,
0.14348503947257996,
0.0015027470653876662,
0.1408361941576004,
-0.16498202085494995,
-0.08244640380144119
] |
null | null |
flair
|
# Dutch NER in Flair (default model)
This is the standard 4-class NER model for Dutch that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **92,58** (CoNLL-03)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| MISC | other name |
Based on Transformer embeddings and LSTM-CRF.
---
# Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-dutch")
# make example sentence
sentence = Sentence("George Washington ging naar Washington")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [1,2]: "George Washington" [− Labels: PER (0.997)]
Span [5]: "Washington" [− Labels: LOC (0.9996)]
```
So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging naar Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import CONLL_03_DUTCH
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. get the corpus
corpus: Corpus = CONLL_03_DUTCH()
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize embeddings
embeddings = TransformerWordEmbeddings('wietsedv/bert-base-dutch-cased')
# 5. initialize sequence tagger
tagger: SequenceTagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
trainer: ModelTrainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/ner-dutch',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following paper when using this model.
```
@inproceedings{akbik-etal-2019-flair,
title = "{FLAIR}: An Easy-to-Use Framework for State-of-the-Art {NLP}",
author = "Akbik, Alan and
Bergmann, Tanja and
Blythe, Duncan and
Rasul, Kashif and
Schweter, Stefan and
Vollgraf, Roland",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
year = "2019",
url = "https://www.aclweb.org/anthology/N19-4010",
pages = "54--59",
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
{"language": "nl", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington ging naar Washington."}]}
|
token-classification
|
flair/ner-dutch
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"nl",
"dataset:conll2003",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"nl"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #nl #dataset-conll2003 #region-us
|
Dutch NER in Flair (default model)
==================================
This is the standard 4-class NER model for Dutch that ships with Flair.
F1-Score: 92,58 (CoNLL-03)
Predicts 4 tags:
Based on Transformer embeddings and LSTM-CRF.
---
Demo: How to use in Flair
=========================
Requires: Flair ('pip install flair')
This yields the following output:
So, the entities "*George Washington*" (labeled as a person) and "*Washington*" (labeled as a location) are found in the sentence "*George Washington ging naar Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following paper when using this model.
---
### Issues?
The Flair issue tracker is available here.
|
[
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #nl #dataset-conll2003 #region-us \n",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
37,
22,
15,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #nl #dataset-conll2003 #region-us \n### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
-0.11560501903295517,
0.07164620608091354,
-0.003440655767917633,
0.10473035275936127,
0.07930689305067062,
0.056879300624132156,
0.10220721364021301,
0.07811843603849411,
-0.010296083055436611,
0.031570352613925934,
0.17703916132450104,
0.10692554712295532,
0.06979160755872726,
0.16308999061584473,
0.014874029904603958,
-0.24638251960277557,
0.010447783395648003,
0.0003641964867711067,
-0.061812255531549454,
0.19166789948940277,
0.08344884216785431,
-0.024003874510526657,
0.03921768069267273,
0.01265049073845148,
-0.10693318396806717,
0.015176703222095966,
-0.0025853586848825216,
-0.05152912437915802,
0.1499054878950119,
-0.0549779050052166,
0.19694235920906067,
0.02415800280869007,
0.10295239835977554,
-0.1586630642414093,
0.06103488802909851,
0.010414600372314453,
-0.00881674699485302,
0.09602128714323044,
0.0711805671453476,
-0.027695612981915474,
0.3380233943462372,
-0.015049870125949383,
0.02838047780096531,
0.03481803461909294,
-0.15284904837608337,
-0.14144352078437805,
-0.11470678448677063,
0.06944501399993896,
0.1607782393693924,
0.09270979464054108,
-0.040292683988809586,
0.1305173635482788,
-0.13835693895816803,
0.06852662563323975,
0.14250724017620087,
-0.2214839607477188,
-0.07065214216709137,
0.15913845598697662,
0.03969021141529083,
-0.05438721179962158,
-0.09101339429616928,
0.06181711331009865,
0.04237690940499306,
0.04901234805583954,
0.011852500028908253,
-0.05115567892789841,
-0.15331611037254333,
0.04852722957730293,
-0.1554715484380722,
-0.08520390838384628,
0.22399257123470306,
0.021137429401278496,
-0.04470694810152054,
-0.013691206462681293,
-0.024327917024493217,
-0.019198721274733543,
0.03522440791130066,
-0.08204773813486099,
-0.014563625678420067,
0.013519711792469025,
0.184489905834198,
-0.07938196510076523,
-0.09785827994346619,
-0.11922287195920944,
0.015867002308368683,
0.034479279071092606,
-0.006284777075052261,
0.07679582387208939,
-0.1763094961643219,
0.13032285869121552,
0.009323257952928543,
-0.08319179713726044,
0.06331338733434677,
-0.05623188987374306,
-0.024294039234519005,
0.006774841342121363,
-0.0037820893339812756,
0.008351919241249561,
0.07355395704507828,
0.15077999234199524,
0.1559896320104599,
0.013775213621556759,
0.013915803283452988,
0.049460556358098984,
-0.004484054632484913,
0.10044948756694794,
-0.07576946914196014,
-0.03929270803928375,
0.09132423251867294,
0.09229283779859543,
0.04341738298535347,
-0.05380811169743538,
-0.11925558000802994,
-0.07313069701194763,
-0.01645790785551071,
0.10996314138174057,
-0.00039031938649713993,
-0.0023613220546394587,
-0.07429458200931549,
-0.015810051932930946,
0.08664169907569885,
-0.07777713239192963,
-0.010099442675709724,
0.039015721529722214,
-0.049962107092142105,
0.047104671597480774,
0.08237148821353912,
0.007982641458511353,
-0.01664595492184162,
0.03474113345146179,
-0.06278949975967407,
0.02468578703701496,
-0.08803509175777435,
-0.09059637039899826,
-0.000725726829841733,
-0.11508369445800781,
0.003442402696236968,
-0.02881082147359848,
-0.20213682949543,
0.012131912633776665,
0.031224986538290977,
-0.009253406897187233,
-0.022967834025621414,
-0.10512734204530716,
-0.09130049496889114,
-0.02154165506362915,
0.039690159261226654,
0.15119829773902893,
-0.03551545366644859,
0.06916192173957825,
0.0023194930981844664,
0.0690341517329216,
-0.05235694348812103,
-0.008882969617843628,
-0.06772320717573166,
0.04959717020392418,
-0.08326402306556702,
-0.0026528749149292707,
-0.044059015810489655,
-0.04840117320418358,
-0.10700707882642746,
-0.1182340756058693,
-0.027176594361662865,
-0.004617462400346994,
0.06091673672199249,
0.23920589685440063,
-0.2949866056442261,
0.011575366370379925,
0.13236601650714874,
-0.12123855203390121,
-0.04882281273603439,
0.11054284125566483,
-0.08747172355651855,
0.04524647071957588,
-0.0035213350784033537,
0.17662103474140167,
0.006626032292842865,
-0.18430079519748688,
0.10173080861568451,
0.04991775006055832,
-0.13992875814437866,
0.016343720257282257,
0.09547266364097595,
-0.021276727318763733,
-0.13797825574874878,
0.003894078778102994,
-0.11087100952863693,
0.027909576892852783,
-0.04420025274157524,
-0.09127823263406754,
-0.0045870766043663025,
-0.01958014816045761,
0.03891146928071976,
-0.011969889514148235,
-0.029704557731747627,
-0.027402589097619057,
-0.07295649498701096,
-0.16604959964752197,
0.12372884899377823,
0.020005259662866592,
-0.014596140943467617,
-0.0480341874063015,
0.10491051524877548,
-0.0034244079142808914,
-0.03727319836616516,
-0.1610567718744278,
-0.0490843802690506,
-0.04660492017865181,
0.049090489745140076,
-0.013934850692749023,
0.027485143393278122,
0.024989096447825432,
0.04532353952527046,
0.011194839142262936,
0.006936973426491022,
-0.022136004641652107,
-0.019122393801808357,
-0.0583091676235199,
-0.11453968286514282,
-0.09680450707674026,
-0.09417662024497986,
0.17514456808567047,
-0.29693084955215454,
0.03637424483895302,
-0.014518902637064457,
0.034859877079725266,
-0.059839967638254166,
-0.018002241849899292,
0.12048312276601791,
0.01154760830104351,
-0.04221777990460396,
-0.07139401882886887,
0.09415964037179947,
-0.05233648791909218,
-0.10368327051401138,
-0.1461542695760727,
-0.05699790269136429,
-0.05808119848370552,
0.07536366581916809,
-0.06051800400018692,
-0.10120407491922379,
-0.09210177510976791,
-0.04561525955796242,
0.0010786508210003376,
-0.022901052609086037,
0.04600529745221138,
0.130057692527771,
0.041043560951948166,
0.07042138278484344,
-0.05694190785288811,
0.02174355648458004,
-0.022470280528068542,
-0.050239961594343185,
0.0025443057529628277,
0.116327665746212,
0.18277689814567566,
-0.02682468667626381,
0.018506372347474098,
0.04567911848425865,
-0.02874656580388546,
0.05799156054854393,
0.02796739526093006,
-0.018477989360690117,
-0.05498515069484711,
-0.011226588860154152,
0.012574778869748116,
0.15373796224594116,
-0.08835756778717041,
0.040678221732378006,
0.06811851263046265,
-0.03879499435424805,
0.051955174654722214,
-0.22979851067066193,
-0.1175912618637085,
0.005223993211984634,
-0.06745008379220963,
-0.17795045673847198,
0.11184119433164597,
-0.029232488945126534,
0.12158889323472977,
-0.0776340439915657,
-0.13273614645004272,
0.010812085121870041,
-0.029107222333550453,
-0.10649461299180984,
0.21551069617271423,
-0.0361970029771328,
-0.16946208477020264,
-0.11216828227043152,
0.060976605862379074,
-0.019340001046657562,
-0.012888167053461075,
0.013652958907186985,
-0.11001553386449814,
-0.005547644104808569,
-0.02951899915933609,
-0.005282232537865639,
-0.038362450897693634,
-0.038236670196056366,
-0.06515878438949585,
-0.02388816885650158,
-0.03906997665762901,
-0.12185023725032806,
-0.041841890662908554,
-0.04696296155452728,
0.038139376789331436,
0.024680087342858315,
-0.04085811600089073,
0.14191286265850067,
0.18324249982833862,
0.025569181889295578,
0.04625481739640236,
-0.03228691592812538,
0.2642099857330322,
-0.0962032824754715,
0.06860458850860596,
0.19637607038021088,
-0.04732959717512131,
0.0359731987118721,
0.07632981985807419,
0.03871963545680046,
-0.11481156945228577,
-0.04482996463775635,
-0.03327937051653862,
-0.10704786330461502,
-0.21830780804157257,
-0.0432724803686142,
-0.08430688828229904,
-0.037220392376184464,
0.006963915657252073,
0.030283082276582718,
-0.012321623973548412,
0.05538182333111763,
0.08119937032461166,
-0.10136956721544266,
-0.009813925251364708,
0.02062971331179142,
0.02189280465245247,
0.004108181223273277,
0.03539838269352913,
-0.05623467266559601,
-0.018043888732790947,
0.04749344661831856,
0.037618238478899,
0.18562796711921692,
0.0557534396648407,
0.06637687236070633,
0.12102451920509338,
0.14212271571159363,
0.05637853592634201,
0.14313562214374542,
0.010522956028580666,
-0.021663857623934746,
-0.021932268515229225,
-0.040011148899793625,
-0.02501167543232441,
-0.016260337084531784,
-0.050617415457963943,
0.046687547117471695,
-0.08651548624038696,
0.04626310244202614,
0.02185821533203125,
0.0011017357464879751,
0.041525837033987045,
-0.2424648553133011,
0.03656987100839615,
0.014283991418778896,
0.08856360614299774,
-0.05840984359383583,
0.057033173739910126,
0.037382446229457855,
-0.14675092697143555,
0.01321676280349493,
-0.07965496182441711,
0.10550481826066971,
0.05197096988558769,
0.008170975372195244,
-0.07290607690811157,
0.11305910348892212,
-0.03137977793812752,
0.1465345174074173,
-0.13494789600372314,
0.3471328318119049,
-0.03228631243109703,
0.06069118529558182,
-0.03914348781108856,
-0.0044699618592858315,
0.09835696220397949,
0.1189168393611908,
0.23801583051681519,
0.011204533278942108,
-0.05320115014910698,
-0.12660349905490875,
-0.12500080466270447,
0.047775257378816605,
0.003777406644076109,
-0.029516465961933136,
0.05049730837345123,
0.03278399258852005,
0.020911067724227905,
0.000368156615877524,
-0.0020886012353003025,
-0.12268628925085068,
-0.07809428125619888,
-0.0030697761103510857,
-0.005254443734884262,
-0.06171237677335739,
-0.03773278370499611,
-0.08860430866479874,
0.01226833462715149,
0.0719849169254303,
-0.038086410611867905,
-0.04837663844227791,
-0.0878017470240593,
0.10515617579221725,
0.10769382119178772,
-0.050484299659729004,
-0.03533857315778732,
-0.020534317940473557,
0.018586741760373116,
-0.06790564209222794,
-0.006589306518435478,
0.014674621634185314,
-0.07127432525157928,
-0.04711258411407471,
-0.046094536781311035,
0.14477995038032532,
0.018775084987282753,
0.029309963807463646,
0.047367263585329056,
0.0245163943618536,
-0.033394183963537216,
-0.13732266426086426,
0.09577158093452454,
-0.027770308777689934,
0.019595643505454063,
0.039110783487558365,
-0.004528931342065334,
-0.023610342293977737,
-0.11210643500089645,
0.044512245804071426,
0.21712644398212433,
0.3047539293766022,
-0.0839526429772377,
0.09676546603441238,
0.03804495930671692,
-0.13110283017158508,
-0.1404847949743271,
-0.016737477853894234,
-0.025377416983246803,
0.030852999538183212,
0.049586281180381775,
-0.1683414727449417,
0.06511811167001724,
0.0909179225564003,
0.011157975532114506,
0.11008550971746445,
-0.3952288031578064,
-0.06421098858118057,
0.18783041834831238,
0.07987756282091141,
0.05821293964982033,
-0.08328785002231598,
-0.01236412301659584,
0.02466079406440258,
-0.11407939344644547,
0.032140664756298065,
0.01872123032808304,
0.06431764364242554,
-0.05521608889102936,
0.0543353408575058,
0.019680563360452652,
-0.09199874103069305,
0.21891404688358307,
0.03816176950931549,
0.10810566693544388,
-0.013440622948110104,
-0.23544877767562866,
0.11767654865980148,
-0.04251224175095558,
0.11271770298480988,
-0.0078344801440835,
0.03136083483695984,
-0.2550102770328522,
-0.03763354569673538,
-0.10205049812793732,
0.07988060265779495,
-0.04949153587222099,
-0.03353326767683029,
-0.027295026928186417,
-0.00653181504458189,
-0.02085260860621929,
-0.012179545126855373,
-0.08307793736457825,
-0.012771831825375557,
0.0640433207154274,
-0.002405323786661029,
0.1478416472673416,
0.13179348409175873,
-0.1392333060503006,
-0.010738699696958065,
0.00005603721001534723,
0.033643729984760284,
-0.0832543596625328,
-0.09678515791893005,
0.12314688414335251,
0.04800982400774956,
0.11329517513513565,
0.08232682943344116,
-0.021149862557649612,
0.04262785241007805,
0.06374295800924301,
-0.1860320270061493,
-0.03924158960580826,
-0.008292124606668949,
-0.08317229896783829,
-0.055575061589479446,
-0.06831490993499756,
0.02037409506738186,
-0.04646506533026695,
-0.03724171966314316,
0.021801194176077843,
0.03189544752240181,
-0.09535211324691772,
0.06929908692836761,
0.14340302348136902,
0.08734756708145142,
-0.07841771841049194,
0.03958488628268242,
0.032384127378463745,
-0.03153292089700699,
0.004985116887837648,
0.09717103093862534,
-0.11430387198925018,
-0.05313561111688614,
0.01261918991804123,
0.30359745025634766,
-0.015974484384059906,
-0.03169650584459305,
-0.06174149736762047,
-0.055318985134363174,
-0.015061777085065842,
0.12740369141101837,
0.11520455777645111,
-0.004572710953652859,
-0.12318418174982071,
-0.03752140328288078,
-0.11147838830947876,
0.04342334717512131,
0.07885608077049255,
-0.04280627891421318,
-0.09588056802749634,
0.12107115238904953,
0.04498206079006195,
0.0706968605518341,
-0.04358799755573273,
-0.06827875971794128,
-0.10848084837198257,
0.04121902585029602,
-0.07732050120830536,
0.030196594074368477,
-0.02983015961945057,
0.008462569676339626,
-0.02528221346437931,
-0.010796367190778255,
-0.03414443880319595,
-0.000017731072148308158,
-0.0795886293053627,
0.028234854340553284,
0.006548989564180374,
0.06688987463712692,
-0.10094854235649109,
-0.043076228350400925,
0.068125419318676,
-0.032425496727228165,
0.047180138528347015,
0.09623581916093826,
-0.005049142520874739,
0.0049846661277115345,
-0.170835942029953,
-0.00026750739198178053,
0.05857795849442482,
0.025438861921429634,
0.010992073453962803,
-0.16758671402931213,
0.013760469853878021,
-0.034703485667705536,
-0.03600156307220459,
0.021776696667075157,
0.0500214509665966,
-0.09260287880897522,
-0.049701087176799774,
0.0723278671503067,
-0.08817320317029953,
-0.018154384568333626,
-0.010587721131742,
0.1405678242444992,
0.0628770962357521,
0.15481482446193695,
-0.015060187317430973,
0.14475581049919128,
-0.14735040068626404,
-0.03160668537020683,
-0.0705891102552414,
-0.03405425697565079,
-0.11573951691389084,
-0.04071240499615669,
0.08174832165241241,
-0.058531589806079865,
0.1290997564792633,
0.04624629393219948,
0.03178427368402481,
-0.030721116811037064,
0.10799160599708557,
0.03814757615327835,
-0.010359159670770168,
0.10120730102062225,
0.02537737786769867,
-0.008317682892084122,
0.09628859907388687,
0.04607405886054039,
0.01839687116444111,
0.09161737561225891,
0.24713623523712158,
0.052654121071100235,
0.08151847869157791,
0.06819833815097809,
-0.012018253095448017,
-0.013194168917834759,
-0.09330711513757706,
-0.0999726727604866,
0.08042815327644348,
0.018280604854226112,
-0.009545418433845043,
0.03737694397568703,
0.13028155267238617,
-0.11782602965831757,
0.06289014220237732,
-0.011722072027623653,
-0.11705886572599411,
-0.14324404299259186,
-0.22439445555210114,
0.003262540092691779,
0.046921830624341965,
0.010420553386211395,
-0.11282472312450409,
-0.014442695304751396,
0.16206663846969604,
0.013464012183248997,
-0.01515235099941492,
0.15827572345733643,
-0.0273183174431324,
-0.004491888452321291,
0.04735269770026207,
-0.009412557817995548,
-0.009464780800044537,
-0.0843885987997055,
0.0518965870141983,
0.02108614519238472,
-0.1259794533252716,
0.023760685697197914,
-0.02239925041794777,
0.02903078868985176,
-0.024225613102316856,
-0.13873617351055145,
-0.06650624424219131,
-0.024724483489990234,
0.036612194031476974,
0.07826345413923264,
0.10471570491790771,
0.08711951971054077,
0.0037323059514164925,
0.004957154393196106,
0.1363854706287384,
0.03007582016289234,
-0.12964355945587158,
-0.079130619764328,
0.26676803827285767,
-0.10897330194711685,
-0.010115577839314938,
-0.038657449185848236,
0.0006707745487801731,
0.06668252497911453,
0.30219924449920654,
0.24958908557891846,
-0.06350119411945343,
-0.03772333264350891,
-0.028638673946261406,
0.016294872388243675,
0.055999938398599625,
0.10108734667301178,
0.0822480171918869,
0.08620862662792206,
-0.12233629077672958,
-0.03253667429089546,
-0.1750054955482483,
-0.03659859299659729,
-0.028020057827234268,
0.0690232515335083,
0.1321425586938858,
-0.03648284077644348,
-0.05120248720049858,
0.18274396657943726,
-0.08473023772239685,
-0.03227914124727249,
-0.005987225100398064,
-0.07652880996465683,
-0.13943129777908325,
-0.014076070860028267,
0.015293738804757595,
0.030555065721273422,
0.07487810403108597,
-0.11306151002645493,
-0.0755825936794281,
0.10723499953746796,
0.005895919632166624,
-0.11522772163152695,
-0.1045781746506691,
0.10492933541536331,
-0.14085491001605988,
0.1693977564573288,
-0.013603145256638527,
0.13623638451099396,
0.08374672383069992,
0.017190415412187576,
-0.09170517325401306,
0.025326427072286606,
0.071790412068367,
0.13962776958942413,
-0.01124001294374466,
0.018100691959261894,
-0.010499014519155025,
-0.1123211607336998,
-0.023642392829060555,
-0.08313099294900894,
0.0251245629042387,
-0.0275130458176136,
-0.04345405474305153,
-0.04585421830415726,
0.12134166061878204,
-0.06015291437506676,
0.10966702550649643,
0.15113075077533722,
-0.06379928439855576,
0.0037706331349909306,
-0.04533834755420685,
0.04661671072244644,
0.05842535197734833,
0.015087221749126911,
-0.024394312873482704,
-0.044363122433423996,
-0.007684454787522554,
-0.01261228509247303,
-0.08081196248531342,
-0.11489526182413101,
-0.08163938671350479,
-0.10647088289260864,
-0.05188237875699997,
-0.007539862301200628,
0.041471388190984726,
0.03843662887811661,
0.07802718877792358,
-0.003124506212770939,
0.04844874143600464,
-0.005380827467888594,
0.1145225390791893,
-0.12861132621765137,
-0.07205766439437866
] |
null | null |
flair
|
## English NER in Flair (fast model)
This is the fast 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **92,92** (corrected CoNLL-03)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| MISC | other name |
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-english-fast")
# make example sentence
sentence = Sentence("George Washington went to Washington")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [1,2]: "George Washington" [− Labels: PER (0.9515)]
Span [5]: "Washington" [− Labels: LOC (0.992)]
```
So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington went to Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import CONLL_03
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. get the corpus
corpus: Corpus = CONLL_03()
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
# GloVe embeddings
WordEmbeddings('glove'),
# contextual string embeddings, forward
FlairEmbeddings('news-forward-fast'),
# contextual string embeddings, backward
FlairEmbeddings('news-backward-fast'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/ner-english',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following paper when using this model.
```
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
{"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington went to Washington"}]}
|
token-classification
|
flair/ner-english-fast
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:conll2003",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #has_space #region-us
|
English NER in Flair (fast model)
---------------------------------
This is the fast 4-class NER model for English that ships with Flair.
F1-Score: 92,92 (corrected CoNLL-03)
Predicts 4 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the entities "*George Washington*" (labeled as a person) and "*Washington*" (labeled as a location) are found in the sentence "*George Washington went to Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following paper when using this model.
---
### Issues?
The Flair issue tracker is available here.
|
[
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington went to Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington went to Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
41,
81,
22,
15,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #has_space #region-us \n### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington went to Washington*\".\n\n\n\n\n---### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
-0.07754179835319519,
0.17645306885242462,
-0.0014712187694385648,
0.06833362579345703,
0.08177177608013153,
0.05396747589111328,
0.11267365515232086,
0.04825665429234505,
0.010602099820971489,
0.053486838936805725,
0.18720762431621552,
0.07780981063842773,
0.11305837333202362,
0.07320278882980347,
0.08347295224666595,
-0.25872859358787537,
0.03124011680483818,
-0.031080935150384903,
-0.0661880373954773,
0.1710071712732315,
0.07460048794746399,
-0.008209152147173882,
0.01566537469625473,
0.03217897564172745,
-0.11547128856182098,
-0.016097035259008408,
-0.02255762740969658,
-0.09116753190755844,
0.17304256558418274,
-0.03867684677243233,
0.2176574319601059,
0.04909152910113335,
0.09997935593128204,
-0.14942097663879395,
0.030922213569283485,
0.04960301145911217,
0.02233264595270157,
0.1008487343788147,
0.047248899936676025,
-0.002577294362708926,
0.29770246148109436,
-0.023222805932164192,
0.0022778487764298916,
0.03184448182582855,
-0.194279283285141,
-0.25129130482673645,
-0.11246374994516373,
0.06667076051235199,
0.04731357470154762,
0.10669522732496262,
-0.029623212292790413,
0.09231632947921753,
-0.05365392565727234,
0.026348991319537163,
0.18664439022541046,
-0.22959259152412415,
-0.029878173023462296,
0.1808956414461136,
0.014837613329291344,
-0.017358366400003433,
-0.04209684580564499,
0.05225979536771774,
-0.0027904964517802,
0.07348155230283737,
-0.038277387619018555,
-0.012397896498441696,
0.019900692626833916,
0.13006621599197388,
-0.16845081746578217,
-0.108916737139225,
0.32628217339515686,
0.033990662544965744,
-0.06865307688713074,
0.0009936346905305982,
0.0048671928234398365,
-0.02796310931444168,
0.06125226244330406,
-0.1016443744301796,
-0.010525999590754509,
0.002147058956325054,
0.16981127858161926,
-0.12401694059371948,
-0.09334787726402283,
-0.09860476851463318,
-0.023727163672447205,
0.04282179847359657,
-0.09350593388080597,
0.10587839037179947,
-0.171022430062294,
0.12357880920171738,
0.06411927193403244,
-0.05157626047730446,
0.04923859238624573,
-0.0730988085269928,
-0.041779667139053345,
0.0125777842476964,
-0.04943787679076195,
0.13106411695480347,
0.03255152329802513,
0.02866257168352604,
0.154957115650177,
-0.04096983000636101,
0.10380399227142334,
0.10950533300638199,
0.005464928690344095,
0.1985224187374115,
-0.1408398151397705,
-0.028763365000486374,
0.025492433458566666,
-0.025497229769825935,
-0.008396552875638008,
-0.05911485478281975,
-0.1629110425710678,
-0.013697267509996891,
-0.07037291675806046,
0.021661145612597466,
0.0364745669066906,
-0.017974253743886948,
-0.08983248472213745,
0.012833666056394577,
0.06407420337200165,
-0.06673172116279602,
-0.03855659067630768,
-0.024492328986525536,
-0.05353779345750809,
0.09334668517112732,
0.08932789415121078,
0.033969949930906296,
0.018869653344154358,
0.13650499284267426,
-0.08181274682283401,
0.048749323934316635,
-0.040186334401369095,
-0.11000388860702515,
-0.025855405256152153,
-0.07450368255376816,
0.01350113283842802,
-0.05998620018362999,
-0.15010225772857666,
-0.014377806335687637,
0.06219375133514404,
0.011581921949982643,
-0.06558139622211456,
-0.042902056127786636,
-0.028128966689109802,
-0.048067715018987656,
0.03784794360399246,
0.06943901628255844,
-0.06656049191951752,
0.07558827847242355,
-0.041824329644441605,
0.10506926476955414,
-0.04709027335047722,
0.014807348139584064,
-0.0919363722205162,
0.009970128536224365,
-0.11978712677955627,
-0.005051698535680771,
-0.01654648594558239,
0.032986126840114594,
-0.11927206814289093,
-0.08522059768438339,
-0.02064075879752636,
0.008532451465725899,
0.018110739067196846,
0.18470415472984314,
-0.2741975486278534,
-0.016154443845152855,
0.11923731118440628,
-0.0970100536942482,
-0.052793316543102264,
0.05080597475171089,
-0.027345797047019005,
0.2000253051519394,
0.03233255073428154,
0.1410668045282364,
-0.026518527418375015,
-0.3641958236694336,
0.11612235754728317,
0.050427693873643875,
-0.1995733678340912,
0.04448068514466286,
0.07041699439287186,
-0.05803046002984047,
-0.18117335438728333,
-0.00035470983129926026,
-0.1054917573928833,
0.03541767597198486,
-0.03451206907629967,
-0.038134872913360596,
0.007946238853037357,
-0.053653083741664886,
0.043425098061561584,
-0.04420396685600281,
-0.03191240131855011,
-0.0008029607124626637,
-0.14804165065288544,
-0.14217549562454224,
0.034968044608831406,
0.11056330800056458,
-0.033926062285900116,
0.015836600214242935,
0.04762740433216095,
0.04205077141523361,
-0.04267682135105133,
-0.11397255957126617,
0.036075618118047714,
-0.08373289555311203,
0.13944672048091888,
0.02140967547893524,
0.15893884003162384,
-0.0326048880815506,
0.01657475344836712,
0.06893139332532883,
0.032006919384002686,
-0.023277686908841133,
0.05343891680240631,
-0.00910012423992157,
0.005924176424741745,
-0.08223745226860046,
-0.09227555245161057,
0.11507019400596619,
-0.1732158064842224,
0.06324122101068497,
0.0345144160091877,
-0.0030428192112594843,
-0.015952076762914658,
-0.020765408873558044,
0.11239534616470337,
0.03155575692653656,
0.016160983592271805,
-0.07194400578737259,
0.0680181011557579,
0.004522040020674467,
-0.05563190579414368,
-0.10820070654153824,
-0.06696484982967377,
-0.032549455761909485,
0.10313135385513306,
-0.031925592571496964,
-0.11245077103376389,
-0.07000447064638138,
-0.04484890401363373,
0.006589848082512617,
-0.03452951833605766,
-0.03343801200389862,
0.15580464899539948,
0.04088863730430603,
0.012611235491931438,
-0.029496170580387115,
0.031581662595272064,
-0.029225578531622887,
-0.03257790207862854,
-0.07219153642654419,
0.05417686328291893,
0.11043953150510788,
0.029020637273788452,
0.030439916998147964,
0.1810978651046753,
0.0025009845849126577,
0.020533539354801178,
0.05543633550405502,
-0.05167628079652786,
-0.1014716774225235,
-0.10627426952123642,
-0.021119778975844383,
0.17995966970920563,
-0.02260744385421276,
0.04118063673377037,
0.04363493621349335,
-0.00604051910340786,
0.03781992197036743,
-0.2039848268032074,
-0.12298339605331421,
0.050411906093358994,
-0.026413410902023315,
-0.1859441101551056,
0.053826894611120224,
-0.05497695505619049,
0.10488542914390564,
-0.02126612327992916,
-0.07224913686513901,
0.04569634795188904,
-0.00010353676043450832,
-0.0773436427116394,
0.10456831753253937,
-0.06604623794555664,
-0.26045143604278564,
-0.10620442777872086,
-0.0641823336482048,
0.1052456796169281,
-0.0048753353767097,
0.025803908705711365,
-0.11017563194036484,
0.03340508043766022,
-0.012199110351502895,
0.05897105112671852,
-0.09708739072084427,
-0.08480245620012283,
-0.06506515294313431,
0.02019714191555977,
-0.01870417408645153,
-0.10990332812070847,
-0.033304858952760696,
-0.05823634937405586,
0.08495926856994629,
0.026999248191714287,
0.018227646127343178,
0.08756991475820541,
0.12908503413200378,
0.02069397270679474,
0.07084877789020538,
-0.04858938977122307,
0.36368730664253235,
-0.08118712902069092,
0.07762788236141205,
0.13007904589176178,
0.012488547712564468,
0.047220781445503235,
0.15282393991947174,
0.06878884881734848,
-0.08269142359495163,
-0.0398922935128212,
-0.07469593733549118,
-0.06922279298305511,
-0.11486057937145233,
-0.051381684839725494,
-0.04355936869978905,
-0.061931077390909195,
0.007631842978298664,
0.02898714318871498,
-0.0849793329834938,
0.026226654648780823,
0.08144693076610565,
-0.04626327380537987,
-0.053528644144535065,
-0.003856563474982977,
0.017222493886947632,
-0.030279308557510376,
0.000022242495106183924,
-0.030871206894516945,
-0.03829526901245117,
0.031054392457008362,
0.09232834726572037,
0.07493927329778671,
0.11867547035217285,
-0.008620659820735455,
0.06487446278333664,
0.10922076553106308,
0.0907936692237854,
0.16689422726631165,
0.08132350444793701,
-0.017172282561659813,
-0.025210438296198845,
-0.0552842877805233,
0.0006747531006112695,
0.03846857324242592,
0.010585397481918335,
-0.05610959231853485,
0.011706947349011898,
-0.10549738258123398,
-0.044250402599573135,
-0.00045780421351082623,
0.12490221112966537,
-0.17725107073783875,
-0.028981702402234077,
0.0025563084054738283,
0.09625837206840515,
-0.04107341915369034,
0.05712779611349106,
0.029471779242157936,
-0.1322060227394104,
0.01304552797228098,
-0.022282704710960388,
0.10046657174825668,
0.032702282071113586,
0.03616068884730339,
-0.08741630613803864,
0.036816470324993134,
-0.05629411339759827,
0.11230190843343735,
-0.19039921462535858,
0.3411136269569397,
-0.029189562425017357,
0.01439796481281519,
-0.04907659441232681,
0.001495462842285633,
0.07830628752708435,
0.049780555069446564,
0.24089691042900085,
0.0072601838037371635,
-0.1557980626821518,
-0.10067134350538254,
-0.11587468534708023,
0.0371759831905365,
-0.02502240054309368,
-0.07582314312458038,
0.06340732425451279,
0.06367435306310654,
0.005864436738193035,
-0.02741561084985733,
0.07760247588157654,
-0.1847614347934723,
-0.10596834123134613,
0.022898240014910698,
-0.03178315609693527,
0.03927309438586235,
-0.009486321359872818,
-0.0540086030960083,
-0.11264348775148392,
-0.008291383273899555,
-0.01637926883995533,
-0.036599185317754745,
-0.08583460003137589,
0.1098761111497879,
0.0486019141972065,
-0.00919925607740879,
-0.03178917616605759,
-0.0005758185870945454,
0.015676327049732208,
-0.0880160927772522,
0.019203219562768936,
0.023837018758058548,
-0.05182203650474548,
0.03307157754898071,
-0.07997714728116989,
0.07993977516889572,
0.04752611368894577,
0.04597796872258186,
0.00774313835427165,
0.06411200016736984,
-0.11032064259052277,
-0.1149287223815918,
0.2012193351984024,
-0.11813875287771225,
-0.024615036323666573,
0.0508619025349617,
0.035559091717004776,
0.006837102118879557,
-0.14715397357940674,
0.022003261372447014,
0.23580947518348694,
0.25653284788131714,
-0.13861101865768433,
0.14306360483169556,
-0.02401386760175228,
-0.11648180335760117,
-0.15072350203990936,
-0.012898522429168224,
-0.06660594046115875,
0.03741202875971794,
0.11839398741722107,
-0.1663983315229416,
0.03581586107611656,
0.11083318293094635,
-0.04651443660259247,
0.18614409863948822,
-0.20854249596595764,
-0.02927589975297451,
0.20170395076274872,
0.040600456297397614,
0.05820350721478462,
-0.05468771979212761,
-0.03231723606586456,
0.01364588737487793,
-0.14487992227077484,
0.06877235323190689,
0.11307153850793839,
0.041834279894828796,
-0.0172274187207222,
0.0787387490272522,
0.0021745148114860058,
-0.0731922835111618,
0.19910576939582825,
-0.025844614952802658,
0.024256490170955658,
-0.04272749647498131,
-0.23798060417175293,
0.12775874137878418,
-0.023064272478222847,
0.08214819431304932,
0.07538721710443497,
0.021028738468885422,
-0.16098462045192719,
-0.014173814095556736,
-0.11875100433826447,
0.07721564173698425,
-0.07301245629787445,
-0.04844633862376213,
-0.0590442456305027,
0.012157578021287918,
-0.10161566734313965,
-0.04514360800385475,
-0.1624065786600113,
-0.07942797243595123,
0.0759822428226471,
0.013675469905138016,
-0.006444314494729042,
0.06654620170593262,
-0.1836836040019989,
0.05705875903367996,
-0.012530838139355183,
0.0619388073682785,
-0.10079565644264221,
-0.12315905094146729,
0.1021762415766716,
0.03662077337503433,
0.08908525109291077,
0.07408330589532852,
-0.06525322049856186,
0.008303400129079819,
0.03823942691087723,
-0.1902492344379425,
-0.061513133347034454,
-0.06060648337006569,
-0.006479691248387098,
-0.01715351827442646,
-0.09824580699205399,
0.030182957649230957,
-0.02474709041416645,
-0.034081727266311646,
-0.0009182289941236377,
-0.002454205881804228,
-0.12701742351055145,
-0.0009491539094597101,
0.13562610745429993,
0.1021547019481659,
-0.04566896706819534,
0.013759877532720566,
0.09578602015972137,
-0.0976477786898613,
-0.015482207760214806,
0.03056846559047699,
-0.05261091887950897,
-0.05242649093270302,
-0.07476341724395752,
0.16592884063720703,
0.07799428701400757,
-0.003889932995662093,
-0.006761424243450165,
-0.08308728784322739,
0.004400869831442833,
0.0834001898765564,
0.09166659414768219,
0.0017428932478651404,
-0.11055802553892136,
-0.08825869113206863,
-0.04522313177585602,
0.022255057469010353,
0.0016010550316423178,
-0.06240902468562126,
-0.13734987378120422,
0.08076385408639908,
0.012935017235577106,
0.12854577600955963,
-0.03758738562464714,
-0.08589383959770203,
-0.1209675520658493,
0.05851154401898384,
-0.00013283985026646405,
0.008335558697581291,
-0.013108356855809689,
0.006929735653102398,
0.0032697003334760666,
-0.04146888107061386,
-0.006699878256767988,
-0.013709691353142262,
-0.10904364287853241,
0.0595129169523716,
-0.00026295066345483065,
0.05749618634581566,
-0.019727669656276703,
-0.035004302859306335,
0.10337471961975098,
-0.028249191120266914,
0.05784662067890167,
0.06588263064622879,
-0.07716593891382217,
-0.052351806312799454,
-0.232272669672966,
0.013977361842989922,
0.01359256450086832,
-0.01938895508646965,
0.01198615599423647,
-0.18718808889389038,
-0.002408674219623208,
-0.07003804296255112,
-0.019620265811681747,
-0.021836137399077415,
0.04376371577382088,
-0.07073947042226791,
-0.04819376766681671,
0.13044919073581696,
-0.05197535455226898,
-0.02548273093998432,
-0.09404639154672623,
0.17698834836483002,
-0.016344599425792694,
0.10929640382528305,
0.026925215497612953,
0.1651221066713333,
-0.13492320477962494,
-0.02287326008081436,
-0.05856097489595413,
-0.022900674492120743,
-0.024268681183457375,
-0.05907032638788223,
0.09327629953622818,
0.011733517982065678,
0.22636036574840546,
0.01423418428748846,
-0.010065902024507523,
-0.009942913427948952,
0.16960115730762482,
0.02611513063311577,
-0.02038286253809929,
0.10359500348567963,
0.021963370963931084,
0.006427949294447899,
0.05541658774018288,
0.04529688507318497,
-0.030264973640441895,
0.18015775084495544,
0.21477608382701874,
0.0011419461807236075,
0.11504117399454117,
0.08320838212966919,
-0.04261947050690651,
0.04139399528503418,
-0.003798088990151882,
-0.12946467101573944,
0.13413143157958984,
-0.0027838568203151226,
-0.038717977702617645,
-0.04559718444943428,
0.10584032535552979,
-0.06193123385310173,
0.05128570646047592,
-0.0501648373901844,
-0.08126737922430038,
-0.10386890918016434,
-0.19279465079307556,
0.042028382420539856,
0.0033682496286928654,
-0.011750648729503155,
-0.045877836644649506,
-0.01831798627972603,
0.18256548047065735,
-0.056898415088653564,
0.015313430689275265,
0.06555616110563278,
0.03392085060477257,
-0.06639061868190765,
0.02865232527256012,
-0.031249510124325752,
0.006010999903082848,
-0.1702151894569397,
0.07115574181079865,
0.004042482003569603,
-0.0891227051615715,
0.038743533194065094,
-0.002058038953691721,
-0.018427273258566856,
-0.0361320823431015,
-0.1450413018465042,
-0.06967693567276001,
-0.03088492341339588,
0.0629301443696022,
-0.02216517925262451,
0.08737204223871231,
0.054675593972206116,
0.04052126407623291,
0.00021603464847430587,
0.13613329827785492,
-0.017200836911797523,
-0.13532908260822296,
-0.05924006924033165,
0.19982117414474487,
-0.16289867460727692,
-0.003894954454153776,
-0.08012817054986954,
-0.01595214754343033,
0.05655684694647789,
0.34926217794418335,
0.29040855169296265,
-0.03533623740077019,
-0.04518170282244682,
-0.02583770826458931,
0.024215908721089363,
0.09732510894536972,
0.03687691688537598,
0.10173441469669342,
0.16557882726192474,
-0.1913149654865265,
-0.039266180247068405,
-0.1793902963399887,
-0.004952968563884497,
-0.018650684505701065,
0.007320149336010218,
0.1155557930469513,
-0.07043810933828354,
-0.04351365938782692,
0.209843248128891,
-0.16912789642810822,
-0.054514169692993164,
0.027548594400286674,
-0.06217918545007706,
-0.11541114002466202,
0.02538352645933628,
0.07309430092573166,
-0.009485145099461079,
0.09940112382173538,
-0.10799267143011093,
-0.09204038232564926,
0.14706911146640778,
-0.04426358640193939,
-0.12115191668272018,
-0.11411548405885696,
0.03939910978078842,
-0.15244382619857788,
0.13161446154117584,
0.004268568009138107,
0.12123951315879822,
0.05961480736732483,
0.08470243960618973,
-0.11262635141611099,
0.1004881039261818,
0.009663550183176994,
0.07560045272111893,
-0.05425167456269264,
-0.05648530274629593,
0.014422115869820118,
-0.05682225525379181,
0.009247985668480396,
-0.15363535284996033,
0.07903418689966202,
0.08833206444978714,
-0.0023426066618412733,
0.005495043005794287,
0.06143582984805107,
-0.04818333312869072,
0.11000506579875946,
0.0969986543059349,
-0.06984512507915497,
0.01432940736413002,
-0.008052513003349304,
-0.012603741139173508,
0.07655569165945053,
-0.04939008504152298,
-0.04133909195661545,
-0.02148480713367462,
0.01191031001508236,
0.006371227093040943,
-0.10161851346492767,
-0.06261949241161346,
-0.07356744259595871,
-0.04364680126309395,
-0.051751863211393356,
0.05409422144293785,
0.030860010534524918,
0.027121830731630325,
0.0734129548072815,
-0.022500816732645035,
0.12983761727809906,
0.0015641379868611693,
0.1542537659406662,
-0.17383043467998505,
-0.07972919940948486
] |
null | null |
flair
|
## English NER in Flair (large model)
This is the large 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **94,36** (corrected CoNLL-03)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| MISC | other name |
Based on document-level XLM-R embeddings and [FLERT](https://arxiv.org/pdf/2011.06993v1.pdf/).
