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BigSalmon/InformalToFormalLincoln32 | e48683a1a97e013219fb90ed35f96054e1702e70 | 2022-03-28T00:48:58.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | BigSalmon | null | BigSalmon/InformalToFormalLincoln32 | 1 | null | transformers | 31,000 | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln32")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln32")
```
```
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:
``` |
CallForEcho/DialoGPT-small-harrypotter | 6c54aa94daacfacb22876e4ab6966c489de2f1e7 | 2022-03-28T00:42:12.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | CallForEcho | null | CallForEcho/DialoGPT-small-harrypotter | 1 | null | transformers | 31,001 | ---
tags:
- conversational
---
# harry Potter DialoGPT Model |
dapang/shuxue-wiki-basic-factor-pair-medium-10257 | c9c6a9c7cc90cae8bd8deb712b68f4d2b729cd00 | 2022-03-28T12:05:34.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | dapang | null | dapang/shuxue-wiki-basic-factor-pair-medium-10257 | 1 | null | transformers | 31,002 | Entry not found |
tau/t5_lm_4_1024_0.3_epoch1 | 43d8aace8e0debb6ec190849784f6957e7ff87a7 | 2022-03-28T04:40:04.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | tau | null | tau/t5_lm_4_1024_0.3_epoch1 | 1 | null | transformers | 31,003 | Entry not found |
dapang/shuxue-wiki-10257 | 3fcee09aec5637892cab385c1faeff561d9713c2 | 2022-03-28T22:21:08.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | dapang | null | dapang/shuxue-wiki-10257 | 1 | null | transformers | 31,004 | Entry not found |
dennisowusuk/wav2vec2-large-xls-r-300m-turkish-colab | 0555cf3b0496aca600919814acb206494f2ffe22 | 2022-03-28T13:28:30.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | dennisowusuk | null | dennisowusuk/wav2vec2-large-xls-r-300m-turkish-colab | 1 | null | transformers | 31,005 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-turkish-colab
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-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3863
- Wer: 0.3095
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.8284 | 3.67 | 400 | 0.6782 | 0.6739 |
| 0.4174 | 7.34 | 800 | 0.4524 | 0.4811 |
| 0.2015 | 11.01 | 1200 | 0.4736 | 0.4311 |
| 0.1371 | 14.68 | 1600 | 0.4254 | 0.3929 |
| 0.0997 | 18.35 | 2000 | 0.4254 | 0.3636 |
| 0.082 | 22.02 | 2400 | 0.3807 | 0.3474 |
| 0.0665 | 25.69 | 2800 | 0.3987 | 0.3236 |
| 0.0523 | 29.36 | 3200 | 0.3863 | 0.3095 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Taekyoon/unicon_v0.5.4_alpha | fa5a69c7a1af08a3fc1b23f6976e3451313a4bcd | 2022-03-28T06:35:18.000Z | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | false | Taekyoon | null | Taekyoon/unicon_v0.5.4_alpha | 1 | null | transformers | 31,006 | Entry not found |
robvanderg/Sem-mmmBERT | 4229c7a4efed00b452b76e531868998bbae446d3 | 2022-03-28T11:28:17.000Z | [
"pytorch",
"bert",
"feature-extraction",
"multilingual",
"dataset:SemEval 2022",
"transformers",
"STILT",
"retraining",
"multi-task learning"
] | feature-extraction | false | robvanderg | null | robvanderg/Sem-mmmBERT | 1 | null | transformers | 31,007 | ---
language:
- multilingual
tags:
- STILT
- retraining
- multi-task learning
datasets:
- SemEval 2022
---
## Sem-mmmBERT
This is the SemEval MaChAmp Multitask Multilingual BERT model. This model is retrained from mBERT (https://huggingface.co/bert-base-multilingual-cased).
The retraining is done based on all SemEval 2022 tasks that are text based, and have annotation on the word, sentence or paragraph level. The retraining is done with MaChAmp (https://machamp-nlp.github.io/), a toolkit focusing on multi-task learning for NLP. More information can be found in the paper (which should be released when the SemEval proceedings are online). |
scasutt/wav2vec2-large-xlsr-53_toy_train_data_fast_10pct | 615d71a17b2b45277988831913fcb002b4bbf469 | 2022-03-28T18:53:54.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | scasutt | null | scasutt/wav2vec2-large-xlsr-53_toy_train_data_fast_10pct | 1 | null | transformers | 31,008 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-53_toy_train_data_fast_10pct
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_toy_train_data_fast_10pct
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6983
- Wer: 0.5026
## 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
- 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: 1000
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.3619 | 1.05 | 250 | 3.4334 | 1.0 |
| 3.0818 | 2.1 | 500 | 3.4914 | 1.0 |
| 2.3245 | 3.15 | 750 | 1.6483 | 0.9486 |
| 1.0233 | 4.2 | 1000 | 0.8817 | 0.7400 |
| 0.7522 | 5.25 | 1250 | 0.7374 | 0.6529 |
| 0.5343 | 6.3 | 1500 | 0.6972 | 0.6068 |
| 0.4452 | 7.35 | 1750 | 0.6757 | 0.5740 |
| 0.4275 | 8.4 | 2000 | 0.6789 | 0.5551 |
| 0.3688 | 9.45 | 2250 | 0.6468 | 0.5394 |
| 0.3363 | 10.5 | 2500 | 0.6798 | 0.5358 |
| 0.3036 | 11.55 | 2750 | 0.6439 | 0.5265 |
| 0.3173 | 12.6 | 3000 | 0.6898 | 0.5196 |
| 0.2985 | 13.65 | 3250 | 0.6791 | 0.5169 |
| 0.288 | 14.7 | 3500 | 0.6442 | 0.5090 |
| 0.2673 | 15.75 | 3750 | 0.6984 | 0.5119 |
| 0.2575 | 16.81 | 4000 | 0.7146 | 0.5084 |
| 0.239 | 17.86 | 4250 | 0.6847 | 0.5040 |
| 0.2266 | 18.91 | 4500 | 0.6900 | 0.5028 |
| 0.22 | 19.96 | 4750 | 0.6983 | 0.5026 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
|
ai4bharat/MultiIndicHeadlineGenerationSS | 36734e347d21b7a25db9354bd0e003a6f8bf40ec | 2022-05-06T10:21:44.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2203.05437",
"transformers",
"multilingual",
"nlp",
"indicnlp",
"autotrain_compatible"
] | text2text-generation | false | ai4bharat | null | ai4bharat/MultiIndicHeadlineGenerationSS | 1 | null | transformers | 31,009 |
---
languages:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
tags:
- multilingual
- nlp
- indicnlp
widget:
