modelId
stringlengths 4
112
| sha
stringlengths 40
40
| lastModified
stringlengths 24
24
| tags
sequence | pipeline_tag
stringclasses 29
values | private
bool 1
class | author
stringlengths 2
38
⌀ | config
null | id
stringlengths 4
112
| downloads
float64 0
36.8M
⌀ | likes
float64 0
712
⌀ | library_name
stringclasses 17
values | __index_level_0__
int64 0
38.5k
| readme
stringlengths 0
186k
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
cestwc/bart-base-l2s | e7ef9d2a7f9f42573f35c570c7594c4483dd02e5 | 2022-06-12T16:10:11.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | cestwc | null | cestwc/bart-base-l2s | 3 | null | transformers | 22,300 | Entry not found |
cestwc/roberta-base-unigram-ternary-wikilingua | 6f2299cb1cda54eb899af996770b182f2f46e8a5 | 2022-05-01T09:11:03.000Z | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | cestwc | null | cestwc/roberta-base-unigram-ternary-wikilingua | 3 | null | transformers | 22,301 | Entry not found |
Muennighoff/p-1-512-fp32 | d5930757a5257094efadac0d0f33bc47292e3fd8 | 2022-05-01T12:32:58.000Z | [
"pytorch",
"t5",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | Muennighoff | null | Muennighoff/p-1-512-fp32 | 3 | null | transformers | 22,302 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: p-1-512-fp32
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.3108
---
<!-- 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. -->
# p-1-512-fp32
This model is a fine-tuned version of [Muennighoff/t5-small-finetuned-xsum-512](https://huggingface.co/Muennighoff/t5-small-finetuned-xsum-512) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 16.6408
- Rouge1: 0.3108
- Rouge2: 0.0
- Rougel: 0.3091
- Rougelsum: 0.3102
- Gen Len: 18.8095
## 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 17.3139 | 1.0 | 7854 | 16.6408 | 0.3108 | 0.0 | 0.3091 | 0.3102 | 18.8095 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.10.2
- Datasets 2.1.0
- Tokenizers 0.12.1
|
cuzeverynameistaken/wav2vec2-base-timit-demo-colab1 | 107649d75fe283600856f7a9e294ad7d185bfd0e | 2022-05-01T19:55:38.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | cuzeverynameistaken | null | cuzeverynameistaken/wav2vec2-base-timit-demo-colab1 | 3 | null | transformers | 22,303 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab1
results: []
---
<!-- 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. -->
# wav2vec2-base-timit-demo-colab1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7170
- Wer: 0.4784
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.1915 | 13.89 | 500 | 3.1318 | 1.0 |
| 1.4993 | 27.78 | 1000 | 0.6736 | 0.5485 |
| 0.3416 | 41.67 | 1500 | 0.7111 | 0.5092 |
| 0.1937 | 55.56 | 2000 | 0.7170 | 0.4784 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
hf-internal-testing/wav2vec2-conformer-frame-class | b2978bb62cbc583ef7c6ca704712d22726c44c23 | 2022-05-01T16:03:38.000Z | [
"pytorch",
"wav2vec2-conformer",
"audio-frame-classification",
"transformers"
] | null | false | hf-internal-testing | null | hf-internal-testing/wav2vec2-conformer-frame-class | 3 | null | transformers | 22,304 | Entry not found |
PSW/mixed_sim3_seed42 | 9184591542fc490d3f4044adf30b68659a75bad1 | 2022-05-02T03:37:02.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/mixed_sim3_seed42 | 3 | null | transformers | 22,305 | Entry not found |
Martin97Bozic/bert-base-multilingual-uncased-finetuned-squad | 6f793af4e417e3b1c4c69dc9647391df7b020137 | 2022-05-04T14:13:34.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | question-answering | false | Martin97Bozic | null | Martin97Bozic/bert-base-multilingual-uncased-finetuned-squad | 3 | null | transformers | 22,306 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-multilingual-uncased-finetuned-squad
results: []
---
<!-- 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. -->
# bert-base-multilingual-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0109
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0252 | 1.0 | 3163 | 0.9733 |
| 0.7401 | 2.0 | 6326 | 0.9607 |
| 0.516 | 3.0 | 9489 | 1.0109 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
waboucay/camembert-base-finetuned-xnli_fr-finetuned-nli-rua_wl | 05756090a2c201536fba0e794abdd99630759a0d | 2022-05-02T14:00:24.000Z | [
"pytorch",
"camembert",
"text-classification",
"fr",
"transformers",
"nli"
] | text-classification | false | waboucay | null | waboucay/camembert-base-finetuned-xnli_fr-finetuned-nli-rua_wl | 3 | null | transformers | 22,307 | ---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 69.9 | 69.9 |
| test | 68.8 | 68.8 | |
niklaspm/linkbert-large-finetuned-squad | ec33b5109830a7e32076a3af678e318a5e9ca574 | 2022-05-03T07:51:30.000Z | [
"pytorch",
"bert",
"question-answering",
"arxiv:2203.15827",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | niklaspm | null | niklaspm/linkbert-large-finetuned-squad | 3 | null | transformers | 22,308 | ---
license: apache-2.0
---
---
license: apache-2.0
---
**Exact Match** 92.68
**F1** 86.5
Checkout [linkbert-base-finetuned-squad](https://huggingface.co/niklaspm/linkbert-base-finetuned-squad)
See [LinkBERT Paper](https://arxiv.org/abs/2203.15827) |
armanc/affiliations-roberta-base-0.0.1 | 9d66b84fd6cd201f2b67bcb38d1049b567dbff76 | 2022-05-02T20:31:34.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | false | armanc | null | armanc/affiliations-roberta-base-0.0.1 | 3 | null | transformers | 22,309 | Entry not found |
veronica320/ADEPT_bert | c014549bc100bd6addfbe22e964c034cff33a438 | 2022-05-03T02:23:38.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | veronica320 | null | veronica320/ADEPT_bert | 3 | null | transformers | 22,310 | Entry not found |
veronica320/ADEPT_roberta | 8fd7efec24b425fb788a84d822824382acf2679a | 2022-05-03T02:25:24.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | veronica320 | null | veronica320/ADEPT_roberta | 3 | null | transformers | 22,311 | Entry not found |
PSW/min_senttrm_del_seed1 | 722ad22042a7b3503fda95d52fd1f7b9bec5694a | 2022-05-03T13:52:08.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/min_senttrm_del_seed1 | 3 | null | transformers | 22,312 | Entry not found |
IsekaiMeta/dapprf3 | 28a9bf9df01c6070b0b852ef8bfb90c028458ffb | 2022-05-03T17:55:50.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | IsekaiMeta | null | IsekaiMeta/dapprf3 | 3 | null | transformers | 22,313 | ---
tags:
- conversational
---
#dapprf3 |
laituan245/molt5-base-smiles2caption | 7b7d4b0ab8b66b351e669b1f66272418ba15c3d9 | 2022-05-03T18:07:57.000Z | [
"pytorch",
"t5",
"text2text-generation",
"arxiv:2204.11817",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | laituan245 | null | laituan245/molt5-base-smiles2caption | 3 | null | transformers | 22,314 | ---
license: apache-2.0
---
This model can be used to generate an input caption from a SMILES string.
## Example Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-base-smiles2caption", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-base-smiles2caption')
input_text = 'C1=CC2=C(C(=C1)[O-])NC(=CC2=O)C(=O)O'
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids, num_beams=5, max_length=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Paper
For more information, please take a look at our paper.