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-english-large")
# make example sentence
sentence = Sentence("George Washington went to Washington")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [1,2]: "George Washington" [− Labels: PER (1.0)]
Span [5]: "Washington" [− Labels: LOC (1.0)]
```
So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington went to Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
import torch
# 1. get the corpus
from flair.datasets import CONLL_03
corpus = CONLL_03()
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize fine-tuneable transformer embeddings WITH document context
from flair.embeddings import TransformerWordEmbeddings
embeddings = TransformerWordEmbeddings(
model='xlm-roberta-large',
layers="-1",
subtoken_pooling="first",
fine_tune=True,
use_context=True,
)
# 5. initialize bare-bones sequence tagger (no CRF, no RNN, no reprojection)
from flair.models import SequenceTagger
tagger = SequenceTagger(
hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type='ner',
use_crf=False,
use_rnn=False,
reproject_embeddings=False,
)
# 6. initialize trainer with AdamW optimizer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus, optimizer=torch.optim.AdamW)
# 7. run training with XLM parameters (20 epochs, small LR)
from torch.optim.lr_scheduler import OneCycleLR
trainer.train('resources/taggers/ner-english-large',
learning_rate=5.0e-6,
mini_batch_size=4,
mini_batch_chunk_size=1,
max_epochs=20,
scheduler=OneCycleLR,
embeddings_storage_mode='none',
weight_decay=0.,
)
)
```
---
### Cite
Please cite the following paper when using this model.
```
@misc{schweter2020flert,
title={FLERT: Document-Level Features for Named Entity Recognition},
author={Stefan Schweter and Alan Akbik},
year={2020},
eprint={2011.06993},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
{"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington went to Washington"}]}
|
token-classification
|
flair/ner-english-large
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:conll2003",
"arxiv:2011.06993",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2011.06993"
] |
[
"en"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us
|
English NER in Flair (large model)
----------------------------------
This is the large 4-class NER model for English that ships with Flair.
F1-Score: 94,36 (corrected CoNLL-03)
Predicts 4 tags:
Based on document-level XLM-R embeddings and FLERT.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the entities "*George Washington*" (labeled as a person) and "*Washington*" (labeled as a location) are found in the sentence "*George Washington went to Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following paper when using this model.
---
### Issues?
The Flair issue tracker is available here.
|
[
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington went to Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington went to Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
50,
81,
22,
15,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us \n### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington went to Washington*\".\n\n\n\n\n---### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
-0.10413747280836105,
0.1745603382587433,
-0.0007235235534608364,
0.07044292241334915,
0.07940594851970673,
0.05360045284032822,
0.1230916902422905,
0.05188778042793274,
0.013948854990303516,
0.04899323359131813,
0.18269319832324982,
0.06860296428203583,
0.10721786320209503,
0.07320466637611389,
0.08739868551492691,
-0.2556147575378418,
0.029148397967219353,
-0.023994499817490578,
-0.07281525433063507,
0.1719951331615448,
0.08074238896369934,
-0.013677768409252167,
0.01810784451663494,
0.032968856394290924,
-0.10419691354036331,
-0.010343062691390514,
-0.032278481870889664,
-0.09707969427108765,
0.18212595582008362,
-0.042507633566856384,
0.21331946551799774,
0.04664183035492897,
0.11574269086122513,
-0.1422918289899826,
0.030273040756583214,
0.04648423194885254,
0.01945398934185505,
0.11429262161254883,
0.05260846018791199,
0.004164467565715313,
0.2970995008945465,
-0.01822115108370781,
-0.0009416030952706933,
0.03122580051422119,
-0.18747001886367798,
-0.26771336793899536,
-0.10919104516506195,
0.0748322457075119,
0.05447103828191757,
0.12448906898498535,
-0.028623340651392937,
0.10902614891529083,
-0.04496137425303459,
0.028482146561145782,
0.17496871948242188,
-0.22136221826076508,
-0.03359467163681984,
0.1755208820104599,
0.012599372304975986,
-0.019702909514307976,
-0.03879614919424057,
0.03802747651934624,
-0.0030178900342434645,
0.07511582225561142,
-0.03160247579216957,
-0.009584644809365273,
0.008488030172884464,
0.11585413664579391,
-0.15541167557239532,
-0.10499328374862671,
0.3376561999320984,
0.0251146350055933,
-0.06448891013860703,
0.010637423023581505,
-0.00808244664222002,
-0.022252433001995087,
0.055526044219732285,
-0.10165529698133469,
-0.012050486169755459,
0.002728717401623726,
0.15615493059158325,
-0.1189204677939415,
-0.11105193942785263,
-0.1038723960518837,
-0.01494250912219286,
0.03423234820365906,
-0.08826567977666855,
0.10105821490287781,
-0.17449626326560974,
0.12477357685565948,
0.07218313217163086,
-0.05886745825409889,
0.04773133620619774,
-0.07920199632644653,
-0.03376701474189758,
0.022550582885742188,
-0.05291961133480072,
0.12700548768043518,
0.024404918774962425,
0.022376352921128273,
0.1767829954624176,
-0.03808126598596573,
0.122339628636837,
0.10942135006189346,
0.004787926562130451,
0.179592564702034,
-0.13720908761024475,
-0.027903351932764053,
0.03225294500589371,
-0.009157605469226837,
-0.005647796671837568,
-0.056848932057619095,
-0.15193186700344086,
-0.025217875838279724,
-0.06449434906244278,
0.02498198114335537,
0.04621993005275726,
-0.025580229237675667,
-0.08342216163873672,
0.020580681040883064,
0.09697916358709335,
-0.06324853748083115,
-0.04119144380092621,
-0.029042143374681473,
-0.06303517520427704,
0.0897643119096756,
0.10029187053442001,
0.03393911197781563,
0.015410765074193478,
0.12463592737913132,
-0.08540493249893188,
0.05229484662413597,
-0.038914769887924194,
-0.10234072059392929,
-0.01819681003689766,
-0.07769586890935898,
0.015652447938919067,
-0.06587062776088715,
-0.15398108959197998,
-0.023977188393473625,
0.06689199060201645,
0.01091114990413189,
-0.061319585889577866,
-0.040633536875247955,
-0.0336758978664875,
-0.03757968172430992,
0.033008333295583725,
0.07044566422700882,
-0.057863831520080566,
0.06917218863964081,
-0.03869625926017761,
0.09952875971794128,
-0.06277798861265182,
0.009295662865042686,
-0.0989612489938736,
0.005422963295131922,
-0.09640520811080933,
-0.007535990327596664,
-0.009429404512047768,
0.03149675950407982,
-0.13178567588329315,
-0.09776253998279572,
-0.030857736244797707,
0.005622429773211479,
0.031941067427396774,
0.18842780590057373,
-0.28495219349861145,
0.004321976099163294,
0.09975382685661316,
-0.09735709428787231,
-0.05498339980840683,
0.04753349721431732,
-0.03286188095808029,
0.20745928585529327,
0.026189373806118965,
0.1280229240655899,
-0.03812757134437561,
-0.33747413754463196,
0.09555374830961227,
0.05828980728983879,
-0.18305152654647827,
0.014070470817387104,
0.08009696751832962,
-0.052700161933898926,
-0.17139126360416412,
-0.0070702629163861275,
-0.12135370075702667,
0.026614859700202942,
-0.0323312021791935,
-0.049769021570682526,
0.002313595963642001,
-0.05500337854027748,
0.028231507167220116,
-0.044706687331199646,
-0.02734297327697277,
0.0011060053948312998,
-0.14644266664981842,
-0.1567814201116562,
0.04191409423947334,
0.10623348504304886,
-0.030443906784057617,
0.023819247260689735,
0.04617192968726158,
0.025940842926502228,
-0.05050937086343765,
-0.11635033041238785,
0.03092259354889393,
-0.0905805230140686,
0.1267412304878235,
0.01783602125942707,
0.15887963771820068,
-0.02598102018237114,
0.029129741713404655,
0.07437777519226074,
0.027170367538928986,
-0.022915376350283623,
0.05076853185892105,
-0.006492906250059605,
0.014031668193638325,
-0.10627378523349762,
-0.09611836820840836,
0.12177008390426636,
-0.16488812863826752,
0.056080449372529984,
0.008370211347937584,
-0.020029796287417412,
-0.03775158151984215,
-0.02202785015106201,
0.10541084408760071,
0.02023238129913807,
0.007953250780701637,
-0.06834899634122849,
0.07223017513751984,
0.00815446488559246,
-0.06362535804510117,
-0.09286816418170929,
-0.07210352271795273,
-0.030725451186299324,
0.09754239767789841,
-0.013993097469210625,
-0.1054283156991005,
-0.06913081556558609,
-0.043489497154951096,
0.009738724678754807,
-0.03253960236907005,
-0.02942514605820179,
0.1820640116930008,
0.043507229536771774,
0.011401711963117123,
-0.026568474248051643,
0.03570229932665825,
-0.028069693595170975,
-0.038070615381002426,
-0.06557094305753708,
0.04323935881257057,
0.08641088753938675,
0.027142131701111794,
0.024127846583724022,
0.17075707018375397,
-0.004897078033536673,
0.0072086285799741745,
0.05919843539595604,
-0.04639032483100891,
-0.09817995876073837,
-0.09467656910419464,
-0.0255714263767004,
0.18345926702022552,
-0.035442326217889786,
0.0386027991771698,
0.046361882239580154,
-0.0030078052077442408,
0.03993832692503929,
-0.20768314599990845,
-0.12903699278831482,
0.052315618842840195,
-0.021443486213684082,
-0.21175028383731842,
0.05986568331718445,
-0.062109995633363724,
0.10338672250509262,
-0.028261814266443253,
-0.07672121375799179,
0.04304242134094238,
-0.00433255173265934,
-0.07367459684610367,
0.11897674947977066,
-0.058908265084028244,
-0.24109208583831787,
-0.1172858253121376,
-0.04383530840277672,
0.11573293060064316,
-0.00284052942879498,
0.027967212721705437,
-0.11188038438558578,
0.023280488327145576,
-0.020734945312142372,
0.0638636127114296,
-0.09537889808416367,
-0.10009167343378067,
-0.05835713446140289,
0.008071422576904297,
-0.00246383692137897,
-0.11837929487228394,
-0.021872665733098984,
-0.04837065562605858,
0.0658302754163742,
0.027176110073924065,
0.030656518414616585,
0.09338260442018509,
0.12033692747354507,
0.021419670432806015,
0.06548851728439331,
-0.03372717276215553,
0.351470410823822,
-0.07389053702354431,
0.08804209530353546,
0.14135831594467163,
0.026987837627530098,
0.04971672594547272,
0.14914754033088684,
0.06870124489068985,
-0.0825042650103569,
-0.0400395542383194,
-0.08304603397846222,
-0.07867259532213211,
-0.12148970365524292,
-0.06054767221212387,
-0.04294848069548607,
-0.07979938387870789,
0.018622051924467087,
0.031561076641082764,
-0.09092458337545395,
0.054324906319379807,
0.07667122036218643,
-0.04431508854031563,
-0.0649292916059494,
-0.001256817253306508,
0.004011294338852167,
-0.025360003113746643,
0.006286131218075752,
-0.03263786435127258,
-0.03907487168908119,
0.028571369126439095,
0.10076715797185898,
0.09582676738500595,
0.08759325742721558,
-0.006652598734945059,
0.06416146457195282,
0.12256703525781631,
0.07810989767313004,
0.18106243014335632,
0.081021249294281,
-0.012186994776129723,
-0.027672171592712402,
-0.05870048329234123,
0.002073507057502866,
0.041579023003578186,
-0.012258407659828663,
-0.060354236513376236,
0.014918271452188492,
-0.0854945257306099,
-0.05435453727841377,
-0.0026896640192717314,
0.11900278925895691,
-0.21203871071338654,
-0.02841346152126789,
-0.004084341693669558,
0.1035543903708458,
-0.049717627465724945,
0.057292595505714417,
0.032624609768390656,
-0.11850908398628235,
0.004331717733293772,
-0.03401786834001541,
0.08898448944091797,
0.03995789960026741,
0.028101587668061256,
-0.0882137343287468,
0.042665161192417145,
-0.04295124113559723,
0.1251257061958313,
-0.1723152995109558,
0.343191921710968,
-0.044036880135536194,
0.017736293375492096,
-0.05523398146033287,
0.003133729798719287,
0.0805269181728363,
0.03518606349825859,
0.24908949434757233,
0.01165855210274458,
-0.130452960729599,
-0.10685836523771286,
-0.12578681111335754,
0.051510535180568695,
-0.03451940417289734,
-0.06487374007701874,
0.05350267514586449,
0.06542385369539261,
0.00692238612100482,
-0.02202014997601509,
0.08926244080066681,
-0.19167397916316986,
-0.11326827853918076,
0.01752762496471405,
-0.029992196708917618,
0.04042504355311394,
-0.0164166446775198,
-0.05170350521802902,
-0.10470587015151978,
-0.03478340804576874,
0.023354068398475647,
-0.02677612006664276,
-0.08844722807407379,
0.10559263080358505,
0.08143571019172668,
-0.012266969308257103,
-0.029343094676733017,
-0.001864883117377758,
0.006845149211585522,
-0.08881645649671555,
0.014898902736604214,
0.025455009192228317,
-0.06880633533000946,
0.025760112330317497,
-0.07824089378118515,
0.08321639895439148,
0.057056594640016556,
0.04517417773604393,
0.01614972949028015,
0.07101099193096161,
-0.1289823055267334,
-0.12097126245498657,
0.18290245532989502,
-0.127309650182724,
-0.0004778954607900232,
0.038615886121988297,
0.037916120141744614,
0.013108880259096622,
-0.13974787294864655,
0.022266853600740433,
0.23304927349090576,
0.2448730319738388,
-0.13391132652759552,
0.12923133373260498,
-0.00772591307759285,
-0.10986914485692978,
-0.1545405089855194,
-0.02961365319788456,
-0.061984751373529434,
0.04623488336801529,
0.1161433681845665,
-0.1751306802034378,
0.05013885349035263,
0.11769895255565643,
-0.04956669360399246,
0.1639191210269928,
-0.23864451050758362,
-0.03401357680559158,
0.20474432408809662,
0.04858136549592018,
0.06185711547732353,
-0.055178046226501465,
-0.017955414950847626,
0.004452402237802744,
-0.14670200645923615,
0.06747513264417648,
0.09369530528783798,
0.03806440159678459,
-0.020220603793859482,
0.0789608508348465,
0.0029774359427392483,
-0.082966148853302,
0.205145463347435,
-0.0033320968504995108,
0.021838845685124397,
-0.04408876970410347,
-0.23550768196582794,
0.1357194185256958,
-0.02087373100221157,
0.1024583950638771,
0.0657232403755188,
0.03249502182006836,
-0.15583936870098114,
-0.011014873161911964,
-0.12735767662525177,
0.07631754875183105,
-0.07261411845684052,
-0.045049998909235,
-0.05711856111884117,
0.015624397434294224,
-0.08463478088378906,
-0.044354818761348724,
-0.150963693857193,
-0.06385059654712677,
0.07391506433486938,
0.03281313180923462,
-0.000155571848154068,
0.06712266802787781,
-0.18108828365802765,
0.05479910224676132,
-0.01031267549842596,
0.06223197653889656,
-0.1068204790353775,
-0.1194954514503479,
0.10545308887958527,
0.032345809042453766,
0.07740925997495651,
0.07263565063476562,
-0.06586456298828125,
0.016829270869493484,
0.04552480950951576,
-0.17818690836429596,
-0.06290280073881149,
-0.057037726044654846,
0.02393738552927971,
-0.019383123144507408,
-0.09304725378751755,
0.02872280217707157,
-0.04447859153151512,
-0.028487611562013626,
-0.007891579531133175,
-0.003016428789123893,
-0.13263113796710968,
0.022852659225463867,
0.12443728744983673,
0.10052590072154999,
-0.05148625001311302,
0.022052044048905373,
0.09386714547872543,
-0.06639569997787476,
-0.011574969626963139,
0.029072808101773262,
-0.051342036575078964,
-0.06316275894641876,
-0.07109949737787247,
0.15275828540325165,
0.0507914200425148,
-0.014527040533721447,
-0.004788383841514587,
-0.08603829145431519,
0.0002379120996920392,
0.053470175713300705,
0.08636203408241272,
-0.0006280643283389509,
-0.12493663281202316,
-0.09518413990736008,
-0.05669853463768959,
0.004514380358159542,
0.022928912192583084,
-0.05005928874015808,
-0.13553184270858765,
0.09168343245983124,
0.010083216242492199,
0.1122828796505928,
-0.036727797240018845,
-0.08384969085454941,
-0.09059667587280273,
0.05270625650882721,
-0.008446695283055305,
0.01489247940480709,
-0.02581462822854519,
0.00561625137925148,
-0.004114258103072643,
-0.031018244102597237,
-0.0005766812828369439,
-0.010590258985757828,
-0.11139016598463058,
0.0482783317565918,
-0.008785544894635677,
0.043268416076898575,
-0.030911287292838097,
-0.030005255714058876,
0.09081272780895233,
-0.02911756932735443,
0.060592323541641235,
0.07107724994421005,
-0.07881679385900497,
-0.04861150681972504,
-0.22144874930381775,
0.019128968939185143,
0.023262811824679375,
-0.014612271450459957,
0.009941908530890942,
-0.18945546448230743,
-0.011268035508692265,
-0.057085633277893066,
-0.02182818204164505,
-0.025398055091500282,
0.05816078558564186,
-0.07015799731016159,
-0.0563761405646801,
0.12105109542608261,
-0.048639047890901566,
-0.029899438843131065,
-0.09334506839513779,
0.17547352612018585,
-0.008197947405278683,
0.11110250651836395,
0.023677337914705276,
0.16366101801395416,
-0.1428760588169098,
-0.022710323333740234,
-0.05679627135396004,
-0.033260930329561234,
-0.05071788653731346,
-0.062155790627002716,
0.08933739364147186,
0.01168186217546463,
0.21314606070518494,
-0.009900502860546112,
-0.013915663585066795,
-0.023213017731904984,
0.18315912783145905,
0.02170882374048233,
-0.037539564073085785,
0.10584010928869247,
0.02190769463777542,
0.007549599278718233,
0.05699198320508003,
0.053206346929073334,
-0.025651579722762108,
0.16816169023513794,
0.21297581493854523,
-0.011467091739177704,
0.13564717769622803,
0.09104938805103302,
-0.03541725128889084,
0.016702989116311073,
0.0038595092482864857,
-0.1281934380531311,
0.12361197173595428,
-0.006411577109247446,
-0.03739672154188156,
-0.051969029009342194,
0.1015903428196907,
-0.06798411160707474,
0.051404036581516266,
-0.05334608629345894,
-0.08322499692440033,
-0.09917397797107697,
-0.20222529768943787,
0.04807303845882416,
0.007983355782926083,
-0.018412236124277115,
-0.0414278581738472,
-0.011442011222243309,
0.1865079700946808,
-0.05394323542714119,
0.012138945050537586,
0.09109349548816681,
0.02527019940316677,
-0.06277301162481308,
0.015656106173992157,
-0.026853857561945915,
0.010311245918273926,
-0.1509847790002823,
0.0715596154332161,
0.0018602749332785606,
-0.08668823540210724,
0.04169469326734543,
-0.004021177999675274,
-0.013445273973047733,
-0.027370603755116463,
-0.12465748935937881,
-0.06715283542871475,
-0.04240516945719719,
0.0672704428434372,
-0.016810733824968338,
0.10131482779979706,
0.057378146797418594,
0.04693185165524483,
-0.0013714209198951721,
0.14066772162914276,
-0.01795312575995922,
-0.12690123915672302,
-0.054675858467817307,
0.19244813919067383,
-0.15097494423389435,
-0.018152112141251564,
-0.06804873794317245,
-0.02096310630440712,
0.04838539659976959,
0.3149496614933014,
0.3045540750026703,
-0.0442916639149189,
-0.04041578993201256,
-0.028562651947140694,
0.020887399092316628,
0.07281971722841263,
0.047788381576538086,
0.11202077567577362,
0.16718216240406036,
-0.1976374089717865,
-0.027938993647694588,
-0.18008975684642792,
-0.0007874522125348449,
-0.02093621902167797,
0.017215849831700325,
0.1064966544508934,
-0.07155615836381912,
-0.0278637558221817,
0.20659297704696655,
-0.1633480042219162,
-0.06401367485523224,
0.02311486192047596,
-0.057745058089494705,
-0.11665032058954239,
0.023024048656225204,
0.07567422091960907,
0.005177158396691084,
0.11433346569538116,
-0.11165832728147507,
-0.08292477577924728,
0.1751791536808014,
-0.03530863672494888,
-0.12906695902347565,
-0.10525976866483688,
0.04307042062282562,
-0.14995238184928894,
0.13894318044185638,
0.007361991796642542,
0.12171207368373871,
0.05953886732459068,
0.08443769812583923,
-0.12478803843259811,
0.11646264791488647,
0.008233350701630116,
0.08150408416986465,
-0.04401925206184387,
-0.05896533653140068,
0.009104957804083824,
-0.06171158701181412,
-0.0017098152311518788,
-0.1577097475528717,
0.07883419841527939,
0.10297931730747223,
0.0123033681884408,
0.0007029986591078341,
0.04898691177368164,
-0.05169475078582764,
0.11442722380161285,
0.09090828895568848,
-0.07160082459449768,
0.006263777147978544,
-0.006594162434339523,
0.0008274931460618973,
0.07259426265954971,
-0.05470627173781395,
-0.03692709282040596,
-0.027300406247377396,
0.01227757427841425,
-0.009806429967284203,
-0.0849025696516037,
-0.0515606626868248,
-0.06596304476261139,
-0.04417622089385986,
-0.06417384743690491,
0.06320751458406448,
0.02043437771499157,
0.021025724709033966,
0.07398165762424469,
-0.0200913455337286,
0.15463760495185852,
0.0009459225693717599,
0.1524367779493332,
-0.17341545224189758,
-0.0813620314002037
] |
null | null |
flair
|
## English NER in Flair (Ontonotes fast model)
This is the fast version of the 18-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **89.3** (Ontonotes)
Predicts 18 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| CARDINAL | cardinal value |
| DATE | date value |
| EVENT | event name |
| FAC | building name |
| GPE | geo-political entity |
| LANGUAGE | language name |
| LAW | law name |
| LOC | location name |
| MONEY | money name |
| NORP | affiliation |
| ORDINAL | ordinal value |
| ORG | organization name |
| PERCENT | percent value |
| PERSON | person name |
| PRODUCT | product name |
| QUANTITY | quantity value |
| TIME | time value |
| WORK_OF_ART | name of work of art |
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-english-ontonotes-fast")
# make example sentence
sentence = Sentence("On September 1st George Washington won 1 dollar.")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [2,3]: "September 1st" [− Labels: DATE (0.9655)]
Span [4,5]: "George Washington" [− Labels: PERSON (0.8243)]
Span [7,8]: "1 dollar" [− Labels: MONEY (0.8022)]
```
So, the entities "*September 1st*" (labeled as a **date**), "*George Washington*" (labeled as a **person**) and "*1 dollar*" (labeled as a **money**) are found in the sentence "*On September 1st George Washington won 1 dollar*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import ColumnCorpus
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. load the corpus (Ontonotes does not ship with Flair, you need to download and reformat into a column format yourself)
corpus: Corpus = ColumnCorpus(
"resources/tasks/onto-ner",
column_format={0: "text", 1: "pos", 2: "upos", 3: "ner"},
tag_to_bioes="ner",
)
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
# GloVe embeddings
WordEmbeddings('en-crawl'),
# contextual string embeddings, forward
FlairEmbeddings('news-forward-fast'),
# contextual string embeddings, backward
FlairEmbeddings('news-backward-fast'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/ner-english-ontonotes-fast',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following paper when using this model.
```
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
{"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["ontonotes"], "widget": [{"text": "On September 1st George Washington won 1 dollar."}]}
|
token-classification
|
flair/ner-english-ontonotes-fast
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:ontonotes",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #has_space #region-us
|
English NER in Flair (Ontonotes fast model)
-------------------------------------------
This is the fast version of the 18-class NER model for English that ships with Flair.
F1-Score: 89.3 (Ontonotes)
Predicts 18 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the entities "*September 1st*" (labeled as a date), "*George Washington*" (labeled as a person) and "*1 dollar*" (labeled as a money) are found in the sentence "*On September 1st George Washington won 1 dollar*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following paper when using this model.
---
### Issues?
The Flair issue tracker is available here.
|
[
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*September 1st*\" (labeled as a date), \"*George Washington*\" (labeled as a person) and \"*1 dollar*\" (labeled as a money) are found in the sentence \"*On September 1st George Washington won 1 dollar*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*September 1st*\" (labeled as a date), \"*George Washington*\" (labeled as a person) and \"*1 dollar*\" (labeled as a money) are found in the sentence \"*On September 1st George Washington won 1 dollar*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
41,
100,
22,
15,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #has_space #region-us \n### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*September 1st*\" (labeled as a date), \"*George Washington*\" (labeled as a person) and \"*1 dollar*\" (labeled as a money) are found in the sentence \"*On September 1st George Washington won 1 dollar*\".\n\n\n\n\n---### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
-0.08316225558519363,
0.10079556703567505,
-0.0019114876631647348,
0.09859561175107956,
0.08915527909994125,
0.03749171644449234,
0.07468868792057037,
0.07941008359193802,
0.011256714351475239,
0.11021765321493149,
0.20103533565998077,
0.06640208512544632,
0.1350521296262741,
0.14261142909526825,
0.03518760949373245,
-0.24320901930332184,
0.011797333136200905,
-0.021585136651992798,
0.08412260562181473,
0.1831733137369156,
0.0932883620262146,
-0.0326807014644146,
0.008811341598629951,
-0.002581997076049447,
-0.11500131338834763,
-0.0231632087379694,
0.005780375562608242,
-0.13265946507453918,
0.13677173852920532,
-0.041492581367492676,
0.17305608093738556,
0.026077525690197945,
0.09537431597709656,
-0.10033538937568665,
0.015361697413027287,
0.003886376041918993,
0.00277880416251719,
0.09290867298841476,
-0.033097341656684875,
0.024312442168593407,
0.20159408450126648,
-0.08779844641685486,
0.023625079542398453,
0.030366642400622368,
-0.22525843977928162,
-0.3146677017211914,
-0.12263697385787964,
0.1526939421892166,
0.07927598059177399,
0.05694747343659401,
-0.04209715500473976,
0.08587772399187088,
-0.02513301558792591,
-0.0063313087448477745,
0.18325577676296234,
-0.22652064263820648,
-0.04710628464818001,
0.1437804400920868,
-0.0021925801411271095,
-0.0013136304914951324,
-0.019787974655628204,
0.023788481950759888,
0.025112366303801537,
0.06092986837029457,
0.00444755656644702,
0.001000404474325478,
0.09044552594423294,
0.11029836535453796,
-0.20154337584972382,
-0.11872992664575577,
0.3508409857749939,
0.05516213923692703,
-0.05819416046142578,
-0.030878249555826187,
0.027527514845132828,
-0.09971772879362106,
0.018031194806098938,
-0.06237305700778961,
0.0038226584438234568,
-0.009890339337289333,
0.18166179955005646,
-0.05168592929840088,
-0.13230836391448975,
-0.015039702877402306,
-0.040640417486429214,
0.08471836149692535,
-0.09175974875688553,
0.11599372327327728,
-0.14444471895694733,
0.08390321582555771,
-0.04794900119304657,
-0.051572464406490326,
0.020039711147546768,
-0.03932471573352814,
0.005759348161518574,
-0.001199529622681439,
-0.07293178141117096,
0.05927314981818199,
0.010882801376283169,
-0.023941421881318092,
0.08591482788324356,
-0.04249372333288193,
0.06909577548503876,
0.12175861746072769,
0.04099413380026817,
0.21659453213214874,
-0.12960435450077057,
-0.05653658136725426,
0.030919168144464493,
0.04142000898718834,
0.021090935915708542,
-0.0478137843310833,
-0.14737407863140106,
0.03299034759402275,
0.011910378001630306,
0.008868847973644733,
0.027130234986543655,
-0.02941679023206234,
-0.09416521340608597,
0.029778867959976196,
0.11051274091005325,
-0.08957868069410324,
-0.01841304823756218,
0.009083504788577557,
-0.10464147478342056,
0.06453833729028702,
0.04933268576860428,
-0.013609249144792557,
0.02278347872197628,
0.11423695832490921,
-0.08564337342977524,
0.0701347216963768,
-0.0339888334274292,
-0.12617909908294678,
0.019716400653123856,
-0.06384170800447464,
-0.04641619324684143,
-0.04097360372543335,
-0.1281115710735321,
-0.03112366981804371,
0.052475836127996445,
0.020123222842812538,
-0.07772515714168549,
-0.02620084583759308,
-0.02564680390059948,
-0.029013635590672493,
0.02010919339954853,
0.1351497769355774,
-0.04438595473766327,
0.08293133974075317,
-0.008363998495042324,
0.10806945711374283,
-0.04011392220854759,
0.02269965410232544,
-0.08755407482385635,
-0.009981929324567318,
-0.15227730572223663,
0.03642553463578224,
0.0022682726848870516,
-0.00025578588247299194,
-0.08218304812908173,
-0.10665016621351242,
-0.06320474296808243,
-0.013682848773896694,
0.019764302298426628,
0.18114353716373444,
-0.2826175391674042,
-0.028415318578481674,
0.11528035998344421,
-0.08964129537343979,
-0.013974024914205074,
0.05248088017106056,
-0.059911441057920456,
0.1942710429430008,
0.056353695690631866,
0.12665194272994995,
-0.0651867464184761,
-0.353809118270874,
0.03344239294528961,
0.035721395164728165,
-0.17346838116645813,
-0.0064885360188782215,
0.09488668292760849,
-0.06494319438934326,
-0.15553857386112213,
0.007789242547005415,
-0.15204069018363953,
-0.014597169123589993,
-0.0660160481929779,
-0.0539834126830101,
-0.009629840962588787,
-0.06818968057632446,
0.10477399080991745,
-0.013248249888420105,
-0.015300046652555466,
-0.045573946088552475,
-0.12798821926116943,
-0.08033065497875214,
0.0020515122450888157,
0.11398109048604965,
-0.018808651715517044,
-0.03379513695836067,
0.08761016279459,
0.03229283168911934,
-0.010957229882478714,
-0.13187718391418457,
0.06382586061954498,
-0.029951488599181175,
0.08723235130310059,
0.04758650064468384,
0.12134704738855362,
-0.028679311275482178,
-0.002308105817064643,
0.062299493700265884,
0.04227179288864136,
-0.011708532460033894,
0.02017076686024666,
-0.020039977505803108,
0.022837918251752853,
-0.09529529511928558,
-0.0938146561384201,
0.1225634515285492,
-0.08705366402864456,
0.06786785274744034,
0.058481182903051376,
-0.07941483706235886,
-0.03214603662490845,
-0.03228491172194481,
0.08769896626472473,
0.02011513151228428,
0.07927748560905457,
-0.03457462415099144,
0.05588062107563019,
0.02763107232749462,
-0.087100550532341,
-0.0931970626115799,
-0.07611129432916641,
-0.09633801877498627,
0.09670329093933105,
-0.031113024801015854,
-0.1261390596628189,
-0.02073996514081955,
-0.041224703192710876,
-0.016499238088726997,
-0.06692378968000412,
-0.06954111903905869,
0.14301645755767822,
0.04259590432047844,
-0.007100790273398161,
-0.029321076348423958,
-0.010559219866991043,
0.018704786896705627,
-0.03837491571903229,
-0.07560523599386215,
0.030957719311118126,
-0.028757356107234955,
-0.01828567311167717,
0.012612530030310154,
0.11716977506875992,
0.03756899759173393,
0.018206823617219925,
0.03191603347659111,
-0.03795967996120453,
-0.13787324726581573,
-0.06254521757364273,
-0.008013740181922913,
0.12359112501144409,
-0.08363622426986694,
-0.039074208587408066,
0.04457593709230423,
0.012640533968806267,
-0.004628119524568319,
-0.20331881940364838,
-0.08295389264822006,
0.013412844389677048,
-0.029603274539113045,
-0.20735494792461395,
0.04761917516589165,
-0.029571371152997017,
0.10246456414461136,
0.011687517166137695,
-0.013061012141406536,
0.06657972931861877,
0.0034081265330314636,
-0.08167224377393723,
0.08499904721975327,
-0.022077174857258797,
-0.2647908329963684,
-0.12704038619995117,
-0.12241344898939133,
0.12564599514007568,
-0.020651139318943024,
0.04669295623898506,
-0.05414632335305214,
0.05718066543340683,
-0.008926781825721264,
0.041542842984199524,
-0.044855453073978424,
-0.09589061886072159,
0.010221130214631557,
-0.0365857370197773,
-0.014854996465146542,
-0.09945108741521835,
-0.04697848856449127,
-0.06033822149038315,
0.11895134299993515,
0.0710437074303627,
0.039821822196245193,
0.0479179248213768,
0.1796146035194397,
-0.007780170999467373,
0.0852576419711113,
-0.06120719015598297,
0.36258190870285034,
-0.060618989169597626,
0.0517374649643898,
0.12525896728038788,
0.06722302734851837,
0.05423782765865326,
0.12066176533699036,
0.09368046373128891,
-0.08391118049621582,
-0.03200184926390648,
-0.050866901874542236,
-0.10713272541761398,
-0.1153402328491211,
-0.04367075115442276,
-0.042184311896562576,
-0.06424086540937424,
0.010644707828760147,
0.014157265424728394,
-0.056015949696302414,
-0.010601567104458809,
0.09062407165765762,
-0.009244890883564949,
0.015547828748822212,
-0.005822288803756237,
0.07172572612762451,
-0.03385578840970993,
0.037167031317949295,
-0.013113767839968204,
-0.012645306065678596,
0.037851594388484955,
-0.009437796659767628,
0.10826565325260162,
0.1272636353969574,
-0.02101839892566204,
0.0696486160159111,
0.057822395116090775,
0.045583415776491165,
0.133970707654953,
0.08899372071027756,
-0.015291751362383366,
-0.02427856996655464,
-0.059323277324438095,
0.017806554213166237,
0.015962306410074234,
0.026493234559893608,
-0.041764818131923676,
-0.0458252988755703,
-0.0815376415848732,
-0.03878547623753548,
0.12156729400157928,
0.10712110251188278,
-0.12181247770786285,
-0.026942258700728416,
0.012435121461749077,
0.05621720850467682,
-0.0054377540946006775,
0.039153072983026505,
-0.07543099671602249,
-0.12186727672815323,
0.033441681414842606,
-0.03394192457199097,
0.0937117412686348,
0.055267076939344406,
0.0234877597540617,
-0.09557481855154037,
0.025298619642853737,
-0.026459068059921265,
0.08539479970932007,
-0.2059215009212494,
0.32412734627723694,
-0.02595926634967327,
0.05853673443198204,
-0.05634666606783867,
-0.017341844737529755,
0.04846195504069328,
0.06317831575870514,
0.22191442549228668,
0.010104524902999401,
-0.16189062595367432,
-0.016694290563464165,
-0.1079927310347557,
0.007729105185717344,
-0.03536679968237877,
-0.06865596771240234,
0.06084318831562996,
0.062222160398960114,
0.010408047586679459,
-0.029809197410941124,
0.15971806645393372,
-0.137426495552063,
-0.0521165169775486,
0.048275288194417953,
-0.054195526987314224,
0.02041393332183361,
-0.02826695330440998,
-0.06938768923282623,
-0.13114707171916962,
-0.07999900728464127,
-0.042031560093164444,
-0.05124383792281151,
-0.0557195283472538,
0.13639786839485168,
0.0411360003054142,
-0.026161614805459976,
-0.07245657593011856,
0.006110088899731636,
0.03044201247394085,
-0.07275664061307907,
0.025847088545560837,
0.01582110859453678,
-0.02635061927139759,
0.025343112647533417,
-0.043443772941827774,
0.03866126760840416,
0.06638714671134949,
0.07115055620670319,
-0.01940624974668026,
0.07056249678134918,
-0.08739098161458969,
-0.0866570994257927,
0.21407416462898254,
-0.20032280683517456,
-0.01923687383532524,
-0.002640657126903534,
0.05162426456809044,
-0.06919605284929276,
-0.16538786888122559,
0.026435483247041702,
0.17054599523544312,
0.266766756772995,
-0.13500486314296722,
0.07983740419149399,
0.09576565772294998,
-0.07621452957391739,
-0.16308340430259705,
-0.020106356590986252,
-0.0635991171002388,
0.023189468309283257,
0.01657947525382042,
-0.13839827477931976,
-0.015609883703291416,
0.12403000146150589,
-0.06423883140087128,
0.15408584475517273,
-0.28378570079803467,
-0.044748641550540924,
0.16627871990203857,
-0.018585961312055588,
0.1360778659582138,
-0.07564985752105713,
-0.048366885632276535,
0.07205940783023834,
-0.1707780510187149,
0.044128019362688065,
0.06109130382537842,
0.053920019418001175,
-0.002702602418139577,
0.0937717854976654,
-0.028296133503317833,
-0.07321329414844513,
0.15027546882629395,
-0.013799860142171383,
0.01837196759879589,
-0.026594385504722595,
-0.2895151376724243,
0.1656656712293625,
-0.050250500440597534,
0.10121581703424454,
0.09210864454507828,
-0.009860238991677761,
-0.11899009346961975,
-0.01483819168061018,
-0.1325434446334839,
0.08483332395553589,
-0.07278843224048615,
-0.034442394971847534,
-0.040398385375738144,
0.0016063580987975001,
-0.07340258359909058,
-0.03749079629778862,
-0.09721805900335312,
-0.08236560225486755,
0.11316623538732529,
0.12060718983411789,
-0.050759393721818924,
-0.001700485940091312,
-0.14887277781963348,
-0.0030883937142789364,
-0.028883403167128563,
0.09161382168531418,
-0.1079411655664444,
-0.1150653064250946,
0.07171206921339035,
0.06907011568546295,
0.14965906739234924,
0.07548091560602188,
-0.06863062083721161,
0.04715072363615036,
0.0397992879152298,
-0.13631640374660492,
-0.14721167087554932,
0.000585209287237376,
-0.05069452151656151,
-0.08934103697538376,
-0.13809996843338013,
-0.005830137990415096,
-0.03118237294256687,
-0.03204825147986412,
-0.0048744212836027145,
0.011298908852040768,
-0.15606068074703217,
0.0496654249727726,
0.11160728335380554,
0.10159850120544434,
-0.07390783727169037,
0.008936894126236439,
0.10637996345758438,
-0.07187143713235855,
-0.02341214194893837,
0.06251567602157593,
-0.014234590344130993,
-0.05943538248538971,
-0.10952029377222061,
0.18767832219600677,
-0.0004133781767450273,
-0.019063051789999008,
-0.011849610134959221,
-0.09027707576751709,
0.056314319372177124,
0.1158367395401001,
0.073533795773983,
0.008413109928369522,
-0.07038146257400513,
-0.02889409102499485,
-0.03611161187291145,
0.01264409814029932,
0.07192353159189224,
-0.04814516752958298,
-0.060174159705638885,
0.1301407516002655,
0.009382949210703373,
0.1311078667640686,
-0.04204103723168373,
-0.0830889344215393,
-0.07619486004114151,
0.048974331468343735,
0.03074405901134014,
0.058384232223033905,
0.017825579270720482,
0.005617854651063681,
0.021150827407836914,
-0.01552216149866581,
-0.03451508656144142,
-0.012735594995319843,
-0.09298450499773026,
0.05443870276212692,
0.00426756776869297,
0.07032009959220886,
-0.0575384758412838,
-0.043425023555755615,
0.053586918860673904,
-0.020288709551095963,
0.05693965032696724,
0.07346418499946594,
-0.06348668783903122,
-0.028101088479161263,
-0.13993141055107117,
0.062157370150089264,
0.02667490765452385,
-0.0366659015417099,
0.04839249327778816,
-0.1720142811536789,
0.020011059939861298,
-0.04362643137574196,
-0.036924924701452255,
0.013832216151058674,
0.01854562573134899,
-0.07283751666545868,
-0.01718832738697529,
0.09418956190347672,
-0.06617586314678192,
-0.02154155634343624,
-0.06947571039199829,
0.14479607343673706,
0.028119971975684166,
0.1130267009139061,
0.0009768647141754627,
0.14077095687389374,
-0.13314436376094818,
-0.011600887402892113,
-0.026015974581241608,
-0.016197333112359047,
-0.02632899582386017,
-0.030397510156035423,
0.10211565345525742,
0.03275115042924881,
0.20164145529270172,
0.040033094584941864,
-0.03724747523665428,
0.01351950690150261,
0.1729055941104889,
-0.026667499914765358,
-0.014567170292139053,
0.05912957340478897,
-0.0182978305965662,
-0.0352620929479599,
0.050952401012182236,
0.05260661616921425,
-0.06883735209703445,
0.08818161487579346,
0.25217151641845703,
0.020126529037952423,
0.1264684647321701,
0.07863844931125641,
-0.054739903658628464,
0.058116354048252106,
-0.0344511903822422,
-0.010747232474386692,
0.12585724890232086,
0.003942588344216347,
-0.037332650274038315,
0.036608461290597916,
0.11150532960891724,
-0.09514997154474258,
0.09439513832330704,
-0.06860962510108948,
-0.07096312940120697,
-0.1022535189986229,
-0.14431947469711304,
0.0270286463201046,
0.0031465436331927776,
0.00965784676373005,
-0.023155495524406433,
0.05854795500636101,
0.08754171431064606,
-0.05936836451292038,
0.018681352958083153,
0.026237977668642998,
-0.020815759897232056,
-0.0031101638451218605,
0.06595505028963089,
-0.06719367206096649,
-0.006735472474247217,
-0.19593258202075958,
0.05238795652985573,
0.02520109713077545,
-0.13197574019432068,
0.0631556585431099,
0.011321702972054482,
-0.02019832469522953,
-0.04960952326655388,
-0.11513327807188034,
-0.08021838217973709,
-0.007291910704225302,
0.03693321347236633,
-0.00966638047248125,
0.17584793269634247,
0.05030819773674011,
0.03308050334453583,
0.010423741303384304,
0.08664146810770035,
-0.02479824796319008,
-0.1576293557882309,
-0.11853888630867004,
0.12645812332630157,
-0.1304926872253418,
0.0164695605635643,
-0.06743859499692917,
-0.03412514552474022,
0.04462841898202896,
0.2927039861679077,
0.19532614946365356,
0.05250449851155281,
-0.028494568541646004,
-0.0031045253854244947,
0.021883534267544746,
0.08225972950458527,
0.026925938203930855,
0.06141097843647003,
0.1493918001651764,
-0.17392311990261078,
0.040210362523794174,
-0.19936394691467285,
0.0068571302108466625,
-0.06800004839897156,
0.0025719378609210253,
0.09980139136314392,
-0.047451961785554886,
-0.04472251608967781,
0.17058604955673218,
-0.13490992784500122,
-0.08826036751270294,
0.0732383206486702,
-0.06328651309013367,
-0.07514587789773941,
0.005678205750882626,
0.04972938820719719,
0.000087964472186286,
0.14703918993473053,
-0.06109211966395378,
-0.07351183891296387,
0.13064032793045044,
-0.04763093963265419,
-0.0942399725317955,
-0.1562129706144333,
0.013413499109447002,
-0.10483404248952866,
0.16316913068294525,
-0.006336118094623089,
0.14121121168136597,
0.055757198482751846,
0.07490039616823196,
-0.07732239365577698,
0.12608802318572998,
-0.009225057438015938,
0.12937165796756744,
-0.06329485774040222,
-0.023733830079436302,
-0.005357259418815374,
-0.035355158150196075,
-0.01089900266379118,
-0.13785628974437714,
0.04332674294710159,
0.059503525495529175,
0.03366152197122574,
-0.03849697485566139,
0.03476104885339737,
-0.024006802588701248,
0.09345661103725433,
0.10824573040008545,
-0.026849236339330673,
0.012220025062561035,
-0.03381827846169472,
-0.009564652107656002,
0.1176799088716507,
0.01601770520210266,
-0.06219296157360077,
-0.05967642739415169,
0.020449776202440262,
0.09098237007856369,
-0.07093772292137146,
-0.12582655251026154,
-0.08428056538105011,
-0.04138968139886856,
-0.061407554894685745,
0.06386832892894745,
0.025512417778372765,
0.04928259924054146,
0.09213174879550934,
-0.025831129401922226,
0.041753169149160385,
0.001855668961070478,
0.17563049495220184,
-0.17203353345394135,
-0.05501732975244522
] |
null | null |
flair
|
## English NER in Flair (Ontonotes large model)
This is the large 18-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **90.93** (Ontonotes)
Predicts 18 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| CARDINAL | cardinal value |
| DATE | date value |
| EVENT | event name |
| FAC | building name |
| GPE | geo-political entity |
| LANGUAGE | language name |
| LAW | law name |
| LOC | location name |
| MONEY | money name |
| NORP | affiliation |
| ORDINAL | ordinal value |
| ORG | organization name |
| PERCENT | percent value |
| PERSON | person name |
| PRODUCT | product name |
| QUANTITY | quantity value |
| TIME | time value |
| WORK_OF_ART | name of work of art |
Based on document-level XLM-R embeddings and [FLERT](https://arxiv.org/pdf/2011.06993v1.pdf/).