- text: वैश्विक व्यापार युद्ध की शिकार हुई तुर्की की मुद्रा लीरा के डूबने से अमेरिकी डॉलर के मुकाबले रुपया अब तक के न्यूनतम स्तर पर पहुंच गया। रुपये में रिकॉर्ड गिरावट से सोने की चमक में निखार नहीं आ सकी। वैश्विक बाजार में सोना करीब आठ महीने के निचले स्तर पर पहुंच गया तो घरेलू बाजार में यह करीब नौ महीने के निचले स्तर पर चला गया। वैश्विक मंदी की आशंका से वैश्विक बाजार में चांदी करीब ढाई साल और घरेलू बाजार में तकरीबन नौ महीने के निचले स्तर पर पहुंच गई। तुर्की की आर्थिक चिंता के कारण अमेरिकी डॉलर के मुकाबले रुपया कारोबार के दौरान 70.80 के स्तर तक गिर गया। यह इसका ऐतिहासिक रिकॉर्ड निम्न स्तर है। कमजोर रुपये से सोने की चमक बढऩे की उम्मीद की जा रही थी लेकिन वैश्विक बाजार में सोने की कीमत गिरकर 1,193.50 डॉलर प्रति औंस पहुंचने के कारण घरेलू बाजार में भी सोने की चमक फीकी पड़ गई। घरेलू बाजार में सोना गिरकर 29,655 रुपये प्रति 10 ग्राम पहुंच गया। घरेलू वायदा बाजार यानी एमसीएक्स पर सोना 29,700 के आस-पास कारोबार कर रहा है। देश में इस साल सोने की मांग में लगातार गिरावट देखने को मिल रही थी। अप्रैल-जून तिमाही में सोने का आयात 25 फीसदी से भी कम हुआ है। चालू महीने में सोने की मांग बढऩे की उम्मीद जगी थी लेकिन यह उम्मीद टूट सकती है क्योंकि दुनिया के सबसे बड़े गोल्ड फंड एसपीडीआर गोल्ड की होल्डिंग अप्रैल के बाद 10 फीसदी गिर चुकी है। इस समय यह पिछले ढाई साल के निचले स्तर पर है। इस साल वैश्विक बाजार में सोना करीब 8.5 फीसदी और घरेलू बाजार में 1.5 फीसदी टूट चुका है। सराफा मामलों के जानकार अनिल अग्रवाल कहते हैं कि वैश्विक हालात ऐसे हैं कि इस समय निवेशक डॉलर में पैसा लगा रहे हैं। इस कारण दूसरी मुद्रा और जिंस दबाव में हैं। हालांकि हालात यही रहे तो सोने में तेज सुधार भी देखने को मिलेगा। वैश्विक मंदी की बढ़ती आशंका का सबसे ज्यादा असर चांदी पर पड़ रहा है। वैश्विक बाजार में चांदी के दाम ढाई साल के निचले स्तर पर पहुंच चुके हैं। वैश्विक बाजार में चांदी की कीमत 15 डॉलर प्रति औंस के करीब चल रही है। इसके पहले अप्रैल 2016 में चांदी इस स्तर पर थी। वैश्विक बाजार में चांदी के दाम दो महीने पहले 18.13 डॉलर प्रति औंस पर चल रहे थे। चांदी कारोबारी राहुल मेहता कहते हैं कि सोना और मूल धातु में कमजोरी से चांदी पर दोहरा दबाव पड़ रहा है। वैश्विक बाजार का व्यापार युद्ध अब मुद्रा युद्ध में बदल गया है। वैश्विक अर्थव्यवस्था एक बार फिर मंदी की गिरफ्त में आ सकती है जिसके कारण औद्योगिक विकास भी प्रभावित होगा। यही वजह है कि चांदी की कीमतें लगातार लुढक़ रही हैं क्योंकि मांग में कमी आने की आशंका बढ़ती जा रही है। फिलहाल घरेलू बाजार में चांदी 37,825 रुपये प्रति किलोग्राम पर बिक रही है। तुर्की के आर्थिक संकट से एक बार फिर वैश्विक मंदी का डर है जिसका असर दुनियाभर के बाजारों पर देखा जा सकता है। इसने विश्व स्तर पर निवेशकों के रुख को प्रभावित किया है और वे डॉलर को एक सुरक्षित निवेश के तौर पर देख रहे हैं। आनंद राठी शेयर्स ऐंड स्टाक ब्रोकर्स में शोध विश्लेषक आर मारू ने कहा कि आयातकों की अधिक मांग से रुपये की विनिमय दर में गिरावट आई। उन्होंने कहा, तुर्की संकट को लेकर अनिश्चितता तथा डॉलर सूचकांक में तेजी को देखते हुए आयातक आक्रमक तरीके से डॉलर की लिवाली कर रहे हैं। दूसरी तरफ आरबीआई की तरफ से आक्रमक हस्तक्षेप न होने से भी रुपया नीचे आया। सरकार ने अमेरिकी डॉलर के मुकाबले रुपये के अब तक के न्यूनतम स्तर पर पहुंचने के लिए बाह्य कारकों को जिम्मेदार ठहराते हुए कहा कि इसमें चिंता की कोई बात नहीं है।</s><2hi>
---
MultiIndicHeadlineGenerationSS is a multilingual, sequence-to-sequence pre-trained model focusing only on Indic languages. It currently supports 11 Indian languages and is finetuned on [IndicBARTSS](https://huggingface.co/ai4bharat/IndicBARTSS) checkpoint. You can use MultiIndicHeadlineGenerationSS model to build natural language generation applications in Indian languages for tasks like summarization, headline generation and other summarization related tasks. Some salient features of the MultiIndicHeadlineGenerationSS are:
<ul>
<li >Supported languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Odiya, Punjabi, Kannada, Malayalam, Tamil, and Telugu. Not all of these languages are supported by mBART50 and mT5. </li>
<li >The model is much smaller than the mBART and mT5(-base) models, so less computationally expensive for finetuning and decoding. </li>
<li> Trained on large Indic language corpora (1.316 million paragraphs and 5.9 million unique tokens) . </li>
<li>Unlike ai4bharat/MultiIndicHeadlineGeneration each language is written in its own script so you do not need to perform any script mapping to/from Devanagari.</li>
</ul>
# Usage:
```
from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM
from transformers import AlbertTokenizer, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ai4bharat/MultiIndicHeadlineGenerationSS", do_lower_case=False, use_fast=False, keep_accents=True)
# Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/MultiIndicHeadlineGenerationSS", do_lower_case=False, use_fast=False, keep_accents=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/MultiIndicHeadlineGenerationSS")
# Or use model = MBartForConditionalGeneration.from_pretrained("ai4bharat/MultiIndicHeadlineGenerationSS")
# Some initial mapping
bos_id = tokenizer._convert_token_to_id_with_added_voc("<s>")
eos_id = tokenizer._convert_token_to_id_with_added_voc("</s>")
pad_id = tokenizer._convert_token_to_id_with_added_voc("<pad>")
# To get lang_id use any of ['<2as>', '<2bn>', '<2gu>', '<2hi>', '<2kn>', '<2ml>', '<2mr>', '<2or>', '<2pa>', '<2ta>', '<2te>']
# First tokenize the input and outputs. The format below is how MultiIndicHeadlineGenerationSS was trained so the input should be "Paragraph </s> <2xx>" where xx is the language code. Similarly, the output should be "<2yy> Sentence </s>".
inp = tokenizer("यूट्यूब या फेसबुक पर वीडियो देखते समय आप भी बफरिंग की वजह से परेशान होते हैं? इसका जवाब हां है तो जल्द ही आपकी सारी समस्या खत्म होने वाली है। दरअसल, टेलीकॉम मिनिस्टर अश्विनी वैष्णव ने पिछले सप्ताह कहा कि अगस्त के अंत तक हर-हाल में '5G' इंटरनेट लॉन्च हो जाएगा। उन्होंने यह भी कहा है कि स्पेक्ट्रम की बिक्री शुरू हो चुकी है और जून तक ये प्रोसेस खत्म होने की संभावना है।</s> <2hi>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids # tensor([[58232, 76, 14514, 53, 5344, 10605, 1052, 680, 83, 648, . . . . , 12126, 725, 19, 13635, 17, 7, 64001, 64007]])
out = tokenizer("<2hi> 5G इंटरनेट का इंतजार हुआ खत्म:अगस्त तक देश में शुरू हो सकती है 5G सर्विस </s>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids # tensor([[64007, 329, 1906, 15429, . . . . ,17, 329, 1906, 27241, 64001]])
model_outputs=model(input_ids=inp, decoder_input_ids=out[:,0:-1], labels=out[:,1:])
# For loss
model_outputs.loss ## This is not label smoothed.