Paper: [Translation between Molecules and Natural Language](https://arxiv.org/abs/2204.11817)
Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji*
|
r4ndomw4lk/distilbert-base-uncased-finetuned-emotion | aa2cb3084007bdb699253e45cd90fa6f43550e2d | 2022-05-03T18:28:02.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | r4ndomw4lk | null | r4ndomw4lk/distilbert-base-uncased-finetuned-emotion | 3 | null | transformers | 22,315 | Entry not found |
chrisvinsen/xlsr-wav2vec2-base-commonvoice-demo-colab-5 | 9a830f2d577f827cf8435cf459835a7a0a06d66a | 2022-05-05T03:52:35.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | chrisvinsen | null | chrisvinsen/xlsr-wav2vec2-base-commonvoice-demo-colab-5 | 3 | null | transformers | 22,316 | Entry not found |
DioLiu/distilroberta-base-less-Taylor | a9ace371d88e0febdc81647dce54cfec434a2d36 | 2022-05-04T06:24:21.000Z | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | DioLiu | null | DioLiu/distilroberta-base-less-Taylor | 3 | null | transformers | 22,317 | Entry not found |
waboucay/camembert-base-finetuned-nli-repnum_wl | 04368878c13ac47105e493c39e2eecc447a9c259 | 2022-05-04T09:27:26.000Z | [
"pytorch",
"camembert",
"text-classification",
"fr",
"transformers",
"nli"
] | text-classification | false | waboucay | null | waboucay/camembert-base-finetuned-nli-repnum_wl | 3 | null | transformers | 22,318 | ---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 74.6 | 74.5 |
| test | 77.8 | 77.8 |
|
waboucay/camembert-base-finetuned-xnli_fr-finetuned-nli-repnum_wl | 71e73c5a65f63da08104c8ae9163a5ae69c63497 | 2022-05-04T09:31:42.000Z | [
"pytorch",
"camembert",
"text-classification",
"fr",
"transformers",
"nli"
] | text-classification | false | waboucay | null | waboucay/camembert-base-finetuned-xnli_fr-finetuned-nli-repnum_wl | 3 | null | transformers | 22,319 | ---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 73.3 | 73.3 |
| test | 69.4 | 69.4 |
|
jenspt/roberta | 38ab3133fa4b720a0e285bd4be76c2eb572b6ce7 | 2022-05-04T13:30:54.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | jenspt | null | jenspt/roberta | 3 | null | transformers | 22,320 | Entry not found |
vblagoje/greaselm-obqa | b92d40155f3783e9dc952b86cf3f77614ef64c94 | 2022-05-28T14:02:15.000Z | [
"pytorch",
"greaselm",
"transformers"
] | null | false | vblagoje | null | vblagoje/greaselm-obqa | 3 | null | transformers | 22,321 | Entry not found |
BigSalmon/MediumInformalToFormalLincoln4 | 31e57f9f27d34c6ec73b383e5f1cb2ce9db15198 | 2022-05-04T21:12:03.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | BigSalmon | null | BigSalmon/MediumInformalToFormalLincoln4 | 3 | null | transformers | 22,322 | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/MediumInformalToFormalLincoln4")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/MediumInformalToFormalLincoln4")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
``` |
PSW/low_resource_percent1_maxsimins_seed1 | 47c324b8bc29509b8cbfe0718a079abc9817b24e | 2022-05-05T06:19:01.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent1_maxsimins_seed1 | 3 | null | transformers | 22,323 | Entry not found |
PSW/low_resource_percent1_maxsimins_seed27 | 5b0452c29e8d86252cbc4fa85c18ceb1c223832c | 2022-05-05T06:30:06.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent1_maxsimins_seed27 | 3 | null | transformers | 22,324 | Entry not found |
PSW/low_resource_percent1_minsimdel_seed1 | 011c9bda560667176ea79ff5a767ce1d2765659e | 2022-05-05T07:24:48.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent1_minsimdel_seed1 | 3 | null | transformers | 22,325 | Entry not found |
PSW/low_resource_percent1_minsimdel_seed27 | b330170e8ed039ab1619d63206042d4a240b4a0e | 2022-05-05T07:35:28.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent1_minsimdel_seed27 | 3 | null | transformers | 22,326 | Entry not found |
CarlCochet/trajectory-transformer-ant-medium-v2 | d05ef7360952a716d8d6852fd8ebe687c09fc1a3 | 2022-05-12T16:57:57.000Z | [
"pytorch",
"trajectory_transformer",
"feature-extraction",
"transformers",
"license:mit"
] | feature-extraction | false | CarlCochet | null | CarlCochet/trajectory-transformer-ant-medium-v2 | 3 | null | transformers | 22,327 | ---
license: mit
---
|
CarlCochet/trajectory-transformer-halfcheetah-medium-expert-v2 | 93b1ac9bcca494f9c07c965548a57b0cbdf9bd4b | 2022-05-12T17:01:20.000Z | [
"pytorch",
"trajectory_transformer",
"feature-extraction",
"transformers",
"license:mit"
] | feature-extraction | false | CarlCochet | null | CarlCochet/trajectory-transformer-halfcheetah-medium-expert-v2 | 3 | null | transformers | 22,328 | ---
license: mit
---
|
CarlCochet/trajectory-transformer-hopper-medium-replay-v2 | 3957253bdb8022c2b6496250b5a007332a2c1c81 | 2022-05-12T17:04:54.000Z | [
"pytorch",
"trajectory_transformer",
"feature-extraction",
"transformers",
"license:mit"
] | feature-extraction | false | CarlCochet | null | CarlCochet/trajectory-transformer-hopper-medium-replay-v2 | 3 | null | transformers | 22,329 | ---
license: mit
---
|
CarlCochet/trajectory-transformer-walker2d-medium-replay-v2 | 2357417b0beede5d88d9f8604faf307a10f820ef | 2022-05-12T17:07:29.000Z | [
"pytorch",
"trajectory_transformer",
"feature-extraction",
"transformers",
"license:mit"
] | feature-extraction | false | CarlCochet | null | CarlCochet/trajectory-transformer-walker2d-medium-replay-v2 | 3 | null | transformers | 22,330 | ---
license: mit
---
|
DioLiu/distilroberta-base-OnlyShakeMask | 92f670fdadc94dfe6e65aaae70d147b5fd15c38a | 2022-05-05T11:29:27.000Z | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | DioLiu | null | DioLiu/distilroberta-base-OnlyShakeMask | 3 | null | transformers | 22,331 | Entry not found |
PSW/low_resource_percent10_minmaxswap_seed42 | 58b55714ce0de848627d9f62e49ee6efac23b587 | 2022-05-05T11:14:48.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_minmaxswap_seed42 | 3 | null | transformers | 22,332 | Entry not found |
ghabin/dystopian_romans | 40279ac918f7f877180cfb745d3a47ffc4ea7f4d | 2022-05-05T11:30:53.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"license:afl-3.0"
] | text-generation | false | ghabin | null | ghabin/dystopian_romans | 3 | null | transformers | 22,333 | ---
license: afl-3.0
---
|
PSW/low_resource_percent10_minsimdel_seed27 | 5b1dcd10f03b4d1454798c319a2a790439d9464f | 2022-05-05T11:44:37.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent10_minsimdel_seed27 | 3 | null | transformers | 22,334 | Entry not found |
catofnull/BERT-fold1 | 1c9d397cac144cc77ec30d0a7f5258dee82d884a | 2022-05-05T11:46:22.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | catofnull | null | catofnull/BERT-fold1 | 3 | null | transformers | 22,335 | Entry not found |
SophieTr/PP0_rm_v1_gpu | 7e87ed07a67080cd1f0a427a1d8f27c716b6c585 | 2022-05-05T12:28:09.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | SophieTr | null | SophieTr/PP0_rm_v1_gpu | 3 | null | transformers | 22,336 | Entry not found |
AlekseyKorshuk/opt-350m | cd1d4cf5293286eb467434036c6aeba040c740ac | 2022-06-25T16:47:22.000Z | [
"pytorch",
"opt",
"text-generation",
"transformers",
"license:apache-2.0"
] | text-generation | false | AlekseyKorshuk | null | AlekseyKorshuk/opt-350m | 3 | 1 | transformers | 22,337 | ---
license: apache-2.0
---
|
dyyyyyyyy/xTune_squad_XLM-RoBERTa-base | 05eecb3bf3a6f7c69ce5b52a28851a0270bc0264 | 2022-05-05T14:08:27.000Z | [
"pytorch",
"xlm-roberta",
"transformers"
] | null | false | dyyyyyyyy | null | dyyyyyyyy/xTune_squad_XLM-RoBERTa-base | 3 | null | transformers | 22,338 | Entry not found |
tau/False_large_pmi_para0_sent1_span2_itFalse_sargmax_rrFalse_8_1024_0.15_1 | c5496aed39d255628b3d2a2e2ce860a9d3962260 | 2022-05-05T14:01:41.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | tau | null | tau/False_large_pmi_para0_sent1_span2_itFalse_sargmax_rrFalse_8_1024_0.15_1 | 3 | null | transformers | 22,339 | Entry not found |
AlekseyKorshuk/opt-125m | 5841a04877d7765fb33c4376d3217158e90a0dca | 2022-05-05T17:42:11.000Z | [
"pytorch",
"opt",
"text-generation",
"transformers",
"license:apache-2.0"
] | text-generation | false | AlekseyKorshuk | null | AlekseyKorshuk/opt-125m | 3 | null | transformers | 22,340 | ---
license: apache-2.0
---
|
nguyenmanhbao/finetuning-sentiment-model-3000-samples | f64c86098f1b069447b44e0f8065867e7ea1fd59 | 2022-05-05T19:18:18.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | nguyenmanhbao | null | nguyenmanhbao/finetuning-sentiment-model-3000-samples | 3 | null | transformers | 22,341 | Entry not found |
ekimz/t5_ttmodel | ac27f40a3622d50e153e0c80d01fdccf6cb68078 | 2022-05-05T19:55:05.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | ekimz | null | ekimz/t5_ttmodel | 3 | null | transformers | 22,342 | Entry not found |
PSW/low_resource_percent20_seed1 | 30c919fcaeaefb1490a7072deaa3c984951bf2fb | 2022-05-05T20:03:33.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_seed1 | 3 | null | transformers | 22,343 | Entry not found |
huggingtweets/theovalpawffice | 3e324906eadb0cc7abd571bc62bd5b69f05c4a21 | 2022-05-05T20:41:08.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/theovalpawffice | 3 | null | transformers | 22,344 | ---
language: en
thumbnail: http://www.huggingtweets.com/theovalpawffice/1651782387551/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1346560834613469184/LJVlGDRS_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">The Oval Pawffice® 🇺🇸 DOTUS Fans</div>
<div style="text-align: center; font-size: 14px;">@theovalpawffice</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from The Oval Pawffice® 🇺🇸 DOTUS Fans.