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-english-ontonotes-large")
# make example sentence
sentence = Sentence("On September 1st George won 1 dollar while watching Game of Thrones.")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [2,3]: "September 1st" [− Labels: DATE (1.0)]
Span [4]: "George" [− Labels: PERSON (1.0)]
Span [6,7]: "1 dollar" [− Labels: MONEY (1.0)]
Span [10,11,12]: "Game of Thrones" [− Labels: WORK_OF_ART (1.0)]
```
So, the entities "*September 1st*" (labeled as a **date**), "*George*" (labeled as a **person**), "*1 dollar*" (labeled as a **money**) and "Game of Thrones" (labeled as a **work of art**) are found in the sentence "*On September 1st George Washington won 1 dollar while watching Game of Thrones*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import ColumnCorpus
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. load the corpus (Ontonotes does not ship with Flair, you need to download and reformat into a column format yourself)
corpus: Corpus = ColumnCorpus(
"resources/tasks/onto-ner",
column_format={0: "text", 1: "pos", 2: "upos", 3: "ner"},
tag_to_bioes="ner",
)
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize fine-tuneable transformer embeddings WITH document context
from flair.embeddings import TransformerWordEmbeddings
embeddings = TransformerWordEmbeddings(
model='xlm-roberta-large',
layers="-1",
subtoken_pooling="first",
fine_tune=True,
use_context=True,
)
# 5. initialize bare-bones sequence tagger (no CRF, no RNN, no reprojection)
from flair.models import SequenceTagger
tagger = SequenceTagger(
hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type='ner',
use_crf=False,
use_rnn=False,
reproject_embeddings=False,
)
# 6. initialize trainer with AdamW optimizer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus, optimizer=torch.optim.AdamW)
# 7. run training with XLM parameters (20 epochs, small LR)
from torch.optim.lr_scheduler import OneCycleLR
trainer.train('resources/taggers/ner-english-ontonotes-large',
learning_rate=5.0e-6,
mini_batch_size=4,
mini_batch_chunk_size=1,
max_epochs=20,
scheduler=OneCycleLR,
embeddings_storage_mode='none',
weight_decay=0.,
)
```
---
### Cite
Please cite the following paper when using this model.
```
@misc{schweter2020flert,
title={FLERT: Document-Level Features for Named Entity Recognition},
author={Stefan Schweter and Alan Akbik},
year={2020},
eprint={2011.06993},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
{"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["ontonotes"], "widget": [{"text": "On September 1st George won 1 dollar while watching Game of Thrones."}]}
|
token-classification
|
flair/ner-english-ontonotes-large
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:ontonotes",
"arxiv:2011.06993",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2011.06993"
] |
[
"en"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #arxiv-2011.06993 #has_space #region-us
|
English NER in Flair (Ontonotes large model)
--------------------------------------------
This is the large 18-class NER model for English that ships with Flair.
F1-Score: 90.93 (Ontonotes)
Predicts 18 tags:
Based on document-level XLM-R embeddings and FLERT.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the entities "*September 1st*" (labeled as a date), "*George*" (labeled as a person), "*1 dollar*" (labeled as a money) and "Game of Thrones" (labeled as a work of art) are found in the sentence "*On September 1st George Washington won 1 dollar while watching Game of Thrones*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following paper when using this model.
---
### Issues?
The Flair issue tracker is available here.
|
[
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*September 1st*\" (labeled as a date), \"*George*\" (labeled as a person), \"*1 dollar*\" (labeled as a money) and \"Game of Thrones\" (labeled as a work of art) are found in the sentence \"*On September 1st George Washington won 1 dollar while watching Game of Thrones*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #arxiv-2011.06993 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*September 1st*\" (labeled as a date), \"*George*\" (labeled as a person), \"*1 dollar*\" (labeled as a money) and \"Game of Thrones\" (labeled as a work of art) are found in the sentence \"*On September 1st George Washington won 1 dollar while watching Game of Thrones*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
50,
118,
22,
15,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #arxiv-2011.06993 #has_space #region-us \n### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*September 1st*\" (labeled as a date), \"*George*\" (labeled as a person), \"*1 dollar*\" (labeled as a money) and \"Game of Thrones\" (labeled as a work of art) are found in the sentence \"*On September 1st George Washington won 1 dollar while watching Game of Thrones*\".\n\n\n\n\n---### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
-0.04395230486989021,
0.06378185749053955,
-0.002694283379241824,
0.1638161689043045,
0.06263026595115662,
0.05287015065550804,
0.03480091691017151,
0.09863538295030594,
0.036095377057790756,
0.07971187680959702,
0.1671987622976303,
0.1012447401881218,
0.09167638421058655,
0.07721450924873352,
0.08367174863815308,
-0.2578028738498688,
-0.02188917249441147,
-0.014272129163146019,
0.09052587300539017,
0.16268093883991241,
0.07287821918725967,
-0.04031844437122345,
0.04040897265076637,
0.00920247845351696,
-0.06373682618141174,
-0.06314750760793686,
-0.015444730408489704,
-0.08536649495363235,
0.16790322959423065,
-0.034350842237472534,
0.10578373819589615,
-0.018338032066822052,
0.04611955210566521,
-0.11690971255302429,
0.038501493632793427,
0.010228892788290977,
-0.014350758865475655,
0.060708772391080856,
-0.010389236733317375,
0.005401280242949724,
0.23533236980438232,
-0.028997406363487244,
0.03576197475194931,
0.05511584132909775,
-0.21218304336071014,
-0.3387517035007477,
-0.07693085074424744,
0.1130533367395401,
0.09007153660058975,
0.07264697551727295,
-0.037795402109622955,
0.07312894612550735,
-0.011654376983642578,
0.010410374961793423,
0.24303796887397766,
-0.2980148196220398,
-0.05787166580557823,
0.13106517493724823,
0.0823131874203682,
0.10737042874097824,
-0.042146485298871994,
0.051491878926754,
-0.03794558718800545,
0.07955233007669449,
0.01247757114470005,
-0.040385033935308456,
0.04920483008027077,
0.10590925067663193,
-0.18791289627552032,
-0.05408529192209244,
0.29101526737213135,
0.036240167915821075,
-0.06625065952539444,
-0.04559837654232979,
0.04551977664232254,
-0.08974502235651016,
-0.01534473430365324,
-0.07380884140729904,
0.013467694632709026,
-0.005212688352912664,
0.1680169254541397,
-0.08432625979185104,
-0.11238425970077515,
-0.034279949963092804,
-0.0013866570079699159,
0.026000551879405975,
-0.08707886934280396,
0.08028996735811234,
-0.12670905888080597,
0.09601844847202301,
-0.1341688483953476,
-0.028815733268857002,
-0.033370453864336014,
-0.04820363596081734,
-0.02476511336863041,
-0.01079639233648777,
-0.054799262434244156,
0.041887469589710236,
0.015270634554326534,
0.029682952910661697,
0.10895895957946777,
0.013322991319000721,
0.03290502354502678,
0.12079140543937683,
0.04241786152124405,
0.11943592131137848,
-0.08245474845170975,
-0.08494361490011215,
0.015020226128399372,
0.04398806765675545,
0.08586479723453522,
-0.06047625094652176,
-0.1491808444261551,
0.0362032912671566,
-0.05216951668262482,
-0.014129120856523514,
0.07846751064062119,
-0.051140666007995605,
-0.11801054328680038,
0.0599813386797905,
0.17252883315086365,
-0.03561447933316231,
-0.020523257553577423,
0.041628990322351456,
-0.06818835437297821,
-0.01226920634508133,
0.03842768445611,
0.03728403151035309,
0.06255583465099335,
0.11006983369588852,
-0.10238967090845108,
0.03170154243707657,
0.043630488216876984,
-0.09112132340669632,
0.06272964924573898,
-0.1248532086610794,
-0.03730573132634163,
-0.045531753450632095,
-0.0772705227136612,
-0.00291267572902143,
0.05179319903254509,
0.004313623998314142,
-0.04610995203256607,
-0.012076041661202908,
-0.05114366486668587,
-0.03194626048207283,
0.052849989384412766,
0.08269611746072769,
-0.062443360686302185,
0.11511147022247314,
-0.02772422507405281,
0.12207911908626556,
-0.08801678568124771,
0.003065119031816721,
-0.11612025648355484,
-0.015131819061934948,
-0.18256613612174988,
0.025705035775899887,
-0.0090791592374444,
-0.0011527055175974965,
-0.05248134955763817,
-0.07716983556747437,
0.011936016380786896,
-0.00022879526659380645,
-0.014499511569738388,
0.13047105073928833,
-0.2984906733036041,
-0.03737373650074005,
0.12715871632099152,
-0.10750330984592438,
-0.1254640370607376,
0.09786561131477356,
-0.032974593341350555,
0.1367771029472351,
0.08402755111455917,
0.16801606118679047,
-0.039989616721868515,
-0.30135655403137207,
-0.009558900259435177,
0.0279154721647501,
-0.15619909763336182,
0.03010271117091179,
0.13462457060813904,
-0.04275594279170036,
0.01923969015479088,
-0.0014088487951084971,
-0.18737779557704926,
-0.05472765490412712,
-0.022402510046958923,
-0.056465648114681244,
0.0033265829551965,
-0.05060802772641182,
0.09962461143732071,
0.0419669933617115,
-0.055438458919525146,
-0.06504879891872406,
-0.12189856171607971,
-0.13339881598949432,
-0.00037004248588345945,
0.110529325902462,
0.04476139694452286,
-0.02160913124680519,
0.15716105699539185,
0.0964852124452591,
-0.008461507968604565,
-0.13765250146389008,
0.049845825880765915,
-0.04576234146952629,
0.08423361927270889,
0.05217067524790764,
0.08438694477081299,
0.007001231890171766,
0.06621203571557999,
0.028131548315286636,
0.05480050668120384,
-0.09701243788003922,
0.025376595556735992,
-0.0539429634809494,
-0.07582655549049377,
-0.1153939962387085,
-0.10819082707166672,
0.11719250679016113,
-0.13627944886684418,
0.04102649167180061,
0.10070903599262238,
-0.05465289205312729,
-0.026580505073070526,
-0.08375348895788193,
0.04490789398550987,
0.03624722734093666,
0.014340115711092949,
-0.03166286647319794,
0.08461607247591019,
0.016555413603782654,
-0.10160964727401733,
-0.10033050924539566,
-0.06939562410116196,
-0.11170010268688202,
0.11731156706809998,
-0.08875861018896103,
-0.11745903640985489,
-0.06140011548995972,
-0.045149341225624084,
0.001840833923779428,
-0.07024559378623962,
-0.06216771900653839,
0.19358685612678528,
0.09681646525859833,
0.011117185465991497,
-0.04251297935843468,
0.0006506023346446455,
0.015830518677830696,
-0.04392364248633385,
-0.05594620481133461,
0.058577679097652435,
-0.052492789924144745,
-0.012460901401937008,
0.0023832391016185284,
0.14307741820812225,
0.04522623494267464,
0.04203654080629349,
0.07649068534374237,
-0.0025293638464063406,
-0.08707859367132187,
-0.04815387725830078,
0.013464562594890594,
0.11839167028665543,
-0.1510784775018692,
-0.01149765308946371,
0.03731323778629303,
-0.016477301716804504,
-0.059715718030929565,
-0.1822410672903061,
-0.12404843419790268,
0.03303499147295952,
-0.03718578442931175,
-0.2163044661283493,
0.07870548963546753,
-0.07380920648574829,
0.11223780363798141,
0.054834019392728806,
-0.005803170148283243,
0.06107319891452789,
-0.009068467654287815,
-0.06482995301485062,
0.08126609772443771,
-0.07157953083515167,
-0.21904680132865906,
-0.07540653645992279,
-0.15665127336978912,
0.0798351913690567,
0.021795060485601425,
0.07541663944721222,
-0.08857376873493195,
0.054357755929231644,
-0.0048123798333108425,
0.00465310038998723,
-0.09885372221469879,
-0.12369704246520996,
-0.02609567530453205,
-0.007831063121557236,
-0.09063351154327393,
-0.06499481946229935,
-0.027926161885261536,
-0.07454372197389603,
0.01485470961779356,
0.049499284476041794,
0.007358141243457794,
0.10757575929164886,
0.1398111879825592,
0.007242465857416391,
0.059465330094099045,
-0.0691179409623146,
0.29564446210861206,
-0.046828631311655045,
0.07209953665733337,
0.1169450581073761,
0.034285277128219604,
0.08526793122291565,
0.08072756230831146,
0.11403010785579681,
-0.030469486489892006,
-0.055197495967149734,
-0.043545130640268326,
-0.10536035150289536,
-0.11157313734292984,
-0.020359743386507034,
-0.055864084511995316,
0.02229306660592556,
0.029438002035021782,
0.042293448001146317,
-0.0017433149041607976,
-0.0009382538846693933,
0.07388638705015182,
-0.04841616377234459,
0.08933547884225845,
0.040306996554136276,
0.0902949646115303,
-0.0556214302778244,
0.001716181985102594,
0.005315334070473909,
0.0521477647125721,
0.07445525377988815,
0.055447403341531754,
0.03836353123188019,
0.09765342622995377,
0.04994963854551315,
0.10434882342815399,
0.06597566604614258,
0.007675801403820515,
0.025207500904798508,
0.057029642164707184,
-0.020534537732601166,
-0.05452907085418701,
-0.08685330301523209,
0.06502240151166916,
0.04945029318332672,
0.01148180477321148,
-0.05233735218644142,
-0.04156758263707161,
-0.01077843178063631,
-0.017892936244606972,
0.0358445905148983,
0.08609601110219955,
-0.11600912362337112,
0.005585105624049902,
0.0424957238137722,
0.10278087854385376,
-0.05898535996675491,
0.06106110289692879,
-0.03581060469150543,
-0.1270631104707718,
0.08498407900333405,
-0.027390211820602417,
0.0812823623418808,
0.038613975048065186,
0.014098312705755234,
-0.027584703639149666,
-0.04207150265574455,
-0.034578293561935425,
0.06812309473752975,
-0.20830461382865906,
0.24865548312664032,
0.004919709637761116,
0.030520789325237274,
-0.024937860667705536,
-0.04815726354718208,
0.05174754559993744,
0.10022988170385361,
0.24834224581718445,
-0.0028698884416371584,
0.0013167464639991522,
-0.058099109679460526,
-0.02800660952925682,
0.0034290780313313007,
-0.036838971078395844,
-0.10300959646701813,
0.02186655066907406,
0.05647677555680275,
0.0005230562528595328,
-0.043536875396966934,
0.15046384930610657,
-0.04432857781648636,
-0.07488866150379181,
0.019379550591111183,
0.029958628118038177,
0.020853908732533455,
-0.0036373331677168608,
-0.07022161781787872,
-0.14170214533805847,
-0.03450429439544678,
-0.03996890410780907,
-0.07054480910301208,
-0.047373656183481216,
0.0890854001045227,
-0.01091825868934393,
-0.020256169140338898,
-0.052814312279224396,
-0.034941960126161575,
0.05378376692533493,
-0.14986516535282135,
0.05177576467394829,
0.018986664712429047,
-0.02500542812049389,
-0.06494252383708954,
-0.08197576552629471,
0.04929029196500778,
0.10877130180597305,
0.08486717194318771,
-0.030988719314336777,
0.07952870428562164,
-0.0761234387755394,
-0.07879234105348587,
0.1974516361951828,
-0.1826547086238861,
-0.01073885802179575,
-0.03526933118700981,
0.07245161384344101,
0.02688329853117466,
-0.15590418875217438,
0.028247421607375145,
0.15593655407428741,
0.27570170164108276,
-0.12742897868156433,
0.12182892858982086,
0.08959443122148514,
-0.05924315005540848,
-0.15002478659152985,
-0.0518295094370842,
-0.06018305569887161,
-0.060027241706848145,
0.07607641816139221,
-0.15337340533733368,
0.016243208199739456,
0.08651816099882126,
-0.05313697084784508,
0.20633423328399658,
-0.2884331941604614,
-0.017640801146626472,
0.06871499121189117,
0.07116395235061646,
0.06644795835018158,
-0.10270233452320099,
-0.022559992969036102,
0.08210104703903198,
-0.20212315022945404,
-0.0063607413321733475,
0.004048505797982216,
0.05391513928771019,
-0.03878836706280708,
-0.0014242156175896525,
-0.03846257925033569,
-0.05341935157775879,
0.1534021645784378,
0.0410025455057621,
-0.009452501311898232,
-0.03321719169616699,
-0.2344435155391693,
0.18194282054901123,
-0.04934518784284592,
-0.017133230343461037,
0.061047472059726715,
-0.042792584747076035,
-0.14726026356220245,
0.02861667610704899,
-0.1343771517276764,
0.0734047144651413,
-0.07967595010995865,
-0.01054653711616993,
-0.0784173458814621,
0.03432444855570793,
-0.08787142485380173,
-0.006342667620629072,
0.0003118595923297107,
-0.08191070705652237,
0.13148418068885803,
0.08593401312828064,
-0.05552605167031288,
0.001447988091968,
-0.22661052644252777,
-0.034166958183050156,
-0.03570650890469551,
0.04210952669382095,
-0.06919439136981964,
-0.1274958699941635,
0.08196935057640076,
0.08607316017150879,
0.08381413668394089,
0.09195175766944885,
-0.0849749892950058,
0.11378137767314911,
0.0458601638674736,
-0.11547552049160004,
-0.08472791314125061,
0.006260599475353956,
-0.09936622530221939,
-0.041787032037973404,
-0.14309558272361755,
-0.006515727378427982,
-0.022793838754296303,
-0.045939695090055466,
0.014872709289193153,
0.02157226763665676,
-0.12322713434696198,
-0.03017060086131096,
0.07975513488054276,
0.06641986966133118,
-0.08488328009843826,
0.06279430538415909,
0.08030843734741211,
-0.06125277653336525,
-0.020736394450068474,
0.1215362697839737,
-0.07436879724264145,
-0.04146689549088478,
-0.1068200170993805,
0.07862651348114014,
-0.04025024175643921,
-0.04540678858757019,
0.02370394766330719,
-0.07536543160676956,
0.043446630239486694,
0.11923599243164062,
0.06652000546455383,
-0.018562261015176773,
-0.09378150850534439,
-0.03318707272410393,
-0.03969964012503624,
0.0781899243593216,
0.005319678224623203,
-0.026303783059120178,
-0.0829591453075409,
0.08613350242376328,
0.03237275034189224,
0.12670908868312836,
-0.010362612083554268,
-0.1206163763999939,
-0.03873860836029053,
0.06655380874872208,
0.06340952962636948,
0.04234369099140167,
-0.03186330571770668,
-0.009167227894067764,
-0.0265558622777462,
-0.014352026395499706,
-0.02058059722185135,
-0.042404502630233765,
-0.06338963657617569,
0.03986280784010887,
0.058772966265678406,
0.05825124308466911,
-0.14780069887638092,
-0.06085456535220146,
0.07039083540439606,
-0.013506554067134857,
0.055851392447948456,
0.04402238503098488,
-0.07578978687524796,
-0.008169662207365036,
-0.14318101108074188,
0.05565779656171799,
0.011135626584291458,
-0.001978611806407571,
0.028169574216008186,
-0.07211454957723618,
-0.025958560407161713,
-0.09484875947237015,
-0.07335926592350006,
0.03182234615087509,
0.07202635705471039,
-0.06477537006139755,
0.026250075548887253,
0.09199949353933334,
-0.07075055688619614,
-0.028398849070072174,
-0.02853425033390522,
0.16883732378482819,
0.005158145911991596,
0.07666844129562378,
0.007193305529654026,
0.11616770923137665,
-0.13997313380241394,
0.003752254182472825,
-0.025146782398223877,
-0.03853268548846245,
-0.07654687017202377,
-0.010724400170147419,
0.04636545479297638,
-0.015482873655855656,
0.11872432380914688,
0.002198917558416724,
0.007224234752357006,
0.016812652349472046,
0.15830765664577484,
-0.07123948633670807,
-0.02392437681555748,
0.08117328584194183,
0.005997664760798216,
-0.0640721321105957,
0.07814795523881912,
0.09289875626564026,
-0.011104685254395008,
0.07549209147691727,
0.23237425088882446,
-0.009133757092058659,
0.1234130933880806,
0.03755916655063629,
-0.04958946630358696,
0.06556949764490128,
-0.13549163937568665,
-0.015274576842784882,
0.15389558672904968,
-0.008339441381394863,
-0.05094236880540848,
0.03497505560517311,
0.14042691886425018,
-0.08191769570112228,
0.045733075588941574,
-0.004750419873744249,
-0.05861964449286461,
-0.09094389528036118,
-0.09128740429878235,
0.04009291157126427,
0.009849260561168194,
-0.013969780877232552,
-0.04054411128163338,
0.096989706158638,
0.04800160974264145,
-0.026072120293974876,
0.018271196633577347,
0.02735087461769581,
-0.050218891352415085,
0.0051234266720712185,
0.07085229456424713,
-0.0561603382229805,
0.022274697199463844,
-0.08201580494642258,
0.0679372176527977,
0.03608478233218193,
-0.14607392251491547,
0.04550960659980774,
0.04050638899207115,
-0.019615432247519493,
-0.007833708077669144,
-0.1332322508096695,
-0.10662560164928436,
-0.021704111248254776,
0.08166350424289703,
-0.024180402979254723,
0.13515911996364594,
0.06040540337562561,
0.007296423427760601,
0.013615628704428673,
0.09840698540210724,
0.001300341566093266,
-0.03886331245303154,
-0.029250971972942352,
0.15839211642742157,
-0.10389801114797592,
-0.06450657546520233,
-0.03890183940529823,
-0.018483301624655724,
0.04336060956120491,
0.2888377904891968,
0.15938451886177063,
0.014403015375137329,
-0.03383495658636093,
0.010544545017182827,
0.02743641659617424,
0.0968872457742691,
0.023340271785855293,
0.033295951783657074,
0.198342964053154,
-0.17865337431430817,
0.016713812947273254,
-0.20836684107780457,
0.01005055382847786,
-0.048644740134477615,
-0.038372620940208435,
0.13280706107616425,
-0.04842201620340347,
-0.06138845905661583,
0.147225484251976,
-0.13907109200954437,
-0.06580574810504913,
0.09965134412050247,
-0.08085693418979645,
-0.024372737854719162,
0.002659347839653492,
0.034898512065410614,
0.007536181714385748,
0.12387019395828247,
-0.005385016091167927,
-0.0944950059056282,
0.12953580915927887,
-0.036889687180519104,
-0.12402451783418655,
-0.1078478991985321,
0.04971214383840561,
-0.11944419890642166,
0.22202864289283752,
-0.054229848086833954,
0.10460870712995529,
0.08029856532812119,
0.07351803779602051,
-0.06341737508773804,
0.05096008628606796,
0.021921908482909203,
0.1772557944059372,
-0.09504149854183197,
-0.022972239181399345,
0.03173937648534775,
-0.04091427102684975,
0.03754652291536331,
-0.05675802752375603,
0.05932131037116051,
0.03239717707037926,
-0.005396685563027859,
-0.05723376199603081,
0.08406733721494675,
-0.03639886528253555,
0.1073637381196022,
0.09261234104633331,
-0.01721072383224964,
-0.025118183344602585,
-0.03564617782831192,
-0.06006953865289688,
0.12625539302825928,
0.05330943316221237,
-0.008891476318240166,
-0.014377114363014698,
0.020859772339463234,
0.06749767810106277,
-0.078809455037117,
-0.17932920157909393,
-0.07449478656053543,
-0.03783709928393364,
-0.05978017300367355,
0.053780827671289444,
-0.013381554745137691,
0.05411753058433533,
0.04437387362122536,
-0.03200311213731766,
0.11267776787281036,
0.013013922609388828,
0.18428584933280945,
-0.1349961757659912,
-0.03697605058550835
] |
null | null |
flair
|
## English NER in Flair (Ontonotes default model)
This is the 18-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **89.27** (Ontonotes)
Predicts 18 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| CARDINAL | cardinal value |
| DATE | date value |
| EVENT | event name |
| FAC | building name |
| GPE | geo-political entity |
| LANGUAGE | language name |
| LAW | law name |
| LOC | location name |
| MONEY | money name |
| NORP | affiliation |
| ORDINAL | ordinal value |
| ORG | organization name |
| PERCENT | percent value |
| PERSON | person name |
| PRODUCT | product name |
| QUANTITY | quantity value |
| TIME | time value |
| WORK_OF_ART | name of work of art |
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-english-ontonotes")
# make example sentence
sentence = Sentence("On September 1st George Washington won 1 dollar.")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [2,3]: "September 1st" [− Labels: DATE (0.8824)]
Span [4,5]: "George Washington" [− Labels: PERSON (0.9604)]
Span [7,8]: "1 dollar" [− Labels: MONEY (0.9837)]
```
So, the entities "*September 1st*" (labeled as a **date**), "*George Washington*" (labeled as a **person**) and "*1 dollar*" (labeled as a **money**) are found in the sentence "*On September 1st George Washington won 1 dollar*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import ColumnCorpus
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. load the corpus (Ontonotes does not ship with Flair, you need to download and reformat into a column format yourself)
corpus: Corpus = ColumnCorpus(
"resources/tasks/onto-ner",
column_format={0: "text", 1: "pos", 2: "upos", 3: "ner"},
tag_to_bioes="ner",
)
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
# GloVe embeddings
WordEmbeddings('en-crawl'),
# contextual string embeddings, forward
FlairEmbeddings('news-forward'),
# contextual string embeddings, backward
FlairEmbeddings('news-backward'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/ner-english-ontonotes',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following paper when using this model.
```
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
{"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["ontonotes"], "widget": [{"text": "On September 1st George Washington won 1 dollar."}]}
|
token-classification
|
flair/ner-english-ontonotes
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:ontonotes",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #has_space #region-us
|
English NER in Flair (Ontonotes default model)
----------------------------------------------
This is the 18-class NER model for English that ships with Flair.
F1-Score: 89.27 (Ontonotes)
Predicts 18 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the entities "*September 1st*" (labeled as a date), "*George Washington*" (labeled as a person) and "*1 dollar*" (labeled as a money) are found in the sentence "*On September 1st George Washington won 1 dollar*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following paper when using this model.