# For logits
model_outputs.logits
# For generation. Pardon the messiness. Note the decoder_start_token_id.
model.eval() # Set dropouts to zero
model_output=model.generate(inp, use_cache=True, num_beams=4, max_length=32, min_length=1, early_stopping=True, pad_token_id=pad_id, bos_token_id=bos_id, eos_token_id=eos_id, decoder_start_token_id=tokenizer._convert_token_to_id_with_added_voc("<2en>"))
# Decode to get output strings
decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(decoded_output) # अगस्त के अंत तक '5G' इंटरनेट लॉन्च हो जाएगा : अश्विनी वैष्णव
```
# Benchmarks
Scores on the `MultiIndicHeadlineGenerationSS` test sets are as follows:
Language | Rouge-1 / Rouge-2 / Rouge-L
---------|----------------------------
as | 48.10 / 32.41 / 46.82
bn | 35.71 / 18.93 / 33.49
gu | 32.41 / 16.95 / 30.87
hi | 38.48 / 18.44 / 33.60
kn | 65.22 / 54.23 / 64.50
ml | 58.52 / 47.02 / 57.60
mr | 34.11 / 18.36 / 33.04
or | 24.83 / 11.00 / 23.74
pa | 45.15 / 27.71 / 42.12
ta | 47.15 / 31.09 / 45.72
te | 36.80 / 20.81 / 35.58
average | 42.41 / 27.00 / 40.64
# Contributors
<ul>
<li> Aman Kumar </li>
<li> Prachi Sahu </li>
<li> Himani Shrotriya </li>
<li> Raj Dabre </li>
<li> Anoop Kunchukuttan </li>
<li> Ratish Puduppully </li>
<li> Mitesh M. Khapra </li>
<li> Pratyush Kumar </li>
</ul>
# Paper
If you use MultiIndicHeadlineGeneration, please cite the following paper:
```
@inproceedings{Kumar2022IndicNLGSM,
title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
year={2022},
url = "https://arxiv.org/abs/2203.05437"
}
```
|
jorge-henao/spanish-t5-small-disco-poetry | 6a9baf81f3d08d23fe0144639655af32a30e993d | 2022-03-28T21:26:45.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | jorge-henao | null | jorge-henao/spanish-t5-small-disco-poetry | 1 | null | transformers | 31,010 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: spanish-t5-small-disco-poetry
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. -->
# spanish-t5-small-disco-poetry
This model is a fine-tuned version of [flax-community/spanish-t5-small](https://huggingface.co/flax-community/spanish-t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0477
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1417 | 1.0 | 1284 | 0.0577 |
| 0.0902 | 2.0 | 2568 | 0.0516 |
| 0.0803 | 3.0 | 3852 | 0.0494 |
| 0.0733 | 4.0 | 5136 | 0.0488 |
| 0.0683 | 5.0 | 6420 | 0.0480 |
| 0.067 | 6.0 | 7704 | 0.0477 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
tau/fewsion_4_1024_0.3_epoch1 | 0e9211c1681c7184d0f448c7f4f740ed0769ac07 | 2022-03-28T18:37:35.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | tau | null | tau/fewsion_4_1024_0.3_epoch1 | 1 | null | transformers | 31,011 | Entry not found |
Vkt/first_model | f6e8f35abd92f017888eb529f6cdbf134792dcdb | 2022-05-20T13:56:39.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Vkt | null | Vkt/first_model | 1 | null | transformers | 31,012 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: test-model
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. -->
# test-model
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0161
- Wer: 0.0141
## 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: 0.0003
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.8062 | 0.29 | 400 | 2.0576 | 1.0 |
| 0.9633 | 0.57 | 800 | 0.5862 | 0.6023 |
| 0.6079 | 0.86 | 1200 | 0.4897 | 0.4824 |
| 0.4993 | 1.14 | 1600 | 0.3823 | 0.3989 |
| 0.4269 | 1.43 | 2000 | 0.3749 | 0.3761 |
| 0.4049 | 1.72 | 2400 | 0.3501 | 0.3536 |
| 0.3998 | 2.0 | 2800 | 0.3527 | 0.3381 |
| 0.3172 | 2.29 | 3200 | 0.3188 | 0.3257 |
| 0.3161 | 2.57 | 3600 | 0.3217 | 0.3185 |
| 0.3213 | 2.86 | 4000 | 0.2988 | 0.3007 |
| 0.3035 | 3.15 | 4400 | 0.3036 | 0.3288 |
| 0.261 | 3.43 | 4800 | 0.3095 | 0.2947 |
| 0.2639 | 3.72 | 5200 | 0.2818 | 0.2767 |
| 0.2771 | 4.0 | 5600 | 0.2739 | 0.2812 |
| 0.2343 | 4.29 | 6000 | 0.2820 | 0.2700 |
| 0.2452 | 4.57 | 6400 | 0.2663 | 0.2697 |
| 0.2344 | 4.86 | 6800 | 0.2679 | 0.2666 |
| 0.2215 | 5.15 | 7200 | 0.2687 | 0.2571 |
| 0.2032 | 5.43 | 7600 | 0.2791 | 0.2624 |
| 0.2092 | 5.72 | 8000 | 0.2682 | 0.2616 |
| 0.2122 | 6.0 | 8400 | 0.2770 | 0.2591 |
| 0.1878 | 6.29 | 8800 | 0.2760 | 0.2584 |
| 0.1884 | 6.58 | 9200 | 0.2641 | 0.2515 |
| 0.194 | 6.86 | 9600 | 0.2500 | 0.2415 |
| 0.175 | 7.15 | 10000 | 0.2635 | 0.2532 |
| 0.1658 | 7.43 | 10400 | 0.2588 | 0.2371 |
| 0.177 | 7.72 | 10800 | 0.2813 | 0.2493 |
| 0.1786 | 8.01 | 11200 | 0.2628 | 0.2437 |
| 0.1509 | 8.29 | 11600 | 0.2592 | 0.2453 |
| 0.1597 | 8.58 | 12000 | 0.2737 | 0.2523 |
| 0.1646 | 8.86 | 12400 | 0.2556 | 0.2436 |
| 0.1587 | 9.15 | 12800 | 0.2669 | 0.2453 |
| 0.1489 | 9.44 | 13200 | 0.2596 | 0.2353 |
| 0.1468 | 9.72 | 13600 | 0.2620 | 0.2419 |
| 0.1482 | 10.01 | 14000 | 0.2622 | 0.2334 |
| 0.1285 | 10.29 | 14400 | 0.2531 | 0.2258 |
| 0.1335 | 10.58 | 14800 | 0.2512 | 0.2273 |
| 0.1335 | 10.86 | 15200 | 0.2475 | 0.2246 |
| 0.132 | 11.15 | 15600 | 0.2575 | 0.2275 |
| 0.1249 | 11.44 | 16000 | 0.2503 | 0.2223 |
| 0.1229 | 11.72 | 16400 | 0.2817 | 0.2297 |
| 0.1274 | 12.01 | 16800 | 0.2707 | 0.2211 |
| 0.1115 | 12.29 | 17200 | 0.2647 | 0.2175 |
| 0.117 | 12.58 | 17600 | 0.2501 | 0.2178 |
| 0.1164 | 12.87 | 18000 | 0.2579 | 0.2216 |
| 0.1085 | 13.15 | 18400 | 0.2636 | 0.2130 |
| 0.1033 | 13.44 | 18800 | 0.2643 | 0.2184 |
| 0.1066 | 13.72 | 19200 | 0.2519 | 0.2158 |
| 0.1032 | 14.01 | 19600 | 0.2322 | 0.2082 |
| 0.0981 | 14.3 | 20000 | 0.2613 | 0.2125 |
| 0.1009 | 14.58 | 20400 | 0.2479 | 0.2076 |
| 0.1 | 14.87 | 20800 | 0.2464 | 0.2058 |
| 0.0886 | 15.15 | 21200 | 0.2595 | 0.2014 |
| 0.0888 | 15.44 | 21600 | 0.2565 | 0.2048 |
| 0.0916 | 15.73 | 22000 | 0.2470 | 0.2000 |
| 0.095 | 16.01 | 22400 | 0.2539 | 0.1997 |
| 0.0875 | 16.3 | 22800 | 0.2576 | 0.1995 |
| 0.0833 | 16.58 | 23200 | 0.2514 | 0.1990 |