| Data | The Oval Pawffice® 🇺🇸 DOTUS Fans |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 68 |
| Short tweets | 106 |
| Tweets kept | 3076 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/uraeqzqr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @theovalpawffice's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3jrqovr7) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3jrqovr7/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/theovalpawffice')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
armanc/affiliations-roberta-base-step18K-loss-0.099 | 2e6afb8881cb7e26fd112b5c2c2dd01f90a2931f | 2022-05-06T02:53:52.000Z | [
"pytorch",
"transformers"
] | null | false | armanc | null | armanc/affiliations-roberta-base-step18K-loss-0.099 | 3 | null | transformers | 22,345 | Entry not found |
xingqiang/nezha-zh-address-match-finetuned | b1ed84e39535ecbf4eb59a589fc18afe8d1f9967 | 2022-06-03T07:47:03.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | xingqiang | null | xingqiang/nezha-zh-address-match-finetuned | 3 | null | transformers | 22,346 | ### 中文地址匹配任务 |
shoubhik/electra_abbv_20k_data_multilabel_auc_0.89 | 21a5dc5758c332f59cfbfb7b731d88604b21f2fb | 2022-05-06T05:40:59.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | false | shoubhik | null | shoubhik/electra_abbv_20k_data_multilabel_auc_0.89 | 3 | null | transformers | 22,347 | Entry not found |
catofnull/Pretrain3-fold1 | 0b0c2dfe02fa6cfd76ed0c8733419cb134211f4a | 2022-05-11T07:04:32.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | catofnull | null | catofnull/Pretrain3-fold1 | 3 | null | transformers | 22,348 | Entry not found |
PrajwalS/wav2vec2_custom_model_50 | b43addea9e403fd96a74bfbcaab3ade9670ae798 | 2022-05-08T16:33:22.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | PrajwalS | null | PrajwalS/wav2vec2_custom_model_50 | 3 | null | transformers | 22,349 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2_custom_model_50
results: []
---
<!-- 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. -->
# wav2vec2_custom_model_50
This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the None 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 1.18.4
- Tokenizers 0.11.6
|
crabz/exp4 | 1ee8b52b44562c7e31d6d200f158163807b67154 | 2022-05-06T10:02:47.000Z | [
"pytorch",
"roberta",
"transformers"
] | null | false | crabz | null | crabz/exp4 | 3 | null | transformers | 22,350 | Entry not found |
lucifermorninstar011/autotrain-lucifer_multi_auto-831626529 | 936872923add3566410905f1daff8a889f618b84 | 2022-05-07T05:22:24.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:lucifermorninstar011/autotrain-data-lucifer_multi_auto",
"transformers",
"autotrain",
"co2_eq_emissions"
] | text-classification | false | lucifermorninstar011 | null | lucifermorninstar011/autotrain-lucifer_multi_auto-831626529 | 3 | null | transformers | 22,351 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- lucifermorninstar011/autotrain-data-lucifer_multi_auto
co2_eq_emissions: 1418.583772776962
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 831626529
- CO2 Emissions (in grams): 1418.583772776962
## Validation Metrics
- Loss: 0.019245266914367676
- Accuracy: 0.9971231760498559
- Macro F1: 0.9917225353498834
- Micro F1: 0.9971231760498559
- Weighted F1: 0.9971219017846226
- Macro Precision: 0.9903556981858435
- Micro Precision: 0.9971231760498559
- Weighted Precision: 0.9971268798191825
- Macro Recall: 0.9931423442532272
- Micro Recall: 0.9971231760498559
- Weighted Recall: 0.9971231760498559
## 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 AutoTrain"}' https://api-inference.huggingface.co/models/lucifermorninstar011/autotrain-lucifer_multi_auto-831626529
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("lucifermorninstar011/autotrain-lucifer_multi_auto-831626529", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("lucifermorninstar011/autotrain-lucifer_multi_auto-831626529", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
huggingtweets/finnegansreader | 2dc88f45cea10537930d2798a1aa6efa6ea276d2 | 2022-05-06T19:04:03.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/finnegansreader | 3 | null | transformers | 22,352 | ---
language: en
thumbnail: http://www.huggingtweets.com/finnegansreader/1651863836821/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/378800000425274798/e6f9ae4914b86c7be5bd1e68d451b2cd_400x400.jpeg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Finnegans Wake</div>
<div style="text-align: center; font-size: 14px;">@finnegansreader</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Finnegans Wake.
| Data | Finnegans Wake |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 0 |
| Short tweets | 0 |
| Tweets kept | 3250 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/26stpp9q/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @finnegansreader's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2s557xc1) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2s557xc1/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/finnegansreader')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
allenai/tk-instruct-3b-pos | 33868e0032073f2a6183a87a19d0cc1a7bc1bee8 | 2022-05-27T06:30:40.000Z | [
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:natural instructions v2.0",
"arxiv:1910.10683",
"arxiv:2204.07705",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | text2text-generation | false | allenai | null | allenai/tk-instruct-3b-pos | 3 | null | transformers | 22,353 | ---
language: en
license: apache-2.0
datasets:
- natural instructions v2.0
---
# Model description
Tk-Instruct is a series of encoder-decoder Transformer models that are trained to solve various NLP tasks by following in-context instructions (plain language task definitions, k-shot examples, explanations, etc). Built upon the pre-trained [T5 models](https://arxiv.org/abs/1910.10683), they are fine-tuned on a large number of tasks & instructions that are collected in the [Natural Instructions benchmark](https://github.com/allenai/natural-instructions), which contains 1600+ tasks in 70+ broach categories in total. This enables the model to not only process the training tasks, but also generalize to many unseen tasks without further parameter update.