---
### Issues?
The Flair issue tracker is available here.
|
[
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*September 1st*\" (labeled as a date), \"*George Washington*\" (labeled as a person) and \"*1 dollar*\" (labeled as a money) are found in the sentence \"*On September 1st George Washington won 1 dollar*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*September 1st*\" (labeled as a date), \"*George Washington*\" (labeled as a person) and \"*1 dollar*\" (labeled as a money) are found in the sentence \"*On September 1st George Washington won 1 dollar*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
41,
100,
22,
15,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-ontonotes #has_space #region-us \n### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*September 1st*\" (labeled as a date), \"*George Washington*\" (labeled as a person) and \"*1 dollar*\" (labeled as a money) are found in the sentence \"*On September 1st George Washington won 1 dollar*\".\n\n\n\n\n---### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
-0.08316225558519363,
0.10079556703567505,
-0.0019114876631647348,
0.09859561175107956,
0.08915527909994125,
0.03749171644449234,
0.07468868792057037,
0.07941008359193802,
0.011256714351475239,
0.11021765321493149,
0.20103533565998077,
0.06640208512544632,
0.1350521296262741,
0.14261142909526825,
0.03518760949373245,
-0.24320901930332184,
0.011797333136200905,
-0.021585136651992798,
0.08412260562181473,
0.1831733137369156,
0.0932883620262146,
-0.0326807014644146,
0.008811341598629951,
-0.002581997076049447,
-0.11500131338834763,
-0.0231632087379694,
0.005780375562608242,
-0.13265946507453918,
0.13677173852920532,
-0.041492581367492676,
0.17305608093738556,
0.026077525690197945,
0.09537431597709656,
-0.10033538937568665,
0.015361697413027287,
0.003886376041918993,
0.00277880416251719,
0.09290867298841476,
-0.033097341656684875,
0.024312442168593407,
0.20159408450126648,
-0.08779844641685486,
0.023625079542398453,
0.030366642400622368,
-0.22525843977928162,
-0.3146677017211914,
-0.12263697385787964,
0.1526939421892166,
0.07927598059177399,
0.05694747343659401,
-0.04209715500473976,
0.08587772399187088,
-0.02513301558792591,
-0.0063313087448477745,
0.18325577676296234,
-0.22652064263820648,
-0.04710628464818001,
0.1437804400920868,
-0.0021925801411271095,
-0.0013136304914951324,
-0.019787974655628204,
0.023788481950759888,
0.025112366303801537,
0.06092986837029457,
0.00444755656644702,
0.001000404474325478,
0.09044552594423294,
0.11029836535453796,
-0.20154337584972382,
-0.11872992664575577,
0.3508409857749939,
0.05516213923692703,
-0.05819416046142578,
-0.030878249555826187,
0.027527514845132828,
-0.09971772879362106,
0.018031194806098938,
-0.06237305700778961,
0.0038226584438234568,
-0.009890339337289333,
0.18166179955005646,
-0.05168592929840088,
-0.13230836391448975,
-0.015039702877402306,
-0.040640417486429214,
0.08471836149692535,
-0.09175974875688553,
0.11599372327327728,
-0.14444471895694733,
0.08390321582555771,
-0.04794900119304657,
-0.051572464406490326,
0.020039711147546768,
-0.03932471573352814,
0.005759348161518574,
-0.001199529622681439,
-0.07293178141117096,
0.05927314981818199,
0.010882801376283169,
-0.023941421881318092,
0.08591482788324356,
-0.04249372333288193,
0.06909577548503876,
0.12175861746072769,
0.04099413380026817,
0.21659453213214874,
-0.12960435450077057,
-0.05653658136725426,
0.030919168144464493,
0.04142000898718834,
0.021090935915708542,
-0.0478137843310833,
-0.14737407863140106,
0.03299034759402275,
0.011910378001630306,
0.008868847973644733,
0.027130234986543655,
-0.02941679023206234,
-0.09416521340608597,
0.029778867959976196,
0.11051274091005325,
-0.08957868069410324,
-0.01841304823756218,
0.009083504788577557,
-0.10464147478342056,
0.06453833729028702,
0.04933268576860428,
-0.013609249144792557,
0.02278347872197628,
0.11423695832490921,
-0.08564337342977524,
0.0701347216963768,
-0.0339888334274292,
-0.12617909908294678,
0.019716400653123856,
-0.06384170800447464,
-0.04641619324684143,
-0.04097360372543335,
-0.1281115710735321,
-0.03112366981804371,
0.052475836127996445,
0.020123222842812538,
-0.07772515714168549,
-0.02620084583759308,
-0.02564680390059948,
-0.029013635590672493,
0.02010919339954853,
0.1351497769355774,
-0.04438595473766327,
0.08293133974075317,
-0.008363998495042324,
0.10806945711374283,
-0.04011392220854759,
0.02269965410232544,
-0.08755407482385635,
-0.009981929324567318,
-0.15227730572223663,
0.03642553463578224,
0.0022682726848870516,
-0.00025578588247299194,
-0.08218304812908173,
-0.10665016621351242,
-0.06320474296808243,
-0.013682848773896694,
0.019764302298426628,
0.18114353716373444,
-0.2826175391674042,
-0.028415318578481674,
0.11528035998344421,
-0.08964129537343979,
-0.013974024914205074,
0.05248088017106056,
-0.059911441057920456,
0.1942710429430008,
0.056353695690631866,
0.12665194272994995,
-0.0651867464184761,
-0.353809118270874,
0.03344239294528961,
0.035721395164728165,
-0.17346838116645813,
-0.0064885360188782215,
0.09488668292760849,
-0.06494319438934326,
-0.15553857386112213,
0.007789242547005415,
-0.15204069018363953,
-0.014597169123589993,
-0.0660160481929779,
-0.0539834126830101,
-0.009629840962588787,
-0.06818968057632446,
0.10477399080991745,
-0.013248249888420105,
-0.015300046652555466,
-0.045573946088552475,
-0.12798821926116943,
-0.08033065497875214,
0.0020515122450888157,
0.11398109048604965,
-0.018808651715517044,
-0.03379513695836067,
0.08761016279459,
0.03229283168911934,
-0.010957229882478714,
-0.13187718391418457,
0.06382586061954498,
-0.029951488599181175,
0.08723235130310059,
0.04758650064468384,
0.12134704738855362,
-0.028679311275482178,
-0.002308105817064643,
0.062299493700265884,
0.04227179288864136,
-0.011708532460033894,
0.02017076686024666,
-0.020039977505803108,
0.022837918251752853,
-0.09529529511928558,
-0.0938146561384201,
0.1225634515285492,
-0.08705366402864456,
0.06786785274744034,
0.058481182903051376,
-0.07941483706235886,
-0.03214603662490845,
-0.03228491172194481,
0.08769896626472473,
0.02011513151228428,
0.07927748560905457,
-0.03457462415099144,
0.05588062107563019,
0.02763107232749462,
-0.087100550532341,
-0.0931970626115799,
-0.07611129432916641,
-0.09633801877498627,
0.09670329093933105,
-0.031113024801015854,
-0.1261390596628189,
-0.02073996514081955,
-0.041224703192710876,
-0.016499238088726997,
-0.06692378968000412,
-0.06954111903905869,
0.14301645755767822,
0.04259590432047844,
-0.007100790273398161,
-0.029321076348423958,
-0.010559219866991043,
0.018704786896705627,
-0.03837491571903229,
-0.07560523599386215,
0.030957719311118126,
-0.028757356107234955,
-0.01828567311167717,
0.012612530030310154,
0.11716977506875992,
0.03756899759173393,
0.018206823617219925,
0.03191603347659111,
-0.03795967996120453,
-0.13787324726581573,
-0.06254521757364273,
-0.008013740181922913,
0.12359112501144409,
-0.08363622426986694,
-0.039074208587408066,
0.04457593709230423,
0.012640533968806267,
-0.004628119524568319,
-0.20331881940364838,
-0.08295389264822006,
0.013412844389677048,
-0.029603274539113045,
-0.20735494792461395,
0.04761917516589165,
-0.029571371152997017,
0.10246456414461136,
0.011687517166137695,
-0.013061012141406536,
0.06657972931861877,
0.0034081265330314636,
-0.08167224377393723,
0.08499904721975327,
-0.022077174857258797,
-0.2647908329963684,
-0.12704038619995117,
-0.12241344898939133,
0.12564599514007568,
-0.020651139318943024,
0.04669295623898506,
-0.05414632335305214,
0.05718066543340683,
-0.008926781825721264,
0.041542842984199524,
-0.044855453073978424,
-0.09589061886072159,
0.010221130214631557,
-0.0365857370197773,
-0.014854996465146542,
-0.09945108741521835,
-0.04697848856449127,
-0.06033822149038315,
0.11895134299993515,
0.0710437074303627,
0.039821822196245193,
0.0479179248213768,
0.1796146035194397,
-0.007780170999467373,
0.0852576419711113,
-0.06120719015598297,
0.36258190870285034,
-0.060618989169597626,
0.0517374649643898,
0.12525896728038788,
0.06722302734851837,
0.05423782765865326,
0.12066176533699036,
0.09368046373128891,
-0.08391118049621582,
-0.03200184926390648,
-0.050866901874542236,
-0.10713272541761398,
-0.1153402328491211,
-0.04367075115442276,
-0.042184311896562576,
-0.06424086540937424,
0.010644707828760147,
0.014157265424728394,
-0.056015949696302414,
-0.010601567104458809,
0.09062407165765762,
-0.009244890883564949,
0.015547828748822212,
-0.005822288803756237,
0.07172572612762451,
-0.03385578840970993,
0.037167031317949295,
-0.013113767839968204,
-0.012645306065678596,
0.037851594388484955,
-0.009437796659767628,
0.10826565325260162,
0.1272636353969574,
-0.02101839892566204,
0.0696486160159111,
0.057822395116090775,
0.045583415776491165,
0.133970707654953,
0.08899372071027756,
-0.015291751362383366,
-0.02427856996655464,
-0.059323277324438095,
0.017806554213166237,
0.015962306410074234,
0.026493234559893608,
-0.041764818131923676,
-0.0458252988755703,
-0.0815376415848732,
-0.03878547623753548,
0.12156729400157928,
0.10712110251188278,
-0.12181247770786285,
-0.026942258700728416,
0.012435121461749077,
0.05621720850467682,
-0.0054377540946006775,
0.039153072983026505,
-0.07543099671602249,
-0.12186727672815323,
0.033441681414842606,
-0.03394192457199097,
0.0937117412686348,
0.055267076939344406,
0.0234877597540617,
-0.09557481855154037,
0.025298619642853737,
-0.026459068059921265,
0.08539479970932007,
-0.2059215009212494,
0.32412734627723694,
-0.02595926634967327,
0.05853673443198204,
-0.05634666606783867,
-0.017341844737529755,
0.04846195504069328,
0.06317831575870514,
0.22191442549228668,
0.010104524902999401,
-0.16189062595367432,
-0.016694290563464165,
-0.1079927310347557,
0.007729105185717344,
-0.03536679968237877,
-0.06865596771240234,
0.06084318831562996,
0.062222160398960114,
0.010408047586679459,
-0.029809197410941124,
0.15971806645393372,
-0.137426495552063,
-0.0521165169775486,
0.048275288194417953,
-0.054195526987314224,
0.02041393332183361,
-0.02826695330440998,
-0.06938768923282623,
-0.13114707171916962,
-0.07999900728464127,
-0.042031560093164444,
-0.05124383792281151,
-0.0557195283472538,
0.13639786839485168,
0.0411360003054142,
-0.026161614805459976,
-0.07245657593011856,
0.006110088899731636,
0.03044201247394085,
-0.07275664061307907,
0.025847088545560837,
0.01582110859453678,
-0.02635061927139759,
0.025343112647533417,
-0.043443772941827774,
0.03866126760840416,
0.06638714671134949,
0.07115055620670319,
-0.01940624974668026,
0.07056249678134918,
-0.08739098161458969,
-0.0866570994257927,
0.21407416462898254,
-0.20032280683517456,
-0.01923687383532524,
-0.002640657126903534,
0.05162426456809044,
-0.06919605284929276,
-0.16538786888122559,
0.026435483247041702,
0.17054599523544312,
0.266766756772995,
-0.13500486314296722,
0.07983740419149399,
0.09576565772294998,
-0.07621452957391739,
-0.16308340430259705,
-0.020106356590986252,
-0.0635991171002388,
0.023189468309283257,
0.01657947525382042,
-0.13839827477931976,
-0.015609883703291416,
0.12403000146150589,
-0.06423883140087128,
0.15408584475517273,
-0.28378570079803467,
-0.044748641550540924,
0.16627871990203857,
-0.018585961312055588,
0.1360778659582138,
-0.07564985752105713,
-0.048366885632276535,
0.07205940783023834,
-0.1707780510187149,
0.044128019362688065,
0.06109130382537842,
0.053920019418001175,
-0.002702602418139577,
0.0937717854976654,
-0.028296133503317833,
-0.07321329414844513,
0.15027546882629395,
-0.013799860142171383,
0.01837196759879589,
-0.026594385504722595,
-0.2895151376724243,
0.1656656712293625,
-0.050250500440597534,
0.10121581703424454,
0.09210864454507828,
-0.009860238991677761,
-0.11899009346961975,
-0.01483819168061018,
-0.1325434446334839,
0.08483332395553589,
-0.07278843224048615,
-0.034442394971847534,
-0.040398385375738144,
0.0016063580987975001,
-0.07340258359909058,
-0.03749079629778862,
-0.09721805900335312,
-0.08236560225486755,
0.11316623538732529,
0.12060718983411789,
-0.050759393721818924,
-0.001700485940091312,
-0.14887277781963348,
-0.0030883937142789364,
-0.028883403167128563,
0.09161382168531418,
-0.1079411655664444,
-0.1150653064250946,
0.07171206921339035,
0.06907011568546295,
0.14965906739234924,
0.07548091560602188,
-0.06863062083721161,
0.04715072363615036,
0.0397992879152298,
-0.13631640374660492,
-0.14721167087554932,
0.000585209287237376,
-0.05069452151656151,
-0.08934103697538376,
-0.13809996843338013,
-0.005830137990415096,
-0.03118237294256687,
-0.03204825147986412,
-0.0048744212836027145,
0.011298908852040768,
-0.15606068074703217,
0.0496654249727726,
0.11160728335380554,
0.10159850120544434,
-0.07390783727169037,
0.008936894126236439,
0.10637996345758438,
-0.07187143713235855,
-0.02341214194893837,
0.06251567602157593,
-0.014234590344130993,
-0.05943538248538971,
-0.10952029377222061,
0.18767832219600677,
-0.0004133781767450273,
-0.019063051789999008,
-0.011849610134959221,
-0.09027707576751709,
0.056314319372177124,
0.1158367395401001,
0.073533795773983,
0.008413109928369522,
-0.07038146257400513,
-0.02889409102499485,
-0.03611161187291145,
0.01264409814029932,
0.07192353159189224,
-0.04814516752958298,
-0.060174159705638885,
0.1301407516002655,
0.009382949210703373,
0.1311078667640686,
-0.04204103723168373,
-0.0830889344215393,
-0.07619486004114151,
0.048974331468343735,
0.03074405901134014,
0.058384232223033905,
0.017825579270720482,
0.005617854651063681,
0.021150827407836914,
-0.01552216149866581,
-0.03451508656144142,
-0.012735594995319843,
-0.09298450499773026,
0.05443870276212692,
0.00426756776869297,
0.07032009959220886,
-0.0575384758412838,
-0.043425023555755615,
0.053586918860673904,
-0.020288709551095963,
0.05693965032696724,
0.07346418499946594,
-0.06348668783903122,
-0.028101088479161263,
-0.13993141055107117,
0.062157370150089264,
0.02667490765452385,
-0.0366659015417099,
0.04839249327778816,
-0.1720142811536789,
0.020011059939861298,
-0.04362643137574196,
-0.036924924701452255,
0.013832216151058674,
0.01854562573134899,
-0.07283751666545868,
-0.01718832738697529,
0.09418956190347672,
-0.06617586314678192,
-0.02154155634343624,
-0.06947571039199829,
0.14479607343673706,
0.028119971975684166,
0.1130267009139061,
0.0009768647141754627,
0.14077095687389374,
-0.13314436376094818,
-0.011600887402892113,
-0.026015974581241608,
-0.016197333112359047,
-0.02632899582386017,
-0.030397510156035423,
0.10211565345525742,
0.03275115042924881,
0.20164145529270172,
0.040033094584941864,
-0.03724747523665428,
0.01351950690150261,
0.1729055941104889,
-0.026667499914765358,
-0.014567170292139053,
0.05912957340478897,
-0.0182978305965662,
-0.0352620929479599,
0.050952401012182236,
0.05260661616921425,
-0.06883735209703445,
0.08818161487579346,
0.25217151641845703,
0.020126529037952423,
0.1264684647321701,
0.07863844931125641,
-0.054739903658628464,
0.058116354048252106,
-0.0344511903822422,
-0.010747232474386692,
0.12585724890232086,
0.003942588344216347,
-0.037332650274038315,
0.036608461290597916,
0.11150532960891724,
-0.09514997154474258,
0.09439513832330704,
-0.06860962510108948,
-0.07096312940120697,
-0.1022535189986229,
-0.14431947469711304,
0.0270286463201046,
0.0031465436331927776,
0.00965784676373005,
-0.023155495524406433,
0.05854795500636101,
0.08754171431064606,
-0.05936836451292038,
0.018681352958083153,
0.026237977668642998,
-0.020815759897232056,
-0.0031101638451218605,
0.06595505028963089,
-0.06719367206096649,
-0.006735472474247217,
-0.19593258202075958,
0.05238795652985573,
0.02520109713077545,
-0.13197574019432068,
0.0631556585431099,
0.011321702972054482,
-0.02019832469522953,
-0.04960952326655388,
-0.11513327807188034,
-0.08021838217973709,
-0.007291910704225302,
0.03693321347236633,
-0.00966638047248125,
0.17584793269634247,
0.05030819773674011,
0.03308050334453583,
0.010423741303384304,
0.08664146810770035,
-0.02479824796319008,
-0.1576293557882309,
-0.11853888630867004,
0.12645812332630157,
-0.1304926872253418,
0.0164695605635643,
-0.06743859499692917,
-0.03412514552474022,
0.04462841898202896,
0.2927039861679077,
0.19532614946365356,
0.05250449851155281,
-0.028494568541646004,
-0.0031045253854244947,
0.021883534267544746,
0.08225972950458527,
0.026925938203930855,
0.06141097843647003,
0.1493918001651764,
-0.17392311990261078,
0.040210362523794174,
-0.19936394691467285,
0.0068571302108466625,
-0.06800004839897156,
0.0025719378609210253,
0.09980139136314392,
-0.047451961785554886,
-0.04472251608967781,
0.17058604955673218,
-0.13490992784500122,
-0.08826036751270294,
0.0732383206486702,
-0.06328651309013367,
-0.07514587789773941,
0.005678205750882626,
0.04972938820719719,
0.000087964472186286,
0.14703918993473053,
-0.06109211966395378,
-0.07351183891296387,
0.13064032793045044,
-0.04763093963265419,
-0.0942399725317955,
-0.1562129706144333,
0.013413499109447002,
-0.10483404248952866,
0.16316913068294525,
-0.006336118094623089,
0.14121121168136597,
0.055757198482751846,
0.07490039616823196,
-0.07732239365577698,
0.12608802318572998,
-0.009225057438015938,
0.12937165796756744,
-0.06329485774040222,
-0.023733830079436302,
-0.005357259418815374,
-0.035355158150196075,
-0.01089900266379118,
-0.13785628974437714,
0.04332674294710159,
0.059503525495529175,
0.03366152197122574,
-0.03849697485566139,
0.03476104885339737,
-0.024006802588701248,
0.09345661103725433,
0.10824573040008545,
-0.026849236339330673,
0.012220025062561035,
-0.03381827846169472,
-0.009564652107656002,
0.1176799088716507,
0.01601770520210266,
-0.06219296157360077,
-0.05967642739415169,
0.020449776202440262,
0.09098237007856369,
-0.07093772292137146,
-0.12582655251026154,
-0.08428056538105011,
-0.04138968139886856,
-0.061407554894685745,
0.06386832892894745,
0.025512417778372765,
0.04928259924054146,
0.09213174879550934,
-0.025831129401922226,
0.041753169149160385,
0.001855668961070478,
0.17563049495220184,
-0.17203353345394135,
-0.05501732975244522
] |
null | null |
flair
|
## English NER in Flair (default model)
This is the standard 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **93,06** (corrected CoNLL-03)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| MISC | other name |
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-english")
# make example sentence
sentence = Sentence("George Washington went to Washington")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [1,2]: "George Washington" [− Labels: PER (0.9968)]
Span [5]: "Washington" [− Labels: LOC (0.9994)]
```
So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington went to Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import CONLL_03
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. get the corpus
corpus: Corpus = CONLL_03()
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
# GloVe embeddings
WordEmbeddings('glove'),
# contextual string embeddings, forward
FlairEmbeddings('news-forward'),
# contextual string embeddings, backward
FlairEmbeddings('news-backward'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/ner-english',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following paper when using this model.
```
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
{"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington went to Washington"}]}
|
token-classification
|
flair/ner-english
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:conll2003",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #has_space #region-us
|
English NER in Flair (default model)
------------------------------------
This is the standard 4-class NER model for English that ships with Flair.
F1-Score: 93,06 (corrected CoNLL-03)
Predicts 4 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the entities "*George Washington*" (labeled as a person) and "*Washington*" (labeled as a location) are found in the sentence "*George Washington went to Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following paper when using this model.
---
### Issues?
The Flair issue tracker is available here.
|
[
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington went to Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington went to Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
41,
81,
22,
15,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #dataset-conll2003 #has_space #region-us \n### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington went to Washington*\".\n\n\n\n\n---### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
-0.07754179835319519,
0.17645306885242462,
-0.0014712187694385648,
0.06833362579345703,
0.08177177608013153,
0.05396747589111328,
0.11267365515232086,
0.04825665429234505,
0.010602099820971489,
0.053486838936805725,
0.18720762431621552,
0.07780981063842773,
0.11305837333202362,
0.07320278882980347,
0.08347295224666595,
-0.25872859358787537,
0.03124011680483818,
-0.031080935150384903,
-0.0661880373954773,
0.1710071712732315,
0.07460048794746399,
-0.008209152147173882,
0.01566537469625473,
0.03217897564172745,
-0.11547128856182098,
-0.016097035259008408,
-0.02255762740969658,
-0.09116753190755844,
0.17304256558418274,
-0.03867684677243233,
0.2176574319601059,
0.04909152910113335,
0.09997935593128204,
-0.14942097663879395,
0.030922213569283485,
0.04960301145911217,
0.02233264595270157,
0.1008487343788147,
0.047248899936676025,
-0.002577294362708926,
0.29770246148109436,
-0.023222805932164192,
0.0022778487764298916,
0.03184448182582855,
-0.194279283285141,
-0.25129130482673645,
-0.11246374994516373,
0.06667076051235199,
0.04731357470154762,
0.10669522732496262,
-0.029623212292790413,
0.09231632947921753,
-0.05365392565727234,
0.026348991319537163,
0.18664439022541046,
-0.22959259152412415,
-0.029878173023462296,
0.1808956414461136,
0.014837613329291344,
-0.017358366400003433,
-0.04209684580564499,
0.05225979536771774,
-0.0027904964517802,
0.07348155230283737,
-0.038277387619018555,
-0.012397896498441696,
0.019900692626833916,
0.13006621599197388,
-0.16845081746578217,
-0.108916737139225,
0.32628217339515686,
0.033990662544965744,
-0.06865307688713074,
0.0009936346905305982,
0.0048671928234398365,
-0.02796310931444168,
0.06125226244330406,
-0.1016443744301796,
-0.010525999590754509,
0.002147058956325054,
0.16981127858161926,
-0.12401694059371948,
-0.09334787726402283,
-0.09860476851463318,
-0.023727163672447205,
0.04282179847359657,
-0.09350593388080597,
0.10587839037179947,
-0.171022430062294,
0.12357880920171738,
0.06411927193403244,
-0.05157626047730446,
0.04923859238624573,
-0.0730988085269928,
-0.041779667139053345,
0.0125777842476964,
-0.04943787679076195,
0.13106411695480347,
0.03255152329802513,
0.02866257168352604,
0.154957115650177,
-0.04096983000636101,
0.10380399227142334,
0.10950533300638199,
0.005464928690344095,
0.1985224187374115,
-0.1408398151397705,
-0.028763365000486374,
0.025492433458566666,
-0.025497229769825935,
-0.008396552875638008,
-0.05911485478281975,
-0.1629110425710678,
-0.013697267509996891,
-0.07037291675806046,
0.021661145612597466,
0.0364745669066906,
-0.017974253743886948,
-0.08983248472213745,
0.012833666056394577,
0.06407420337200165,
-0.06673172116279602,
-0.03855659067630768,
-0.024492328986525536,
-0.05353779345750809,
0.09334668517112732,
0.08932789415121078,
0.033969949930906296,
0.018869653344154358,
0.13650499284267426,
-0.08181274682283401,
0.048749323934316635,
-0.040186334401369095,
-0.11000388860702515,
-0.025855405256152153,
-0.07450368255376816,
0.01350113283842802,
-0.05998620018362999,
-0.15010225772857666,
-0.014377806335687637,
0.06219375133514404,
0.011581921949982643,
-0.06558139622211456,
-0.042902056127786636,
-0.028128966689109802,
-0.048067715018987656,
0.03784794360399246,
0.06943901628255844,
-0.06656049191951752,
0.07558827847242355,
-0.041824329644441605,
0.10506926476955414,
-0.04709027335047722,
0.014807348139584064,
-0.0919363722205162,
0.009970128536224365,
-0.11978712677955627,
-0.005051698535680771,
-0.01654648594558239,
0.032986126840114594,
-0.11927206814289093,
-0.08522059768438339,
-0.02064075879752636,
0.008532451465725899,
0.018110739067196846,
0.18470415472984314,
-0.2741975486278534,
-0.016154443845152855,
0.11923731118440628,
-0.0970100536942482,
-0.052793316543102264,
0.05080597475171089,
-0.027345797047019005,
0.2000253051519394,
0.03233255073428154,
0.1410668045282364,
-0.026518527418375015,
-0.3641958236694336,
0.11612235754728317,
0.050427693873643875,
-0.1995733678340912,
0.04448068514466286,
0.07041699439287186,
-0.05803046002984047,
-0.18117335438728333,
-0.00035470983129926026,
-0.1054917573928833,
0.03541767597198486,
-0.03451206907629967,
-0.038134872913360596,
0.007946238853037357,
-0.053653083741664886,
0.043425098061561584,
-0.04420396685600281,
-0.03191240131855011,
-0.0008029607124626637,
-0.14804165065288544,
-0.14217549562454224,
0.034968044608831406,
0.11056330800056458,
-0.033926062285900116,
0.015836600214242935,
0.04762740433216095,
0.04205077141523361,
-0.04267682135105133,
-0.11397255957126617,
0.036075618118047714,
-0.08373289555311203,
0.13944672048091888,
0.02140967547893524,
0.15893884003162384,
-0.0326048880815506,
0.01657475344836712,
0.06893139332532883,
0.032006919384002686,
-0.023277686908841133,
0.05343891680240631,
-0.00910012423992157,
0.005924176424741745,
-0.08223745226860046,
-0.09227555245161057,
0.11507019400596619,
-0.1732158064842224,
0.06324122101068497,
0.0345144160091877,
-0.0030428192112594843,
-0.015952076762914658,
-0.020765408873558044,
0.11239534616470337,
0.03155575692653656,
0.016160983592271805,
-0.07194400578737259,
0.0680181011557579,
0.004522040020674467,
-0.05563190579414368,
-0.10820070654153824,
-0.06696484982967377,
-0.032549455761909485,
0.10313135385513306,
-0.031925592571496964,
-0.11245077103376389,
-0.07000447064638138,
-0.04484890401363373,
0.006589848082512617,
-0.03452951833605766,
-0.03343801200389862,
0.15580464899539948,
0.04088863730430603,
0.012611235491931438,
-0.029496170580387115,
0.031581662595272064,
-0.029225578531622887,
-0.03257790207862854,
-0.07219153642654419,
0.05417686328291893,
0.11043953150510788,
0.029020637273788452,
0.030439916998147964,
0.1810978651046753,
0.0025009845849126577,
0.020533539354801178,
0.05543633550405502,
-0.05167628079652786,
-0.1014716774225235,
-0.10627426952123642,
-0.021119778975844383,
0.17995966970920563,
-0.02260744385421276,
0.04118063673377037,
0.04363493621349335,
-0.00604051910340786,
0.03781992197036743,
-0.2039848268032074,
-0.12298339605331421,
0.050411906093358994,
-0.026413410902023315,
-0.1859441101551056,
0.053826894611120224,
-0.05497695505619049,
0.10488542914390564,
-0.02126612327992916,
-0.07224913686513901,
0.04569634795188904,
-0.00010353676043450832,
-0.0773436427116394,
0.10456831753253937,
-0.06604623794555664,
-0.26045143604278564,
-0.10620442777872086,
-0.0641823336482048,
0.1052456796169281,
-0.0048753353767097,
0.025803908705711365,
-0.11017563194036484,
0.03340508043766022,
-0.012199110351502895,
0.05897105112671852,
-0.09708739072084427,
-0.08480245620012283,
-0.06506515294313431,
0.02019714191555977,
-0.01870417408645153,
-0.10990332812070847,
-0.033304858952760696,
-0.05823634937405586,
0.08495926856994629,
0.026999248191714287,
0.018227646127343178,
0.08756991475820541,
0.12908503413200378,
0.02069397270679474,
0.07084877789020538,
-0.04858938977122307,
0.36368730664253235,
-0.08118712902069092,
0.07762788236141205,
0.13007904589176178,
0.012488547712564468,
0.047220781445503235,
0.15282393991947174,
0.06878884881734848,
-0.08269142359495163,
-0.0398922935128212,
-0.07469593733549118,
-0.06922279298305511,
-0.11486057937145233,
-0.051381684839725494,
-0.04355936869978905,
-0.061931077390909195,
0.007631842978298664,
0.02898714318871498,
-0.0849793329834938,
0.026226654648780823,
0.08144693076610565,
-0.04626327380537987,
-0.053528644144535065,
-0.003856563474982977,
0.017222493886947632,
-0.030279308557510376,
0.000022242495106183924,
-0.030871206894516945,
-0.03829526901245117,
0.031054392457008362,
0.09232834726572037,
0.07493927329778671,
0.11867547035217285,
-0.008620659820735455,
0.06487446278333664,
0.10922076553106308,
0.0907936692237854,
0.16689422726631165,
0.08132350444793701,
-0.017172282561659813,
-0.025210438296198845,
-0.0552842877805233,
0.0006747531006112695,
0.03846857324242592,
0.010585397481918335,
-0.05610959231853485,
0.011706947349011898,
-0.10549738258123398,
-0.044250402599573135,
-0.00045780421351082623,
0.12490221112966537,
-0.17725107073783875,
-0.028981702402234077,
0.0025563084054738283,
0.09625837206840515,
-0.04107341915369034,
0.05712779611349106,
0.029471779242157936,
-0.1322060227394104,
0.01304552797228098,
-0.022282704710960388,
0.10046657174825668,
0.032702282071113586,
0.03616068884730339,
-0.08741630613803864,
0.036816470324993134,
-0.05629411339759827,
0.11230190843343735,
-0.19039921462535858,
0.3411136269569397,
-0.029189562425017357,
0.01439796481281519,
-0.04907659441232681,
0.001495462842285633,
0.07830628752708435,
0.049780555069446564,
0.24089691042900085,
0.0072601838037371635,
-0.1557980626821518,
-0.10067134350538254,
-0.11587468534708023,
0.0371759831905365,
-0.02502240054309368,
-0.07582314312458038,
0.06340732425451279,
0.06367435306310654,
0.005864436738193035,
-0.02741561084985733,
0.07760247588157654,
-0.1847614347934723,
-0.10596834123134613,
0.022898240014910698,
-0.03178315609693527,
0.03927309438586235,
-0.009486321359872818,
-0.0540086030960083,
-0.11264348775148392,
-0.008291383273899555,
-0.01637926883995533,
-0.036599185317754745,
-0.08583460003137589,
0.1098761111497879,
0.0486019141972065,
-0.00919925607740879,
-0.03178917616605759,
-0.0005758185870945454,
0.015676327049732208,
-0.0880160927772522,
0.019203219562768936,
0.023837018758058548,
-0.05182203650474548,
0.03307157754898071,
-0.07997714728116989,
0.07993977516889572,
0.04752611368894577,
0.04597796872258186,
0.00774313835427165,
0.06411200016736984,
-0.11032064259052277,
-0.1149287223815918,
0.2012193351984024,
-0.11813875287771225,
-0.024615036323666573,
0.0508619025349617,
0.035559091717004776,
0.006837102118879557,
-0.14715397357940674,
0.022003261372447014,
0.23580947518348694,
0.25653284788131714,
-0.13861101865768433,
0.14306360483169556,
-0.02401386760175228,
-0.11648180335760117,
-0.15072350203990936,
-0.012898522429168224,
-0.06660594046115875,
0.03741202875971794,
0.11839398741722107,
-0.1663983315229416,
0.03581586107611656,
0.11083318293094635,
-0.04651443660259247,
0.18614409863948822,
-0.20854249596595764,
-0.02927589975297451,
0.20170395076274872,
0.040600456297397614,
0.05820350721478462,
-0.05468771979212761,
-0.03231723606586456,
0.01364588737487793,
-0.14487992227077484,
0.06877235323190689,
0.11307153850793839,
0.041834279894828796,
-0.0172274187207222,
0.0787387490272522,
0.0021745148114860058,
-0.0731922835111618,
0.19910576939582825,
-0.025844614952802658,
0.024256490170955658,
-0.04272749647498131,
-0.23798060417175293,
0.12775874137878418,
-0.023064272478222847,
0.08214819431304932,
0.07538721710443497,
0.021028738468885422,
-0.16098462045192719,
-0.014173814095556736,
-0.11875100433826447,
0.07721564173698425,
-0.07301245629787445,
-0.04844633862376213,
-0.0590442456305027,
0.012157578021287918,
-0.10161566734313965,
-0.04514360800385475,
-0.1624065786600113,
-0.07942797243595123,
0.0759822428226471,
0.013675469905138016,
-0.006444314494729042,
0.06654620170593262,
-0.1836836040019989,
0.05705875903367996,
-0.012530838139355183,
0.0619388073682785,
-0.10079565644264221,
-0.12315905094146729,
0.1021762415766716,
0.03662077337503433,
0.08908525109291077,
0.07408330589532852,
-0.06525322049856186,
0.008303400129079819,
0.03823942691087723,
-0.1902492344379425,
-0.061513133347034454,
-0.06060648337006569,
-0.006479691248387098,
-0.01715351827442646,
-0.09824580699205399,
0.030182957649230957,
-0.02474709041416645,
-0.034081727266311646,
-0.0009182289941236377,
-0.002454205881804228,
-0.12701742351055145,
-0.0009491539094597101,
0.13562610745429993,
0.1021547019481659,
-0.04566896706819534,
0.013759877532720566,
0.09578602015972137,
-0.0976477786898613,
-0.015482207760214806,
0.03056846559047699,
-0.05261091887950897,
-0.05242649093270302,
-0.07476341724395752,
0.16592884063720703,
0.07799428701400757,
-0.003889932995662093,
-0.006761424243450165,
-0.08308728784322739,
0.004400869831442833,
0.0834001898765564,
0.09166659414768219,
0.0017428932478651404,
-0.11055802553892136,
-0.08825869113206863,
-0.04522313177585602,
0.022255057469010353,
0.0016010550316423178,
-0.06240902468562126,
-0.13734987378120422,
0.08076385408639908,
0.012935017235577106,
0.12854577600955963,
-0.03758738562464714,
-0.08589383959770203,
-0.1209675520658493,
0.05851154401898384,
-0.00013283985026646405,
0.008335558697581291,
-0.013108356855809689,
0.006929735653102398,
0.0032697003334760666,
-0.04146888107061386,
-0.006699878256767988,
-0.013709691353142262,
-0.10904364287853241,
0.0595129169523716,
-0.00026295066345483065,
0.05749618634581566,
-0.019727669656276703,
-0.035004302859306335,
0.10337471961975098,
-0.028249191120266914,
0.05784662067890167,
0.06588263064622879,
-0.07716593891382217,
-0.052351806312799454,
-0.232272669672966,
0.013977361842989922,
0.01359256450086832,
-0.01938895508646965,
0.01198615599423647,
-0.18718808889389038,
-0.002408674219623208,
-0.07003804296255112,
-0.019620265811681747,
-0.021836137399077415,
0.04376371577382088,
-0.07073947042226791,
-0.04819376766681671,
0.13044919073581696,
-0.05197535455226898,
-0.02548273093998432,
-0.09404639154672623,
0.17698834836483002,
-0.016344599425792694,
0.10929640382528305,
0.026925215497612953,
0.1651221066713333,
-0.13492320477962494,
-0.02287326008081436,
-0.05856097489595413,
-0.022900674492120743,
-0.024268681183457375,
-0.05907032638788223,
0.09327629953622818,
0.011733517982065678,
0.22636036574840546,
0.01423418428748846,
-0.010065902024507523,
-0.009942913427948952,
0.16960115730762482,
0.02611513063311577,
-0.02038286253809929,
0.10359500348567963,
0.021963370963931084,
0.006427949294447899,
0.05541658774018288,
0.04529688507318497,
-0.030264973640441895,
0.18015775084495544,
0.21477608382701874,
0.0011419461807236075,
0.11504117399454117,
0.08320838212966919,
-0.04261947050690651,
0.04139399528503418,
-0.003798088990151882,
-0.12946467101573944,
0.13413143157958984,
-0.0027838568203151226,
-0.038717977702617645,
-0.04559718444943428,
0.10584032535552979,
-0.06193123385310173,
0.05128570646047592,
-0.0501648373901844,
-0.08126737922430038,
-0.10386890918016434,
-0.19279465079307556,
0.042028382420539856,
0.0033682496286928654,
-0.011750648729503155,
-0.045877836644649506,
-0.01831798627972603,
0.18256548047065735,
-0.056898415088653564,
0.015313430689275265,
0.06555616110563278,
0.03392085060477257,
-0.06639061868190765,
0.02865232527256012,
-0.031249510124325752,
0.006010999903082848,
-0.1702151894569397,
0.07115574181079865,
0.004042482003569603,
-0.0891227051615715,
0.038743533194065094,
-0.002058038953691721,
-0.018427273258566856,
-0.0361320823431015,
-0.1450413018465042,
-0.06967693567276001,
-0.03088492341339588,
0.0629301443696022,
-0.02216517925262451,
0.08737204223871231,
0.054675593972206116,
0.04052126407623291,
0.00021603464847430587,
0.13613329827785492,
-0.017200836911797523,
-0.13532908260822296,
-0.05924006924033165,
0.19982117414474487,
-0.16289867460727692,
-0.003894954454153776,
-0.08012817054986954,
-0.01595214754343033,
0.05655684694647789,
0.34926217794418335,
0.29040855169296265,
-0.03533623740077019,
-0.04518170282244682,
-0.02583770826458931,
0.024215908721089363,
0.09732510894536972,
0.03687691688537598,
0.10173441469669342,
0.16557882726192474,
-0.1913149654865265,
-0.039266180247068405,
-0.1793902963399887,
-0.004952968563884497,
-0.018650684505701065,
0.007320149336010218,
0.1155557930469513,
-0.07043810933828354,
-0.04351365938782692,
0.209843248128891,
-0.16912789642810822,
-0.054514169692993164,
0.027548594400286674,
-0.06217918545007706,
-0.11541114002466202,
0.02538352645933628,
0.07309430092573166,
-0.009485145099461079,
0.09940112382173538,
-0.10799267143011093,
-0.09204038232564926,
0.14706911146640778,
-0.04426358640193939,
-0.12115191668272018,
-0.11411548405885696,
0.03939910978078842,
-0.15244382619857788,
0.13161446154117584,
0.004268568009138107,
0.12123951315879822,
0.05961480736732483,
0.08470243960618973,
-0.11262635141611099,
0.1004881039261818,
0.009663550183176994,
0.07560045272111893,
-0.05425167456269264,
-0.05648530274629593,
0.014422115869820118,
-0.05682225525379181,
0.009247985668480396,
-0.15363535284996033,
0.07903418689966202,
0.08833206444978714,
-0.0023426066618412733,
0.005495043005794287,
0.06143582984805107,
-0.04818333312869072,
0.11000506579875946,
0.0969986543059349,
-0.06984512507915497,
0.01432940736413002,
-0.008052513003349304,
-0.012603741139173508,
0.07655569165945053,
-0.04939008504152298,
-0.04133909195661545,
-0.02148480713367462,
0.01191031001508236,
0.006371227093040943,
-0.10161851346492767,
-0.06261949241161346,
-0.07356744259595871,
-0.04364680126309395,
-0.051751863211393356,
0.05409422144293785,
0.030860010534524918,
0.027121830731630325,
0.0734129548072815,
-0.022500816732645035,
0.12983761727809906,
0.0015641379868611693,
0.1542537659406662,
-0.17383043467998505,
-0.07972919940948486
] |
null | null |
flair
|
## French NER in Flair (default model)
This is the standard 4-class NER model for French that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **90,61** (WikiNER)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| MISC | other name |
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-french")
# make example sentence
sentence = Sentence("George Washington est allé à Washington")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [1,2]: "George Washington" [− Labels: PER (0.7394)]
Span [6]: "Washington" [− Labels: LOC (0.9161)]
```
So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington est allé à Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import WIKINER_FRENCH
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. get the corpus
corpus: Corpus = WIKINER_FRENCH()
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
# GloVe embeddings
WordEmbeddings('fr'),
# contextual string embeddings, forward
FlairEmbeddings('fr-forward'),
# contextual string embeddings, backward
FlairEmbeddings('fr-backward'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/ner-french',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following paper when using this model.