| 0.0813 | 16.87 | 23600 | 0.2522 | 0.2020 |
| 0.0845 | 17.16 | 24000 | 0.2522 | 0.2045 |
| 0.0879 | 17.44 | 24400 | 0.2629 | 0.2183 |
| 0.0854 | 17.73 | 24800 | 0.2464 | 0.2000 |
| 0.0795 | 18.01 | 25200 | 0.2526 | 0.2078 |
| 0.075 | 18.3 | 25600 | 0.2519 | 0.1971 |
| 0.0724 | 18.58 | 26000 | 0.2551 | 0.1965 |
| 0.0735 | 18.87 | 26400 | 0.2536 | 0.1934 |
| 0.0735 | 19.16 | 26800 | 0.2504 | 0.1916 |
| 0.0676 | 19.44 | 27200 | 0.2532 | 0.1884 |
| 0.0687 | 19.73 | 27600 | 0.2498 | 0.1849 |
| 0.0652 | 20.01 | 28000 | 0.2490 | 0.1847 |
| 0.0617 | 20.3 | 28400 | 0.2547 | 0.1899 |
| 0.0627 | 20.59 | 28800 | 0.2509 | 0.1834 |
| 0.0639 | 20.87 | 29200 | 0.2472 | 0.1812 |
| 0.0611 | 21.16 | 29600 | 0.2486 | 0.1827 |
| 0.0559 | 21.44 | 30000 | 0.2530 | 0.1825 |
| 0.0564 | 21.73 | 30400 | 0.2484 | 0.1785 |
| 0.0593 | 22.02 | 30800 | 0.2425 | 0.1781 |
| 0.0517 | 22.3 | 31200 | 0.2613 | 0.1775 |
| 0.0528 | 22.59 | 31600 | 0.2517 | 0.1759 |
| 0.0556 | 22.87 | 32000 | 0.2494 | 0.1811 |
| 0.0507 | 23.16 | 32400 | 0.2522 | 0.1761 |
| 0.0485 | 23.45 | 32800 | 0.2344 | 0.1717 |
| 0.0504 | 23.73 | 33200 | 0.2458 | 0.1772 |
| 0.0485 | 24.02 | 33600 | 0.2497 | 0.1748 |
| 0.0436 | 24.3 | 34000 | 0.2405 | 0.1738 |
| 0.0468 | 24.59 | 34400 | 0.2446 | 0.1735 |
| 0.0443 | 24.87 | 34800 | 0.2514 | 0.1709 |
| 0.0417 | 25.16 | 35200 | 0.2515 | 0.1711 |
| 0.0399 | 25.45 | 35600 | 0.2452 | 0.1664 |
| 0.0416 | 25.73 | 36000 | 0.2438 | 0.1664 |
| 0.0412 | 26.02 | 36400 | 0.2457 | 0.1662 |
| 0.0406 | 26.3 | 36800 | 0.2475 | 0.1659 |
| 0.0376 | 26.59 | 37200 | 0.2454 | 0.1682 |
| 0.0365 | 26.88 | 37600 | 0.2511 | 0.1650 |
| 0.0355 | 27.16 | 38000 | 0.2518 | 0.1633 |
| 0.032 | 27.45 | 38400 | 0.2479 | 0.1604 |
| 0.0348 | 27.73 | 38800 | 0.2391 | 0.1599 |
| 0.0331 | 28.02 | 39200 | 0.2417 | 0.1617 |
| 0.0349 | 28.31 | 39600 | 0.2358 | 0.1590 |
| 0.0347 | 28.59 | 40000 | 0.2388 | 0.1582 |
| 0.0325 | 28.88 | 40400 | 0.2412 | 0.1564 |
| 0.0332 | 29.16 | 40800 | 0.2390 | 0.1545 |
| 0.0613 | 29.45 | 41200 | 0.0167 | 0.0141 |
| 0.0563 | 29.74 | 41600 | 0.0161 | 0.0141 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.8.1+cu111
- Datasets 2.2.1
- Tokenizers 0.12.1
|
MU-NLPC/CzeGPT-2_headline_generator | 9fdfe42e4f968873a23b2179855cb70c012fc6cf | 2022-05-17T15:49:13.000Z | [
"pytorch",
"gpt2",
"text-generation",
"cs",
"dataset:csTenTen17",
"transformers",
"license:cc-by-nc-sa-4.0"
] | text-generation | false | MU-NLPC | null | MU-NLPC/CzeGPT-2_headline_generator | 1 | null | transformers | 31,013 | ---
language: cs
license: cc-by-nc-sa-4.0
datasets:
- csTenTen17
---
# CzeGPT-2 headline generator
CzeGPT-2_headline_generator is a Czech summarizer built upon the <a href="https://huggingface.co/MU-NLPC/CzeGPT-2">CzeGPT-2</a> model. The model has the same architectural dimensions as the GPT-2 small (12 layers, 12 heads, 1024 tokens on input/output, and embedding vectors with 768 dimensions) resulting in 124M trainable parameters. It was fine-tuned and evaluated on the <a href="https://aclanthology.org/L18-1551.pdf">SumeCzech</a> summarization dataset containing about 1M Czech news articles.
## Tokenizer
Along, we also provide a Czech trained tokenizer (vocab and merges) with vocab size of 50257 that was used during the pre-training phase and fine-tuning. It is the byte-level BPE tokenizer as used in the original GPT-2 paper.
## Training results
The model was evaluated on the *test* and *ood-test* partitions of the SumeCzech dataset and compared to the best summarizers yet evaluated on this benchmark (the results taken from <a href="https://ufal.mff.cuni.cz/sumeczech">here</a>).
The headline generator is trained to decide itself when to stop (generate an <|endoftext|> token). If you want a variable summary length, refer to our <a href="https://huggingface.co/MU-NLPC/CzeGPT-2_summarizer">summary generator</a>
We manage to exceed current state-of-the art on all standard metrics.
Test set
| Model | ROUGE<sub>RAW</sub>-1 | ROUGE<sub>RAW</sub>-2 | ROUGE<sub>RAW</sub>-L |
| :---: | :------: | :-----: | :-----: |
| CzeGPT-2 | **17.3**/**17.0**/**16.7** | **4.4**/**4.3**/**4.2** | **15.5**/**15.2**/**14.9**|
| First | 7.4/13.5/8.9 | 1.1/2.2/1.3 | 6.5/11.7/7.7 |
| TextRank | 6.0/16.5/8.3 | 0.8/2.3/1.1 | 5.0/13.8/6.9 |
|Tensor2Tensor | 8.8/7.0/7.5 | 0.8/0.6/0.7 | 8.1/6.5/7.0 |
|NE Density | 6.6/10.7/7.3 | 0.8/1.4/0.9 | 5.9/9.4/6.4 |
|Seq2Seq | 16.1/14.1/14.6 | 2.5/2.1/2.2 | 14.6/12.8/13.2|
|Seq2Seq<sub>NER</sub> | 16.2/14.1/14.7 | 2.5/2.1/2.2 | 14.7/12.8/13.3|
OOD test set
| Model | ROUGE<sub>RAW</sub>-1 | ROUGE<sub>RAW</sub>-2 | ROUGE<sub>RAW</sub>-L |
| :---: | :------: | :-----: | :-----: |
|CzeGPT-2 | **17.9**/**17.6**/**17.2** | **5.9**/**5.7**/**5.5** | **16.4**/**16.2**/**15.8** |
|First | 6.7/13.6/8.3 | 1.3/2.8/1.6 | 5.9/12.0/7.4 |
|TextRank | 5.8/16.9/8.1 | 1.1/3.4/1.5 | 5.0/14.5/6.9 |
|Tensor2Tensor | 6.3/5.1/5.5 | 0.5/0.4/0.4 | 5.9/4.8/5.1 |
|NE Density | 6.3/11.4/7.1 | 1.3/2.3/1.4 | 5.7/10.2/6.3 |
|Seq2Seq | 13.1/11.8/12.0 | 2.0/1.7/1.8 | 12.1/11.0/11.2 |
|Seq2SeqNER | 16.2/14.1/14.7 | 2.5/2.1/2.2 | 14.7/12.8/13.3 |
The numbers in the tables denote *precision/recall/F1-score*
## Error Analysis
As we think the current standard ROUGE<sub>RAW</sub> metric is not suitable enough for the summarization task (even though it is the best we have at the time), we performed also a manual error analysis of the generated summaries using human annotators. You can find more about the methodology and results in our paper referenced at the bottom of this card.
## Running the predictions
The repository includes a simple Jupyter Notebook that can help with first steps when using the model.
## Summary generator
See also our model fine-tuned for <a href="https://huggingface.co/MU-NLPC/CzeGPT-2_summarizer">summary generation task</a>.