More resources for using the model:
- **Paper**: [link](https://arxiv.org/abs/2204.07705)
- **Code repository**: [Tk-Instruct](https://github.com/yizhongw/Tk-Instruct)
- **Official Website**: [Natural Instructions](https://instructions.apps.allenai.org/)
- **All released models**: [allenai/tk-instruct](https://huggingface.co/models?search=allenai/tk-instruct)
## Intended uses & limitations
Tk-Instruct can be used to do many NLP tasks by following instructions.
### How to use
When instructing the model, task definition or demonstration examples or explanations should be prepended to the original input and fed into the model. You can easily try Tk-Instruct models as follows:
```python
>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
>>> tokenizer = AutoTokenizer.from_pretrained("allenai/tk-instruct-3b-def")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("allenai/tk-instruct-3b-def")
>>> input_ids = tokenizer.encode(
"Definition: return the currency of the given country. Now complete the following example - Input: India. Output:",
return_tensors="pt")
>>> output = model.generate(input_ids, max_length=10)
>>> output = tokenizer.decode(output[0], skip_special_tokens=True) # model should output 'Indian Rupee'
>>> input_ids = tokenizer.encode(
"Definition: negate the following sentence. Input: John went to school. Output:",
return_tensors="pt")
>>> output = model.generate(input_ids, max_length=10)
>>> output = tokenizer.decode(output[0], skip_special_tokens=True) # model should output 'John did not go to shool.'
```
### Limitations
We are still working on understanding the behaviors of these models, but here are several issues we have found:
- Models are generally sensitive to the instruction. Sometimes rewording the instruction can lead to very different output.
- Models are not always compliant to the instruction. Sometimes the model don't follow your instruction (e.g., when you ask the model to generate one sentence, it might still generate one word or a long story).
- Models might totally fail on some tasks.
If you find serious issues or any interesting result, you are welcome to share with us!
## Training data
Tk-Instruct is trained using the tasks & instructions in [Natural Instructions benchmark](https://github.com/allenai/natural-instructions), which contains 1600+ tasks in 70+ broach categories in total. We follow the official train/test split. Tk-Instruct model series were trained using 757 tasks, and mTk-Instruct series were trained using 1271 tasks (including some non-English tasks).
The training tasks are in 64 broad categories, such as text categorization / question answering / sentiment analysis / summarization / grammar error detection / dialogue generation / etc. The other 12 categories are selected for evaluation.
## Training procedure
All our models are initialized from either T5 models or mT5 models. Because generating the output can be regarded as a form of language modeling, we used their [LM adapted version](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k). All data is converted into a text-to-text format, and models are fine-tuned to maximize the likelihood of the output sequence.
Our [released models](https://huggingface.co/models?search=allenai/tk-instruct) are in different sizes, and each of them was trained with a specific type of instruction encoding. For instance, `tk-instruct-3b-def-pos` was initialized from [t5-xl-lm-adapt](https://huggingface.co/google/t5-xl-lm-adapt), and it saw task definition & 2 positive examples as the instruction during training time.
Although they are trained with only one type of instruction encodings, we found they can usually work with other type of encodings at test time (see more in our paper).
### BibTeX entry and citation info
```bibtex
@article{wang2022benchmarking,
title={Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and A. Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and M. Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddharth Deepak Mishra and Sujan C. Reddy and Sumanta Patro and Tanay Dixit and Xu-dong Shen and Chitta Baral and Yejin Choi and Hannaneh Hajishirzi and Noah A. Smith and Daniel Khashabi},
year={2022},
archivePrefix={arXiv},
eprint={2204.07705},
primaryClass={cs.CL},
}
``` |
kneis/distilbert-sentiment-adversarial-training | f0b2dae7a63cd8cdd3a1a1ece676a92b79d20237 | 2022-05-06T21:54:42.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | false | kneis | null | kneis/distilbert-sentiment-adversarial-training | 3 | null | transformers | 22,354 | Entry not found |
VoltaicDaniel/distilgpt2-finetuned-wikitext2 | 61b6c50cb45c41be24bb348d0caf4ee4cd2adc9e | 2022-05-08T03:51:55.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-generation | false | VoltaicDaniel | null | VoltaicDaniel/distilgpt2-finetuned-wikitext2 | 3 | null | transformers | 22,355 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- 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. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1909
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 18 | 4.2070 |
| No log | 2.0 | 36 | 4.1958 |
| No log | 3.0 | 54 | 4.1909 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
bko/bert-base-uncased-finetuned-swag | 088f1f2b7226f71a7b4fffabb1bfeddc969588cc | 2022-05-07T11:41:33.000Z | [
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"dataset:swag",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | multiple-choice | false | bko | null | bko/bert-base-uncased-finetuned-swag | 3 | null | transformers | 22,356 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- swag
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-swag
results: []
---
<!-- 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. -->
# bert-base-uncased-finetuned-swag
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the swag dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0099
- Accuracy: 0.7917
## 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: 5e-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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7577 | 1.0 | 4597 | 0.6133 | 0.7624 |
| 0.3729 | 2.0 | 9194 | 0.6351 | 0.7841 |
| 0.1405 | 3.0 | 13791 | 1.0099 | 0.7917 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Khalsuu/english-filipino-wav2vec2-l-xls-r-test-07 | da15d6464af6a42cb2d7bac9dbff2f9a4003c496 | 2022-05-07T19:19:09.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:filipino_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Khalsuu | null | Khalsuu/english-filipino-wav2vec2-l-xls-r-test-07 | 3 | null | transformers | 22,357 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- filipino_voice
model-index:
- name: english-filipino-wav2vec2-l-xls-r-test-07
results: []
---
<!-- 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. -->
# english-filipino-wav2vec2-l-xls-r-test-07
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the filipino_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6768
- Wer: 0.3755
## 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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.9255 | 2.09 | 400 | 0.7742 | 0.7694 |
| 0.5792 | 4.19 | 800 | 0.5368 | 0.5250 |
| 0.3611 | 6.28 | 1200 | 0.4796 | 0.4718 |
| 0.2742 | 8.38 | 1600 | 0.5308 | 0.4764 |
| 0.201 | 10.47 | 2000 | 0.5885 | 0.4723 |
| 0.164 | 12.57 | 2400 | 0.5595 | 0.4750 |
| 0.1374 | 14.66 | 2800 | 0.5836 | 0.4366 |
| 0.1138 | 16.75 | 3200 | 0.6110 | 0.4628 |
| 0.0991 | 18.85 | 3600 | 0.6179 | 0.4174 |
| 0.0837 | 20.94 | 4000 | 0.6681 | 0.4170 |
| 0.0722 | 23.04 | 4400 | 0.6665 | 0.4103 |
| 0.0576 | 25.13 | 4800 | 0.7538 | 0.4068 |
| 0.052 | 27.23 | 5200 | 0.6808 | 0.3844 |
| 0.0449 | 29.32 | 5600 | 0.6768 | 0.3755 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
scasutt/wav2vec2-large-xlsr-52_Swiss_German | b34b5ecf37d128ca97bf37e5516e9323cb6d20c1 | 2022-05-25T10:38:04.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | scasutt | null | scasutt/wav2vec2-large-xlsr-52_Swiss_German | 3 | null | transformers | 22,358 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-53_full_train
results: []
---
<!-- 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. -->
# wav2vec2-large-xlsr-53_full_train
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the Swissdial dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2811
- Wer: 0.2909
## Model description
Wav2Vec2-XLSR-53 trained on augmented Swiss Dial dataset
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.7666 | 2.69 | 1000 | 0.4356 | 0.4954 |
| 0.7868 | 5.39 | 2000 | 0.2693 | 0.3180 |
| 0.6948 | 8.09 | 3000 | 0.2811 | 0.2909 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.1
- Tokenizers 0.12.1
|
vuiseng9/nncf-qat-kd-bert-l-squadv1.1-sl256 | c65b8ea22ab613209d6941bf2ef87139d2e8ef31 | 2022-05-07T17:15:19.000Z | [
"pytorch",
"onnx",
"bert",
"dataset:squad",
"transformers",
"license:apache-2.0",
"model-index"
] | null | false | vuiseng9 | null | vuiseng9/nncf-qat-kd-bert-l-squadv1.1-sl256 | 3 | null | transformers | 22,359 | ---
license: apache-2.0
datasets:
- squad
model-index:
- name: nncf-qat-kd-bert-l-squadv1.1-sl256
results: []
---
This model is quantized version of ```vuiseng9/bert-l-squadv1.1-sl256``` using OpenVINO NNCF.