```
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
{"language": "fr", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington est all\u00e9 \u00e0 Washington"}]}
|
token-classification
|
flair/ner-french
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"fr",
"dataset:conll2003",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"fr"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #fr #dataset-conll2003 #has_space #region-us
|
French NER in Flair (default model)
-----------------------------------
This is the standard 4-class NER model for French that ships with Flair.
F1-Score: 90,61 (WikiNER)
Predicts 4 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the entities "*George Washington*" (labeled as a person) and "*Washington*" (labeled as a location) are found in the sentence "*George Washington est allé à Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following paper when using this model.
---
### Issues?
The Flair issue tracker is available here.
|
[
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington est allé à Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #fr #dataset-conll2003 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington est allé à Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
41,
82,
22,
15,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #fr #dataset-conll2003 #has_space #region-us \n### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington est allé à Washington*\".\n\n\n\n\n---### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
-0.08365882933139801,
0.15097683668136597,
-0.0015571448020637035,
0.08468350768089294,
0.08389682322740555,
0.05138925462961197,
0.11425533145666122,
0.03942098468542099,
0.01564592309296131,
0.04754920303821564,
0.1935599446296692,
0.06445895880460739,
0.11225450783967972,
0.08996524661779404,
0.0660267174243927,
-0.2605012357234955,
0.020195120945572853,
-0.014275937341153622,
-0.057200945913791656,
0.16208775341510773,
0.07861386984586716,
0.0024298301432281733,
0.022882860153913498,
0.03356640413403511,
-0.13402916491031647,
-0.014239925891160965,
-0.02297198586165905,
-0.09361322224140167,
0.1720016449689865,
-0.028234921395778656,
0.21859487891197205,
0.046971190720796585,
0.1029105931520462,
-0.14252176880836487,
0.030575426295399666,
0.041905924677848816,
0.014005550183355808,
0.09983565658330917,
0.04616498947143555,
0.0013570269802585244,
0.31630322337150574,
-0.03334290534257889,
0.0037380645517259836,
0.025874348357319832,
-0.1936904489994049,
-0.24951983988285065,
-0.0994843915104866,
0.07151798158884048,
0.045901108533144,
0.10013974457979202,
-0.030316896736621857,
0.09613993763923645,
-0.06407597661018372,
0.029538344591856003,
0.17376667261123657,
-0.22766803205013275,
-0.04215679317712784,
0.17372263967990875,
0.01774631440639496,
-0.025732379406690598,
-0.04842977225780487,
0.04378073289990425,
0.0031577623449265957,
0.0760035589337349,
-0.03233363851904869,
-0.018168048933148384,
0.025256438180804253,
0.11548715084791183,
-0.1629744917154312,
-0.08923295885324478,
0.33049651980400085,
0.0441344790160656,
-0.06839350610971451,
0.018539849668741226,
0.016589701175689697,
-0.005293074529618025,
0.05077318847179413,
-0.10747019946575165,
-0.010647827759385109,
-0.009917034767568111,
0.17993998527526855,
-0.11943168193101883,
-0.0959928035736084,
-0.09241721779108047,
-0.022874318063259125,
0.044309377670288086,
-0.08891106396913528,
0.0972323939204216,
-0.1670491099357605,
0.11556731909513474,
0.028931254521012306,
-0.047920286655426025,
0.04320970177650452,
-0.07845903187990189,
-0.047247644513845444,
0.012430001050233841,
-0.03956634923815727,
0.12119882553815842,
0.02996235340833664,
0.029708784073591232,
0.1254854053258896,
-0.030890585854649544,
0.10992567986249924,
0.10643109679222107,
0.0013602334074676037,
0.19019660353660583,
-0.11888470500707626,
-0.05056878179311752,
0.026146484538912773,
-0.01687304861843586,
-0.019524771720170975,
-0.05757167562842369,
-0.17246907949447632,
-0.02483411319553852,
-0.07409430295228958,
0.005284099839627743,
0.02535097487270832,
-0.020335109904408455,
-0.0912184938788414,
0.01477343961596489,
0.0823163092136383,
-0.0650450736284256,
-0.026950202882289886,
-0.02337855100631714,
-0.04591085761785507,
0.10148175060749054,
0.08841309696435928,
0.03367960825562477,
0.020155644044280052,
0.1193428710103035,
-0.07483461499214172,
0.04445250704884529,
-0.0432555228471756,
-0.10908731818199158,
-0.032922204583883286,
-0.07914517819881439,
0.020441962406039238,
-0.06433181464672089,
-0.1456228345632553,
-0.0194307379424572,
0.0737089216709137,
-0.001457232516258955,
-0.07584337145090103,
-0.04989062249660492,
-0.03973044455051422,
-0.04472669959068298,
0.04973302781581879,
0.06853413581848145,
-0.059171974658966064,
0.06396470218896866,
-0.02897803857922554,
0.11566943675279617,
-0.07758515328168869,
0.018056346103549004,
-0.07572849839925766,
0.010427390225231647,
-0.11481640487909317,
-0.0034486392978578806,
-0.013405382633209229,
0.02134828455746174,
-0.10609854012727737,
-0.09359567612409592,
-0.02185533009469509,
0.013757151551544666,
0.023827772587537766,
0.18027363717556,
-0.259060263633728,
-0.009249239228665829,
0.108338363468647,
-0.09947381168603897,
-0.05381060019135475,
0.06357710063457489,
-0.023510105907917023,
0.1971474587917328,
0.030439626425504684,
0.1435176283121109,
-0.035786326974630356,
-0.3513360917568207,
0.12292660027742386,
0.0690632164478302,
-0.1920531690120697,
0.017548933625221252,
0.07184725254774094,
-0.03944897651672363,
-0.1866174191236496,
-0.0010744667379185557,
-0.11382488906383514,
0.03305724635720253,
-0.040367867797613144,
-0.047354698181152344,
0.010120359249413013,
-0.05733199045062065,
0.03672537952661514,
-0.038803890347480774,
-0.03177310898900032,
-0.0012997094308957458,
-0.1385093331336975,
-0.14810818433761597,
0.03584201633930206,
0.10430362075567245,
-0.037011876702308655,
0.009448482654988766,
0.04258517175912857,
0.049433790147304535,
-0.045253898948431015,
-0.11362433433532715,
0.03974944353103638,
-0.08822375535964966,
0.13811340928077698,
0.025004571303725243,
0.17228390276432037,
-0.026734912768006325,
0.02670106291770935,
0.0662795677781105,
0.036310482770204544,
-0.03728077933192253,
0.05062628909945488,
-0.012345432303845882,
0.0021722549572587013,
-0.07833857089281082,
-0.09408120810985565,
0.11827170848846436,
-0.2012646645307541,
0.053747400641441345,
0.03342002257704735,
-0.016329580917954445,
-0.00762199005112052,
-0.013718033209443092,
0.09605251997709274,
0.036585185676813126,
0.007828408852219582,
-0.06108877435326576,
0.06618473678827286,
0.0027020385023206472,
-0.05483214557170868,
-0.1148827001452446,
-0.04251103103160858,
-0.010046624578535557,
0.09389109164476395,
-0.030097592622041702,
-0.12170274555683136,
-0.0774843692779541,
-0.04129191115498543,
0.007054339628666639,
-0.02950117737054825,
-0.03436562418937683,
0.16277585923671722,
0.0366501547396183,
0.01709478162229061,
-0.02764272131025791,
0.03170222043991089,
-0.02902253344655037,
-0.03418796882033348,
-0.07525070011615753,
0.05542757734656334,
0.11596734076738358,
0.04170884191989899,
0.024450676515698433,
0.17914065718650818,
-0.017237458378076553,
0.014492672868072987,
0.05407709255814552,
-0.04228195175528526,
-0.09383039176464081,
-0.09874927997589111,
-0.015848957002162933,
0.17662067711353302,
-0.04145975038409233,
0.03560453653335571,
0.04142903909087181,
-0.01646234281361103,
0.03578175604343414,
-0.19966286420822144,
-0.1268831342458725,
0.05330333858728409,
-0.02733681909739971,
-0.1917114406824112,
0.05255705118179321,
-0.05822000280022621,
0.10281805694103241,
-0.020678138360381126,
-0.10441753268241882,
0.043537482619285583,
0.004146823659539223,
-0.07438099384307861,
0.10992658883333206,
-0.05104546248912811,
-0.26637208461761475,
-0.09623659402132034,
-0.049822088330984116,
0.09918246418237686,
0.000036596149584511295,
0.021768124774098396,
-0.09956352412700653,
0.033275309950113297,
-0.01402384601533413,
0.05135359987616539,
-0.08269640058279037,
-0.07531602680683136,
-0.05438985675573349,
0.016866600140929222,
-0.01692316122353077,
-0.10869279503822327,
-0.03391779214143753,
-0.0564759261906147,
0.08369258046150208,
0.036024924367666245,
-0.0011935657821595669,
0.10385322570800781,
0.1278783231973648,
0.00951910950243473,
0.06923121958971024,
-0.04230209439992905,
0.3817793130874634,
-0.08522947132587433,
0.0926930159330368,
0.11235885322093964,
0.016765352338552475,
0.047883931547403336,
0.14648999273777008,
0.06527220457792282,
-0.08834616839885712,
-0.03645818680524826,
-0.07425118237733841,
-0.07775917649269104,
-0.1122363731265068,
-0.0506899319589138,
-0.048846278339624405,
-0.07685660570859909,
0.0050447057001292706,
0.024148421362042427,
-0.08002424985170364,
0.036807142198085785,
0.09176260977983475,
-0.018295567482709885,
-0.057938896119594574,
-0.018564341589808464,
0.009521791711449623,
-0.02005101926624775,
0.0046115173026919365,
-0.029442233964800835,
-0.05096692964434624,
0.027103018015623093,
0.100104421377182,
0.09459054470062256,
0.1149129569530487,
-0.02631525881588459,
0.06533288955688477,
0.1015920341014862,
0.08747028559446335,
0.17632745206356049,
0.0690055564045906,
-0.024375420063734055,
-0.029622413218021393,
-0.053661223500967026,
0.007376234512776136,
0.04503817856311798,
-0.0034272661432623863,
-0.05898350104689598,
0.007230695802718401,
-0.09226536005735397,
-0.047692473977804184,
-0.016333848237991333,
0.11693327128887177,
-0.18567965924739838,
-0.016113493591547012,
-0.001076429383829236,
0.1036805659532547,
-0.04274981468915939,
0.0576997734606266,
0.04020650312304497,
-0.1304830014705658,
0.011642921715974808,
-0.018140994012355804,
0.10038185864686966,
0.03508244454860687,
0.03727364167571068,
-0.07310963422060013,
0.0446162223815918,
-0.050083111971616745,
0.11084029823541641,
-0.1956794112920761,
0.35604479908943176,
-0.03696226701140404,
0.02603721432387829,
-0.03293480724096298,
0.004094993229955435,
0.07638903707265854,
0.08549031615257263,
0.2393881231546402,
0.007331946864724159,
-0.14462162554264069,
-0.1011410728096962,
-0.09526104480028152,
0.04089062288403511,
-0.035197820514440536,
-0.0519365519285202,
0.05403520166873932,
0.06991053372621536,
0.011238094419240952,
-0.020361287519335747,
0.07346885651350021,
-0.20300336182117462,
-0.1170273944735527,
0.008901556022465229,
-0.024277450516819954,
0.023020757362246513,
-0.006062022875994444,
-0.054684873670339584,
-0.11006984114646912,
-0.026549987494945526,
-0.011642158031463623,
-0.03457246720790863,
-0.08275184035301208,
0.09467358887195587,
0.03378317505121231,
-0.01451652031391859,
-0.03522256389260292,
0.0025423336774110794,
0.01726466603577137,
-0.09315235912799835,
0.02696281112730503,
0.021215559914708138,
-0.060950104147195816,
0.04814973846077919,
-0.06865067780017853,
0.05618209019303322,
0.06305304914712906,
0.04854234308004379,
0.025644052773714066,
0.05495274439454079,
-0.10840737074613571,
-0.1206473633646965,
0.19496697187423706,
-0.1341806799173355,
-0.03667373210191727,
0.04089193046092987,
0.029825497418642044,
-0.007257198914885521,
-0.13072550296783447,
0.02679925039410591,
0.22563901543617249,
0.2525259256362915,
-0.13355189561843872,
0.1302540898323059,
-0.01659221202135086,
-0.11094274371862411,
-0.16421689093112946,
0.0017506727017462254,
-0.05000662803649902,
0.04689955338835716,
0.11835838109254837,
-0.17797845602035522,
0.04312514886260033,
0.11314141750335693,
-0.03482305631041527,
0.17507027089595795,
-0.20606733858585358,
-0.02383098192512989,
0.2104025036096573,
0.04335130751132965,
0.06995625793933868,
-0.0445968322455883,
-0.02056237682700157,
0.0035418651532381773,
-0.13927710056304932,
0.06513597071170807,
0.10252072662115097,
0.03923502936959267,
-0.026242028921842575,
0.06343480199575424,
0.0009986236691474915,
-0.0621727854013443,
0.20334219932556152,
0.00041544673149473965,
0.03627762943506241,
-0.04373571649193764,
-0.24148224294185638,
0.11756440997123718,
-0.02046455256640911,
0.09309878200292587,
0.05828271061182022,
0.012322228401899338,
-0.16380156576633453,
-0.018017880618572235,
-0.11408551037311554,
0.09265175461769104,
-0.07192641496658325,
-0.049518439918756485,
-0.0620773583650589,
0.008512386120855808,
-0.10012948513031006,
-0.03731865808367729,
-0.1577790528535843,
-0.0682106614112854,
0.06497670710086823,
0.020136592909693718,
-0.0017142602009698749,
0.05204145610332489,
-0.173226997256279,
0.05860891193151474,
-0.008214467205107212,
0.06422383338212967,
-0.09668601304292679,
-0.12101250141859055,
0.09972217679023743,
0.02896629087626934,
0.09241335839033127,
0.0751018151640892,
-0.06603571772575378,
0.008743515238165855,
0.051458802074193954,
-0.16862927377223969,
-0.06581655144691467,
-0.06866574287414551,
-0.027197279036045074,
-0.008049949072301388,
-0.09752693772315979,
0.024085557088255882,
-0.03368252515792847,
-0.028227360919117928,
-0.008148517459630966,
-0.0029852462466806173,
-0.13468961417675018,
0.0027751298621296883,
0.14730919897556305,
0.09658975899219513,
-0.04551612213253975,
-0.008305642753839493,
0.09512710571289062,
-0.09099029749631882,
-0.013210684061050415,
0.03977707400918007,
-0.04898875951766968,
-0.05688050761818886,
-0.05232052877545357,
0.18459536135196686,
0.053997837007045746,
-0.015753991901874542,
-0.0027451347559690475,
-0.09785652160644531,
0.008335019461810589,
0.06907297670841217,
0.09661819785833359,
-0.007812453433871269,
-0.09666900336742401,
-0.08725998550653458,
-0.05414111539721489,
0.021382462233304977,
0.00734288664534688,
-0.04986971616744995,
-0.13468106091022491,
0.0660352110862732,
0.00878587644547224,
0.12791544198989868,
-0.038509611040353775,
-0.08034072071313858,
-0.12676586210727692,
0.05232858657836914,
-0.01147262193262577,
0.013684588484466076,
-0.005689026787877083,
0.010010884143412113,
0.005566876847296953,
-0.044035639613866806,
-0.002663880353793502,
-0.003040237119421363,
-0.10579398274421692,
0.06274498254060745,
0.008489473722875118,
0.053961608558893204,
-0.01968444138765335,
-0.03434410318732262,
0.09716176986694336,
-0.027792034670710564,
0.05342274531722069,
0.051195669919252396,
-0.07507835328578949,
-0.05325720086693764,
-0.2617686092853546,
0.023017821833491325,
0.011794730089604855,
-0.00946225505322218,
0.02330225147306919,
-0.18312829732894897,
-0.00024018458498176187,
-0.0617830753326416,
-0.0021564168855547905,
-0.014558196067810059,
0.04881320148706436,
-0.07351892441511154,
-0.03378046303987503,
0.11000140011310577,
-0.06744357943534851,
-0.023247962817549706,
-0.07225949317216873,
0.17549417912960052,
-0.01527130976319313,
0.11574000865221024,
0.025032563135027885,
0.15177275240421295,
-0.11968660354614258,
-0.02232557162642479,
-0.051036275923252106,
-0.01780899055302143,
-0.031102923676371574,
-0.06591098755598068,
0.08786052465438843,
0.015066423453390598,
0.20962315797805786,
0.03528023138642311,
-0.02939523011445999,
-0.01446167565882206,
0.14778390526771545,
0.01674346998333931,
-0.023077858611941338,
0.0966925323009491,
0.02354787103831768,
0.003838712815195322,
0.05080561339855194,
0.04734215885400772,
-0.01833832450211048,
0.19357825815677643,
0.22837619483470917,
0.0006502437754534185,
0.08565659075975418,
0.0836503803730011,
-0.026133911684155464,
0.05148313194513321,
-0.00457674078643322,
-0.1167910024523735,
0.13189347088336945,
-0.0036658644676208496,
-0.040878478437662125,
-0.05842168629169464,
0.09791133552789688,
-0.06808213889598846,
0.05567452684044838,
-0.0509069599211216,
-0.09752397984266281,
-0.10375151038169861,
-0.1894219070672989,
0.03264635056257248,
0.009300412610173225,
-0.017007838934659958,
-0.045580532401800156,
-0.026812493801116943,
0.17738188803195953,
-0.05187036842107773,
0.015727324411273003,
0.08048748970031738,
0.014861158095300198,
-0.042259957641363144,
0.04309673234820366,
-0.03648078814148903,
0.001099026994779706,
-0.1651734709739685,
0.07576894760131836,
-0.003977196291089058,
-0.08285265415906906,
0.03231769800186157,
-0.005028645973652601,
-0.02490406483411789,
-0.04144374653697014,
-0.13657937943935394,
-0.06444922089576721,
-0.035157959908246994,
0.06301981955766678,
-0.01687377318739891,
0.07788848131895065,
0.052889272570610046,
0.02866613119840622,
0.0031503746286034584,
0.13252763450145721,
-0.009585139341652393,
-0.14411593973636627,
-0.06643089652061462,
0.18842872977256775,
-0.14898909628391266,
0.0007999194203875959,
-0.07648103684186935,
-0.017002150416374207,
0.06496093422174454,
0.34397074580192566,
0.29764413833618164,
-0.03623931109905243,
-0.04033944383263588,
-0.03474230319261551,
0.02825099788606167,
0.09211103618144989,
0.04801503196358681,
0.10507841408252716,
0.16320215165615082,
-0.18021956086158752,
-0.040582820773124695,
-0.1832226663827896,
0.0003581531345844269,
-0.022142551839351654,
0.0021497742272913456,
0.12649604678153992,
-0.06848347932100296,
-0.04118629917502403,
0.2144036889076233,
-0.16216519474983215,
-0.05590847507119179,
0.030182557180523872,
-0.05780896171927452,
-0.12047655135393143,
0.024671589955687523,
0.04996789991855621,
0.0000878656210261397,
0.10641379654407501,
-0.11291153728961945,
-0.09500625729560852,
0.15117280185222626,
-0.0436224602162838,
-0.11927758902311325,
-0.09999653697013855,
0.04189909249544144,
-0.1561603844165802,
0.14264963567256927,
0.010512829758226871,
0.1278936117887497,
0.053916361182928085,
0.07807007431983948,
-0.1022152453660965,
0.09899228066205978,
0.010059014894068241,
0.10906879603862762,
-0.039859022945165634,
-0.05507751926779747,
0.011707243509590626,
-0.06348291784524918,
0.0008683200576342642,
-0.15679757297039032,
0.07035853713750839,
0.10293351858854294,
-0.003319165203720331,
0.00031425702036358416,
0.0660402849316597,
-0.056765373796224594,
0.10801186412572861,
0.10008209198713303,
-0.06070317327976227,
0.010738925077021122,
-0.006203854456543922,
-0.004120723810046911,
0.07029532641172409,
-0.0480964295566082,
-0.04360814020037651,
-0.014992838725447655,
0.012044031172990799,
-0.007076658308506012,
-0.09139351546764374,
-0.06727619469165802,
-0.07023782283067703,
-0.06388484686613083,
-0.04754164442420006,
0.05498012527823448,
0.02399083785712719,
0.038605161011219025,
0.07164324820041656,
-0.02432352863252163,
0.13405519723892212,
-0.005403961520642042,
0.14696232974529266,
-0.14751093089580536,
-0.08033975958824158
] |
null | null |
flair
|
## German NER in Flair (large model)
This is the large 4-class NER model for German that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **92,31** (CoNLL-03 German revised)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| MISC | other name |
Based on document-level XLM-R embeddings and [FLERT](https://arxiv.org/pdf/2011.06993v1.pdf).
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-german-large")
# make example sentence
sentence = Sentence("George Washington ging nach Washington")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [1,2]: "George Washington" [− Labels: PER (1.0)]
Span [5]: "Washington" [− Labels: LOC (1.0)]
```
So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging nach Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
import torch
# 1. get the corpus
from flair.datasets import CONLL_03_GERMAN
corpus = CONLL_03_GERMAN()
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize fine-tuneable transformer embeddings WITH document context
from flair.embeddings import TransformerWordEmbeddings
embeddings = TransformerWordEmbeddings(
model='xlm-roberta-large',
layers="-1",
subtoken_pooling="first",
fine_tune=True,
use_context=True,
)
# 5. initialize bare-bones sequence tagger (no CRF, no RNN, no reprojection)
from flair.models import SequenceTagger
tagger = SequenceTagger(
hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type='ner',
use_crf=False,
use_rnn=False,
reproject_embeddings=False,
)
# 6. initialize trainer with AdamW optimizer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus, optimizer=torch.optim.AdamW)
# 7. run training with XLM parameters (20 epochs, small LR)
from torch.optim.lr_scheduler import OneCycleLR
trainer.train('resources/taggers/ner-german-large',
learning_rate=5.0e-6,
mini_batch_size=4,
mini_batch_chunk_size=1,
max_epochs=20,
scheduler=OneCycleLR,
embeddings_storage_mode='none',
weight_decay=0.,
)
)
```
---
### Cite
Please cite the following paper when using this model.
```
@misc{schweter2020flert,
title={FLERT: Document-Level Features for Named Entity Recognition},
author={Stefan Schweter and Alan Akbik},
year={2020},
eprint={2011.06993},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
{"language": "de", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington ging nach Washington"}]}
|
token-classification
|
flair/ner-german-large
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"de",
"dataset:conll2003",
"arxiv:2011.06993",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2011.06993"
] |
[
"de"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #de #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us
|
German NER in Flair (large model)
---------------------------------
This is the large 4-class NER model for German that ships with Flair.
F1-Score: 92,31 (CoNLL-03 German revised)
Predicts 4 tags:
Based on document-level XLM-R embeddings and FLERT.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the entities "*George Washington*" (labeled as a person) and "*Washington*" (labeled as a location) are found in the sentence "*George Washington ging nach Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following paper when using this model.
---
### Issues?
The Flair issue tracker is available here.
|
[
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging nach Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #de #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging nach Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
50,
81,
22,
15,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #de #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us \n### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging nach Washington*\".\n\n\n\n\n---### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
-0.11078167706727982,
0.1729535609483719,
-0.0006081386818550527,
0.07418359071016312,
0.07372473925352097,
0.05407562106847763,
0.12066252529621124,
0.05047163739800453,
0.014446025714278221,
0.04779127240180969,
0.18543291091918945,
0.0643002986907959,
0.1057700365781784,
0.09478871524333954,
0.0761103630065918,
-0.2595694661140442,
0.025450384244322777,
-0.013675931841135025,
-0.06434838473796844,
0.16318033635616302,
0.08622192591428757,
-0.01223866455256939,
0.019385771825909615,
0.02510707825422287,
-0.11199945956468582,
-0.009973539039492607,
-0.035290636122226715,
-0.09869996458292007,
0.17866408824920654,
-0.04827551916241646,
0.20904085040092468,
0.05055471137166023,
0.11953853815793991,
-0.13892652094364166,
0.02971350960433483,
0.04217119142413139,
0.014464914798736572,
0.10964648425579071,
0.05345357954502106,
0.004590907599776983,
0.3154265284538269,
-0.031206239014863968,
-0.004984348546713591,
0.0299741979688406,
-0.18884870409965515,
-0.26515740156173706,
-0.10632189363241196,
0.06890403479337692,
0.057248685508966446,
0.11704837530851364,
-0.030529135838150978,
0.10934370756149292,
-0.05448909476399422,
0.0234995037317276,
0.16570697724819183,
-0.21631230413913727,
-0.03921036049723625,
0.1854885369539261,
0.019742464646697044,
-0.025723731145262718,
-0.05049311742186546,
0.04233691096305847,
0.005360891576856375,
0.07647953927516937,
-0.027041122317314148,
-0.01118680089712143,
0.014675252139568329,
0.11031673103570938,
-0.15282560884952545,
-0.09713146090507507,
0.3371601104736328,
0.03136691078543663,
-0.0636197030544281,
0.012712986208498478,
-0.000003113856791969738,
-0.018340328708291054,
0.04894265532493591,
-0.10807696729898453,
-0.014262471348047256,
-0.0030428858008235693,
0.15224683284759521,
-0.1069507896900177,
-0.1112285703420639,
-0.10072848200798035,
-0.008969714865088463,
0.03524978086352348,
-0.08544265478849411,
0.09145072102546692,
-0.17078028619289398,
0.12239350378513336,
0.0550362654030323,
-0.0655936747789383,
0.04293877258896828,
-0.08186198025941849,
-0.032213255763053894,
0.02283928357064724,
-0.04467960074543953,
0.12283746153116226,
0.02950459159910679,
0.03043496422469616,
0.16377414762973785,
-0.03182554244995117,
0.12483439594507217,
0.10254573076963425,
0.004394461400806904,
0.17183254659175873,
-0.12294739484786987,
-0.053335756063461304,
0.03885412588715553,
-0.002921546343713999,
-0.004924640525132418,
-0.0568082369863987,
-0.1549115628004074,
-0.03739824891090393,
-0.05567740276455879,
0.022650843486189842,
0.040106743574142456,
-0.025843508541584015,
-0.08571465313434601,
0.019061949104070663,
0.10943479090929031,
-0.05975198745727539,
-0.035524096339941025,
-0.027529632672667503,
-0.058361176401376724,
0.0909537747502327,
0.09262949228286743,
0.031626079231500626,
0.01847946271300316,
0.11015753448009491,
-0.08570944517850876,
0.0430774949491024,
-0.046116963028907776,
-0.09902238845825195,
-0.015946757048368454,
-0.08693084865808487,
0.01859894022345543,
-0.07029769569635391,
-0.15635493397712708,
-0.026454241946339607,
0.06391715258359909,
0.0020312131382524967,
-0.06258182227611542,
-0.04910427704453468,
-0.03979517146945,
-0.03450338542461395,
0.03346693143248558,
0.07158056646585464,
-0.05550341680645943,
0.06366388499736786,
-0.023947207257151604,
0.105413056910038,
-0.06931980699300766,
0.011818070895969868,
-0.09300783276557922,
0.010096567682921886,
-0.09546510875225067,
-0.0010184386046603322,
-0.014581415802240372,
0.03044750913977623,
-0.1271820366382599,
-0.09756981581449509,
-0.03202783688902855,
0.0038566479925066233,
0.03800318390130997,
0.1949528157711029,
-0.29317668080329895,
0.008953823707997799,
0.0893053486943245,
-0.09621964395046234,
-0.05482468008995056,
0.05280733481049538,
-0.03655467927455902,
0.20727916061878204,
0.029560724273324013,
0.14174674451351166,
-0.03602217137813568,
-0.32466205954551697,
0.09576999396085739,
0.06029210239648819,
-0.169237419962883,
0.007386712823063135,
0.07846605032682419,
-0.04566946253180504,
-0.16640569269657135,
-0.003474486293271184,
-0.1312287598848343,
0.026261067017912865,
-0.034110911190509796,
-0.05523346737027168,
0.007140197325497866,
-0.06128950044512749,
0.03589845448732376,
-0.03962947055697441,
-0.027264323085546494,
-0.004085345659404993,
-0.14041876792907715,
-0.16290131211280823,
0.047285228967666626,
0.09719270467758179,
-0.03576216846704483,
0.017252685502171516,
0.037501364946365356,
0.027032649144530296,
-0.047345053404569626,
-0.10889638215303421,
0.023956570774316788,
-0.08596917241811752,
0.11219749599695206,
0.015041711740195751,
0.15981130301952362,
-0.019754862412810326,
0.032519303262233734,
0.07034040242433548,
0.02791762351989746,
-0.03221100941300392,
0.053242843598127365,
-0.008059431798756123,
0.00829231832176447,
-0.10709699243307114,
-0.0962686762213707,
0.12363778054714203,
-0.17824651300907135,
0.04808832332491875,
0.008983386680483818,
-0.01731918565928936,
-0.0360201932489872,
-0.021536922082304955,
0.10354235768318176,
0.021006399765610695,
0.003732206765562296,
-0.06757040321826935,
0.06889484077692032,
0.0035259269643574953,
-0.06457813084125519,
-0.09823059290647507,
-0.06378667056560516,
-0.02961750142276287,
0.09214253723621368,
-0.009919669479131699,
-0.1062706932425499,
-0.0694078877568245,
-0.03976694494485855,
0.0074994973838329315,
-0.02767248824238777,
-0.029983071610331535,
0.17985080182552338,
0.03602301701903343,
0.00703743752092123,
-0.02624819613993168,
0.035671550780534744,
-0.03260797634720802,
-0.03236563131213188,
-0.06870071589946747,
0.04488165304064751,
0.09086203575134277,
0.02892307937145233,
0.025359710678458214,
0.170136496424675,
-0.01514704804867506,
0.007505292538553476,
0.058141350746154785,
-0.04505079239606857,
-0.0958101823925972,
-0.094307079911232,
-0.02344561368227005,
0.18647098541259766,
-0.047022584825754166,
0.03596596047282219,
0.044416189193725586,
-0.006840050686150789,
0.04608551412820816,
-0.19934023916721344,
-0.13397371768951416,
0.048636529594659805,
-0.02229832112789154,
-0.2068440169095993,
0.06745128333568573,
-0.06746400147676468,
0.10143663734197617,
-0.026048699393868446,
-0.09256037324666977,
0.042472440749406815,
-0.0023643739987164736,
-0.07261388003826141,
0.12412174046039581,
-0.053669799119234085,
-0.24065515398979187,
-0.10313644260168076,
-0.02824331447482109,
0.10782227665185928,
0.00386088527739048,
0.02585536800324917,
-0.10747295618057251,
0.01865031197667122,
-0.017984915524721146,
0.05936703085899353,
-0.09063771367073059,
-0.09520575404167175,
-0.05230560526251793,
0.005172831006348133,
-0.006842218805104494,
-0.12159129977226257,
-0.02233111672103405,
-0.04213908314704895,
0.06848238408565521,
0.03089264966547489,
0.018794791772961617,
0.10890112072229385,
0.12291614711284637,
0.01913580112159252,
0.0649479478597641,
-0.029179846867918968,
0.35449597239494324,
-0.081545390188694,
0.09539048373699188,
0.13271038234233856,
0.028587581589818,
0.045262616127729416,
0.1392887830734253,
0.0654241219162941,
-0.0905192643404007,
-0.04038308933377266,
-0.08539015799760818,
-0.08356507867574692,
-0.12187865376472473,
-0.062463875859975815,
-0.05025407671928406,
-0.08506599068641663,
0.016220038756728172,
0.028457248583436012,
-0.07030262798070908,
0.05736895650625229,
0.0762326791882515,
-0.03211916610598564,
-0.06607455015182495,
-0.004294241778552532,
0.005617010407149792,
-0.01831461489200592,
0.005353646352887154,
-0.03473186120390892,
-0.045654840767383575,
0.02424073964357376,
0.10104314237833023,
0.10574635118246078,
0.09052418917417526,
-0.0010492503643035889,
0.06457161158323288,
0.1183939054608345,
0.07587765157222748,
0.17936986684799194,
0.07738017290830612,
-0.01687653362751007,
-0.03015066310763359,
-0.06322212517261505,
0.002567761577665806,
0.047564033418893814,
-0.02103104069828987,
-0.06731738895177841,
0.015895353630185127,
-0.08811315149068832,
-0.05125536024570465,
-0.017591023817658424,
0.12424279004335403,
-0.22202670574188232,
-0.021208370104432106,
0.002797913271933794,
0.11418380588293076,
-0.044401615858078,
0.05464161932468414,
0.03014804981648922,
-0.1143539547920227,
0.012471072375774384,
-0.026866301894187927,
0.08915835618972778,
0.037010062485933304,
0.031449094414711,
-0.08889519423246384,
0.05351865664124489,
-0.040121566504240036,
0.12731583416461945,
-0.18196506798267365,
0.3427995443344116,
-0.04837478697299957,
0.027632903307676315,
-0.05089308321475983,
0.0028484745416790247,
0.07809733599424362,
0.048162031918764114,
0.2457561045885086,
0.011763839982450008,
-0.12896029651165009,
-0.09587137401103973,
-0.11694546043872833,
0.05217762663960457,
-0.038883741945028305,
-0.059592533856630325,
0.049124933779239655,
0.070253387093544,
0.008664855733513832,
-0.017469901591539383,
0.08640876412391663,
-0.2086653709411621,
-0.1120503693819046,
0.01568002626299858,
-0.03067464753985405,
0.04030618071556091,
-0.013308368623256683,
-0.04980805143713951,
-0.0997985377907753,
-0.034854572266340256,
0.01985359564423561,
-0.026518741622567177,
-0.0869336724281311,
0.09916847944259644,
0.08022405952215195,
-0.013447817414999008,
-0.03324122726917267,
0.006935945712029934,
0.012088784947991371,
-0.09041953831911087,
0.011662199161946774,
0.02296588011085987,
-0.07067316770553589,
0.029927929863333702,
-0.07283160090446472,
0.07561691850423813,
0.0685301274061203,
0.045596979558467865,
0.028948914259672165,
0.06718103587627411,
-0.12107362598180771,
-0.12462838739156723,
0.18370935320854187,
-0.12762291729450226,
-0.004102869424968958,
0.024010909721255302,
0.03708074614405632,
0.007624260149896145,
-0.1316065788269043,
0.02399996481835842,
0.21857967972755432,
0.24649354815483093,
-0.1335221230983734,
0.1225484237074852,
-0.00024670103448443115,
-0.1050037294626236,
-0.16449937224388123,
-0.01945723406970501,
-0.05855727195739746,
0.04899326711893082,
0.11015278100967407,
-0.17803578078746796,
0.04968544840812683,
0.1191326305270195,
-0.04495275020599365,
0.17241303622722626,
-0.24684955179691315,
-0.03411996737122536,
0.2100714147090912,
0.04569889232516289,
0.06569342315196991,
-0.05574772134423256,
-0.015039459802210331,
-0.00528736412525177,
-0.14097414910793304,
0.06288903206586838,
0.09348098933696747,
0.036021482199430466,
-0.024131251499056816,
0.07943118363618851,
0.0044325897470116615,
-0.08022259920835495,
0.21442513167858124,
0.012962738052010536,
0.03046649880707264,
-0.047997474670410156,
-0.23536209762096405,
0.13805344700813293,
-0.025112813338637352,
0.11033132672309875,
0.05390465259552002,
0.02994718961417675,
-0.15773136913776398,
-0.013986163772642612,
-0.12404321879148483,
0.08766418695449829,
-0.07251182943582535,
-0.05158756673336029,
-0.05854862555861473,
0.015991194173693657,
-0.08727336674928665,
-0.04245669022202492,
-0.14428097009658813,
-0.055563077330589294,
0.0703742727637291,
0.022751430049538612,
0.009583641774952412,
0.06400688737630844,
-0.169049933552742,
0.05598882585763931,
-0.01149170845746994,
0.06434738636016846,
-0.10651591420173645,
-0.12004084885120392,
0.10367484390735626,
0.032518479973077774,
0.07668012380599976,
0.0708896815776825,
-0.07021142542362213,
0.0162661150097847,
0.05385328829288483,
-0.17175792157649994,
-0.06327351182699203,
-0.05683770403265953,
0.012919018976390362,
-0.010866916738450527,
-0.08071823418140411,
0.025430886074900627,
-0.05129344388842583,
-0.02617599628865719,
-0.010316947475075722,
0.0006186262471601367,
-0.13381779193878174,
0.030088165774941444,
0.13181829452514648,
0.0980042964220047,
-0.05393359437584877,
0.016877278685569763,
0.09504169970750809,
-0.06179828196763992,
-0.010033953003585339,
0.03999825567007065,
-0.05480214208364487,
-0.06541170924901962,
-0.061956051737070084,
0.16657671332359314,
0.03896809741854668,
-0.014393637888133526,
-0.0005867790314368904,
-0.08712455630302429,
-0.00031383821624331176,
0.04782608896493912,
0.08786796778440475,
0.0003506964130792767,
-0.11672773212194443,
-0.09084511548280716,
-0.06097876653075218,
0.003501643193885684,
0.02645174227654934,
-0.03922626003623009,
-0.13142673671245575,
0.09046798199415207,
0.007159093860536814,
0.10693839192390442,
-0.03861484304070473,
-0.07697290182113647,
-0.09244167059659958,
0.04886141046881676,
-0.0269522313028574,
0.01129046082496643,
-0.028360219672322273,
0.0054560499265789986,
-0.008353113196790218,
-0.033303093165159225,
-0.002425789600238204,
-0.0063718222081661224,
-0.10831985622644424,
0.047020621597766876,
-0.006284073460847139,
0.041693560779094696,
-0.032689180225133896,
-0.03228956088423729,
0.08438573777675629,
-0.03412673994898796,
0.06086663529276848,
0.0665818452835083,
-0.06871401518583298,
-0.044982317835092545,
-0.22733375430107117,
0.021654587239027023,
0.024860594421625137,
-0.01212060172110796,
0.013229108415544033,
-0.18718454241752625,
-0.013087733648717403,
-0.052979644387960434,
-0.01410323940217495,
-0.022924695163965225,
0.05765881761908531,
-0.07158409804105759,
-0.046782828867435455,
0.1118445172905922,
-0.0564417839050293,
-0.028098465874791145,
-0.08062928915023804,
0.17905747890472412,
-0.007197213359177113,
0.11030151695013046,
0.022037047892808914,
0.15901106595993042,
-0.13530169427394867,
-0.026483800262212753,
-0.057975467294454575,
-0.03342226892709732,
-0.055159103125333786,
-0.059083860367536545,
0.09343298524618149,
0.008314655162394047,
0.20565354824066162,
-0.0038630173075944185,
-0.022759204730391502,
-0.025063827633857727,
0.16970916092395782,
0.022308113053441048,
-0.031538549810647964,
0.11258768290281296,
0.02720748260617256,
0.004732121713459492,
0.04620293527841568,
0.060099996626377106,
-0.020551929250359535,
0.164747416973114,
0.219853013753891,
-0.005328914616256952,
0.1125851422548294,
0.08858208358287811,
-0.027431707829236984,
0.0066237132996320724,
-0.009655706584453583,
-0.11372916400432587,
0.12196702510118484,
-0.006451229564845562,
-0.03767151013016701,
-0.05524977296590805,
0.10306931287050247,
-0.07520896196365356,
0.05083534121513367,
-0.0540275014936924,
-0.08930258452892303,
-0.09524649381637573,
-0.20489518344402313,
0.03972652181982994,
0.007851764559745789,
-0.019344184547662735,
-0.04243422672152519,
-0.016858849674463272,
0.17942647635936737,
-0.052847836166620255,
0.003525838255882263,
0.0990867167711258,
0.026497529819607735,
-0.055005453526973724,
0.022575857117772102,
-0.025056635960936546,
0.0035884256940335035,
-0.1395774483680725,
0.0690794587135315,
-0.0031497941818088293,
-0.07956340909004211,
0.03878507763147354,
-0.011148602701723576,
-0.017163444310426712,
-0.030657337978482246,
-0.11476265639066696,
-0.064881831407547,
-0.04335713014006615,
0.06691928207874298,
-0.011913929134607315,
0.09849588572978973,
0.05757857486605644,
0.03684232011437416,
0.0037527582608163357,
0.1292974203824997,
-0.009419238194823265,
-0.13162972033023834,
-0.053166307508945465,
0.18901796638965607,
-0.14018180966377258,
-0.011939244344830513,
-0.0624690018594265,
-0.022841567173600197,
0.055512480437755585,
0.3043859601020813,
0.3142399191856384,
-0.04530547559261322,
-0.039240721613168716,
-0.04068749025464058,
0.022405195981264114,
0.06643234193325043,
0.05524616688489914,
0.1051882654428482,
0.17213332653045654,
-0.18840456008911133,
-0.028064092621207237,
-0.17832209169864655,
-0.0024505911860615015,
-0.025621071457862854,
0.021143199875950813,
0.10944507271051407,
-0.07239370793104172,
-0.031437426805496216,
0.20968076586723328,
-0.15773887932300568,
-0.06127235293388367,
0.020960276946425438,
-0.06201305612921715,
-0.1194273978471756,
0.02337118797004223,
0.06101902574300766,
0.0023309173993766308,
0.11557881534099579,
-0.11471462994813919,
-0.07981414347887039,
0.1682577282190323,
-0.0321953184902668,
-0.12659071385860443,
-0.10023559629917145,
0.049350980669260025,
-0.1321873515844345,
0.14273372292518616,
0.005093518644571304,
0.1271931380033493,
0.0606926828622818,
0.08208594471216202,
-0.12412270158529282,
0.1154453456401825,
0.013909713365137577,
0.09524955600500107,
-0.0336361899971962,
-0.05553201958537102,
0.0004995805211365223,
-0.0635792687535286,
0.0006204497185535729,
-0.15752793848514557,
0.07029400020837784,
0.1159246489405632,
0.013946960680186749,
-0.004718446638435125,
0.05561415106058121,
-0.05386538431048393,
0.1153939738869667,
0.08661811053752899,
-0.0687795951962471,
0.001525399973616004,
-0.006427733227610588,
0.010716335847973824,
0.07425536960363388,
-0.05081215128302574,
-0.03982756286859512,
-0.027454297989606857,
0.011482537724077702,
-0.01342987734824419,
-0.07936602830886841,
-0.05345652997493744,
-0.06332408636808395,
-0.056671712547540665,
-0.060752276331186295,
0.06481326371431351,
0.02131848968565464,
0.02299259975552559,
0.06840471923351288,
-0.02243855781853199,
0.15208150446414948,
-0.004574290011078119,
0.14298005402088165,
-0.16464337706565857,
-0.08555936813354492
] |
null | null |
flair
|
## NER for German Legal Text in Flair (default model)
This is the legal NER model for German that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **96,35** (LER German dataset)
Predicts 19 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| AN | Anwalt |
| EUN | Europäische Norm |
| GS | Gesetz |
| GRT | Gericht |
| INN | Institution |
| LD | Land |
| LDS | Landschaft |
| LIT | Literatur |
| MRK | Marke |
| ORG | Organisation |
| PER | Person |
| RR | Richter |
| RS | Rechtssprechung |
| ST | Stadt |
| STR | Straße |
| UN | Unternehmen |
| VO | Verordnung |
| VS | Vorschrift |
| VT | Vertrag |
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
More details on the Legal NER dataset [here](https://github.com/elenanereiss/Legal-Entity-Recognition)
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-german-legal")
# make example sentence (don't use tokenizer since Rechtstexte are badly handled)
sentence = Sentence("Herr W. verstieß gegen § 36 Abs. 7 IfSG.", use_tokenizer=False)
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [2]: "W." [− Labels: PER (0.9911)]
Span [5,6,7,8,9]: "§ 36 Abs. 7 IfSG." [− Labels: GS (0.5353)]
```
So, the entities "*W.*" (labeled as a **person**) and "*§ 36 Abs. 7 IfSG*" (labeled as a **Gesetz**) are found in the sentence "*Herr W. verstieß gegen § 36 Abs. 7 IfSG.*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import LER_GERMAN
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. get the corpus
corpus: Corpus = LER_GERMAN()
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
# GloVe embeddings
WordEmbeddings('de'),
# contextual string embeddings, forward
FlairEmbeddings('de-forward'),
# contextual string embeddings, backward
FlairEmbeddings('de-backward'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/ner-german-legal',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following papers when using this model.