## How to cite
@unpublished{hajek_horak2022,<br>
author = "Adam Hájek and Aleš Horák",<br>
title = "CzeGPT-2 – New Model for Czech Summarization Task",<br>
note = "preprint available at \url{https://openreview.net/forum?id=H43eQtxZefq}",<br>
month = "3",<br>
year = "2022",<br>
} |
jorge-henao/gpt2-small-spanish-disco-poetry-15 | 5d6dbbd46cd5a4e798d9c0e093408607fc57d1fe | 2022-03-29T05:17:49.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-generation | false | jorge-henao | null | jorge-henao/gpt2-small-spanish-disco-poetry-15 | 1 | null | transformers | 31,014 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: gpt2-small-spanish-disco-poetry-15
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. -->
# gpt2-small-spanish-disco-poetry-15
This model is a fine-tuned version of [datificate/gpt2-small-spanish](https://huggingface.co/datificate/gpt2-small-spanish) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2465
## 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Rishav-hub/xlm-roberta-base-finetuned-panx-de | abda1d0f55df44356c91a242483207feb0af04a5 | 2022-03-29T11:05:37.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | Rishav-hub | null | Rishav-hub/xlm-roberta-base-finetuned-panx-de | 1 | null | transformers | 31,015 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8591260810195721
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1352
- F1: 0.8591
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.257 | 1.0 | 525 | 0.1512 | 0.8302 |
| 0.1305 | 2.0 | 1050 | 0.1401 | 0.8447 |
| 0.0817 | 3.0 | 1575 | 0.1352 | 0.8591 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Serranito/wav2vec2-base-timit-demo-colab | 4000767c5e76a082687820778a8ecf2ccf054f43 | 2022-04-18T11:32:59.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Serranito | null | Serranito/wav2vec2-base-timit-demo-colab | 1 | null | transformers | 31,016 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
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-colab
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:
- eval_loss: 0.4283
- eval_wer: 0.3847
- eval_runtime: 133.4799
- eval_samples_per_second: 12.586
- eval_steps_per_second: 1.573
- epoch: 12.0
- step: 1500
## 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
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
BeamBee/DialoGPT-small-LavenzaNumTwo | 1f9ee4135cb45060b1e64961ec5ec08bf6f878c3 | 2022-03-29T16:30:52.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | BeamBee | null | BeamBee/DialoGPT-small-LavenzaNumTwo | 1 | null | transformers | 31,017 | ---
tags:
- conversational
---
# LavenzaNumTwo DialoGPT Model |
DrishtiSharma/poem-gen-spanish-t5-small-v6 | d8ef64d4f3f3dea855461eacb967d91dd415fc4f | 2022-03-29T23:45:09.000Z | [
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | DrishtiSharma | null | DrishtiSharma/poem-gen-spanish-t5-small-v6 | 1 | null | transformers | 31,018 | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: poem-gen-spanish-t5-small-v6
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. -->
# poem-gen-spanish-t5-small-v6
This model is a fine-tuned version of [hackathon-pln-es/poem-gen-spanish-t5-small](https://huggingface.co/hackathon-pln-es/poem-gen-spanish-t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8831
## 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 2.8551 | 0.73 | 30000 | 2.9296 |
| 2.6961 | 1.46 | 60000 | 2.9005 |
| 2.5756 | 2.19 | 90000 | 2.8786 |
| 2.5095 | 2.93 | 120000 | 2.8621 |
| 2.4061 | 3.66 | 150000 | 2.8830 |
| 2.3161 | 4.39 | 180000 | 2.8865 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
jfreiwa/asr-crdnn-german | e591802bd809b91d95332aa50e93c9b364a47955 | 2022-05-23T21:06:32.000Z | [
"de",
"arxiv:2106.04624",
"speechbrain",
"automatic-speech-recognition",
"CTC",
"Attention",
"pytorch",
"license:cc-by-sa-4.0"
] | automatic-speech-recognition | false | jfreiwa | null | jfreiwa/asr-crdnn-german | 1 | null | speechbrain | 31,019 | ---
license: cc-by-sa-4.0
language: "de"
thumbnail:
tags:
- automatic-speech-recognition
- CTC
- Attention
- pytorch
- speechbrain
metrics:
- wer
---
# German ASR
This model is trained on the Mozilla Common Voice 6.1, the Spoken Wikipedia Corpus and the m-ailabs corpus.
- https://nats.gitlab.io/swc/
- https://commonvoice.mozilla.org/de/datasets
- https://www.caito.de/2019/01/03/the-m-ailabs-speech-dataset/
We do not provide a language model.
You can find the training codes [here](https://github.com/rub-ksv/asr-crdnn-german).
# Performance
This model has a WER of 7.24%.
(You can find an updated version of this model here: https://huggingface.co/jfreiwa/asr-crdnn-german-umlaute)
# Model application
## Install SpeechBrain
First of all, please install SpeechBrain with the following command:
```
pip install speechbrain
```
Please notice that we encourage you to read the tutorials and learn more about
[SpeechBrain](https://speechbrain.github.io).
## Using the model
```
from speechbrain.pretrained import EncoderDecoderASR
asr_model = EncoderDecoderASR.from_hparams(source="jfreiwa/asr-crdnn-german", savedir="pretrained_models/asr-crdnn-german")
asr_model.transcribe_file("jfreiwa/asr-crdnn-german/example-de.wav")
```
## Inference on GPU
To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
# Limitations
We do not provide any warranty on the performance achieved by this model when used on other datasets.
# **About SpeechBrain**
- Website: https://speechbrain.github.io/
- Code: https://github.com/speechbrain/speechbrain/
- HuggingFace: https://huggingface.co/speechbrain/
# **Citing SpeechBrain**
Please, cite SpeechBrain if you use it for your research or business.
```bibtex
@misc{speechbrain,
title={{SpeechBrain}: A General-Purpose Speech Toolkit},
author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
year={2021},
eprint={2106.04624},
archivePrefix={arXiv},
primaryClass={eess.AS},
note={arXiv:2106.04624}
}
```
# **Citing our paper**
Please, cite our paper, when you use this model in your research.
```bibtex
@inproceedings{freiwald2022,
author={J. Freiwald and P. Pracht and S. Gergen and D. Kolossa},
title={Open-Source End-To-End Learning for Privacy-Preserving German {ASR}},
year=2022,
booktitle={DAGA 2022}
}
```
# Acknowledgements
This work was funded by the German Federal Ministry of Education and Research (BMBF)
within the “Innovations for Tomorrow’s Production, Services, and
Work” Program (02L19C200), a project that is implemented by
the Project Management Agency Karlsruhe (PTKA). The authors
are responsible for the content of this publication. |
negfir/bert_uncased_L-12_H-768_A-12 | 54ab1afa89403e9a1cd3754e71b0f3b19dbce95b | 2022-04-07T17:20:01.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | negfir | null | negfir/bert_uncased_L-12_H-768_A-12 | 1 | null | transformers | 31,020 | Entry not found |
BigSalmon/PointsOneSent | 515fe1dbd7ffa293d603aaf56449aadf4c94e50d | 2022-03-29T21:26:49.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | BigSalmon | null | BigSalmon/PointsOneSent | 1 | null | transformers | 31,021 | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/PointsOneSent")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/PointsOneSent")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-founder stacy spikes
text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes.
***
-
```
It should also be able to do all that this can: https://huggingface.co/BigSalmon/InformalToFormalLincoln27 |
negfir/bert_uncased_L-12_H-256_A-4 | 81899a3bf624d1a3b185968124a24187df6b4715 | 2022-04-05T22:30:47.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | negfir | null | negfir/bert_uncased_L-12_H-256_A-4 | 1 | null | transformers | 31,022 | Entry not found |
negfir/bert_uncased_L-12_H-128_A-2 | dc2d5be31d67844160de6b63853b3effbb25e90d | 2022-04-05T22:43:38.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | negfir | null | negfir/bert_uncased_L-12_H-128_A-2 | 1 | null | transformers | 31,023 | Entry not found |
princeton-nlp/CoFi-SQuAD-s60 | 9850f111e653a096833845a8575c4152d33bbe66 | 2022-05-01T01:15:01.000Z | [
"pytorch",
"bert",
"question-answering",
"arxiv:2204.00408",
"transformers",
"autotrain_compatible"
] | question-answering | false | princeton-nlp | null | princeton-nlp/CoFi-SQuAD-s60 | 1 | null | transformers | 31,024 | 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 SQuAD 1.1. 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. |
negfir/bert_uncased_L-8_H-256_A-4 | bfc3572e589e7dd91436d3b665e4aa9485d15f4e | 2022-04-06T01:54:08.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | negfir | null | negfir/bert_uncased_L-8_H-256_A-4 | 1 | null | transformers | 31,025 | Entry not found |
negfir/bert_uncased_L-8_H-128_A-2 | 6c4457399566ef728369bd7bb9bb53a02625ca84 | 2022-04-06T02:04:15.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | negfir | null | negfir/bert_uncased_L-8_H-128_A-2 | 1 | null | transformers | 31,026 | Entry not found |
negfir/bert_uncased_L-6_H-512_A-8 | 2af6904f5f9ffc525846afba99d026658a214447 | 2022-04-06T03:00:29.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | negfir | null | negfir/bert_uncased_L-6_H-512_A-8 | 1 | null | transformers | 31,027 | Entry not found |
negfir/bert_uncased_L-6_H-256_A-4 | cc5bacf51fe54526fc327125128565b84b2f74d4 | 2022-04-06T03:12:22.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | negfir | null | negfir/bert_uncased_L-6_H-256_A-4 | 1 | null | transformers | 31,028 | Entry not found |