### Training
```bash
# used 4xV100 GPUS
# --fp16 for lower turnaround and resource requirement
python run_qa.py \
--model_name_or_path vuiseng9/bert-l-squadv1.1-sl256 \
--dataset_name squad \
--do_eval \
--do_train \
--evaluation_strategy steps \
--eval_steps 250 \
--learning_rate 3e-5 \
--fp16 \
--num_train_epochs 2 \
--per_device_eval_batch_size 64 \
--per_device_train_batch_size 8 \
--max_seq_length 256 \
--doc_stride 128 \
--save_steps 500 \
--logging_steps 1 \
--overwrite_output_dir \
--nncf_config nncf_bert_config_squad_kd.json \ #stock config which has seq.len modified to 256.
--run_name $RUNID \
--output_dir $OUTDIR
```
### Evaluation
Require ```vuiseng9/transformers (fork)``` , commit: ```ff24569b```, NNCF v2.1+ commit (```8e26365```)
```bash
git clone https://huggingface.co/vuiseng9/nncf-qat-kd-bert-l-squadv1.1-sl256
python run_qa.py \
--model_name_or_path ./nncf-qat-kd-bert-l-squadv1.1-sl256 \
--dataset_name squad \
--nncf_config ./nncf-qat-kd-bert-l-squadv1.1-sl256/nncf_bert_config_squad_kd.json \
--nncf_ckpt ./nncf-qat-kd-bert-l-squadv1.1-sl256 \
--do_eval \
--per_device_eval_batch_size 128 \
--max_seq_length 256 \
--doc_stride 128 \
--output_dir /tmp/eval-nncf-qat-kd-bert-l-squadv1.1-sl256 \
--overwrite_output_dir
```
### Results
```
eval_exact_match = 87.1902
eval_f1 = 93.0286
eval_samples = 12097
``` |
lucifermorninstar011/autotrain-lucifer_ner_multi-838326726 | f648c2bb4a3d8e9d048bee5be108eed68f3b0913 | 2022-05-08T11:40:07.000Z | [
"pytorch",
"distilbert",
"token-classification",
"en",
"dataset:lucifermorninstar011/autotrain-data-lucifer_ner_multi",
"transformers",
"autotrain",
"co2_eq_emissions",
"autotrain_compatible"
] | token-classification | false | lucifermorninstar011 | null | lucifermorninstar011/autotrain-lucifer_ner_multi-838326726 | 3 | null | transformers | 22,360 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- lucifermorninstar011/autotrain-data-lucifer_ner_multi
co2_eq_emissions: 3.409136901758606
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 838326726
- CO2 Emissions (in grams): 3.409136901758606
## Validation Metrics
- Loss: 0.003970975521951914
- Accuracy: 0.9991803230435035
- Precision: 0.9969928464523109
- Recall: 0.997096050476826
- F1: 0.9970444457939075
## 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 AutoTrain"}' https://api-inference.huggingface.co/models/lucifermorninstar011/autotrain-lucifer_ner_multi-838326726
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("lucifermorninstar011/autotrain-lucifer_ner_multi-838326726", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("lucifermorninstar011/autotrain-lucifer_ner_multi-838326726", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
Jiexing/sparc_add_coref_and_depen_t5_3b-2304 | 29ceb5b160e8cf12bdff8add554301672c78eabc | 2022-05-08T04:57:08.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Jiexing | null | Jiexing/sparc_add_coref_and_depen_t5_3b-2304 | 3 | null | transformers | 22,361 | Entry not found |
DioLiu/distilroberta-base-OnlyWikiMask | b3389921de0485bf4cc7c39871f39b8e8994d981 | 2022-05-08T08:23:50.000Z | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | DioLiu | null | DioLiu/distilroberta-base-OnlyWikiMask | 3 | null | transformers | 22,362 | Entry not found |
anuragshas/wav2vec2-xls-r-300m-mr-cv9-with-lm | d97a00805336f02b81f689d53dc2ef0523875277 | 2022-05-17T22:48:20.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mr",
"dataset:mozilla-foundation/common_voice_9_0",
"transformers",
"mozilla-foundation/common_voice_9_0",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | anuragshas | null | anuragshas/wav2vec2-xls-r-300m-mr-cv9-with-lm | 3 | null | transformers | 22,363 | ---
language:
- mr
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_9_0
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_9_0
metrics:
- wer
model-index:
- name: XLS-R-300M - Marathi
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_9_0
name: Common Voice 9
args: mr
metrics:
- type: wer
value: 23.841
name: Test WER
- name: Test CER
type: cer
value: 5.522
---
<!-- 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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - MR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3642
- Wer: 0.4190
- Cer: 0.0946
## 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: 7.5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 6124
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 3.5184 | 12.9 | 400 | 3.4210 | 1.0 | 1.0 |
| 2.3797 | 25.81 | 800 | 1.1068 | 0.8389 | 0.2584 |
| 1.5022 | 38.71 | 1200 | 0.5278 | 0.6280 | 0.1517 |
| 1.3181 | 51.61 | 1600 | 0.4254 | 0.5587 | 0.1297 |
| 1.2037 | 64.52 | 2000 | 0.3836 | 0.5143 | 0.1176 |
| 1.1245 | 77.42 | 2400 | 0.3643 | 0.4871 | 0.1111 |
| 1.0582 | 90.32 | 2800 | 0.3562 | 0.4676 | 0.1062 |
| 1.0027 | 103.23 | 3200 | 0.3530 | 0.4625 | 0.1058 |
| 0.9382 | 116.13 | 3600 | 0.3388 | 0.4442 | 0.1002 |
| 0.8915 | 129.03 | 4000 | 0.3430 | 0.4427 | 0.1000 |
| 0.853 | 141.94 | 4400 | 0.3536 | 0.4375 | 0.1000 |
| 0.8127 | 154.84 | 4800 | 0.3511 | 0.4344 | 0.0986 |
| 0.7861 | 167.74 | 5200 | 0.3595 | 0.4372 | 0.0993 |
| 0.7619 | 180.65 | 5600 | 0.3628 | 0.4316 | 0.0985 |
| 0.7537 | 193.55 | 6000 | 0.3633 | 0.4174 | 0.0943 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.1.1.dev0
- Tokenizers 0.12.1
|
lvwerra/gpt2-imdb-pos-v2 | 42792eafd6ad310c8cc41fddc52fd7e8f14ede4c | 2022-05-08T20:09:07.000Z | [
"pytorch",
"gpt2",
"transformers"
] | null | false | lvwerra | null | lvwerra/gpt2-imdb-pos-v2 | 3 | null | transformers | 22,364 | Entry not found |
theojolliffe/bart-cnn-pubmed-arxiv-pubmed-v3-e12 | 0d9cf814802dc6b319322a0378423472c9dc8bae | 2022-05-08T23:01:48.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | theojolliffe | null | theojolliffe/bart-cnn-pubmed-arxiv-pubmed-v3-e12 | 3 | null | transformers | 22,365 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-cnn-pubmed-arxiv-pubmed-v3-e12
results: []
---
<!-- 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. -->
# bart-cnn-pubmed-arxiv-pubmed-v3-e12
This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8658
- Rouge1: 57.2678
- Rouge2: 43.347
- Rougel: 47.0854
- Rougelsum: 55.4167
- Gen Len: 142.0