```
@inproceedings{leitner2019fine,
author = {Elena Leitner and Georg Rehm and Julian Moreno-Schneider},
title = {{Fine-grained Named Entity Recognition in Legal Documents}},
booktitle = {Semantic Systems. The Power of AI and Knowledge
Graphs. Proceedings of the 15th International Conference
(SEMANTiCS 2019)},
year = 2019,
pages = {272--287},
pdf = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-33220-4_20.pdf}}
```
```
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
{"language": "de", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["legal"], "widget": [{"text": "Herr W. verstie\u00df gegen \u00a7 36 Abs. 7 IfSG."}]}
|
token-classification
|
flair/ner-german-legal
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"de",
"dataset:legal",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"de"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #de #dataset-legal #region-us
|
NER for German Legal Text in Flair (default model)
--------------------------------------------------
This is the legal NER model for German that ships with Flair.
F1-Score: 96,35 (LER German dataset)
Predicts 19 tags:
Based on Flair embeddings and LSTM-CRF.
More details on the Legal NER dataset here
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the entities "*W.*" (labeled as a person) and "*§ 36 Abs. 7 IfSG*" (labeled as a Gesetz) are found in the sentence "*Herr W. verstieß gegen § 36 Abs. 7 IfSG.*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following papers when using this model.
---
### Issues?
The Flair issue tracker is available here.
|
[
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*W.*\" (labeled as a person) and \"*§ 36 Abs. 7 IfSG*\" (labeled as a Gesetz) are found in the sentence \"*Herr W. verstieß gegen § 36 Abs. 7 IfSG.*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following papers when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #de #dataset-legal #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*W.*\" (labeled as a person) and \"*§ 36 Abs. 7 IfSG*\" (labeled as a Gesetz) are found in the sentence \"*Herr W. verstieß gegen § 36 Abs. 7 IfSG.*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following papers when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
35,
96,
22,
16,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #de #dataset-legal #region-us \n### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*W.*\" (labeled as a person) and \"*§ 36 Abs. 7 IfSG*\" (labeled as a Gesetz) are found in the sentence \"*Herr W. verstieß gegen § 36 Abs. 7 IfSG.*\".\n\n\n\n\n---### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---### Cite\n\n\nPlease cite the following papers when using this model.\n\n\n\n\n---### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
-0.1392635703086853,
0.011637435294687748,
-0.002655262127518654,
0.11012160032987595,
0.07922672480344772,
0.03900466486811638,
0.12187520414590836,
0.07661261409521103,
0.13722257316112518,
0.048563141375780106,
0.14665107429027557,
-0.012679665349423885,
0.05817720666527748,
0.15676115453243256,
0.04525208845734596,
-0.24912019073963165,
0.017436858266592026,
0.0015811373014003038,
-0.04396623745560646,
0.18413881957530975,
0.12823154032230377,
0.018271833658218384,
0.020867399871349335,
0.02768862433731556,
-0.10894086956977844,
-0.01260780356824398,
-0.01640928164124489,
-0.03975933417677879,
0.13047119975090027,
-0.025902265682816505,
0.12747174501419067,
0.026846883818507195,
0.13246680796146393,
-0.1524168848991394,
0.029156288132071495,
0.0059769065119326115,
-0.014890147373080254,
0.05570666864514351,
0.028027724474668503,
-0.033618681132793427,
0.2718132436275482,
-0.06662608683109283,
-0.055104199796915054,
0.034415699541568756,
-0.18005618453025818,
-0.1888437271118164,
-0.10324663668870926,
0.04038447514176369,
0.051875196397304535,
0.11822721362113953,
-0.06313535571098328,
0.09986845403909683,
-0.14134882390499115,
0.04308219999074936,
0.1807510405778885,
-0.23409751057624817,
-0.06335435807704926,
0.12641915678977966,
0.061945099383592606,
-0.03152836859226227,
-0.10575638711452484,
0.05787380412220955,
0.0393221452832222,
0.07383827120065689,
-0.014031380414962769,
-0.04439469426870346,
0.021456001326441765,
0.062104981392621994,
-0.17449738085269928,
-0.07684392482042313,
0.30312585830688477,
0.04107730835676193,
-0.11306942254304886,
0.012633667327463627,
-0.005384292919188738,
-0.05009395629167557,
0.06486120820045471,
-0.08983717113733292,
0.003188763977959752,
-0.01556343398988247,
0.19769717752933502,
-0.016758901998400688,
-0.08889502286911011,
-0.13909943401813507,
0.01711663044989109,
0.11405468732118607,
-0.04957905784249306,
0.06373211741447449,
-0.13441744446754456,
0.0944378450512886,
-0.03083544597029686,
-0.12261898070573807,
-0.010421323589980602,
-0.05597827583551407,
-0.045348770916461945,
0.035036250948905945,
-0.030390750616788864,
0.024839045479893684,
0.04582586511969566,
0.12149222195148468,
0.04967019334435463,
-0.0013932966394349933,
0.1103329136967659,
0.0815458744764328,
0.015458292327821255,
0.12396785616874695,
-0.04886624962091446,
-0.05015697330236435,
0.03293079882860184,
0.048350412398576736,
0.023174360394477844,
-0.06076036021113396,
-0.10128093510866165,
-0.11785656958818436,
-0.03370009735226631,
0.02816009521484375,
-0.04298172518610954,
0.018901892006397247,
-0.06751187145709991,
0.0046912734396755695,
0.052779749035835266,
-0.08247612416744232,
-0.014147029258310795,
0.019788378849625587,
-0.05496617406606674,
0.008203374221920967,
0.10258578509092331,
0.007226007059216499,
-0.0061473543755710125,
0.05982547998428345,
-0.042497195303440094,
0.04173404723405838,
-0.0901082456111908,
-0.10004604607820511,
-0.016554420813918114,
-0.07604489475488663,
-0.019583385437726974,
-0.06607521325349808,
-0.1791035681962967,
-0.03196609765291214,
0.04613335058093071,
-0.020346583798527718,
-0.008495750837028027,
-0.07294123619794846,
-0.03387041762471199,
-0.03811720013618469,
0.037368159741163254,
0.07689355313777924,
-0.04837697371840477,
0.0387248769402504,
0.0820147842168808,
0.08423246443271637,
-0.10543759167194366,
-0.017059311270713806,
-0.12004652619361877,
0.03395824879407883,
-0.12884804606437683,
0.003987561911344528,
-0.1038379892706871,
-0.11904542148113251,
-0.09973016381263733,
-0.12092693150043488,
0.000007165605893533211,
0.007574801798909903,
0.08378075063228607,
0.2010219693183899,
-0.22636547684669495,
0.017098331823945045,
0.14739921689033508,
-0.14165541529655457,
-0.04151896759867668,
0.14958734810352325,
-0.06503119319677353,
0.13210228085517883,
-0.043291497975587845,
0.17691323161125183,
0.10190023481845856,
-0.23040466010570526,
0.09981000423431396,
0.049085117876529694,
-0.08574489504098892,
0.08814593404531479,
0.11127112805843353,
-0.02972348779439926,
-0.17570216953754425,
0.028112567961215973,
-0.248537078499794,
0.037050761282444,
-0.030879508703947067,
-0.07303963601589203,
0.01503763347864151,
-0.008389245718717575,
0.13119447231292725,
-0.008519747294485569,
-0.028739823028445244,
-0.016102466732263565,
-0.11414062231779099,
-0.20925994217395782,
0.05285882577300072,
0.00041717628482729197,
-0.024398507550358772,
-0.07012300193309784,
0.11128769814968109,
0.046040937304496765,
-0.03193070739507675,
-0.13592024147510529,
0.03271103277802467,
-0.03683796152472496,
0.03214140236377716,
0.014320770278573036,
0.12191224843263626,
0.027345797047019005,
0.07020178437232971,
0.03429035842418671,
0.011321226134896278,
-0.11804725974798203,
-0.004543814342468977,
-0.004347179085016251,
-0.05813449248671532,
-0.1201959028840065,
-0.08048813045024872,
0.1884879469871521,
-0.22739706933498383,
0.04604051262140274,
-0.0481763631105423,
-0.03891744092106819,
-0.07979720085859299,
-0.015407223254442215,
0.03957429155707359,
0.02346028760075569,
-0.02452072501182556,
-0.024491263553500175,
0.09289918839931488,
0.0013051837449893355,
-0.07430637627840042,
-0.10008084028959274,
-0.012380682863295078,
-0.1384570598602295,
0.0430222786962986,
0.03303230553865433,
-0.11278343200683594,
-0.14355139434337616,
-0.04802805930376053,
-0.00019192599575035274,
-0.07974667102098465,
-0.010645163245499134,
0.2072925716638565,
0.03555021435022354,
0.005544866900891066,
-0.040489643812179565,
0.03467237576842308,
0.007071070373058319,
-0.10940296947956085,
0.04370556026697159,
0.07037903368473053,
0.12693436443805695,
0.04068625718355179,
0.007768819108605385,
0.051286231726408005,
-0.07419633865356445,
0.07647314667701721,
0.0295309666544199,
0.005260644014924765,
-0.0856134369969368,
0.021467315033078194,
0.005015792325139046,
0.22683781385421753,
-0.19129520654678345,
0.0016162249958142638,
0.05907740071415901,
-0.016671661287546158,
-0.0007916101021692157,
-0.18007172644138336,
-0.08846134692430496,
0.027643505483865738,
-0.08315969258546829,
-0.20744459331035614,
0.0942230224609375,
-0.11805916577577591,
0.08473484218120575,
-0.09295348823070526,
-0.1272219717502594,
0.03405735269188881,
-0.01809270679950714,
-0.12565146386623383,
0.17715312540531158,
0.022363409399986267,
-0.18756850063800812,
-0.11727825552225113,
0.021879034116864204,
-0.023771461099386215,
0.007454368285834789,
0.02690434642136097,
-0.06584435701370239,
-0.012798759154975414,
0.016104914247989655,
0.09719966351985931,
-0.05474358797073364,
-0.08651910722255707,
-0.046621173620224,
-0.03956885635852814,
0.00271294335834682,
-0.12174917757511139,
-0.041606396436691284,
-0.012620082125067711,
0.07926817983388901,
0.06865902990102768,
-0.026262585073709488,
0.184556245803833,
0.08247224241495132,
-0.015715522691607475,
0.033096350729465485,
-0.04352402687072754,
0.31356093287467957,
-0.06767424196004868,
0.0940273106098175,
0.1952620893716812,
-0.026668621227145195,
0.05260074883699417,
0.10162050276994705,
0.04396878182888031,
-0.08268262445926666,
-0.019594287499785423,
-0.05120145529508591,
-0.07847803086042404,
-0.14008043706417084,
-0.08170535415410995,
-0.08849547058343887,
-0.04937129095196724,
-0.007380734197795391,
0.004439074080437422,
-0.04185550659894943,
0.08548910915851593,
0.07310273498296738,
-0.024450993165373802,
-0.04083070158958435,
0.006499570328742266,
0.04787353426218033,
0.036477454006671906,
0.053397152572870255,
-0.023335600271821022,
-0.026221703737974167,
0.006429429166018963,
0.008471938781440258,
0.14329129457473755,
0.03870708495378494,
0.03319786116480827,
0.1355750560760498,
0.13065838813781738,
0.07684873044490814,
0.17760445177555084,
0.019639912992715836,
-0.0074575673788785934,
-0.038974400609731674,
-0.08428355306386948,
-0.05340175703167915,
-0.013026547618210316,
-0.10276391357183456,
0.016943518072366714,
-0.0760953277349472,
0.06052922084927559,
0.025329111143946648,
0.04392733797430992,
0.039076682180166245,
-0.19789187610149384,
-0.024153724312782288,
-0.01255672425031662,
0.13344992697238922,
-0.05321697145700455,
-0.006889252457767725,
0.07734943181276321,
-0.13581524789333344,
0.055600352585315704,
-0.06860873103141785,
0.07709409296512604,
0.11271358281373978,
0.03606383875012398,
0.01099986769258976,
0.08802413195371628,
-0.01872555911540985,
0.18511395156383514,
-0.30547699332237244,
0.30622828006744385,
-0.06039126217365265,
0.06094512343406677,
-0.09105274826288223,
0.02681872434914112,
0.03325635939836502,
0.09377241134643555,
0.2971353828907013,
0.054085493087768555,
-0.0893719270825386,
-0.04654955863952637,
-0.02462492510676384,
0.0685679242014885,
0.005348426289856434,
-0.022024331614375114,
0.09646335989236832,
0.050735119730234146,
0.03640684857964516,
-0.01775800809264183,
0.16489556431770325,
-0.1756397783756256,
-0.05571112409234047,
0.01118545699864626,
-0.05826149508357048,
0.006151163950562477,
-0.02823788858950138,
-0.05948950722813606,
-0.10934820771217346,
0.027535544708371162,
-0.05096054449677467,
-0.032298628240823746,
-0.06646374613046646,
0.1409107893705368,
0.025088487192988396,
-0.04226350039243698,
-0.0770144909620285,
0.008866498246788979,
0.04224533215165138,
-0.11357108503580093,
0.031388040632009506,
0.02578614465892315,
-0.06833920627832413,
-0.08039688318967819,
-0.0636177510023117,
0.058114536106586456,
0.06283135712146759,
0.04691452905535698,
0.04001964256167412,
0.008627507835626602,
-0.03331228345632553,
-0.17411518096923828,
0.13000640273094177,
-0.08718034625053406,
-0.07570326328277588,
-0.015598075464367867,
-0.0336189791560173,
-0.011712339706718922,
-0.11156821250915527,
0.04561181366443634,
0.15177208185195923,
0.2789185047149658,
-0.09298598021268845,
0.0813220664858818,
0.07849961519241333,
-0.12468793988227844,
-0.18940052390098572,
-0.0006203429074957967,
0.042401887476444244,
0.09607233852148056,
0.03640048950910568,
-0.15822938084602356,
0.10255902260541916,
0.1110701858997345,
-0.00688889529556036,
0.1055741012096405,
-0.2578275799751282,
-0.041560158133506775,
0.12253424525260925,
0.03140623867511749,
0.03739810734987259,
-0.05672500655055046,
0.004556864500045776,
-0.011411494575440884,
-0.1524745374917984,
0.002272173296660185,
0.0318433977663517,
0.05227614566683769,
-0.0364849828183651,
0.08516504615545273,
0.0042945281602442265,
-0.07653491944074631,
0.21033291518688202,
0.0609956830739975,
0.07101824879646301,
-0.023592276498675346,
-0.14683668315410614,
0.1912405639886856,
-0.039717886596918106,
0.17406117916107178,
-0.07329200953245163,
-0.004566543269902468,
-0.18776273727416992,
-0.024363825097680092,
-0.12521544098854065,
0.15857547521591187,
-0.08063355833292007,
-0.08032058924436569,
-0.009319346398115158,
0.022161519154906273,
-0.04439273476600647,
-0.015854980796575546,
-0.1526808887720108,
-0.0675521120429039,
0.04358154907822609,
-0.0017417522612959146,
0.0757378488779068,
0.07851982861757278,
-0.21323652565479279,
0.023789245635271072,
0.03113926760852337,
0.06090877577662468,
-0.05159863084554672,
-0.09542643278837204,
0.106629878282547,
0.08596533536911011,
0.10303610563278198,
0.08456826955080032,
-0.08209487050771713,
0.04025983437895775,
0.04128524661064148,
-0.15996912121772766,
-0.029078586027026176,
-0.010753961279988289,
-0.011967156082391739,
-0.09007422626018524,
-0.06781206279993057,
0.03214196488261223,
-0.06560761481523514,
-0.007146371528506279,
-0.021907323971390724,
0.016284365206956863,
-0.13475054502487183,
0.05992427468299866,
0.11213047802448273,
0.1185285747051239,
-0.07517295330762863,
0.017758140340447426,
0.04379004240036011,
-0.047777287662029266,
0.0012116930447518826,
0.028614835813641548,
-0.11639778316020966,
-0.05412450060248375,
-0.02830820344388485,
0.2578060030937195,
0.03529563173651695,
-0.04815046861767769,
-0.04569793865084648,
-0.10044249147176743,
-0.039482247084379196,
0.03991150110960007,
0.1071729063987732,
-0.04252075031399727,
-0.09020918607711792,
-0.04777256399393082,
-0.09834616631269455,
0.036440152674913406,
0.07977867871522903,
0.012519234791398048,
-0.09665191918611526,
0.07342435419559479,
0.00036574125988408923,
0.07426799833774567,
-0.046480514109134674,
-0.0526743084192276,
-0.11126451939344406,
0.05480396747589111,
-0.017127033323049545,
0.05128498747944832,
-0.0366327278316021,
0.009466198273003101,
-0.02342170849442482,
-0.013792715966701508,
0.012590217404067516,
0.04609125107526779,
-0.05218275263905525,
0.025873756036162376,
0.035687580704689026,
0.01504978071898222,
-0.06718479841947556,
-0.04274393618106842,
0.0619250126183033,
-0.06775125861167908,
0.04918928071856499,
0.07080351561307907,
-0.06368819624185562,
-0.020505035296082497,
-0.2267778366804123,
0.04186571016907692,
0.04562541842460632,
-0.0006306184222921729,
0.01789429597556591,
-0.18590423464775085,
0.03626667708158493,
-0.036646317690610886,
-0.0038837401662021875,
0.013675475493073463,
0.11819887906312943,
-0.09259840846061707,
0.03612799569964409,
0.10070125758647919,
-0.049665164202451706,
-0.050949662923812866,
0.019594036042690277,
0.08015646785497665,
0.07706877589225769,
0.15413831174373627,
-0.037769630551338196,
0.1455432027578354,
-0.14101557433605194,
-0.016920121386647224,
-0.02254607528448105,
0.007164834067225456,
-0.1465110331773758,
-0.04479365050792694,
0.09308532625436783,
-0.04735901206731796,
0.13413037359714508,
0.09962520748376846,
0.10933828353881836,
-0.056534502655267715,
0.05140027776360512,
0.032038263976573944,
-0.050881873816251755,
0.07712970674037933,
-0.034140005707740784,
-0.0007285433821380138,
0.04247809574007988,
0.06379209458827972,
-0.020755738019943237,
0.12286685407161713,
0.22193995118141174,
0.05731961876153946,
0.02613251842558384,
0.06962867081165314,
0.008866802789270878,
-0.06229781731963158,
-0.11866797506809235,
-0.07577645778656006,
0.14097341895103455,
0.03757767751812935,
-0.0594056099653244,
-0.010067436844110489,
0.043062783777713776,
-0.11564235389232635,
0.08200521022081375,
-0.031568173319101334,
-0.13041658699512482,
-0.10164287686347961,
-0.22808361053466797,
0.04470372945070267,
-0.007034115493297577,
0.019067782908678055,
-0.06892839074134827,
-0.0298173725605011,
0.11556840687990189,
-0.032398246228694916,
-0.028557443991303444,
0.14043457806110382,
-0.061922330409288406,
-0.051785532385110855,
0.04848320782184601,
-0.043146271258592606,
0.027931759133934975,
-0.05447191745042801,
0.09344683587551117,
0.08122435212135315,
-0.06908354163169861,
0.054441168904304504,
-0.0024304573889821768,
0.04424724727869034,
-0.04033510759472847,
-0.071573406457901,
-0.062674880027771,
-0.03858773782849312,
0.04465178772807121,
-0.0129877720028162,
0.14720109105110168,
0.07967104762792587,
0.02285071648657322,
-0.020284906029701233,
0.06711259484291077,
0.007246114779263735,
-0.1888076514005661,
-0.10583605617284775,
0.27869006991386414,
-0.11294462531805038,
0.025785500183701515,
-0.08217721432447433,
-0.050651200115680695,
0.09258609265089035,
0.21187441051006317,
0.21390481293201447,
-0.028975768014788628,
-0.01702667772769928,
-0.011816579848527908,
0.008926774375140667,
0.04711100459098816,
0.07210225611925125,
0.07090640068054199,
0.165397509932518,
-0.13347961008548737,
0.03131769970059395,
-0.1741059571504593,
-0.04564356058835983,
-0.03375258669257164,
0.09208157658576965,
0.09965118020772934,
-0.03795621544122696,
-0.0682927593588829,
0.15318608283996582,
-0.09588255733251572,
-0.06935293227434158,
0.015624661929905415,
-0.01355522871017456,
-0.08007204532623291,
-0.0014656295534223318,
0.005166425369679928,
0.048417601734399796,
0.13041886687278748,
-0.1003013402223587,
-0.04623488336801529,
0.1246708407998085,
0.010364428162574768,
-0.05669811740517616,
-0.0313122421503067,
0.06981781125068665,
-0.10929324477910995,
0.15546566247940063,
-0.021934598684310913,
0.18778708577156067,
0.06777933239936829,
0.07304513454437256,
-0.10389437526464462,
0.09661545604467392,
0.026820894330739975,
0.12956136465072632,
-0.013340053148567677,
-0.004772083833813667,
0.007692469283938408,
-0.05576173588633537,
-0.008157875388860703,
-0.09952987730503082,
0.053799908608198166,
0.08878619223833084,
-0.01248213555663824,
-0.08891148865222931,
0.1074552983045578,
-0.03348863869905472,
0.11164576560258865,
0.13732948899269104,
-0.03740236908197403,
-0.020373530685901642,
-0.09398946166038513,
0.06045578792691231,
0.06501805782318115,
0.01084168627858162,
-0.013605812564492226,
-0.06812476366758347,
0.016033120453357697,
0.08165021240711212,
-0.048123642802238464,
-0.13053272664546967,
-0.053366437554359436,
-0.04895105957984924,
-0.03969026356935501,
0.10753773152828217,
0.00560148386284709,
0.04787999764084816,
0.1012449637055397,
-0.02348104491829872,
0.016727114096283913,
-0.013138780370354652,
0.14813090860843658,
-0.12426964193582535,
-0.07834672927856445
] |
null | null |
flair
|
## German NER in Flair (default model)
This is the standard 4-class NER model for German that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **87,94** (CoNLL-03 German revised)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| MISC | other name |
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-german")
# make example sentence
sentence = Sentence("George Washington ging nach Washington")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [1,2]: "George Washington" [− Labels: PER (0.9977)]
Span [5]: "Washington" [− Labels: LOC (0.9895)]
```
So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging nach Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import CONLL_03_GERMAN
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. get the corpus
corpus: Corpus = CONLL_03_GERMAN()
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
# GloVe embeddings
WordEmbeddings('de'),
# contextual string embeddings, forward
FlairEmbeddings('de-forward'),
# contextual string embeddings, backward
FlairEmbeddings('de-backward'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/ner-german',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following paper when using this model.
```
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
{"language": "de", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington ging nach Washington"}]}
|
token-classification
|
flair/ner-german
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"de",
"dataset:conll2003",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"de"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #de #dataset-conll2003 #has_space #region-us
|
German NER in Flair (default model)
-----------------------------------
This is the standard 4-class NER model for German that ships with Flair.
F1-Score: 87,94 (CoNLL-03 German revised)
Predicts 4 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the entities "*George Washington*" (labeled as a person) and "*Washington*" (labeled as a location) are found in the sentence "*George Washington ging nach Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following paper when using this model.
---
### Issues?
The Flair issue tracker is available here.