negfir/bert_uncased_L-2_H-512_A-8 | 4c20f09c13c5a558842953f2ad43d7ebaee3d31a | 2022-04-06T04:55:26.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | negfir | null | negfir/bert_uncased_L-2_H-512_A-8 | 1 | null | transformers | 31,029 | Entry not found |
202015004/MY_st1_training_shreya_fixed_27_march_labled-decoded_level2_re | d4509c060e29dc2f3d2b5a7d0525a71c0a5d0679 | 2022-03-30T10:38:42.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | 202015004 | null | 202015004/MY_st1_training_shreya_fixed_27_march_labled-decoded_level2_re | 1 | null | transformers | 31,030 | Entry not found |
vesteinn/ScandiBERT-NER | 0a4146750451865d8c7b88a1aeec967febdd096a | 2022-03-30T09:16:03.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | vesteinn | null | vesteinn/ScandiBERT-NER | 1 | null | transformers | 31,031 | Entry not found |
negfir/bert_uncased_L-10_H-256_A-4 | eb35edd0e6758e8abc81a19bdd2fa8c2b29ed271 | 2022-04-06T00:20:07.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | negfir | null | negfir/bert_uncased_L-10_H-256_A-4 | 1 | null | transformers | 31,032 | Entry not found |
Mads/xlsr-0330 | 3534645b44f7a85db4d4b18830fef648e15b6a45 | 2022-03-31T07:24:58.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | Mads | null | Mads/xlsr-0330 | 1 | null | transformers | 31,033 | Entry not found |
bemich/DialoGPT-small-GeorgeCostanza | 3dd7d7a35037c96e094ec3ea83528866c16df660 | 2022-03-31T03:12:47.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | bemich | null | bemich/DialoGPT-small-GeorgeCostanza | 1 | null | transformers | 31,034 | ---
tags:
- conversational
---
# George Costanza DialoGPT model |
Kuray107/librispeech-100h-supervised-aug | 746f11ff47e2a09da984fea126a996f37d13ff23 | 2022-04-05T12:57:44.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Kuray107 | null | Kuray107/librispeech-100h-supervised-aug | 1 | null | transformers | 31,035 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: librispeech-100h-supervised-aug
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. -->
# librispeech-100h-supervised-aug
This model is a fine-tuned version of [Kuray107/librispeech-5h-supervised](https://huggingface.co/Kuray107/librispeech-5h-supervised) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0776
- Wer: 0.0327
## 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.3099 | 1.12 | 1000 | 0.0748 | 0.0521 |
| 0.1873 | 2.24 | 2000 | 0.0674 | 0.0440 |
| 0.146 | 3.36 | 3000 | 0.0671 | 0.0406 |
| 0.1233 | 4.48 | 4000 | 0.0619 | 0.0381 |
| 0.1098 | 5.61 | 5000 | 0.0618 | 0.0381 |
| 0.0985 | 6.73 | 6000 | 0.0590 | 0.0355 |
| 0.0907 | 7.85 | 7000 | 0.0659 | 0.0352 |
| 0.0837 | 8.97 | 8000 | 0.0679 | 0.0359 |
| 0.0762 | 10.09 | 9000 | 0.0701 | 0.0349 |
| 0.0707 | 11.21 | 10000 | 0.0715 | 0.0348 |
| 0.0666 | 12.33 | 11000 | 0.0719 | 0.0346 |
| 0.0631 | 13.45 | 12000 | 0.0746 | 0.0347 |
| 0.0593 | 14.57 | 13000 | 0.0757 | 0.0340 |
| 0.0554 | 15.7 | 14000 | 0.0746 | 0.0337 |
| 0.053 | 16.82 | 15000 | 0.0757 | 0.0331 |
| 0.0525 | 17.94 | 16000 | 0.0752 | 0.0327 |
| 0.0514 | 19.06 | 17000 | 0.0776 | 0.0327 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.2
- Tokenizers 0.10.3
|
nikhil6041/wav2vec2-commonvoice-hindi | a80b62165ebc9aea1b6344dcfde5ba1926e8fa9f | 2022-04-02T04:48:26.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | nikhil6041 | null | nikhil6041/wav2vec2-commonvoice-hindi | 1 | null | transformers | 31,036 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-commonvoice-hindi
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-commonvoice-hindi
This model is a fine-tuned version of [theainerd/Wav2Vec2-large-xlsr-hindi](https://huggingface.co/theainerd/Wav2Vec2-large-xlsr-hindi) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9825
- Wer: 0.6763
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 20.0 | 100 | 0.8801 | 0.6754 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
mimi/test_KE-T5 | 8449155d180df3abc383e186924a1edb7c296cbf | 2022-04-07T19:51:54.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | mimi | null | mimi/test_KE-T5 | 1 | null | transformers | 31,037 | Entry not found |
AnonymousSub/news-pretrain-roberta | b49be0bb8ee97bbfe2cae9f777f11dba0f2681a9 | 2022-03-31T07:52:33.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | AnonymousSub | null | AnonymousSub/news-pretrain-roberta | 1 | null | transformers | 31,038 | Entry not found |
AnonymousSub/news-pretrain-bert | edfd332f99bdfb9c6516386e0030fe69cf8a2498 | 2022-03-31T07:53:34.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | AnonymousSub | null | AnonymousSub/news-pretrain-bert | 1 | null | transformers | 31,039 | Entry not found |
r1ck/bi-encoder-vi_wikiqa | f8e29f915e6e96f90feecfbb81bc935678f63fcd | 2022-03-31T08:39:47.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
] | sentence-similarity | false | r1ck | null | r1ck/bi-encoder-vi_wikiqa | 1 | null | sentence-transformers | 31,040 | ---
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 8625 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss` with parameters:
```
{'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 2500,
"evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 1e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel
(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 --> |
scasutt/wav2vec2-base_toy_train_data_random_low_pass | e53f3505e9b10c9d6fc6a12a566514729758533e | 2022-03-31T10:42:02.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | scasutt | null | scasutt/wav2vec2-base_toy_train_data_random_low_pass | 1 | null | transformers | 31,041 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base_toy_train_data_random_low_pass
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_toy_train_data_random_low_pass
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: 1.3227
- Wer: 0.7288
## 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
- 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: 1000
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.0795 | 2.1 | 500 | 3.2227 | 0.9982 |
| 1.21 | 4.2 | 1000 | 1.3713 | 0.8879 |
| 0.742 | 6.3 | 1500 | 1.2660 | 0.8296 |
| 0.5877 | 8.4 | 2000 | 1.2921 | 0.7794 |
| 0.4823 | 10.5 | 2500 | 1.2899 | 0.7565 |
| 0.4036 | 12.6 | 3000 | 1.3486 | 0.7494 |
| 0.391 | 14.7 | 3500 | 1.2701 | 0.7466 |
| 0.3426 | 16.81 | 4000 | 1.3570 | 0.7279 |
| 0.3015 | 18.91 | 4500 | 1.3227 | 0.7288 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Khalsuu/2nd-wav2vec2-l-xls-r-300m-turkish-test | 0aa193770df7e4fc92a7258a05036e6c81728dfe | 2022-03-31T12:09:32.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | Khalsuu | null | Khalsuu/2nd-wav2vec2-l-xls-r-300m-turkish-test | 1 | null | transformers | 31,042 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: 2nd-wav2vec2-l-xls-r-300m-turkish-test
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. -->
# 2nd-wav2vec2-l-xls-r-300m-turkish-test
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6019
- Wer: 0.4444
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.0522 | 3.67 | 400 | 0.7773 | 0.7296 |
| 0.5369 | 7.34 | 800 | 0.6282 | 0.5888 |
| 0.276 | 11.01 | 1200 | 0.5998 | 0.5330 |
| 0.1725 | 14.68 | 1600 | 0.5859 | 0.4908 |
| 0.1177 | 18.35 | 2000 | 0.6019 | 0.4444 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
YiTian/wav2vec2-common_voice-tr-demo | c0ddbaab434fdc2090e3bd1650af8c28fd96db2e | 2022-03-31T11:40:04.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | YiTian | null | YiTian/wav2vec2-common_voice-tr-demo | 1 | null | transformers | 31,043 | ---
language:
- tr
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-common_voice-tr-demo
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-common_voice-tr-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9841
- Wer: 0.9999
## 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: 0.0003
- train_batch_size: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- 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: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 7.14 | 100 | 3.6689 | 1.0 |
| No log | 14.29 | 200 | 3.0280 | 0.9999 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.9.0
- Datasets 1.18.0
- Tokenizers 0.11.6
|
202015004/Teacher_model_31_march | 5e99825971eca3a7c34f2dbddf2ab57b79a3ed9e | 2022-03-31T20:15:44.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | 202015004 | null | 202015004/Teacher_model_31_march | 1 | null | transformers | 31,044 | Entry not found |
creynier/wav2vec2-base-swbd-turn-eos-half | 1f60aed7ccfda5736f3cd233c4de203e66512101 | 2022-04-14T15:46:06.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | creynier | null | creynier/wav2vec2-base-swbd-turn-eos-half | 1 | null | transformers | 31,045 | Entry not found |
huggingtweets/timdingmanlive | 000a447683e43c5211ed8e02f707c044e718246d | 2022-03-31T14:30:05.000Z | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"huggingtweets"
] | text-generation | false | huggingtweets | null | huggingtweets/timdingmanlive | 1 | null | transformers | 31,046 | ---
language: en
thumbnail: http://www.huggingtweets.com/timdingmanlive/1648736999131/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/2844974270/7bb6450b90b65f8712d9433b8d5e1971_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">Tim Dingman</div>
<div style="text-align: center; font-size: 14px;">@timdingmanlive</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 Tim Dingman.