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 1.2548 | 1.0 | 795 | 0.9154 | 53.4249 | 34.0377 | 36.4396 | 50.9884 | 141.8889 |
| 0.6994 | 2.0 | 1590 | 0.8213 | 54.7613 | 35.9428 | 38.3899 | 51.9527 | 142.0 |
| 0.5272 | 3.0 | 2385 | 0.7703 | 53.8561 | 35.4871 | 38.0502 | 51.131 | 141.8889 |
| 0.3407 | 4.0 | 3180 | 0.7764 | 53.9514 | 35.8553 | 39.1935 | 51.7005 | 142.0 |
| 0.2612 | 5.0 | 3975 | 0.7529 | 54.4056 | 36.2605 | 40.8003 | 52.0424 | 142.0 |
| 0.1702 | 6.0 | 4770 | 0.8105 | 54.2251 | 37.1441 | 41.2472 | 52.2803 | 142.0 |
| 0.1276 | 7.0 | 5565 | 0.8004 | 56.49 | 40.4009 | 44.018 | 54.2404 | 141.5556 |
| 0.0978 | 8.0 | 6360 | 0.7890 | 56.6339 | 40.9867 | 43.9603 | 54.4468 | 142.0 |
| 0.0711 | 9.0 | 7155 | 0.8285 | 56.0469 | 40.7758 | 44.1395 | 53.9668 | 142.0 |
| 0.0649 | 10.0 | 7950 | 0.8498 | 56.9873 | 42.4721 | 46.705 | 55.2188 | 142.0 |
| 0.0471 | 11.0 | 8745 | 0.8547 | 57.7898 | 43.4238 | 46.5868 | 56.0858 | 142.0 |
| 0.0336 | 12.0 | 9540 | 0.8658 | 57.2678 | 43.347 | 47.0854 | 55.4167 | 142.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
NTUYG/FG-CodeBERT | 7cb0c7083e138c6a1bc3f9af56b47b04456e35b4 | 2022-05-09T05:12:11.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | false | NTUYG | null | NTUYG/FG-CodeBERT | 3 | null | transformers | 22,366 | ---
license: apache-2.0
---
|
soni69/DialoGPT-medium-holmes | f1d63b374739927a9283121aa5215f622662f77a | 2022-05-09T18:54:44.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | soni69 | null | soni69/DialoGPT-medium-holmes | 3 | null | transformers | 22,367 | ---
tags:
- conversational
---
# Sherlock Holmes DialoGPT Model |
lewtun/test-hub-pr-1 | 5c498ac40721a0b74f5683d4888f93a6766bbde8 | 2022-05-23T13:30:02.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:lewtun/autotrain-data-my-eval-project-615",
"transformers",
"autotrain",
"model-index",
"co2_eq_emissions"
] | text-classification | false | lewtun | null | lewtun/test-hub-pr-1 | 3 | null | transformers | 22,368 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- lewtun/autotrain-data-my-eval-project-615
co2_eq_emissions: 172.04481351504182
model-index:
- name: bhadresh-savani/distilbert-base-uncased-emotion
results:
- task:
name: Multi-class Classification
type: text-classification
dataset:
type: emotion
name: Emotion
config: default
split: test
metrics:
- name: Loss
type: loss
value: 0.17404702305793762
- name: Accuracy
type: accuracy
value: 0.927
- name: Macro F1
type: macro_f1
value: 0.8825061528287809
- name: Recall
type: micro_f1
value: 0.927
- name: Weighted F1
type: weighted_f1
value: 0.926876082854655
- name: Macro Precision
type: macro_precision
value: 0.8880230732280744
- name: Micro Precision
type: micro_precision
value: 0.927
- name: Weighted Precision
type: weighted_precision
value: 0.9272902840835793
- name: Macro Recall
type: macro_recall
value: 0.8790126653780703
- name: Micro Recall
type: micro_recall
value: 0.927
- name: Weighted Recall
type: weighted_recall
value: 0.927
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 5694363
- CO2 Emissions (in grams): 172.04481351504182
## Validation Metrics
- Loss: 0.2228243350982666
- Accuracy: 0.9298
- Precision: 0.9434585224927775
- Recall: 0.9144
- AUC: 0.9566112000000001
- F1: 0.9287020109689214
## 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 AutoTrain"}' https://api-inference.huggingface.co/models/lewtun/autotrain-my-eval-project-615-5694363
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("lewtun/autotrain-my-eval-project-615-5694363", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("lewtun/autotrain-my-eval-project-615-5694363", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
lsb/tironiculum | b932d8d9d3111cc156cee9e7abcd5292266e839e | 2022-05-10T22:18:05.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | lsb | null | lsb/tironiculum | 3 | null | transformers | 22,369 | Entry not found |
ybelkada/opt-125m-debug | ef108af6e570b1b8921d81a058f1fa3d88c4d1a4 | 2022-05-26T15:39:17.000Z | [
"pytorch",
"opt",
"feature-extraction",
"transformers"
] | feature-extraction | false | ybelkada | null | ybelkada/opt-125m-debug | 3 | null | transformers | 22,370 | # OPT-125m debug
Debug model for OPT-125m |
Nonegom/roberta_finetune_twice | 7786ba15fcb6e4b08da4ddcc729a69ccae2ebc3b | 2022-05-09T10:25:20.000Z | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | Nonegom | null | Nonegom/roberta_finetune_twice | 3 | 1 | transformers | 22,371 | Entry not found |
princeton-nlp/CoFi-RTE-s60 | 9d59239167f6572742b23ad1c1b4c28831591663 | 2022-05-09T15:23:20.000Z | [
"pytorch",
"bert",
"text-classification",
"arxiv:2204.00408",
"transformers"
] | text-classification | false | princeton-nlp | null | princeton-nlp/CoFi-RTE-s60 | 3 | null | transformers | 22,372 | This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 60% sparsity on dataset RTE. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the model for inference. Note that you would have to use the model class specified in our repository to load the model.
|
princeton-nlp/CoFi-RTE-s96 | 28013e693b5db0daf32ee30b9a0a35d08dfbaad6 | 2022-05-09T15:21:16.000Z | [
"pytorch",
"bert",
"text-classification",
"arxiv:2204.00408",
"transformers"
] | text-classification | false | princeton-nlp | null | princeton-nlp/CoFi-RTE-s96 | 3 | null | transformers | 22,373 | This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 96% sparsity on dataset RTE. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the model for inference. Note that you would have to use the model class specified in our repository to load the model.
|
princeton-nlp/CoFi-CoLA-s60 | 72e0a5de8d8675335084e44eb1aff7b1104d20f7 | 2022-05-09T15:23:43.000Z | [
"pytorch",
"bert",
"text-classification",
"arxiv:2204.00408",
"transformers"
] | text-classification | false | princeton-nlp | null | princeton-nlp/CoFi-CoLA-s60 | 3 | null | transformers | 22,374 | This is a model checkpoint for "[Structured Pruning Learns Compact and Accurate Models](https://arxiv.org/pdf/2204.00408.pdf)". The model is pruned from `bert-base-uncased` to a 60% sparsity on dataset CoLA. Please go to [our repository](https://github.com/princeton-nlp/CoFiPruning) for more details on how to use the model for inference. Note that you would have to use the model class specified in our repository to load the model.