|
[
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging nach Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #de #dataset-conll2003 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging nach Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
41,
81,
22,
15,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #de #dataset-conll2003 #has_space #region-us \n### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging nach Washington*\".\n\n\n\n\n---### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
-0.0864473283290863,
0.165631964802742,
-0.001327722566202283,
0.07345331460237503,
0.07478407025337219,
0.05472071096301079,
0.10950032621622086,
0.048354219645261765,
0.016054406762123108,
0.054282985627651215,
0.18960101902484894,
0.07065895199775696,
0.11090267449617386,
0.0997697114944458,
0.07220839709043503,
-0.2636707127094269,
0.028059523552656174,
-0.018817676231265068,
-0.058018509298563004,
0.16060185432434082,
0.08286600559949875,
-0.004159227944910526,
0.018055932596325874,
0.027475278824567795,
-0.12582845985889435,
-0.011434728279709816,
-0.025223078206181526,
-0.0938408225774765,
0.16939519345760345,
-0.04020872712135315,
0.21536578238010406,
0.052473124116659164,
0.10029531270265579,
-0.14396725594997406,
0.031531743705272675,
0.042537622153759,
0.016841992735862732,
0.09778443723917007,
0.048217058181762695,
-0.0003678301000036299,
0.3162868022918701,
-0.03843638673424721,
-0.0010800942545756698,
0.029733775183558464,
-0.1956697553396225,
-0.2513427138328552,
-0.1066872626543045,
0.059406913816928864,
0.04893193766474724,
0.09930085390806198,
-0.03319986164569855,
0.09409483522176743,
-0.06645538657903671,
0.023234155029058456,
0.1815926432609558,
-0.22865794599056244,
-0.03589159995317459,
0.19373685121536255,
0.025462063029408455,
-0.0242240559309721,
-0.055456265807151794,
0.053792551159858704,
0.005558941513299942,
0.0737217366695404,
-0.03260303661227226,
-0.012466262094676495,
0.02781209722161293,
0.11993203312158585,
-0.1655099093914032,
-0.10078230500221252,
0.3224119246006012,
0.03709806874394417,
-0.06789164990186691,
0.00366398598998785,
0.012401587329804897,
-0.02157040312886238,
0.05125044286251068,
-0.1053982675075531,
-0.013839476741850376,
-0.0040856883861124516,
0.1622350811958313,
-0.10911044478416443,
-0.09361134469509125,
-0.0973391905426979,
-0.010357639752328396,
0.04104127734899521,
-0.08842572569847107,
0.09488877654075623,
-0.16703392565250397,
0.12193126231431961,
0.04572547227144241,
-0.058760739862918854,
0.04200955480337143,
-0.07739697396755219,
-0.0419280119240284,
0.013555111363530159,
-0.040474239736795425,
0.12958812713623047,
0.038553111255168915,
0.04247665032744408,
0.14235293865203857,
-0.03275952488183975,
0.10967521369457245,
0.10460911691188812,
0.002643601968884468,
0.18594154715538025,
-0.12506088614463806,
-0.05300676450133324,
0.03155682608485222,
-0.017675818875432014,
-0.0070897797122597694,
-0.06170859560370445,
-0.16454754769802094,
-0.026712946593761444,
-0.06201329454779625,
0.019380653277039528,
0.028993993997573853,
-0.01696988008916378,
-0.08986230939626694,
0.01104006264358759,
0.07964864373207092,
-0.06474729627370834,
-0.028570832684636116,
-0.023348962888121605,
-0.04406445473432541,
0.09304238110780716,
0.08403739333152771,
0.03045511059463024,
0.02033698558807373,
0.11995518207550049,
-0.08290446549654007,
0.04097398370504379,
-0.047462284564971924,
-0.10733480006456375,
-0.021982286125421524,
-0.08575581759214401,
0.015077874064445496,
-0.06474585086107254,
-0.1484186351299286,
-0.014555816538631916,
0.05946502834558487,
0.003173608100041747,
-0.06793341785669327,
-0.04790271073579788,
-0.03672143444418907,
-0.040912363678216934,
0.04146680235862732,
0.07285463064908981,
-0.06558670848608017,
0.0682310238480568,
-0.023712558671832085,
0.1107793003320694,
-0.0610378198325634,
0.016283929347991943,
-0.08536966145038605,
0.01478628534823656,
-0.12152118235826492,
-0.0011148180346935987,
-0.02572285756468773,
0.030263181775808334,
-0.11344484239816666,
-0.08642810583114624,
-0.02287234179675579,
0.009732179343700409,
0.026033679023385048,
0.18516361713409424,
-0.2818530797958374,
-0.011141140945255756,
0.1142200380563736,
-0.10080614686012268,
-0.052956417202949524,
0.06140947714447975,
-0.030983872711658478,
0.19698891043663025,
0.03374265879392624,
0.15631507337093353,
-0.021675128489732742,
-0.35448896884918213,
0.1137622743844986,
0.055543430149555206,
-0.1835213601589203,
0.03317296877503395,
0.06967269629240036,
-0.05002962425351143,
-0.1791507750749588,
0.004829187877476215,
-0.11804922670125961,
0.032153673470020294,
-0.035873185843229294,
-0.045560602098703384,
0.00817255862057209,
-0.05814484879374504,
0.04656994342803955,
-0.03828296810388565,
-0.03160844370722771,
-0.007721066474914551,
-0.14335419237613678,
-0.14933548867702484,
0.03987814113497734,
0.09975267946720123,
-0.036222461611032486,
0.005931753199547529,
0.04588444158434868,
0.04450562596321106,
-0.039212558418512344,
-0.10635397583246231,
0.03014177270233631,
-0.07798215001821518,
0.11492139846086502,
0.018793568015098572,
0.15770606696605682,
-0.026852790266275406,
0.023637626320123672,
0.06368076801300049,
0.03436091169714928,
-0.036160968244075775,
0.05247342213988304,
-0.011742044240236282,
-0.000697659095749259,
-0.0872667208313942,
-0.09156420081853867,
0.11849888414144516,
-0.19214729964733124,
0.053954146802425385,
0.03375527635216713,
-0.0015280147781595588,
-0.017745964229106903,
-0.019375063478946686,
0.10709167271852493,
0.03266728296875954,
0.009958179667592049,
-0.06865384429693222,
0.0660010278224945,
0.0009563884814269841,
-0.05689152330160141,
-0.11353407800197601,
-0.05922946333885193,
-0.0291852168738842,
0.09529507160186768,
-0.02465318888425827,
-0.11330613493919373,
-0.07138239592313766,
-0.04232635721564293,
0.004253525752574205,
-0.03230695426464081,
-0.037347614765167236,
0.14899319410324097,
0.029223237186670303,
0.007473848760128021,
-0.031084509566426277,
0.03303062543272972,
-0.033287156373262405,
-0.029053233563899994,
-0.07304005324840546,
0.05387464910745621,
0.11469457298517227,
0.035587746649980545,
0.03110455721616745,
0.17667165398597717,
-0.01415874995291233,
0.026432745158672333,
0.05235178396105766,
-0.050863269716501236,
-0.09811799973249435,
-0.10384225100278854,
-0.017152845859527588,
0.1829826533794403,
-0.035727884620428085,
0.03711230307817459,
0.042939912527799606,
-0.009428570047020912,
0.04462910816073418,
-0.1949303299188614,
-0.12865109741687775,
0.045492518693208694,
-0.02761233225464821,
-0.1861216127872467,
0.0631999596953392,
-0.05838332697749138,
0.10205132514238358,
-0.0201309435069561,
-0.09005744755268097,
0.04470313712954521,
0.00105274620000273,
-0.07441221177577972,
0.11042910069227219,
-0.057471614331007004,
-0.26088953018188477,
-0.09714845567941666,
-0.04771433770656586,
0.09854857623577118,
0.0009219518979080021,
0.02573290653526783,
-0.10376486927270889,
0.02775651030242443,
-0.013020901009440422,
0.05583551526069641,
-0.09111050516366959,
-0.0780121460556984,
-0.0602806955575943,
0.017119577154517174,
-0.019475551322102547,
-0.11501031368970871,
-0.034150708466768265,
-0.05261494591832161,
0.08659441024065018,
0.030640168115496635,
0.005522923544049263,
0.10740765929222107,
0.13401494920253754,
0.019032472744584084,
0.06948638707399368,
-0.04448847472667694,
0.36960357427597046,
-0.08753228187561035,
0.08551451563835144,
0.11685582995414734,
0.011166119948029518,
0.043923430144786835,
0.1436159461736679,
0.06555095314979553,
-0.09239834547042847,
-0.03955741226673126,
-0.07540473341941833,
-0.07920543104410172,
-0.1152944266796112,
-0.05192364752292633,
-0.0532841831445694,
-0.06591450423002243,
0.006264917552471161,
0.027490487322211266,
-0.061540793627500534,
0.027504177764058113,
0.08251167088747025,
-0.034746237099170685,
-0.05346184968948364,
-0.006078402511775494,
0.01638278178870678,
-0.017613442614674568,
0.002826416864991188,
-0.035489603877067566,
-0.0451677106320858,
0.023963933810591698,
0.09274423122406006,
0.09270823001861572,
0.1213291734457016,
0.0015717993956059217,
0.069449782371521,
0.10890103131532669,
0.0939125195145607,
0.16488690674304962,
0.07843657582998276,
-0.02221536450088024,
-0.027176203206181526,
-0.062025271356105804,
0.002947406843304634,
0.04411982744932175,
-0.0001968063588719815,
-0.06410346180200577,
0.012689587660133839,
-0.10756363719701767,
-0.04196301847696304,
-0.013155263848602772,
0.13091835379600525,
-0.18838945031166077,
-0.022766821086406708,
0.007353803142905235,
0.1052415668964386,
-0.03460897132754326,
0.05330328643321991,
0.025772271677851677,
-0.12468239665031433,
0.019415006041526794,
-0.01696205884218216,
0.10096614062786102,
0.03287247568368912,
0.03940849378705025,
-0.08429589867591858,
0.04668228328227997,
-0.052348792552948,
0.11422432214021683,
-0.1994786262512207,
0.3445565402507782,
-0.03510146960616112,
0.02865622565150261,
-0.041513241827487946,
0.003609639825299382,
0.07589647173881531,
0.06268473714590073,
0.23199449479579926,
0.0087816771119833,
-0.15442651510238647,
-0.09514708071947098,
-0.10590840876102448,
0.03848477080464363,
-0.028348742052912712,
-0.06737754493951797,
0.057898227125406265,
0.06891564279794693,
0.009974634274840355,
-0.021343020722270012,
0.08182225376367569,
-0.2051374316215515,
-0.1096683144569397,
0.02268552966415882,
-0.03269341215491295,
0.0375928059220314,
-0.007878106087446213,
-0.05341567471623421,
-0.10787525773048401,
-0.008890221826732159,
-0.02182525023818016,
-0.03559994697570801,
-0.08392633497714996,
0.1031191274523735,
0.048503514379262924,
-0.010173255577683449,
-0.03528731316328049,
0.006679277401417494,
0.02496732957661152,
-0.08976899832487106,
0.016928046941757202,
0.02088337391614914,
-0.054208241403102875,
0.03472917899489403,
-0.07363111525774002,
0.07524212449789047,
0.05802147462964058,
0.045040156692266464,
0.02266530506312847,
0.06310300529003143,
-0.10008230060338974,
-0.12140662223100662,
0.19976577162742615,
-0.1212092936038971,
-0.03158097341656685,
0.03307611867785454,
0.03447874262928963,
0.004514877684414387,
-0.1372167319059372,
0.026766883209347725,
0.2195744812488556,
0.26094332337379456,
-0.13677673041820526,
0.13413754105567932,
-0.014414628967642784,
-0.11341394484043121,
-0.1630844622850418,
-0.006620117463171482,
-0.06142439693212509,
0.04060008004307747,
0.11229230463504791,
-0.17134340107440948,
0.04120902344584465,
0.11370030790567398,
-0.041859205812215805,
0.19285668432712555,
-0.21722355484962463,
-0.0318429209291935,
0.20453007519245148,
0.03725430741906166,
0.062001828104257584,
-0.0561172254383564,
-0.028545379638671875,
0.0042596012353897095,
-0.1415700763463974,
0.0637945905327797,
0.11228307336568832,
0.04219292104244232,
-0.02311144769191742,
0.07328730821609497,
0.0014436189085245132,
-0.07089413702487946,
0.20889365673065186,
-0.0035610096529126167,
0.03404783830046654,
-0.046369921416044235,
-0.2335587590932846,
0.1309761106967926,
-0.029300982132554054,
0.09118098020553589,
0.05692112445831299,
0.017436832189559937,
-0.16355986893177032,
-0.017354298382997513,
-0.115167036652565,
0.09485811740159988,
-0.07450571656227112,
-0.054317407310009,
-0.06206698343157768,
0.011164982803165913,
-0.10382812470197678,
-0.04038059338927269,
-0.14903505146503448,
-0.06900060176849365,
0.07591356337070465,
0.005151227582246065,
0.0004070939321536571,
0.059561096131801605,
-0.17439031600952148,
0.06060978025197983,
-0.01406576856970787,
0.06636287271976471,
-0.09637577831745148,
-0.1220996081829071,
0.09821461141109467,
0.036718931049108505,
0.08672408014535904,
0.07372236251831055,
-0.07084392756223679,
0.0068072848953306675,
0.049038272351026535,
-0.18077352643013,
-0.059485774487257004,
-0.05674349144101143,
-0.017128536477684975,
-0.00869669672101736,
-0.08876767009496689,
0.027589084580540657,
-0.032512009143829346,
-0.031123178079724312,
-0.00395136559382081,
0.002544267103075981,
-0.12855422496795654,
0.0077727800235152245,
0.1434042900800705,
0.100351482629776,
-0.050259508192539215,
0.006985838059335947,
0.09680359065532684,
-0.09277975559234619,
-0.012466437183320522,
0.04061039909720421,
-0.05699826776981354,
-0.05592719465494156,
-0.06887912005186081,
0.1817152351140976,
0.06860245764255524,
-0.007435525301843882,
-0.0016846905928105116,
-0.08428789675235748,
0.00470705283805728,
0.07596909254789352,
0.09385421872138977,
0.004326063673943281,
-0.1018138974905014,
-0.08099143207073212,
-0.05098705738782883,
0.020646709948778152,
0.006964482367038727,
-0.04651714488863945,
-0.13185597956180573,
0.0792316198348999,
0.013159428723156452,
0.11966945230960846,
-0.038983024656772614,
-0.07783667743206024,
-0.12429419159889221,
0.0508543998003006,
-0.01831742376089096,
0.007296751718968153,
-0.014379593543708324,
0.0049707829020917416,
-0.002841574838384986,
-0.0420323982834816,
-0.009533002972602844,
-0.00860342476516962,
-0.10585170984268188,
0.05565613880753517,
0.002496795728802681,
0.056751228868961334,
-0.022879045456647873,
-0.03515184670686722,
0.09755437821149826,
-0.033932656049728394,
0.05729324743151665,
0.061141014099121094,
-0.06737925857305527,
-0.04679423198103905,
-0.23875704407691956,
0.020662138238549232,
0.015233000740408897,
-0.017435016110539436,
0.014123819768428802,
-0.18616880476474762,
-0.004509044345468283,
-0.06419779360294342,
-0.007597156800329685,
-0.01813642308115959,
0.043570928275585175,
-0.07440048456192017,
-0.03556923568248749,
0.12008803337812424,
-0.06297570466995239,
-0.02106386236846447,
-0.08126669377088547,
0.18184144794940948,
-0.014204503037035465,
0.11052581667900085,
0.021280931308865547,
0.15617656707763672,
-0.12678757309913635,
-0.026775673031806946,
-0.05890317261219025,
-0.022410929203033447,
-0.033974044024944305,
-0.058205679059028625,
0.09458430856466293,
0.007008551154285669,
0.2217494398355484,
0.022126605734229088,
-0.023525230586528778,
-0.011709422804415226,
0.15345489978790283,
0.0253580454736948,
-0.01563906855881214,
0.11165159195661545,
0.02761845663189888,
0.0042380765080451965,
0.049214448779821396,
0.04929395765066147,
-0.02316788211464882,
0.1784166544675827,
0.22365108132362366,
0.01167635340243578,
0.09273460507392883,
0.07980117946863174,
-0.03206894174218178,
0.030458485707640648,
-0.020209072157740593,
-0.1151980385184288,
0.13084785640239716,
0.0000783056893851608,
-0.03904300183057785,
-0.04644649475812912,
0.10575652867555618,
-0.06810948997735977,
0.05200977995991707,
-0.05104733631014824,
-0.09066151827573776,
-0.10147473961114883,
-0.19474177062511444,
0.033571816980838776,
0.0019183787517249584,
-0.014907143078744411,
-0.047281473875045776,
-0.023065235465765,
0.17887546122074127,
-0.05477912724018097,
0.006576859392225742,
0.0789022371172905,
0.033554963767528534,
-0.05367119982838631,
0.035842664539813995,
-0.031670499593019485,
-0.003049196908250451,
-0.15667515993118286,
0.06915123015642166,
-0.00014891692262608558,
-0.077821284532547,
0.03403867781162262,
-0.008372415788471699,
-0.02003924734890461,
-0.03982161730527878,
-0.13529060781002045,
-0.0667538121342659,
-0.03171030431985855,
0.06365437805652618,
-0.016905225813388824,
0.0834413543343544,
0.05554730072617531,
0.028205694630742073,
0.005054074339568615,
0.1258920580148697,
-0.007829622365534306,
-0.14090079069137573,
-0.0588567778468132,
0.19696441292762756,
-0.151302769780159,
0.002884229179471731,
-0.07828786224126816,
-0.018619051203131676,
0.06211160868406296,
0.3344199061393738,
0.2989075183868408,
-0.030830416828393936,
-0.042798444628715515,
-0.03885810077190399,
0.025625670328736305,
0.09038126468658447,
0.047791142016649246,
0.09473346173763275,
0.17198488116264343,
-0.1778194010257721,
-0.040432967245578766,
-0.1785961240530014,
-0.008423839695751667,
-0.02838488668203354,
0.011688129976391792,
0.11794304847717285,
-0.07176320999860764,
-0.04587026312947273,
0.21095186471939087,
-0.16698728501796722,
-0.05421515181660652,
0.023736463859677315,
-0.06387390941381454,
-0.11779472231864929,
0.028003033250570297,
0.05594930797815323,
-0.014699556864798069,
0.10198129713535309,
-0.11232948303222656,
-0.08719455450773239,
0.1396418809890747,
-0.03991944342851639,
-0.11626507341861725,
-0.1075366958975792,
0.04647337272763252,
-0.1374921053647995,
0.1337154507637024,
0.0018527703359723091,
0.12701416015625,
0.06199934706091881,
0.08375795185565948,
-0.1119784563779831,
0.1001194417476654,
0.014989013783633709,
0.09245875477790833,
-0.04222904145717621,
-0.05003930255770683,
0.006178693845868111,
-0.06250763684511185,
0.011585786938667297,
-0.15607386827468872,
0.0671437531709671,
0.10250096023082733,
0.002249914687126875,
-0.0017030569724738598,
0.06470907479524612,
-0.052254028618335724,
0.11132411658763885,
0.09589345753192902,
-0.06735197454690933,
0.008085503242909908,
-0.008374614641070366,
0.0006493401597253978,
0.0775614008307457,
-0.04947430640459061,
-0.04581080377101898,
-0.030340788885951042,
0.0125211700797081,
0.0020406688563525677,
-0.09657732397317886,
-0.06456887722015381,
-0.07107251137495041,
-0.057300906628370285,
-0.04658262059092522,
0.05448892340064049,
0.03365844860672951,
0.03134593740105629,
0.06699182838201523,
-0.023244129493832588,
0.125849649310112,
-0.0038847702089697123,
0.1449090838432312,
-0.1631743609905243,
-0.08303119987249374
] |
null | null |
flair
|
## 4-Language NER in Flair (English, German, Dutch and Spanish)
This is the fast 4-class NER model for 4 CoNLL-03 languages that ships with [Flair](https://github.com/flairNLP/flair/). Also kind of works for related languages like French.
F1-Score: **91,51** (CoNLL-03 English), **85,72** (CoNLL-03 German revised), **86,22** (CoNLL-03 Dutch), **85,78** (CoNLL-03 Spanish)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| MISC | other name |
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-multi-fast")
# make example sentence in any of the four languages
sentence = Sentence("George Washington ging nach Washington")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [1,2]: "George Washington" [− Labels: PER (0.9977)]
Span [5]: "Washington" [− Labels: LOC (0.9895)]
```
So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging nach Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import CONLL_03, CONLL_03_GERMAN, CONLL_03_DUTCH, CONLL_03_SPANISH
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. get the multi-language corpus
corpus: Corpus = MultiCorpus([
CONLL_03(), # English corpus
CONLL_03_GERMAN(), # German corpus
CONLL_03_DUTCH(), # Dutch corpus
CONLL_03_SPANISH(), # Spanish corpus
])
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
# GloVe embeddings
WordEmbeddings('glove'),
# FastText embeddings
WordEmbeddings('de'),
# contextual string embeddings, forward
FlairEmbeddings('multi-forward-fast'),
# contextual string embeddings, backward
FlairEmbeddings('multi-backward-fast'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/ner-multi-fast',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following papers when using this model.
```
@misc{akbik2019multilingual,
title={Multilingual sequence labeling with one model},
author={Akbik, Alan and Bergmann, Tanja and Vollgraf, Roland}
booktitle = {{NLDL} 2019, Northern Lights Deep Learning Workshop},
year = {2019}
}
```
```
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
|
{"language": ["en", "de", "nl", "es"], "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington ging nach Washington"}]}
|
token-classification
|
flair/ner-multi-fast
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"de",
"nl",
"es",
"dataset:conll2003",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en",
"de",
"nl",
"es"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #de #nl #es #dataset-conll2003 #has_space #region-us
|
4-Language NER in Flair (English, German, Dutch and Spanish)
------------------------------------------------------------
This is the fast 4-class NER model for 4 CoNLL-03 languages that ships with Flair. Also kind of works for related languages like French.
F1-Score: 91,51 (CoNLL-03 English), 85,72 (CoNLL-03 German revised), 86,22 (CoNLL-03 Dutch), 85,78 (CoNLL-03 Spanish)
Predicts 4 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the entities "*George Washington*" (labeled as a person) and "*Washington*" (labeled as a location) are found in the sentence "*George Washington ging nach Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following papers when using this model.
|
[
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging nach Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following papers when using this model."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #de #nl #es #dataset-conll2003 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging nach Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following papers when using this model."
] |
[
47,
81,
22,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #de #nl #es #dataset-conll2003 #has_space #region-us \n### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging nach Washington*\".\n\n\n\n\n---### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---### Cite\n\n\nPlease cite the following papers when using this model."
] |
[
-0.07798746228218079,
0.13751034438610077,
-0.0016217634547501802,
0.05840683728456497,
0.06904684752225876,
0.05137156695127487,
0.11104044318199158,
0.03018762730062008,
0.04033200070261955,
0.027104903012514114,
0.1829613596200943,
0.061503540724515915,
0.10693472623825073,
0.05472923815250397,
0.10640428960323334,
-0.2887724041938782,
0.023204226046800613,
-0.006411223206669092,
-0.09386599063873291,
0.15231400728225708,
0.06529606133699417,
0.0023837920743972063,
0.015670597553253174,
0.033570822328329086,
-0.09152854979038239,
-0.016167616471648216,
-0.027963358908891678,
-0.08922359347343445,
0.1789291650056839,
-0.04391629993915558,
0.2048395872116089,
0.044952407479286194,
0.11458812654018402,
-0.1567380726337433,
0.019967414438724518,
0.040198393166065216,
0.011026979424059391,
0.09299708157777786,
0.03218025341629982,
-0.006587785203009844,
0.3473920524120331,
-0.015135226771235466,
-0.006863153073936701,
0.013914473354816437,
-0.20398719608783722,
-0.25578421354293823,
-0.054320141673088074,
0.05314680188894272,
0.016155024990439415,
0.1127796545624733,
-0.02492554858326912,
0.07856868207454681,
-0.09147044271230698,
0.021298350766301155,
0.1472444087266922,
-0.20248742401599884,
-0.028191519901156425,
0.2125968039035797,
0.015208151191473007,
-0.035254377871751785,
-0.03704756125807762,
0.03436113893985748,
0.0032503651455044746,
0.07338503003120422,
-0.05981512740254402,
-0.0011611242080107331,
0.05009085685014725,
0.12665416300296783,
-0.14921776950359344,
-0.10113555938005447,
0.3210931122303009,
0.02426031604409218,
-0.05428730696439743,
0.020833034068346024,
0.015416666865348816,
-0.004385168198496103,
0.042929694056510925,
-0.09651874005794525,
-0.017899004742503166,
0.009343206882476807,
0.14354747533798218,
-0.09419132769107819,
-0.09261848777532578,
-0.08475282043218613,
0.005307924468070269,
0.03161286562681198,
-0.085856132209301,
0.09093345701694489,
-0.1813141256570816,
0.12581245601177216,
0.0528629794716835,
-0.06293564289808273,
0.049359653145074844,
-0.07688560336828232,
-0.019389137625694275,
0.02573511004447937,
-0.042346153408288956,
0.17368857562541962,
0.021591683849692345,
0.024142133072018623,
0.17371338605880737,
-0.02673693373799324,
0.1317591667175293,
0.11445578932762146,
-0.009411649778485298,
0.1618015468120575,
-0.10970708727836609,
-0.03850311413407326,
0.0012859179405495524,
-0.03155377879738808,
-0.02403968572616577,
-0.05601758509874344,
-0.16703401505947113,
-0.02155268006026745,
-0.0864824503660202,
-0.0056332373060286045,
0.011028661392629147,
0.002763621509075165,
-0.09043844044208527,
0.025394674390554428,
0.07589510083198547,
-0.04509774222970009,
-0.04308394342660904,
-0.031858671456575394,
-0.061533477157354355,
0.10208605229854584,
0.08943542093038559,
0.03978237137198448,
0.02069966122508049,
0.1183137372136116,
-0.08511126786470413,
0.060414496809244156,
-0.05104946717619896,
-0.07948312163352966,
-0.01966228522360325,
-0.05305800586938858,
0.03191991150379181,
-0.06460637599229813,
-0.15468229353427887,
-0.02288052812218666,
0.08532033115625381,
0.0031819194555282593,
-0.06544796377420425,
-0.07351728528738022,
-0.025491204112768173,
-0.04587271809577942,
0.032748520374298096,
0.06055450439453125,
-0.0653572678565979,
0.06400094926357269,
-0.04091953858733177,
0.1078178733587265,
-0.054197512567043304,
0.013247378170490265,
-0.09723737835884094,
0.008090049028396606,
-0.11136485636234283,
-0.004448849242180586,
-0.0028181455563753843,
0.035773877054452896,
-0.10823208838701248,
-0.09492649883031845,
-0.04254978895187378,
0.02200414426624775,
0.016909778118133545,
0.1899646669626236,
-0.2643081843852997,
-0.005300403572618961,
0.1350688487291336,
-0.08793147653341293,
-0.03937901556491852,
0.043602921068668365,
-0.039299361407756805,
0.26257333159446716,
0.04848093166947365,
0.14488357305526733,
-0.03564587980508804,
-0.3438657522201538,
0.14250627160072327,
0.09021662175655365,
-0.16199396550655365,
0.009390468709170818,
0.05654346942901611,
-0.03470339626073837,
-0.19768524169921875,
0.008483916521072388,
-0.12185975164175034,
0.05126147344708443,
-0.03797953575849533,
-0.04672553017735481,
0.032767102122306824,
-0.0679682269692421,
0.05360803380608559,
-0.042904824018478394,
-0.025287965312600136,
0.0012983942870050669,
-0.13916829228401184,
-0.14493288099765778,
0.04741835594177246,
0.09405609220266342,
-0.0331103689968586,
0.018067002296447754,
-0.023529281839728355,
0.08588724583387375,
-0.038516487926244736,
-0.08912664651870728,
0.04805239289999008,
-0.1010471060872078,
0.13370634615421295,
0.01797071471810341,
0.20957154035568237,
-0.022025322541594505,
0.026284534484148026,
0.07443595677614212,
0.04067128524184227,
-0.03772294893860817,
0.055768754333257675,
0.014915230683982372,
0.011667327955365181,
-0.0869087427854538,
-0.09126821160316467,
0.10631711781024933,
-0.16427968442440033,
0.057481132447719574,
-0.0009369032923132181,
-0.018858911469578743,
-0.02383284829556942,
-0.016126062721014023,
0.0832764208316803,
0.04270484670996666,
0.020163724198937416,
-0.04801883175969124,
0.0784786120057106,
0.012213245965540409,
-0.06286879628896713,
-0.08874206990003586,
-0.06066959723830223,
0.0037339210975915194,
0.08097571134567261,
-0.05788968876004219,
-0.0998014435172081,
-0.06667830795049667,
-0.03723650053143501,
-0.002199113368988037,
-0.012897440232336521,
-0.049787361174821854,
0.1961609125137329,
0.019533807411789894,
0.004868572577834129,
-0.03296816721558571,
0.025737082585692406,
-0.03811898082494736,
-0.030980607494711876,
-0.069101482629776,
0.048074666410684586,
0.11496119201183319,
0.03952313959598541,
0.028994297608733177,
0.1807912439107895,
-0.011265013366937637,
0.02210865542292595,
0.04918241128325462,
-0.032440751791000366,
-0.1062261164188385,
-0.08495520800352097,
-0.03493323177099228,
0.18170392513275146,
-0.08772038668394089,
0.03118942119181156,
0.026393214240670204,
-0.012514553032815456,
0.05488142743706703,
-0.19492845237255096,
-0.14413030445575714,
0.050802819430828094,
-0.021297750994563103,
-0.1868038922548294,
0.05217132344841957,
-0.08621908724308014,
0.09482428431510925,
-0.03626537695527077,
-0.13212750852108002,
0.059037014842033386,
0.0027347691357135773,
-0.0762583538889885,
0.108114093542099,
-0.05139721930027008,
-0.25781989097595215,
-0.08960629999637604,
-0.08303501456975937,
0.12844833731651306,
-0.0003927077050320804,
0.010347140952944756,
-0.10004536807537079,
0.03265903517603874,
0.0017519796965643764,
0.07477860897779465,
-0.09359131753444672,
-0.09739506989717484,
-0.06763740628957748,
0.016335515305399895,
-0.008231342770159245,
-0.11379928141832352,
-0.027938617393374443,
-0.06323839724063873,
0.07842257618904114,
0.018496589735150337,
-0.0003237799392081797,
0.07393242418766022,
0.1240958571434021,
0.035310856997966766,
0.06722038239240646,
-0.030593667179346085,
0.3835040330886841,
-0.06814681738615036,
0.07562986761331558,
0.11544091999530792,
0.014548050239682198,
0.03679530322551727,
0.14753808081150055,
0.07688651233911514,
-0.08744849264621735,
-0.04887997359037399,
-0.08941061049699783,
-0.08380096405744553,
-0.08780910819768906,
-0.06793884932994843,
-0.04887174814939499,
-0.0642886832356453,
0.026135193184018135,
0.02373216673731804,
-0.06278598308563232,
0.04327160492539406,
0.08484113216400146,
-0.024263452738523483,
-0.05303793027997017,
-0.032293617725372314,
-0.007004409562796354,
-0.029983948916196823,
-0.005343705415725708,
-0.016498709097504616,
-0.08336370438337326,
-0.00818634033203125,
0.10651911795139313,
0.05628727003931999,
0.12686771154403687,
-0.029579445719718933,
0.04708058387041092,
0.09339956194162369,
0.0830978974699974,
0.1894007921218872,
0.07263857126235962,
-0.02652924135327339,
-0.026264972984790802,
-0.05754891037940979,
-0.014608517289161682,
0.05106896162033081,
-0.006604139693081379,
-0.08937511593103409,
0.022837016731500626,
-0.11815720796585083,
-0.0547119602560997,
-0.06971673667430878,
0.12421764433383942,
-0.1952967792749405,
-0.020272063091397285,
-0.004694131202995777,
0.11174124479293823,
-0.030502336099743843,
0.07507484406232834,
0.04258469492197037,
-0.12050046026706696,
0.018408235162496567,
-0.01228402927517891,
0.08295241743326187,
0.029300834983587265,
0.05446162074804306,
-0.09129288792610168,
0.03524639084935188,
-0.026522016152739525,
0.1325576901435852,
-0.21213506162166595,
0.3320060074329376,
-0.05684014782309532,
0.029606396332383156,
-0.053254276514053345,
-0.013412347063422203,
0.05586254596710205,
0.03306286409497261,
0.2475258857011795,
0.01873643510043621,
-0.16956648230552673,
-0.10247743129730225,
-0.12514343857765198,
0.049267061054706573,
-0.03522248938679695,
-0.0650329440832138,
0.05233945697546005,
0.0797901526093483,
0.008231449872255325,
-0.004157283343374729,
0.0687689259648323,
-0.20144470036029816,
-0.12153960019350052,
-0.00202388153411448,
-0.0325281135737896,
0.03590669855475426,
-0.008141305297613144,
-0.03819514065980911,
-0.11265762895345688,
-0.0379638671875,
0.016329865902662277,
-0.02918372116982937,
-0.06057102978229523,
0.0990973636507988,
0.04022037982940674,
-0.012965885922312737,
-0.027918463572859764,
0.018369514495134354,
-0.014117655344307423,
-0.10208776593208313,
0.0031446374487131834,
0.018500586971640587,
-0.06767652928829193,
0.05757913365960121,
-0.06339037418365479,
0.08164092153310776,
0.091236911714077,
0.05621866509318352,
0.031442366540431976,
0.04203177988529205,
-0.10567217320203781,
-0.11857825517654419,
0.18168050050735474,
-0.13834813237190247,
-0.044549427926540375,
0.02037140540778637,
0.021384362131357193,
0.04935254156589508,
-0.1229175329208374,
0.03821694105863571,
0.22621707618236542,
0.21729779243469238,
-0.12175178527832031,
0.1396138072013855,
0.012627588585019112,
-0.1091330498456955,
-0.1780693680047989,
-0.02555992268025875,
-0.06791135668754578,
0.045250482857227325,
0.11126848310232162,
-0.21327701210975647,
0.051767464727163315,
0.09605582803487778,
-0.03809741511940956,
0.15899860858917236,
-0.2386111468076706,
-0.02794799767434597,
0.23033283650875092,
0.03911460563540459,
0.12904977798461914,
-0.0015332234324887395,
-0.015069393441081047,
-0.0003043626493308693,
-0.09300790727138519,
0.08832892030477524,
0.11422345787286758,
0.03690742328763008,
-0.015289973467588425,
0.08430556207895279,
0.013146737590432167,
-0.06976291537284851,
0.20034508407115936,
0.020212935283780098,
0.031187765300273895,
-0.05899563431739807,
-0.22401851415634155,
0.1274295300245285,
0.000642277009319514,
0.08870790898799896,
0.04355591535568237,
0.00261552631855011,
-0.13101573288440704,
-0.015539026819169521,
-0.13417984545230865,
0.08337809145450592,
-0.07108628749847412,
-0.05721888318657875,
-0.056567784398794174,
0.021252930164337158,
-0.08734115213155746,
-0.027745738625526428,
-0.1547132134437561,
-0.06108466908335686,
0.07004643976688385,
0.009921349585056305,
-0.004407051019370556,
0.0748256966471672,
-0.17099925875663757,
0.05752081423997879,
-0.008681043051183224,
0.07360393553972244,
-0.08034317195415497,
-0.13538649678230286,
0.10458847880363464,
0.025620680302381516,
0.08084440976381302,
0.08080793917179108,
-0.08023194968700409,
-0.017432013526558876,
0.07100933790206909,
-0.17991259694099426,
-0.08899319171905518,
-0.08816515654325485,
-0.011504939757287502,
0.0192946158349514,
-0.0909242108464241,
0.020016631111502647,
-0.046305667608976364,
-0.011947168968617916,
-0.009103422984480858,
-0.008671226911246777,
-0.14746427536010742,
0.010561435483396053,
0.12436734139919281,
0.10074812918901443,
-0.05755438655614853,
-0.008031889796257019,
0.08008521050214767,
-0.07990159839391708,
-0.016480179503560066,
0.02578418329358101,
-0.05113181471824646,
-0.061499252915382385,
-0.09365867078304291,
0.17876318097114563,
0.06041192635893822,
-0.024186285212635994,
0.009262138046324253,
-0.10280763357877731,
0.008500993251800537,
0.038099948316812515,
0.10196702927350998,
0.006339358631521463,
-0.10676360130310059,
-0.08172924071550369,
-0.026000145822763443,
0.003129541175439954,
0.00845851469784975,
-0.06306815892457962,
-0.11814355105161667,
0.10274939984083176,
0.01866181381046772,
0.13215354084968567,
-0.03850442171096802,
-0.08353504538536072,
-0.11364249140024185,
0.07044614851474762,
0.006768116727471352,
0.0039514098316431046,
-0.02322903275489807,
0.008096219971776009,
0.004506603814661503,
-0.018688170239329338,
0.0018472112715244293,
-0.01553858071565628,
-0.11894307285547256,
0.06459371000528336,
0.0023509557358920574,
0.04438295587897301,
0.0020900890231132507,
-0.025377444922924042,
0.09371894598007202,
-0.04274316877126694,
0.048286546021699905,
0.05956275761127472,
-0.08639135211706161,
-0.018552202731370926,
-0.2477317899465561,
0.029834849759936333,
0.026607559993863106,
-0.012189331464469433,
-0.00046917484723962843,
-0.1584911197423935,
-0.010449511930346489,
-0.06283193826675415,
-0.004471845459192991,
-0.03338494151830673,
0.02249179407954216,
-0.07515899091959,
-0.01674518547952175,
0.13955211639404297,
-0.06160209700465202,
-0.02365236170589924,
-0.09501203894615173,
0.14415358006954193,
-0.015871431678533554,
0.0941455066204071,
0.02975252829492092,
0.15471157431602478,
-0.12002535909414291,
-0.020019620656967163,
-0.04141292721033096,
-0.03269098699092865,
-0.01969834230840206,
-0.07274855673313141,
0.08421705663204193,
0.007855422794818878,
0.22161270678043365,
0.00916008185595274,
-0.06568975746631622,
-0.03960449993610382,
0.15314450860023499,
0.033845461905002594,
-0.015271427109837532,
0.12557418644428253,
0.03401431068778038,
0.010973067954182625,
0.024544833227992058,
0.04955372214317322,
-0.02124890871345997,
0.21891753375530243,
0.2216762900352478,
-0.011654598638415337,
0.11377114057540894,
0.08687995374202728,
-0.03042038530111313,
0.03715468943119049,
0.010378154926002026,
-0.12228000909090042,
0.1313769668340683,
-0.0018851010827347636,
-0.0360560305416584,
-0.05861285701394081,
0.08453504741191864,
-0.05226447805762291,
0.04940040409564972,
-0.042572926729917526,
-0.09019223600625992,
-0.09880618751049042,
-0.2527622580528259,
0.05306633189320564,
0.011137334629893303,
-0.024769214913249016,
-0.028293389827013016,
-0.04983154311776161,
0.18058277666568756,
-0.05505409464240074,
-0.00044862666982226074,
0.07683518528938293,
0.02751697413623333,
-0.0632767453789711,
0.03722291812300682,
-0.03221476823091507,
0.004867770243436098,
-0.1593879610300064,
0.05143263190984726,
-0.02507401444017887,
-0.0659337267279625,
0.030040087178349495,
-0.0067985886707901955,
-0.02339046075940132,
-0.03178992494940758,
-0.11368736624717712,
-0.05634797737002373,
-0.03969566151499748,
0.049222636967897415,
-0.0180263239890337,
0.06978950649499893,
0.050521742552518845,
0.031143000349402428,
0.004394059535115957,
0.09966213256120682,
0.003576352959498763,
-0.15525367856025696,
-0.0500505194067955,
0.20068778097629547,
-0.13414441049098969,
0.0026785738300532103,
-0.06271252036094666,
0.005832779221236706,
0.044671591371297836,
0.33931639790534973,
0.33710241317749023,
-0.04917256534099579,
-0.030306093394756317,
-0.04920850321650505,
0.028643447905778885,
0.10021320730447769,
0.08019809424877167,
0.09048347920179367,
0.20875057578086853,
-0.19236336648464203,
-0.0461285263299942,
-0.17289422452449799,
-0.011792926117777824,
-0.02067435160279274,
0.008081091567873955,
0.1314287930727005,
-0.08412124216556549,
-0.05237257853150368,
0.20711861550807953,
-0.21424823999404907,
-0.008341985754668713,
0.006726271938532591,
-0.061382636427879333,
-0.10580363124608994,
0.019012020900845528,
0.056117840111255646,
0.015975724905729294,
0.11765660345554352,
-0.12584711611270905,
-0.10001424700021744,
0.1355840265750885,
-0.03548480197787285,
-0.14732706546783447,
-0.09191282093524933,
0.04245176538825035,
-0.07981416583061218,
0.113393135368824,
0.007554339244961739,
0.13367417454719543,
0.042500413954257965,
0.08919413387775421,
-0.0856151208281517,
0.10851647704839706,
0.009207835420966148,
0.10035163164138794,
-0.05728272721171379,
-0.09010530263185501,
0.011178798042237759,
-0.07409455627202988,
-0.002607760252431035,
-0.17943379282951355,
0.07886137813329697,
0.1466357260942459,
-0.0016698372783139348,
0.008604628965258598,
0.04819885268807411,
-0.04262913763523102,
0.10245832800865173,
0.08723599463701248,
-0.05486151948571205,
-0.004145643208175898,
0.004307651426643133,
-0.004031631629914045,
0.054171230643987656,
-0.08388853073120117,
-0.050846733152866364,
-0.022413220256567,
-0.009560815058648586,
-0.011405082419514656,
-0.08078517764806747,
-0.010909287258982658,
-0.06263640522956848,
-0.04152553528547287,
-0.0475827232003212,
0.06003334000706673,
0.022789878770709038,
0.020471369847655296,
0.06777368485927582,
-0.019975122064352036,
0.1608298420906067,
0.0023591851349920034,
0.1313333511352539,
-0.14818882942199707,
-0.1017763614654541
] |
null | null |
flair
|
## 4-Language NER in Flair (English, German, Dutch and Spanish)
This is the standard 4-class NER model for 4 CoNLL-03 languages that ships with [Flair](https://github.com/flairNLP/flair/). Also kind of works for related languages like French.
F1-Score: **92,16** (CoNLL-03 English), **87,33** (CoNLL-03 German revised), **88,96** (CoNLL-03 Dutch), **86,65** (CoNLL-03 Spanish)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| MISC | other name |
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-multi")
# make example sentence in any of the four languages
sentence = Sentence("George Washington ging nach Washington")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [1,2]: "George Washington" [− Labels: PER (0.9977)]
Span [5]: "Washington" [− Labels: LOC (0.9895)]
```
So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging nach Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import CONLL_03, CONLL_03_GERMAN, CONLL_03_DUTCH, CONLL_03_SPANISH
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. get the multi-language corpus
corpus: Corpus = MultiCorpus([
CONLL_03(), # English corpus
CONLL_03_GERMAN(), # German corpus
CONLL_03_DUTCH(), # Dutch corpus
CONLL_03_SPANISH(), # Spanish corpus
])
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
# GloVe embeddings
WordEmbeddings('glove'),
# FastText embeddings
WordEmbeddings('de'),
# contextual string embeddings, forward
FlairEmbeddings('multi-forward'),
# contextual string embeddings, backward
FlairEmbeddings('multi-backward'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/ner-multi',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following paper when using this model.
```
@misc{akbik2019multilingual,
title={Multilingual sequence labeling with one model},
author={Akbik, Alan and Bergmann, Tanja and Vollgraf, Roland}
booktitle = {{NLDL} 2019, Northern Lights Deep Learning Workshop},
year = {2019}
}
```
```
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
|
{"language": ["en", "de", "nl", "es", "multilingual"], "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington ging nach Washington"}]}
|
token-classification
|
flair/ner-multi
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"de",
"nl",
"es",
"multilingual",
"dataset:conll2003",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en",
"de",
"nl",
"es",
"multilingual"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #de #nl #es #multilingual #dataset-conll2003 #region-us
|
4-Language NER in Flair (English, German, Dutch and Spanish)
------------------------------------------------------------
This is the standard 4-class NER model for 4 CoNLL-03 languages that ships with Flair. Also kind of works for related languages like French.
F1-Score: 92,16 (CoNLL-03 English), 87,33 (CoNLL-03 German revised), 88,96 (CoNLL-03 Dutch), 86,65 (CoNLL-03 Spanish)
Predicts 4 tags:
Based on Flair embeddings and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the entities "*George Washington*" (labeled as a person) and "*Washington*" (labeled as a location) are found in the sentence "*George Washington ging nach Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following paper when using this model.
|
[
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging nach Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #de #nl #es #multilingual #dataset-conll2003 #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging nach Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model."
] |
[
47,
81,
22,
14
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #de #nl #es #multilingual #dataset-conll2003 #region-us \n### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington ging nach Washington*\".\n\n\n\n\n---### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---### Cite\n\n\nPlease cite the following paper when using this model."