| Data | Tim Dingman |
| --- | --- |
| Tweets downloaded | 3240 |
| Retweets | 555 |
| Short tweets | 138 |
| Tweets kept | 2547 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/7yvdv2z7/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 @timdingmanlive's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/311pu3zj) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/311pu3zj/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/timdingmanlive')
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)
|
benwoodyear/t5-small-cryptic-crosswords | 81faa7a48ccf6b14a9a19bd9871aca02a5d6768c | 2022-03-31T21:46:31.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | benwoodyear | null | benwoodyear/t5-small-cryptic-crosswords | 1 | null | transformers | 31,047 | Entry not found |
benwoodyear/t5-large-cryptic-crosswords | 9ed147d1e7b6b559acf21f96fe3138fd2640b896 | 2022-03-31T21:57:54.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | benwoodyear | null | benwoodyear/t5-large-cryptic-crosswords | 1 | null | transformers | 31,048 | Entry not found |
Ramil/wav2vec2-large-xlsr-300m-turkish-lm | 4a1534d987d08ad9e43af5f9e37e0bbedd9321d1 | 2022-04-01T00:10:57.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | Ramil | null | Ramil/wav2vec2-large-xlsr-300m-turkish-lm | 1 | null | transformers | 31,049 | Entry not found |
Teyronebigdick/DialoGPT-small-harrypotter | 5d645c327752430fa95a4c385ccec5d222b54cf2 | 2022-04-01T00:11:48.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Teyronebigdick | null | Teyronebigdick/DialoGPT-small-harrypotter | 1 | null | transformers | 31,050 | ---
tags:
- conversational
---
# Harry Potter Model |
Splend1dchan/t5lephone-small-squad1024 | e6f9e358ba1f11dee6c29b1a577abd30228c5781 | 2022-04-06T12:36:43.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Splend1dchan | null | Splend1dchan/t5lephone-small-squad1024 | 1 | null | transformers | 31,051 | Entry not found |
FrankCorrigan/results | e53474f877b87c3371df484f3b87a0c8a887ced5 | 2022-04-01T18:15:40.000Z | [
"pytorch",
"bart",
"text2text-generation",
"dataset:samsum",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | false | FrankCorrigan | null | FrankCorrigan/results | 1 | null | transformers | 31,052 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [linydub/bart-large-samsum](https://huggingface.co/linydub/bart-large-samsum) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0158
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | 0.9563 |
| No log | 2.0 | 2 | 0.9877 |
| No log | 3.0 | 3 | 1.0158 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
dchung117/distilbert-base-uncased-finetuned-squad-d5716d28 | e26569e115efcae2ff91ca049ff9bd442299e6d7 | 2022-04-01T02:02:28.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"en",
"dataset:squad",
"arxiv:1910.01108",
"transformers",
"question-answering",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | false | dchung117 | null | dchung117/distilbert-base-uncased-finetuned-squad-d5716d28 | 1 | null | transformers | 31,053 | ---
language:
- en
thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg
tags:
- question-answering
license: apache-2.0
datasets:
- squad
metrics:
- squad
---
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation.
In this version, the following pre-trained models were used:
* Student: `distilbert-base-uncased`
* Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1`
## Training data
This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows:
```python
from datasets import load_dataset
squad = load_dataset('squad')
```
## Training procedure
## Eval results
| | Exact Match | F1 |
|------------------|-------------|------|
| DistilBERT paper | 79.1 | 86.9 |
| Ours | 78.4 | 86.5 |
The scores were calculated using the `squad` metric from `datasets`.
### BibTeX entry and citation info
```bibtex
@misc{sanh2020distilbert,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
year={2020},
eprint={1910.01108},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
Sammith/DialoGPT-small-miachael | e8089df82645ff99fe98b782612209b858cb60c9 | 2022-04-01T04:34:10.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Sammith | null | Sammith/DialoGPT-small-miachael | 1 | null | transformers | 31,054 | ---
tags:
- conversational
---
# my chatbot model |
202015004/Teacher_model_31_march1 | 9ded0aaeadb65563fbb3f066c14357b2f25cbe1f | 2022-04-01T07:47:52.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | 202015004 | null | 202015004/Teacher_model_31_march1 | 1 | null | transformers | 31,055 | Entry not found |
Yingda/myfirstmodel | 3ecfd82613e3721931c70daf08edb64daec4114e | 2022-05-23T06:43:37.000Z | [
"pytorch",
"albert",
"text-generation",
"transformers",
"license:apache-2.0",
"token-classification"
] | token-classification | false | Yingda | null | Yingda/myfirstmodel | 1 | null | transformers | 31,056 | ---
license: apache-2.0
pipeline_tag: token-classification
---
This is my model card |
Nxtxn01/DialoGPT-small-harrypotter | b0e48ec3170eb280321b6b407314e97e337ef8e6 | 2022-04-01T08:13:15.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | Nxtxn01 | null | Nxtxn01/DialoGPT-small-harrypotter | 1 | null | transformers | 31,057 | ---
tags:
- conversational
---
# Harry Potter DialoGPT Model |
birgermoell/psst-base-rep | a7a9982f8943b094d6cca5a203e33955533f3e6a | 2022-04-01T12:02:45.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | birgermoell | null | birgermoell/psst-base-rep | 1 | null | transformers | 31,058 | The model is a reproduction of the baseline trained with Wav2vec2-small on PSST
pssteval INFO: ASR metrics for split `valid` FER: 10.4% PER: 23.1% |
202015004/Teacher_model_1_april | 7dc241930bc66d8ce0ae87ae7624b1106fdfbd72 | 2022-04-01T10:24:17.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | 202015004 | null | 202015004/Teacher_model_1_april | 1 | null | transformers | 31,059 | Entry not found |
nn007/wikineural-multilingual-ner | 6c8d81d181ece0c4679423e8937c7a25605663d3 | 2022-04-11T17:44:24.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | false | nn007 | null | nn007/wikineural-multilingual-ner | 1 | null | transformers | 31,060 | Entry not found |
RichardWang/test | 6549220ab7bc55871d4e3707b1d49cb36bc9aa75 | 2022-05-08T03:02:57.000Z | [
"pytorch",
"tsp",
"transformers"
] | null | false | RichardWang | null | RichardWang/test | 1 | null | transformers | 31,061 | Entry not found |
scasutt/wav2vec2-large-xlsr-53_toy_train_data_random_high_pass | a900587cd450d762165486a2ff9c93f3b3f81100 | 2022-04-01T17:35:45.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | scasutt | null | scasutt/wav2vec2-large-xlsr-53_toy_train_data_random_high_pass | 1 | null | transformers | 31,062 | Entry not found |
deepakvk/albert-base-v2-squad2 | f41b21b0dea1ac3e26eebb46a9c4f435aa69d21c | 2022-04-02T13:36:31.000Z | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | deepakvk | null | deepakvk/albert-base-v2-squad2 | 1 | null | transformers | 31,063 | Entry not found |
202015004/Teacher_model_1_april_proper | acbf12912e5d8dc65d4020df2a86c064a4ae297c | 2022-04-02T07:10:23.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | 202015004 | null | 202015004/Teacher_model_1_april_proper | 1 | null | transformers | 31,064 | Entry not found |
AnonymousSub/fpdm_triplet_bert_FT_newsqa | 9934be36d282b9455d9176c261f3e34b4e93c83d | 2022-04-01T21:52:03.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | AnonymousSub | null | AnonymousSub/fpdm_triplet_bert_FT_newsqa | 1 | null | transformers | 31,065 | Entry not found |
AnonymousSub/news_pretrain_bert_FT_newsqa | e3de8eb805039b38af637e180d22d79db7bdedaa | 2022-04-01T21:54:05.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | AnonymousSub | null | AnonymousSub/news_pretrain_bert_FT_newsqa | 1 | null | transformers | 31,066 | Entry not found |