|
kushaljoseph/tiny-bert-sst2-distilled | ce30490503b207c79623122c76c36caa8d670e3a | 2022-05-10T05:07:08.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | kushaljoseph | null | kushaljoseph/tiny-bert-sst2-distilled | 3 | null | transformers | 22,375 | Entry not found |
nithya/project3-model | d2361f58d995f7949d80c6231f1f7d809a81e236 | 2022-05-09T16:57:17.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | nithya | null | nithya/project3-model | 3 | null | transformers | 22,376 | Entry not found |
lucifermorninstar011/autotrain-lucifer_multi-844026969 | 7905fbf47fbf093d3aaa61e06a19594c28cf2a4d | 2022-05-09T21:46:40.000Z | [
"pytorch",
"distilbert",
"text-classification",
"en",
"dataset:lucifermorninstar011/autotrain-data-lucifer_multi",
"transformers",
"autotrain",
"co2_eq_emissions"
] | text-classification | false | lucifermorninstar011 | null | lucifermorninstar011/autotrain-lucifer_multi-844026969 | 3 | null | transformers | 22,377 | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- lucifermorninstar011/autotrain-data-lucifer_multi
co2_eq_emissions: 114.27071200298751
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 844026969
- CO2 Emissions (in grams): 114.27071200298751
## Validation Metrics
- Loss: 0.01150986272841692
- Accuracy: 0.99642966866208
- Macro F1: 0.9962909855453217
- Micro F1: 0.99642966866208
- Weighted F1: 0.9964296206983974
- Macro Precision: 0.9963861124818623
- Micro Precision: 0.99642966866208
- Weighted Precision: 0.9964357526967369
- Macro Recall: 0.9962012842304059
- Micro Recall: 0.99642966866208
- Weighted Recall: 0.99642966866208
## 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 AutoTrain"}' https://api-inference.huggingface.co/models/lucifermorninstar011/autotrain-lucifer_multi-844026969
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("lucifermorninstar011/autotrain-lucifer_multi-844026969", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("lucifermorninstar011/autotrain-lucifer_multi-844026969", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
veronica320/EPC_ADEPT_roberta-l_200 | 9cc1ed8580cabb6cd780c0c8c4ffedbed24ef003 | 2022-05-09T21:33:02.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | veronica320 | null | veronica320/EPC_ADEPT_roberta-l_200 | 3 | null | transformers | 22,378 | Entry not found |
theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv | b1bbe260e92e27bfe17ddf467a4ce77f404c991b | 2022-05-11T04:45:29.000Z | [
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"dataset:scientific_papers",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | theojolliffe | null | theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv | 3 | null | transformers | 22,379 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: bart-cnn-pubmed-arxiv-pubmed-arxiv
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: scientific_papers
type: scientific_papers
args: arxiv
metrics:
- name: Rouge1
type: rouge
value: 42.1723
---
<!-- 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. -->
# bart-cnn-pubmed-arxiv-pubmed-arxiv
This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed) on the scientific_papers dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1382
- Rouge1: 42.1723
- Rouge2: 15.7664
- Rougel: 24.5336
- Rougelsum: 37.7532
- Gen Len: 127.6382
## 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: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 2.125 | 1.0 | 67679 | 2.1382 | 42.1723 | 15.7664 | 24.5336 | 37.7532 | 127.6382 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
nepp1d0/prot_bert_classification_finetuned_no_finetune | 5fcd13075e5a5e75790438933059c69a9c747282 | 2022-05-10T12:27:27.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | false | nepp1d0 | null | nepp1d0/prot_bert_classification_finetuned_no_finetune | 3 | null | transformers | 22,380 | ---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: prot_bert_classification_finetuned_no_finetune
results: []
---
<!-- 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. -->
# prot_bert_classification_finetuned_no_finetune
This model is a fine-tuned version of [Rostlab/prot_bert](https://huggingface.co/Rostlab/prot_bert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6212
- Accuracy: 0.6473
- F1: 0.6623
- Precision: 0.6201
- Recall: 0.7107
## 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: 1e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 3
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6494 | 1.0 | 3332 | 0.6479 | 0.6439 | 0.6679 | 0.6116 | 0.7357 |
| 0.5357 | 2.0 | 6664 | 0.6440 | 0.6148 | 0.6459 | 0.5845 | 0.7218 |
| 0.4661 | 3.0 | 9996 | 0.6265 | 0.6283 | 0.6414 | 0.6047 | 0.6829 |
| 0.506 | 4.0 | 13328 | 0.6192 | 0.6439 | 0.6567 | 0.6187 | 0.6996 |
| 0.4204 | 5.0 | 16660 | 0.6122 | 0.6567 | 0.6752 | 0.6259 | 0.7330 |
| 0.6071 | 6.0 | 19992 | 0.6212 | 0.6473 | 0.6623 | 0.6201 | 0.7107 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ofirzaf/bert-large-uncased-mnli | 58ce3f764986a701dab1f30dfc6f63663ddc453f | 2022-05-09T23:58:32.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | ofirzaf | null | ofirzaf/bert-large-uncased-mnli | 3 | null | transformers | 22,381 | Entry not found |
masakhane/m2m100_418M_pcm_en_news | ebad1cc7a87bb830025242a899da764d4df57e84 | 2022-05-10T11:47:56.000Z | [
"pytorch",
"m2m_100",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | masakhane | null | masakhane/m2m100_418M_pcm_en_news | 3 | null | transformers | 22,382 | ---
license: afl-3.0
---
|
nielsr/pix2seq-base | 1294a0b6b3e281c2cee678c1815c11ff7e1fc297 | 2022-05-10T10:13:20.000Z | [
"pytorch",
"pix2seq",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | nielsr | null | nielsr/pix2seq-base | 3 | null | transformers | 22,383 | Entry not found |
SreyanG-NVIDIA/bert-base-uncased-finetuned-squad | b9edb783c8346ea3e7915ed0989db8dc98cfd1f8 | 2022-05-10T12:54:15.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | SreyanG-NVIDIA | null | SreyanG-NVIDIA/bert-base-uncased-finetuned-squad | 3 | null | transformers | 22,384 | Entry not found |
moshew/MiniLM-L12-clinc-distilled | b2bb28600332d59a58440afcce6a605a4517f8eb | 2022-05-10T19:18:00.000Z | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"transformers"
] | text-classification | false | moshew | null | moshew/MiniLM-L12-clinc-distilled | 3 | null | transformers | 22,385 | Entry not found |
ceggian/bert_post_trained_reddit_batch128 | 2e47ad8dac3381e57e3d746d157833a186a3ff25 | 2022-05-11T06:21:58.000Z | [
"pytorch",
"bert",
"pretraining",
"transformers"
] | null | false | ceggian | null | ceggian/bert_post_trained_reddit_batch128 | 3 | null | transformers | 22,386 | Entry not found |
ceggian/sbert_standard_reddit_softmax | a92f8e96687ad9a6b7c4e8d81e4b41b1b40a6796 | 2022-05-11T06:49:38.000Z | [
"pytorch",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | ceggian | null | ceggian/sbert_standard_reddit_softmax | 3 | null | sentence-transformers | 22,387 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 117759 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 11775,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
ceggian/sbert_pt_reddit_mnr_512 | f7d39c12f7631a9d456686812ee04a74993e2791 | 2022-05-11T13:33:48.000Z | [
"pytorch",
"bert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | ceggian | null | ceggian/sbert_pt_reddit_mnr_512 | 3 | 1 | sentence-transformers | 22,388 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 39289 with parameters:
```
{'batch_size': 8}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 3928,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
orenpereg/paraphrase-mpnet-base-v2_sst2_64samps | 8789633f4d275ddc1ced554042aa384c734890b7 | 2022-05-11T13:40:33.000Z | [
"pytorch",