] |
[
-0.0833059698343277,
0.11088046431541443,
-0.002851977478712797,
0.0648057758808136,
0.09773474931716919,
0.06724124401807785,
0.1374470591545105,
0.01561618410050869,
0.04028002917766571,
0.004797354340553284,
0.16926580667495728,
0.07804197072982788,
0.12113681435585022,
0.02815619669854641,
0.10788269340991974,
-0.30111557245254517,
0.008531792089343071,
-0.00851465854793787,
-0.06275956332683563,
0.15114521980285645,
0.05794324725866318,
0.03186040371656418,
0.012942931614816189,
0.029085354879498482,
-0.09321630746126175,
-0.021728962659835815,
-0.016438059508800507,
-0.08343399316072464,
0.17854487895965576,
-0.020209191367030144,
0.19196072220802307,
0.03375902771949768,
0.10877156257629395,
-0.17925794422626495,
0.017381062731146812,
0.036649029701948166,
0.020162271335721016,
0.070530466735363,
0.04841139167547226,
-0.02195408195257187,
0.3340262770652771,
0.008162284269928932,
-0.0026346698869019747,
0.025616735219955444,
-0.19213107228279114,
-0.22718119621276855,
-0.050444941967725754,
0.06452241539955139,
0.011951909400522709,
0.10344351083040237,
-0.009544367901980877,
0.06902073323726654,
-0.07836522907018661,
0.030711159110069275,
0.10383930802345276,
-0.21201515197753906,
-0.03483544662594795,
0.200745090842247,
0.0012143065687268972,
-0.041184939444065094,
-0.033168692141771317,
0.035347193479537964,
-0.007671920582652092,
0.08996325731277466,
-0.1202039048075676,
-0.012243242003023624,
0.06414654850959778,
0.11353350430727005,
-0.1340557336807251,
-0.07226977497339249,
0.31053587794303894,
0.035162217915058136,
-0.07614358514547348,
0.03324909508228302,
0.03703879565000534,
0.03242599219083786,
0.032197631895542145,
-0.09178747236728668,
-0.02833588793873787,
0.020015036687254906,
0.1409536600112915,
-0.10176847875118256,
-0.07152267545461655,
-0.08992448449134827,
-0.01206114236265421,
0.054820578545331955,
-0.06552337110042572,
0.09513316303491592,
-0.18722456693649292,
0.11196227371692657,
0.029300721362233162,
-0.03795117512345314,
0.04056234285235405,
-0.079921193420887,
-0.0243961401283741,
0.03161343187093735,
-0.02319967746734619,
0.15340477228164673,
0.039315689355134964,
0.010089111514389515,
0.1858261674642563,
-0.011309013701975346,
0.08697079867124557,
0.10939178615808487,
-0.0101071922108531,
0.15734505653381348,
-0.09072604030370712,
-0.03662751242518425,
0.009881006553769112,
-0.0562736801803112,
-0.02038879692554474,
-0.04992026463150978,
-0.16765043139457703,
-0.005181773565709591,
-0.09252548217773438,
0.018017835915088654,
-0.00578268663957715,
0.009159069508314133,
-0.09987485408782959,
0.006689716596156359,
0.05159619450569153,
-0.057091183960437775,
-0.047792915254831314,
-0.03298048675060272,
-0.05076600983738899,
0.16477249562740326,
0.06849761307239532,
0.036534037441015244,
-0.012395056895911694,
0.1214531734585762,
-0.06340999901294708,
0.07349832355976105,
-0.0504894033074379,
-0.07307168841362,
-0.01465902104973793,
-0.03407343477010727,
0.02362256497144699,
-0.05051378533244133,
-0.17866533994674683,
-0.01773972623050213,
0.07788734883069992,
-0.0009826811728999019,
-0.05552765727043152,
-0.09680137038230896,
-0.0037724182475358248,
-0.06306734681129456,
0.04507135972380638,
0.0369238555431366,
-0.06015773117542267,
0.06675749272108078,
-0.03801203519105911,
0.08419524878263474,
-0.06218875199556351,
0.014078286476433277,
-0.08157137036323547,
0.0049930899403989315,
-0.10098295658826828,
0.027818599715828896,
0.008711131289601326,
0.030000394210219383,
-0.10532217472791672,
-0.0930958166718483,
-0.031384341418743134,
0.02234467677772045,
0.0035662679001688957,
0.19508574903011322,
-0.247097909450531,
-0.019808832556009293,
0.18534928560256958,
-0.09655244648456573,
-0.02869660221040249,
0.06485293060541153,
-0.03300413861870766,
0.247140035033226,
0.08621740341186523,
0.11684557795524597,
-0.02217702753841877,
-0.3525518476963043,
0.17964006960391998,
0.09696628153324127,
-0.18257461488246918,
0.019788796082139015,
0.0502593070268631,
-0.05535097047686577,
-0.22612452507019043,
0.023494193330407143,
-0.10559936612844467,
0.06754688173532486,
-0.047186434268951416,
-0.037038110196590424,
0.04447763040661812,
-0.06378971040248871,
0.03209979459643364,
-0.04772989824414253,
-0.03339477255940437,
-0.003897559829056263,
-0.12015293538570404,
-0.1478756219148636,
0.05159839242696762,
0.07205390930175781,
-0.02465810626745224,
-0.009995343163609505,
-0.05521845445036888,
0.11077906936407089,
-0.02194351516664028,
-0.09164826571941376,
0.046460289508104324,
-0.09123800694942474,
0.1442507952451706,
0.028792228549718857,
0.16868267953395844,
-0.0281546488404274,
0.04534794017672539,
0.06441162526607513,
0.04130346700549126,
-0.02963322587311268,
0.039621297270059586,
0.022778954356908798,
-0.0038743040058761835,
-0.05684572830796242,
-0.0839560404419899,
0.11184141039848328,
-0.14186139404773712,
0.05493656545877457,
-0.01770714670419693,
-0.028047064319252968,
-0.022739743813872337,
-0.009149101562798023,
0.07120025157928467,
0.06226124241948128,
0.026321273297071457,
-0.04626724123954773,
0.1042059138417244,
0.015017061494290829,
-0.0827532634139061,
-0.0944865494966507,
-0.03778960555791855,
-0.01805531606078148,
0.06990868598222733,
-0.0671592503786087,
-0.09617269039154053,
-0.08550436794757843,
-0.027782948687672615,
-0.018375391140580177,
-0.0126648535951972,
-0.03952706605195999,
0.23773476481437683,
0.04629111289978027,
0.011581135913729668,
-0.025357596576213837,
0.037498973309993744,
-0.03667861968278885,
-0.02936411462724209,
-0.060500916093587875,
0.04781348630785942,
0.12120049446821213,
0.008421852253377438,
0.008211880922317505,
0.1338677853345871,
0.0140346335247159,
0.030679693445563316,
0.038378048688173294,
-0.026003334671258926,
-0.10477805882692337,
-0.058056894689798355,
-0.019681889563798904,
0.14761120080947876,
-0.09113907068967819,
0.04032658040523529,
0.01543845608830452,
-0.004520037677139044,
0.04325395077466965,
-0.19236122071743011,
-0.13762642443180084,
0.024212194606661797,
-0.036555271595716476,
-0.1821918785572052,
0.03747433423995972,
-0.0828503966331482,
0.09219730645418167,
-0.04465140029788017,
-0.1247897520661354,
0.08274218440055847,
0.003274722956120968,
-0.09616031497716904,
0.1229841411113739,
-0.06773391366004944,
-0.28940045833587646,
-0.10488574206829071,
-0.13813526928424835,
0.10486998409032822,
-0.014602918177843094,
0.0013100029900670052,
-0.095120869576931,
0.03060106560587883,
0.02852161042392254,
0.10511309653520584,
-0.09080716222524643,
-0.11265154927968979,
-0.05811310559511185,
0.029598163440823555,
-0.014894986525177956,
-0.102593332529068,
-0.026292001828551292,
-0.049273863434791565,
0.0710836872458458,
0.017427997663617134,
-0.029270118102431297,
0.05349501222372055,
0.12765859067440033,
0.053924668580293655,
0.06592918932437897,
-0.03155851736664772,
0.3552684485912323,
-0.055350057780742645,
0.06266806274652481,
0.1042211502790451,
-0.0011733219726011157,
0.03799126669764519,
0.1278563141822815,
0.08613818883895874,
-0.08961092680692673,
-0.04554446041584015,
-0.08044786751270294,
-0.07231169939041138,
-0.11919029802083969,
-0.06541714817285538,
-0.04527471587061882,
-0.035274967551231384,
0.0049849748611450195,
0.02236831560730934,
-0.08077318221330643,
0.028630008921027184,
0.08342183381319046,
-0.015120983123779297,
-0.0245564766228199,
-0.03038317896425724,
0.017705636098980904,
-0.03451944515109062,
-0.01953415386378765,
-0.0167026836425066,
-0.07882919162511826,
-0.002001563087105751,
0.11137780547142029,
0.05102100223302841,
0.1408413201570511,
-0.026298517361283302,
0.050738103687763214,
0.06324351578950882,
0.0881194919347763,
0.19454260170459747,
0.07240168750286102,
-0.02447289414703846,
-0.016966234892606735,
-0.043957263231277466,
-0.01786607690155506,
0.017843538895249367,
-0.0057807015255093575,
-0.08378720283508301,
0.024969957768917084,
-0.08505039662122726,
-0.04922494664788246,
-0.059463828802108765,
0.10140060633420944,
-0.1607988178730011,
-0.02158823423087597,
-0.00577735248953104,
0.10935152322053909,
-0.051848918199539185,
0.08577438443899155,
0.010646569542586803,
-0.15973268449306488,
0.05563418194651604,
-0.022368162870407104,
0.09269518405199051,
-0.007885429076850414,
0.04500555247068405,
-0.09293381869792938,
0.06158255785703659,
-0.018371837213635445,
0.14904940128326416,
-0.23807817697525024,
0.31701532006263733,
-0.04533009976148605,
0.05251344293355942,
-0.04485331103205681,
-0.00934470072388649,
0.04521401226520538,
0.06284112483263016,
0.24571770429611206,
0.02326946146786213,
-0.12202909588813782,
-0.09931515157222748,
-0.1378011852502823,
0.05215322971343994,
-0.0161745548248291,
-0.03213011100888252,
0.04613050818443298,
0.0734766498208046,
0.005297286435961723,
-0.02858949638903141,
-0.0075097098015248775,
-0.21678690612316132,
-0.13306023180484772,
-0.0008551508653908968,
-0.05310749635100365,
0.04390529915690422,
-0.0031693778000772,
-0.04276305064558983,
-0.08092506229877472,
-0.007697660010308027,
-0.018202250823378563,
-0.04547450318932533,
-0.0562865287065506,
0.10688288509845734,
0.017432134598493576,
-0.026406029239296913,
-0.04126899316906929,
0.006076621823012829,
-0.024400265887379646,
-0.10900929570198059,
-0.0003412782389204949,
0.008620450273156166,
-0.06564224511384964,
0.06537318229675293,
-0.05377945303916931,
0.11129345744848251,
0.10595105588436127,
0.05224348604679108,
0.03223690018057823,
0.005150584038347006,
-0.060633234679698944,
-0.13019327819347382,
0.16908256709575653,
-0.12038290500640869,
-0.06963822990655899,
0.045462604612112045,
-0.008661421947181225,
0.033768147230148315,
-0.1432010531425476,
0.02451683208346367,
0.23727302253246307,
0.20878587663173676,
-0.0841832309961319,
0.14517956972122192,
0.04386782646179199,
-0.126686230301857,
-0.1623932421207428,
-0.05326109007000923,
-0.06279514729976654,
0.04226711392402649,
0.07721827924251556,
-0.23308375477790833,
0.061790935695171356,
0.10014118999242783,
-0.01301256287842989,
0.17384997010231018,
-0.24837493896484375,
-0.028977228328585625,
0.22833256423473358,
0.0503418892621994,
0.12658603489398956,
-0.0000526202202308923,
-0.03936367481946945,
-0.021927474066615105,
-0.10390686243772507,
0.07147213071584702,
0.13244733214378357,
0.04278722405433655,
-0.015475672669708729,
0.080649733543396,
0.007280121557414532,
-0.06278468668460846,
0.1867770552635193,
0.03299414739012718,
0.023447073996067047,
-0.05703350529074669,
-0.20592786371707916,
0.12856793403625488,
0.0319068506360054,
0.0903274267911911,
0.04820888116955757,
-0.008750314824283123,
-0.0886501744389534,
-0.03606749698519707,
-0.13185352087020874,
0.06801231950521469,
-0.06740739196538925,
-0.04944450780749321,
-0.03786708787083626,
0.014959986321628094,
-0.1132497563958168,
-0.029749758541584015,
-0.1723894476890564,
-0.057632315903902054,
0.04109203815460205,
-0.024829726666212082,
0.03015495277941227,
0.07385385036468506,
-0.18092043697834015,
0.011902359314262867,
-0.014547646977007389,
0.05590079352259636,
-0.024121200665831566,
-0.13595692813396454,
0.10443467646837234,
0.01899387687444687,
0.10369621217250824,
0.07482481002807617,
-0.08976325392723083,
-0.017602723091840744,
0.0651639997959137,
-0.17201200127601624,
-0.09626854956150055,
-0.09147708863019943,
-0.015202884562313557,
0.0270064789801836,
-0.07326100021600723,
0.03310159593820572,
-0.020352240651845932,
-0.017781099304556847,
-0.001777817727997899,
0.00931644905358553,
-0.15012896060943604,
0.01316736824810505,
0.111461341381073,
0.08809248358011246,
-0.06296879053115845,
-0.0020957125816494226,
0.0627501979470253,
-0.09170260280370712,
-0.029633840546011925,
0.047704681754112244,
-0.06485770642757416,
-0.04734547436237335,
-0.0790027603507042,
0.21745114028453827,
0.049150459468364716,
-0.04858732968568802,
0.002167105209082365,
-0.11521563678979874,
0.01785903610289097,
0.03833884373307228,
0.08789266645908356,
0.01910719834268093,
-0.12107297778129578,
-0.09280207753181458,
-0.02585209161043167,
-0.0014230810338631272,
0.009244351647794247,
-0.08469658344984055,
-0.11623673141002655,
0.09183093905448914,
0.044871918857097626,
0.15244553983211517,
-0.02938794158399105,
-0.10762572288513184,
-0.11129573732614517,
0.08069920539855957,
-0.0046752216294407845,
0.020955916494131088,
-0.028482744470238686,
0.030816752463579178,
0.008511710911989212,
-0.013145309872925282,
0.012413710355758667,
-0.01515002828091383,
-0.10308736562728882,
0.060104720294475555,
0.01109117642045021,
0.04359829053282738,
0.01743701472878456,
-0.028326554223895073,
0.089392751455307,
-0.04670623689889908,
0.054585233330726624,
0.07802736014127731,
-0.09330590814352036,
0.0069188689813017845,
-0.27191492915153503,
0.04012596234679222,
0.02692759409546852,
-0.010117792524397373,
-0.007998323999345303,
-0.1647154986858368,
-0.004071313422173262,
-0.0436357781291008,
-0.0035379938781261444,
-0.021280277520418167,
0.03220195323228836,
-0.07793043553829193,
-0.02083543688058853,
0.13391424715518951,
-0.07846536487340927,
-0.036227453500032425,
-0.09240303188562393,
0.12762820720672607,
-0.011918621137738228,
0.09324699640274048,
0.009490367025136948,
0.1721123903989792,
-0.09886494278907776,
-0.022016871720552444,
-0.03323565796017647,
-0.0215300340205431,
-0.006365580949932337,
-0.08010270446538925,
0.07557246088981628,
0.010841129347682,
0.21999295055866241,
-0.015168936923146248,
-0.037896525114774704,
-0.04587554931640625,
0.1405876874923706,
0.013288440182805061,
-0.01401849091053009,
0.12111188471317291,
0.022180400788784027,
0.02201877534389496,
0.018953373655676842,
0.03500983119010925,
-0.034568581730127335,
0.2310781627893448,
0.23549965023994446,
-0.03269749507308006,
0.10187365859746933,
0.07795307785272598,
-0.02674081176519394,
0.06463251262903214,
0.03207813575863838,
-0.12559431791305542,
0.1455310732126236,
-0.013994934968650341,
-0.015118665061891079,
-0.02547604776918888,
0.06667080521583557,
-0.04767041653394699,
0.048907577991485596,
-0.02433554269373417,
-0.11707087606191635,
-0.11214485764503479,
-0.2674994170665741,
0.04963761195540428,
0.019196394830942154,
-0.01183052733540535,
-0.035958144813776016,
-0.045621879398822784,
0.15134282410144806,
-0.036092374473810196,
-0.012828887440264225,
0.059705376625061035,
0.016035186126828194,
-0.07580599188804626,
0.05274439975619316,
-0.0373077355325222,
0.011083636432886124,
-0.13842695951461792,
0.051391758024692535,
-0.04062435030937195,
-0.06374887377023697,
0.03843744099140167,
-0.0013859787723049521,
-0.002595340134575963,
-0.02353181689977646,
-0.13725996017456055,
-0.053886521607637405,
-0.022580675780773163,
0.04759443551301956,
-0.0005016533541493118,
0.07408146560192108,
0.06121295318007469,
0.016758007928729057,
0.014173205941915512,
0.07894545793533325,
0.02832789532840252,
-0.1934763491153717,
-0.05706005170941353,
0.20786172151565552,
-0.13464993238449097,
0.0004764071782119572,
-0.05243441462516785,
0.023982662707567215,
0.05484072491526604,
0.36811718344688416,
0.32511869072914124,
-0.04360254108905792,
-0.051547929644584656,
-0.05478915199637413,
0.030239734798669815,
0.1373482346534729,
0.09784412384033203,
0.07654841244220734,
0.21854956448078156,
-0.1795405149459839,
-0.05390741303563118,
-0.18007740378379822,
-0.014696336351335049,
-0.03276875242590904,
0.012262032367289066,
0.1248619481921196,
-0.08178763836622238,
-0.04373408481478691,
0.2402680665254593,
-0.22296638786792755,
0.010258994065225124,
-0.009283228777348995,
-0.027255360037088394,
-0.10680019110441208,
0.0013831109972670674,
0.04381285980343819,
0.03336824104189873,
0.10954511165618896,
-0.12084788829088211,
-0.09629718214273453,
0.15480922162532806,
-0.030796648934483528,
-0.1723768711090088,
-0.09990810602903366,
0.031200317665934563,
-0.07795239984989166,
0.07796063274145126,
0.024832677096128464,
0.15890531241893768,
0.03405485302209854,
0.09082047641277313,
-0.05988655984401703,
0.11845429986715317,
-0.00522605562582612,
0.1325109601020813,
-0.033617325127124786,
-0.08243676275014877,
0.008244184777140617,
-0.071627177298069,
-0.010629243217408657,
-0.1445927619934082,
0.07546281069517136,
0.15686173737049103,
-0.022472402080893517,
-0.001802422571927309,
0.07604435086250305,
-0.06615708768367767,
0.09539700299501419,
0.10498451441526413,
-0.048358067870140076,
-0.009181511588394642,
-0.0054788230918347836,
-0.001242970465682447,
0.04654490202665329,
-0.08640067279338837,
-0.04175417497754097,
-0.03403359279036522,
-0.020571405068039894,
-0.0335383340716362,
-0.07567055523395538,
-0.031175464391708374,
-0.05727706477046013,
-0.04904051125049591,
-0.03789246454834938,
0.025768917053937912,
0.035710763186216354,
0.0051240455359220505,
0.07445728778839111,
-0.016007740050554276,
0.1406855434179306,
0.0006990987458266318,
0.12357216328382492,
-0.13881957530975342,
-0.10732855647802353
] |
null | null |
flair
|
## Spanish NER in Flair (large model)
This is the large 4-class NER model for Spanish that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **90,54** (CoNLL-03 Spanish)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| MISC | other name |
Based on document-level XLM-R embeddings and [FLERT](https://arxiv.org/pdf/2011.06993v1.pdf/).
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/ner-spanish-large")
# make example sentence
sentence = Sentence("George Washington fue a Washington")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [1,2]: "George Washington" [− Labels: PER (1.0)]
Span [5]: "Washington" [− Labels: LOC (1.0)]
```
So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington fue a Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
import torch
# 1. get the corpus
from flair.datasets import CONLL_03_SPANISH
corpus = CONLL_03_SPANISH()
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize fine-tuneable transformer embeddings WITH document context
from flair.embeddings import TransformerWordEmbeddings
embeddings = TransformerWordEmbeddings(
model='xlm-roberta-large',
layers="-1",
subtoken_pooling="first",
fine_tune=True,
use_context=True,
)
# 5. initialize bare-bones sequence tagger (no CRF, no RNN, no reprojection)
from flair.models import SequenceTagger
tagger = SequenceTagger(
hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type='ner',
use_crf=False,
use_rnn=False,
reproject_embeddings=False,
)
# 6. initialize trainer with AdamW optimizer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus, optimizer=torch.optim.AdamW)
# 7. run training with XLM parameters (20 epochs, small LR)
from torch.optim.lr_scheduler import OneCycleLR
trainer.train('resources/taggers/ner-spanish-large',
learning_rate=5.0e-6,
mini_batch_size=4,
mini_batch_chunk_size=1,
max_epochs=20,
scheduler=OneCycleLR,
embeddings_storage_mode='none',
weight_decay=0.,
)
)
```
---
### Cite
Please cite the following paper when using this model.
```
@misc{schweter2020flert,
title={FLERT: Document-Level Features for Named Entity Recognition},
author={Stefan Schweter and Alan Akbik},
year={2020},
eprint={2011.06993},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
|
{"language": "es", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003"], "widget": [{"text": "George Washington fue a Washington"}]}
|
token-classification
|
flair/ner-spanish-large
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"es",
"dataset:conll2003",
"arxiv:2011.06993",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2011.06993"
] |
[
"es"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #es #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us
|
Spanish NER in Flair (large model)
----------------------------------
This is the large 4-class NER model for Spanish that ships with Flair.
F1-Score: 90,54 (CoNLL-03 Spanish)
Predicts 4 tags:
Based on document-level XLM-R embeddings and FLERT.
---
### Demo: How to use in Flair
Requires: Flair ('pip install flair')
This yields the following output:
So, the entities "*George Washington*" (labeled as a person) and "*Washington*" (labeled as a location) are found in the sentence "*George Washington fue a Washington*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
---
### Cite
Please cite the following paper when using this model.
---
### Issues?
The Flair issue tracker is available here.
|
[
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington fue a Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #es #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us \n",
"### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington fue a Washington*\".\n\n\n\n\n---",
"### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---",
"### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---",
"### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
50,
81,
22,
15,
15
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #es #dataset-conll2003 #arxiv-2011.06993 #has_space #region-us \n### Demo: How to use in Flair\n\n\nRequires: Flair ('pip install flair')\n\n\nThis yields the following output:\n\n\nSo, the entities \"*George Washington*\" (labeled as a person) and \"*Washington*\" (labeled as a location) are found in the sentence \"*George Washington fue a Washington*\".\n\n\n\n\n---### Training: Script to train this model\n\n\nThe following Flair script was used to train this model:\n\n\n\n\n---### Cite\n\n\nPlease cite the following paper when using this model.\n\n\n\n\n---### Issues?\n\n\nThe Flair issue tracker is available here."
] |
[
-0.11084988713264465,
0.17583386600017548,
-0.000726116995792836,
0.07776138931512833,
0.07994335889816284,
0.05660754069685936,
0.12025811523199081,
0.050116248428821564,
0.017603948712348938,
0.049200206995010376,
0.1878618597984314,
0.05754449963569641,
0.10591325908899307,
0.09344106912612915,
0.06911864131689072,
-0.25175362825393677,
0.02783483825623989,
-0.018288718536496162,
-0.059464242309331894,
0.16570156812667847,
0.08378658443689346,
-0.012677089311182499,
0.020183157175779343,
0.02303340844810009,
-0.11886686831712723,
-0.010412121191620827,
-0.02908715046942234,
-0.10018446296453476,
0.17157390713691711,
-0.04017454758286476,
0.21164773404598236,
0.04831181839108467,
0.11574949324131012,
-0.14117901027202606,
0.029272358864545822,
0.04082159325480461,
0.015971509739756584,
0.1145244762301445,
0.05470530316233635,
0.0018587902886793017,
0.29905444383621216,
-0.031337179243564606,
-0.002448560204356909,
0.031124606728553772,
-0.1903248280286789,
-0.2561837136745453,
-0.11081517487764359,
0.07556901127099991,
0.05346132814884186,
0.11972927302122116,
-0.02964414283633232,
0.10912159085273743,
-0.05172378197312355,
0.026089323684573174,
0.16902421414852142,
-0.21929724514484406,
-0.04046130180358887,
0.17273452877998352,
0.011957105249166489,
-0.025836002081632614,
-0.04422999545931816,
0.03754868358373642,
0.0014342801878228784,
0.07646334916353226,
-0.030170409008860588,
-0.013097095303237438,
0.006812028121203184,
0.104322150349617,
-0.1524730622768402,
-0.09574906527996063,
0.3449510633945465,
0.03161085024476051,
-0.06613577902317047,
0.018137022852897644,
0.0011870510643348098,
-0.012855918146669865,
0.04863123223185539,
-0.09864099323749542,
-0.015506983734667301,
-0.004966751206666231,
0.15847018361091614,
-0.1100381463766098,
-0.10922034084796906,
-0.09917625039815903,
-0.01873866282403469,
0.04184867814183235,
-0.08459169417619705,
0.09579271823167801,
-0.16954293847084045,
0.11436650156974792,
0.06245226040482521,
-0.06088370457291603,
0.04414660856127739,
-0.0799683928489685,
-0.04373001679778099,
0.020970338955521584,
-0.04889292269945145,
0.12127052247524261,
0.029570087790489197,
0.023116694763302803,
0.15418492257595062,
-0.03650526702404022,
0.12862929701805115,
0.10581930726766586,
0.010912789031863213,
0.17756621539592743,
-0.12270529568195343,
-0.04470430314540863,
0.03776922821998596,
-0.006448891945183277,
-0.007029397413134575,
-0.05685611441731453,
-0.15249137580394745,
-0.030249854549765587,
-0.0631389170885086,
0.021836210042238235,
0.04240252822637558,
-0.025337934494018555,
-0.08716116100549698,
0.025899449363350868,
0.09654979407787323,
-0.0663657858967781,
-0.03377487510442734,
-0.028359441086649895,
-0.05823855847120285,
0.09770791232585907,
0.0980658009648323,
0.030184825882315636,
0.017749283462762833,
0.10992245376110077,
-0.08551329374313354,
0.04367700219154358,
-0.04112797603011131,
-0.10622638463973999,
-0.01676914282143116,
-0.09115298092365265,
0.016461137682199478,
-0.06794708967208862,
-0.15603482723236084,
-0.027253156527876854,
0.06509704142808914,
0.003741967724636197,
-0.06704743206501007,
-0.04385390505194664,
-0.03993696719408035,
-0.03745551407337189,
0.037099387496709824,
0.06963054835796356,
-0.05325356125831604,
0.06283789873123169,
-0.023288361728191376,
0.10584250837564468,
-0.07738485187292099,
0.010150712914764881,
-0.0929141640663147,
0.007982790470123291,
-0.09407040476799011,
0.002409576438367367,
-0.016214579343795776,
0.030813436955213547,
-0.12417618185281754,
-0.09509997814893723,
-0.026992129161953926,
0.005977326072752476,
0.03236271068453789,
0.19439367949962616,
-0.28492724895477295,
0.007977757602930069,
0.09579075872898102,
-0.10072197765111923,
-0.055292271077632904,
0.05280559882521629,
-0.030934665352106094,
0.199079230427742,
0.03181154280900955,
0.13052837550640106,
-0.04031737893819809,
-0.322478324174881,
0.08744630962610245,
0.06757938116788864,
-0.16711640357971191,
0.007528796326369047,
0.0824747085571289,
-0.045146290212869644,
-0.16362740099430084,
-0.0048073953948915005,
-0.12814189493656158,
0.024718230590224266,
-0.03898634761571884,
-0.05328366532921791,
0.0017353941220790148,
-0.06604361534118652,
0.030750645324587822,
-0.04064194858074188,
-0.027362551540136337,
-0.0008985743625089526,
-0.1425420343875885,
-0.1607925146818161,
0.04236195236444473,
0.10097284615039825,
-0.03364100679755211,
0.014632823877036572,
0.048769477754831314,
0.02689201943576336,
-0.04914042353630066,
-0.1167689710855484,
0.016783103346824646,
-0.08832112699747086,
0.13047274947166443,
0.015822749584913254,
0.14993467926979065,
-0.02384200133383274,
0.034767888486385345,
0.0734545961022377,
0.03032204695045948,
-0.023972854018211365,
0.05034465342760086,
-0.011046173982322216,
0.00934167206287384,
-0.1104341670870781,
-0.09884743392467499,
0.12944039702415466,
-0.17719587683677673,
0.0476292259991169,
0.008354270830750465,
-0.021426239982247353,
-0.031313396990299225,
-0.02315293438732624,
0.10482334345579147,
0.02391502633690834,
0.005157717037945986,
-0.06578133255243301,
0.07007741928100586,
0.005457780789583921,
-0.061722662299871445,
-0.09892719984054565,
-0.05382642149925232,
-0.01989547349512577,
0.09439646452665329,
-0.0005199384177103639,
-0.11077900230884552,
-0.06632670760154724,
-0.041706424206495285,
0.013419529423117638,
-0.03431832790374756,
-0.030926669016480446,
0.18621359765529633,
0.04354691505432129,
0.010714470408856869,
-0.02286345884203911,
0.035955775529146194,
-0.028727201744914055,
-0.036796148866415024,
-0.0696229413151741,
0.046561677008867264,
0.09183820337057114,
0.02760394848883152,
0.02584514580667019,
0.16064193844795227,
-0.01451098546385765,
0.0015665105311200023,
0.05427654832601547,
-0.045333199203014374,
-0.09225307404994965,
-0.09117287397384644,
-0.022287018597126007,
0.18281115591526031,
-0.04628793150186539,
0.0372052900493145,
0.044755514711141586,
-0.012141902931034565,
0.04165532439947128,
-0.20082369446754456,
-0.13150766491889954,
0.05132385343313217,
-0.02176167443394661,
-0.2155539095401764,
0.06140822917222977,
-0.060282159596681595,
0.10362503677606583,
-0.027494845911860466,
-0.0853668749332428,
0.04299201816320419,
-0.003007757244631648,
-0.07360956817865372,
0.12252376228570938,
-0.05572331324219704,
-0.23920094966888428,
-0.10372833907604218,
-0.03506474569439888,
0.11544255912303925,
0.0027198409661650658,
0.023037459701299667,
-0.10841889679431915,
0.020678561180830002,
-0.02238556556403637,
0.057190194725990295,
-0.08789833635091782,
-0.09180086851119995,
-0.043502580374479294,
0.0064202710054814816,
-0.006098187528550625,
-0.11674799770116806,
-0.02421184815466404,
-0.04353535175323486,
0.06352698057889938,
0.02919985167682171,
0.022953201085329056,
0.10579816997051239,
0.11988439410924911,
0.01721142791211605,
0.06723511219024658,
-0.03166479989886284,
0.36155617237091064,
-0.07447801530361176,
0.08995317667722702,
0.12885063886642456,
0.02440936677157879,
0.04536261782050133,
0.14213435351848602,
0.06433317810297012,
-0.08786029368638992,
-0.039694894105196,
-0.0832010880112648,
-0.08044439554214478,
-0.12220840901136398,
-0.06413550674915314,
-0.049078673124313354,
-0.08259135484695435,
0.012164398096501827,
0.02834143303334713,
-0.09620272368192673,
0.05830584838986397,
0.0800272673368454,
-0.031314440071582794,
-0.07175901532173157,
-0.010938204824924469,
0.0021956381388008595,
-0.020519182085990906,
0.004246471915394068,
-0.035831157118082047,
-0.040025416761636734,
0.02671528048813343,
0.097275011241436,
0.10586725175380707,
0.08453794568777084,
-0.010063892230391502,
0.06322193145751953,
0.11965856701135635,
0.07250562310218811,
0.17850646376609802,
0.07497037202119827,
-0.01758674718439579,
-0.02629384957253933,
-0.0619424469769001,
0.0028062264900654554,
0.04057275131344795,
-0.016338543966412544,
-0.06007632985711098,
0.010438145138323307,
-0.07872741669416428,
-0.05142349749803543,
-0.0171903558075428,
0.12313691526651382,
-0.21705761551856995,
-0.024631168693304062,
0.0018230878049507737,
0.10714608430862427,
-0.04574037715792656,
0.05505867674946785,
0.04043508693575859,
-0.11680828034877777,
0.008579960092902184,
-0.024663357064127922,
0.0881161242723465,
0.03604120388627052,
0.02574024721980095,
-0.08813673257827759,
0.04979274049401283,
-0.038889285176992416,
0.12143026292324066,
-0.1736958920955658,
0.3481077551841736,
-0.046825066208839417,
0.026932884007692337,
-0.04690876603126526,
0.004950621165335178,
0.07695378363132477,
0.0492960587143898,
0.24824567139148712,
0.01164959091693163,
-0.12057757377624512,
-0.10613258183002472,
-0.11921999603509903,
0.05322892591357231,
-0.038180332630872726,
-0.054455000907182693,
0.05431707575917244,
0.06727996468544006,
0.008386670611798763,
-0.01857978105545044,
0.09518375247716904,
-0.20919322967529297,
-0.1182820051908493,
0.012425289489328861,
-0.027623072266578674,
0.03467121720314026,
-0.013200432993471622,
-0.051896099001169205,
-0.10609814524650574,
-0.0328732430934906,
0.026153424754738808,
-0.029117463156580925,
-0.0903770700097084,
0.10518110543489456,
0.08115998655557632,
-0.014741683378815651,
-0.03386485576629639,
0.0018307281425222754,
0.015488320030272007,
-0.09035886079072952,
0.01268415991216898,
0.020134149119257927,
-0.07264874875545502,
0.03153754770755768,
-0.07603469491004944,
0.07471620291471481,
0.06446611136198044,
0.04833395034074783,
0.0287251528352499,
0.07062331587076187,
-0.11578827351331711,
-0.1231566071510315,
0.18432769179344177,
-0.1275404691696167,
-0.0031805424951016903,
0.04122773930430412,
0.03497263789176941,
0.0003578497562557459,
-0.13150537014007568,
0.0182771198451519,
0.23460166156291962,
0.24430835247039795,
-0.13328254222869873,
0.12212321907281876,
-0.011462921276688576,
-0.10512728989124298,
-0.16281209886074066,
-0.020879272371530533,
-0.049090053886175156,
0.050191473215818405,
0.11067666858434677,
-0.17951177060604095,
0.05445544049143791,
0.1178014725446701,
-0.0446840301156044,
0.16015943884849548,
-0.25343436002731323,
-0.030480969697237015,
0.20679216086864471,
0.050558894872665405,
0.061854228377342224,
-0.062487684190273285,
-0.018172666430473328,
-0.0036614362616091967,
-0.14449717104434967,
0.07767172157764435,
0.08835826814174652,
0.03792394697666168,
-0.019470183178782463,
0.07166563719511032,
0.003841626923531294,
-0.07685922086238861,
0.20860572159290314,
0.009991684928536415,
0.022865254431962967,
-0.04526066407561302,
-0.24333980679512024,
0.13467305898666382,
-0.019891055300831795,
0.1090494841337204,
0.059949588030576706,
0.02962493896484375,
-0.16135235130786896,
-0.010183380916714668,
-0.12618878483772278,
0.08179070055484772,
-0.07239635288715363,
-0.04414282739162445,
-0.05712189897894859,
0.014174971729516983,
-0.0828532725572586,
-0.042089708149433136,
-0.14711984992027283,
-0.05602264776825905,
0.07665193825960159,
0.03215184062719345,
0.003904573852196336,
0.05996512249112129,
-0.17785044014453888,
0.05568869039416313,
-0.012259960174560547,
0.0590427964925766,
-0.1016155555844307,
-0.12151331454515457,
0.10215862840414047,
0.03520426154136658,
0.0803036317229271,
0.07113127410411835,
-0.06564917415380478,
0.01849963143467903,
0.0544358491897583,
-0.1732739508152008,
-0.06661935895681381,
-0.05481909215450287,
0.012468484230339527,
-0.02024419605731964,
-0.09430046379566193,
0.02470194548368454,
-0.04975554719567299,
-0.024534158408641815,
-0.01359904371201992,
-0.0002086710010189563,
-0.1353820413351059,
0.03164426609873772,
0.13523386418819427,
0.09603238105773926,
-0.05444977059960365,
0.015370151028037071,
0.09205184131860733,
-0.06296884268522263,
-0.009848917834460735,
0.04449566453695297,
-0.05236179381608963,
-0.0658966600894928,
-0.06097257137298584,
0.16095159947872162,
0.04130730777978897,
-0.01357994880527258,
-0.0012178333709016442,
-0.09948597103357315,
0.005269384477287531,
0.0619351826608181,
0.08801635354757309,
-0.0007733728853054345,
-0.11643289774656296,
-0.09494519233703613,
-0.06168261915445328,
0.0016189301386475563,
0.029454847797751427,
-0.04571012407541275,
-0.13895277678966522,
0.09658399969339371,
0.006578268017619848,
0.10713261365890503,
-0.03725447505712509,
-0.0775202065706253,
-0.09634817391633987,
0.050942789763212204,
-0.016577113419771194,
0.02091428078711033,
-0.025099100545048714,
0.006544430740177631,
-0.006045914720743895,
-0.034559350460767746,
-0.0020438465289771557,
-0.01070162933319807,
-0.10863818973302841,
0.04462503269314766,
-0.004760181996971369,
0.040480729192495346,
-0.03406926989555359,
-0.03147332742810249,
0.08625280112028122,
-0.032240454107522964,
0.062011439353227615,
0.06665924191474915,
-0.07539402693510056,
-0.04867929592728615,
-0.2424738109111786,
0.016769926995038986,
0.029934272170066833,
-0.012190332636237144,
0.01837105117738247,
-0.1893644779920578,
-0.01078330259770155,
-0.04995813965797424,
-0.01600673981010914,
-0.01672806777060032,
0.0700545534491539,
-0.07035788893699646,
-0.051055122166872025,
0.10903188586235046,
-0.049533627927303314,
-0.029421547427773476,
-0.08107203990221024,
0.1887093037366867,
-0.010190869681537151,
0.11170434951782227,
0.019040096551179886,
0.15934911370277405,
-0.139051616191864,
-0.02276548556983471,
-0.05867098644375801,
-0.03357105329632759,
-0.06180531531572342,
-0.056543562561273575,
0.09323708713054657,
0.013076338917016983,
0.21065670251846313,
-0.005984197370707989,
-0.018061986193060875,
-0.023458627983927727,
0.17441198229789734,
0.021211082115769386,
-0.03323969617486,
0.10589054226875305,
0.028183240443468094,
0.004008172079920769,
0.05202683061361313,
0.05676786229014397,
-0.020436301827430725,
0.1597089022397995,
0.22638177871704102,
-0.020982351154088974,
0.11977017670869827,
0.09319204092025757,
-0.024582650512456894,
0.009821184910833836,
0.014225058257579803,
-0.11697119474411011,
0.11543674767017365,
-0.005251176189631224,
-0.037562478333711624,
-0.05142822489142418,
0.09832848608493805,
-0.07253295183181763,
0.051772359758615494,
-0.05523747578263283,
-0.09207748621702194,
-0.09552846103906631,
-0.19465938210487366,
0.04328116402029991,
0.012707225978374481,
-0.01857396773993969,
-0.04804433509707451,
-0.012292228639125824,
0.17770493030548096,
-0.053927820175886154,
0.010777858085930347,
0.09495377540588379,
0.022348888218402863,
-0.051298342645168304,
0.024496005848050117,
-0.026963969692587852,
0.005657749250531197,
-0.14382900297641754,
0.07141892611980438,
0.0003238321514800191,
-0.07924918085336685,
0.039057888090610504,
-0.001770573086105287,
-0.019289527088403702,
-0.027692141011357307,
-0.12023062258958817,
-0.06624429672956467,
-0.044373735785484314,
0.06822681427001953,
-0.00917756836861372,
0.10030611604452133,
0.056685920804739,
0.04097874090075493,
-0.00013149947335477918,
0.13740898668766022,
-0.015993904322385788,
-0.12904700636863708,
-0.06018225476145744,
0.18409624695777893,
-0.14221252501010895,
-0.013467665761709213,
-0.06351994723081589,
-0.02512737177312374,
0.0486755333840847,
0.3140629827976227,
0.31220778822898865,
-0.048326145857572556,
-0.03929755091667175,
-0.040466535836458206,
0.022786542773246765,
0.06673085689544678,
0.05332934856414795,
0.10819540172815323,
0.17565950751304626,
-0.19037571549415588,
-0.02548929490149021,
-0.179010808467865,
0.0011121616698801517,
-0.028810760006308556,
0.017782064154744148,
0.11365700513124466,
-0.06691458076238632,
-0.027980852872133255,
0.2104995995759964,
-0.16526609659194946,
-0.06997494399547577,
0.018015265464782715,
-0.07018603384494781,
-0.11965973675251007,
0.0187628585845232,
0.06104458495974541,
0.010017763823270798,
0.11936009675264359,
-0.11207315325737,
-0.07823657244443893,
0.16999968886375427,
-0.0342375747859478,
-0.13065707683563232,
-0.09866613149642944,
0.04356291890144348,
-0.1518034040927887,
0.14388003945350647,
0.01016036607325077,
0.13071036338806152,
0.058473795652389526,
0.08134454488754272,
-0.12539127469062805,
0.11444221436977386,
0.009180882945656776,
0.09565433114767075,
-0.03471670299768448,
-0.053417958319187164,
0.00021896949328947812,
-0.05752360448241234,
-0.0018941806629300117,
-0.14884671568870544,
0.07459786534309387,
0.10422100871801376,
0.015305965207517147,
-0.0006863657617941499,
0.04989583417773247,
-0.05785456299781799,
0.1138872504234314,
0.0958959311246872,
-0.06998573243618011,
0.004951742943376303,
-0.005069128703325987,
0.008871621452271938,
0.07009460777044296,
-0.050394073128700256,
-0.04097267985343933,
-0.030179103836417198,
0.011660672724246979,
-0.01333276741206646,
-0.07994460314512253,
-0.05237177759408951,
-0.06058912351727486,
-0.058181289583444595,
-0.060209885239601135,
0.06090407446026802,
0.019340036436915398,
0.03164708614349365,
0.07070208340883255,
-0.022442810237407684,
0.14947722852230072,
0.0004895447054877877,
0.1457696408033371,
-0.16368915140628815,
-0.07936849445104599
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.