"mpnet",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | orenpereg | null | orenpereg/paraphrase-mpnet-base-v2_sst2_64samps | 3 | null | sentence-transformers | 22,389 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# orenpereg/paraphrase-mpnet-base-v2_sst2_64samps
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('orenpereg/paraphrase-mpnet-base-v2_sst2_64samps')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('orenpereg/paraphrase-mpnet-base-v2_sst2_64samps')
model = AutoModel.from_pretrained('orenpereg/paraphrase-mpnet-base-v2_sst2_64samps')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=orenpereg/paraphrase-mpnet-base-v2_sst2_64samps)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 80 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 3,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
danieleV9H/hubert-base-timit-demo-google-colab-ft30ep_v4 | 66ff2e38a2fe2886a58d4d9396fa8f21646160cd | 2022-05-14T10:32:13.000Z | [
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | danieleV9H | null | danieleV9H/hubert-base-timit-demo-google-colab-ft30ep_v4 | 3 | null | transformers | 22,390 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: hubert-base-timit-demo-google-colab-ft30ep_v4
results: []
---
<!-- 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. -->
# hubert-base-timit-demo-google-colab-ft35ep
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the timit-asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4602
- Wer: 0.3466
## 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: 0.0001
- 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
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.825 | 0.87 | 500 | 2.9521 | 1.0 |
| 2.431 | 1.73 | 1000 | 0.9760 | 0.8013 |
| 1.0089 | 2.6 | 1500 | 0.5934 | 0.5968 |
| 0.6859 | 3.46 | 2000 | 0.5132 | 0.5356 |
| 0.5302 | 4.33 | 2500 | 0.4506 | 0.4894 |
| 0.44 | 5.19 | 3000 | 0.4340 | 0.4670 |
| 0.3926 | 6.06 | 3500 | 0.4506 | 0.4528 |
| 0.3326 | 6.92 | 4000 | 0.4197 | 0.4486 |
| 0.2937 | 7.79 | 4500 | 0.4093 | 0.4193 |
| 0.2568 | 8.65 | 5000 | 0.4098 | 0.4229 |
| 0.2473 | 9.52 | 5500 | 0.4090 | 0.4141 |
| 0.2233 | 10.38 | 6000 | 0.4152 | 0.4125 |
| 0.2108 | 11.25 | 6500 | 0.4586 | 0.4189 |
| 0.2086 | 12.11 | 7000 | 0.4284 | 0.3969 |
| 0.1858 | 12.98 | 7500 | 0.4028 | 0.3946 |
| 0.1641 | 13.84 | 8000 | 0.4679 | 0.4002 |
| 0.1686 | 14.71 | 8500 | 0.4441 | 0.3936 |
| 0.1489 | 15.57 | 9000 | 0.4897 | 0.3828 |
| 0.1541 | 16.44 | 9500 | 0.4953 | 0.3783 |
| 0.1417 | 17.3 | 10000 | 0.4500 | 0.3758 |
| 0.1428 | 18.17 | 10500 | 0.4533 | 0.3796 |
| 0.1306 | 19.03 | 11000 | 0.4474 | 0.3792 |
| 0.1185 | 19.9 | 11500 | 0.4762 | 0.3743 |
| 0.1081 | 20.76 | 12000 | 0.4770 | 0.3699 |
| 0.1253 | 21.63 | 12500 | 0.4749 | 0.3629 |
| 0.1087 | 22.49 | 13000 | 0.4577 | 0.3534 |
| 0.1172 | 23.36 | 13500 | 0.4819 | 0.3525 |
| 0.1086 | 24.22 | 14000 | 0.4709 | 0.3623 |
| 0.089 | 25.09 | 14500 | 0.4852 | 0.3544 |
| 0.086 | 25.95 | 15000 | 0.4602 | 0.3555 |
| 0.086 | 26.82 | 15500 | 0.4861 | 0.3497 |
| 0.086 | 27.68 | 16000 | 0.4527 | 0.3473 |
| 0.0919 | 28.55 | 16500 | 0.4607 | 0.3487 |
| 0.0792 | 29.41 | 17000 | 0.4602 | 0.3466 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
Matthijs/mobilevit-xx-small | 850dc69265907c2019d8a67293a01dd2b31baecd | 2022-05-11T14:42:38.000Z | [
"pytorch",
"mobilevit",
"image-classification",
"transformers"
] | image-classification | false | Matthijs | null | Matthijs/mobilevit-xx-small | 3 | null | transformers | 22,391 | Entry not found |
PSW/low_resource_percent20_min2swap_seed1 | f179846ea51f16e398b22ca3f63bb7f5cf6126ab | 2022-05-12T08:54:14.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_min2swap_seed1 | 3 | null | transformers | 22,392 | Entry not found |
PSW/low_resource_percent20_min2swap_seed42 | 2f0b55efc1bc6d4989fcaacc1a4e6d37bea24c09 | 2022-05-12T09:35:55.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_min2swap_seed42 | 3 | null | transformers | 22,393 | Entry not found |
PSW/low_resource_percent20_max2swap_seed27 | dcd9985851a7ddecdd2ab1a9888a022b2ce33254 | 2022-05-12T10:11:07.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | PSW | null | PSW/low_resource_percent20_max2swap_seed27 | 3 | null | transformers | 22,394 | Entry not found |
monsoon-nlp/czech-movie-rating | ede5a7485caa91dc8803fb9f436515c4e5aaabff | 2022-05-11T22:03:48.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | false | monsoon-nlp | null | monsoon-nlp/czech-movie-rating | 3 | null | transformers | 22,395 | Entry not found |
eslamxm/mt5-base-finetuned-urdu-arabic | aec9b30c6dbae689fa9bc7f59f07e78e215a0443 | 2022-05-12T09:18:16.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"dataset:xlsum",
"transformers",
"summarization",
"arabic",
"ar",
"Abstractive Summarization",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | summarization | false | eslamxm | null | eslamxm/mt5-base-finetuned-urdu-arabic | 3 | null | transformers | 22,396 | ---
license: apache-2.0
tags:
- summarization
- arabic
- ar
- mt5
- Abstractive Summarization
- generated_from_trainer
datasets:
- xlsum
model-index:
- name: mt5-base-finetuned-urdu-finetuned-urdu-arabic
results: []
---
<!-- 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. -->
# mt5-base-finetuned-urdu-finetuned-urdu-arabic
This model is a fine-tuned version of [eslamxm/mt5-base-finetuned-urdu](https://huggingface.co/eslamxm/mt5-base-finetuned-urdu) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3744
- Rouge-1: 22.77
- Rouge-2: 10.15
- Rouge-l: 20.71
- Gen Len: 19.0
- Bertscore: 71.46
## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 4.5155 | 1.0 | 1172 | 3.6895 | 18.81 | 6.77 | 17.01 | 19.0 | 70.27 |
| 3.8315 | 2.0 | 2344 | 3.5047 | 19.75 | 7.79 | 17.95 | 19.0 | 70.58 |
| 3.6122 | 3.0 | 3516 | 3.4231 | 20.46 | 8.44 | 18.7 | 19.0 | 70.8 |
| 3.4735 | 4.0 | 4688 | 3.3835 | 21.12 | 8.86 | 19.21 | 19.0 | 70.98 |
| 3.3855 | 5.0 | 5860 | 3.3744 | 21.48 | 9.01 | 19.57 | 19.0 | 71.17 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
|
enoriega/kw_pubmed_10000_0.000006 | 9ea766f9637655504b9179d18f346ae9e712c4c8 | 2022-05-12T14:25:29.000Z | [
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | enoriega | null | enoriega/kw_pubmed_10000_0.000006 | 3 | null | transformers | 22,397 | Entry not found |
reallycarlaost/emobert-single-binary | 331deac5cc3c8fe0c47f5c1915857b18dd3319e3 | 2022-05-12T13:37:59.000Z | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | false | reallycarlaost | null | reallycarlaost/emobert-single-binary | 3 | null | transformers | 22,398 | Entry not found |
manthan40/wav2vec2-base-finetuned-manthan_base | e90b01ca221f3baf37144f572f45ab4430770521 | 2022-05-13T01:39:46.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"dataset:new_dataset",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | audio-classification | false | manthan40 | null | manthan40/wav2vec2-base-finetuned-manthan_base | 3 | null | transformers | 22,399 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- new_dataset
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-manthan_base
results: []
---
<!-- 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. -->
# wav2vec2-base-finetuned-manthan_base
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the new_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2246
- Accuracy: 0.9691
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.4725 | 0.98 | 12 | 2.4222 | 0.1057 |
| 2.4501 | 1.98 | 24 | 2.2420 | 0.2784 |
| 2.2977 | 2.98 | 36 | 2.0155 | 0.7603 |
| 2.1331 | 3.98 | 48 | 1.8193 | 0.8582 |
| 1.7927 | 4.98 | 60 | 1.6376 | 0.9459 |
| 1.7226 | 5.98 | 72 | 1.4940 | 0.9613 |
| 1.6036 | 6.98 | 84 | 1.3632 | 0.9665 |
| 1.5181 | 7.98 | 96 | 1.2963 | 0.9562 |
| 1.4384 | 8.98 | 108 | 1.2406 | 0.9742 |
| 1.3339 | 9.98 | 120 | 1.2246 | 0.9691 |
### Framework versions
- Transformers 4.19.0
- Pytorch 1.11.0+cu113
- Datasets 1.14.0
- Tokenizers 0.12.1
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.