AnonymousSub/bert_FT_newsqa | 78b86586d8e859291c05b9302712e02418445a0f | 2022-04-01T21:56:34.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | AnonymousSub | null | AnonymousSub/bert_FT_newsqa | 1 | null | transformers | 31,067 | Entry not found |
AnonymousSub/roberta_FT_newsqa | a66a6877190145cd17cea3ee8d04076762d2090a | 2022-04-01T21:57:25.000Z | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | AnonymousSub | null | AnonymousSub/roberta_FT_newsqa | 1 | null | transformers | 31,068 | Entry not found |
clisi2000/codeparrot | a941818d5f3c277743aa3b57088e5ccc4a19022a | 2022-04-02T15:46:55.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | clisi2000 | null | clisi2000/codeparrot | 1 | null | transformers | 31,069 | Entry not found |
clisi2000/codeparrot-small | 535ed67457f0231697ea5dafd72a8a7052f4edca | 2022-04-03T02:24:23.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | clisi2000 | null | clisi2000/codeparrot-small | 1 | null | transformers | 31,070 | Entry not found |
jingwei001/distilgpt2-finetuned-wikitext2 | d8143063a58917aec8f84eaf2dddca23ccfcc226 | 2022-04-02T14:40:16.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-generation | false | jingwei001 | null | jingwei001/distilgpt2-finetuned-wikitext2 | 1 | null | transformers | 31,071 | ---
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: 3.6432
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7607 | 1.0 | 2334 | 3.6664 |
| 3.6323 | 2.0 | 4668 | 3.6461 |
| 3.6075 | 3.0 | 7002 | 3.6432 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
juancavallotti/t5-base-es-en-fr-de | 0db20e29e4eb720c042f6f455fae8ae18f545403 | 2022-04-02T07:34:27.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | juancavallotti | null | juancavallotti/t5-base-es-en-fr-de | 1 | null | transformers | 31,072 | Entry not found |
yusufani/trclip-vitl14-e10 | f49fe60a6a4ffa22aced1297a7f11a0289da64ad | 2022-06-26T10:08:09.000Z | [
"pytorch",
"trclip",
"transformers",
"license:afl-3.0"
] | null | false | yusufani | null | yusufani/trclip-vitl14-e10 | 1 | 1 | transformers | 31,073 | ---
license: afl-3.0
---
|
aiface/test | f8cec0b68618455ab2c6c882931ede815e675707 | 2022-04-02T23:18:32.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | aiface | null | aiface/test | 1 | null | transformers | 31,074 | Entry not found |
vocab-transformers/distilbert-mlm-250k | 424f22300a1198337549f4de8e515e09c4bf019d | 2022-04-02T21:10:59.000Z | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | vocab-transformers | null | vocab-transformers/distilbert-mlm-250k | 1 | null | transformers | 31,075 | distilbert-base-uncased trained for 250K steps with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
|
notexist/ttte | 85a1bd31db50cdfd821833bc5577e4f4da9390b4 | 2022-04-03T01:28:49.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | notexist | null | notexist/ttte | 1 | null | transformers | 31,076 | Entry not found |
ml6team/xlm-roberta-base-nl-emoji-ner | 61e289ecd6fcc60fe2b88baa92d90042686cb34a | 2022-04-20T09:21:12.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"nl",
"transformers",
"sequence-tagger-model",
"autotrain_compatible"
] | token-classification | false | ml6team | null | ml6team/xlm-roberta-base-nl-emoji-ner | 1 | 1 | transformers | 31,077 | ---
language: nl
tags:
- token-classification
- sequence-tagger-model
---
# Goal
This model can be used to add emoji to an input text.
To accomplish this, we framed the problem as a token-classification problem, predicting the emoji that should follow a certain word/token as an entity.
The accompanying demo, which includes all the pre- and postprocessing needed can be found [here](https://huggingface.co/spaces/ml6team/emoji_predictor).
For the moment, this only works for Dutch texts.
# Dataset
For this model, we scraped about 1000 unique tweets per emoji we support:
['😨', '😥', '😍', '😠', '🤯', '😄', '🍾', '🚗', '☕', '💰']
Which could look like this:
```
Wow 😍😍, what a cool car 🚗🚗!
Omg, I hate mondays 😠... I need a drink 🍾
```
After some processing, we can reposition this in a more known NER format:
| Word | Label |
|-------|-----|
| Wow | B-😍|
| , | O |
| what | O |
| a | O |
| cool | O |
| car | O |
| ! | B-🚗|
Which can then be leveraged for training a token classification model.
Unfortunately, Terms of Service prohibit us from sharing the original dataset.
# Training
The model was trained for 4 epochs.
|
kumachan/dummy-model | 322dc9af940e5f3288e1a5b4aac0fdf7b0fc0c43 | 2022-04-03T09:53:08.000Z | [
"pytorch",
"camembert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | false | kumachan | null | kumachan/dummy-model | 1 | null | transformers | 31,078 | Entry not found |
morahil/wav2vec2-large-xls-r-300m-hindi | b38105af6ae4f748b9ad21efc7c5120c261ad9fe | 2022-04-03T17:28:16.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"dataset:common_voice",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | morahil | null | morahil/wav2vec2-large-xls-r-300m-hindi | 1 | null | transformers | 31,079 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-hindi
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-xls-r-300m-hindi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
mustapha/wav2vec_iemocap_session_1 | 21522b1c192c8d50d445f778201b8706747c5029 | 2022-04-06T17:56:28.000Z | [
"pytorch"
] | null | false | mustapha | null | mustapha/wav2vec_iemocap_session_1 | 1 | 1 | null | 31,080 | Entry not found |
BigSalmon/InformalToFormalLincoln34 | 06772a5b0f996e4c30e9e0962a2683493cc2e4f0 | 2022-04-03T20:41:44.000Z | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] | text-generation | false | BigSalmon | null | BigSalmon/InformalToFormalLincoln34 | 1 | null | transformers | 31,081 | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln34")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln34")
```
```
- moviepass to return
- this summer
- swooped up by
- original co-founder stacy spikes
text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes.
***
- middle schools do not have recess
- should get back to doing it
- amazing for communication
- and getting kids to move around
text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity.
***
-
```
```
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. |
jaeyeon/wav2vec2-large-xls-r-300m-en-colab | 4ac322367dc30c3069f124f0946fb171213bd2ef | 2022-04-07T11:06:38.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"dataset:librispeech_asr",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | false | jaeyeon | null | jaeyeon/wav2vec2-large-xls-r-300m-en-colab | 1 | null | transformers | 31,082 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- librispeech_asr
model-index:
- name: wav2vec2-large-xls-r-300m-en-colab
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-xls-r-300m-en-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1169
- Wer: 0.0597
## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.6951 | 0.22 | 100 | 3.1606 | 1.0 |
| 2.924 | 0.45 | 200 | 2.9297 | 1.0 |
| 2.5328 | 0.67 | 300 | 1.4339 | 0.8953 |
| 0.8611 | 0.9 | 400 | 0.6104 | 0.5306 |
| 0.3714 | 1.12 | 500 | 0.2497 | 0.2150 |
| 0.2015 | 1.35 | 600 | 0.1853 | 0.1615 |
| 0.1593 | 1.57 | 700 | 0.1613 | 0.1366 |
| 0.1436 | 1.79 | 800 | 0.1503 | 0.1311 |
| 0.1249 | 2.02 | 900 | 0.1374 | 0.1038 |
| 0.0936 | 2.24 | 1000 | 0.1328 | 0.1016 |
| 0.0896 | 2.47 | 1100 | 0.1234 | 0.0942 |
| 0.0872 | 2.69 | 1200 | 0.1148 | 0.0922 |
| 0.0859 | 2.91 | 1300 | 0.1140 | 0.0892 |
| 0.0733 | 3.14 | 1400 | 0.1134 | 0.0839 |
| 0.0633 | 3.36 | 1500 | 0.1085 | 0.0802 |
| 0.0567 | 3.59 | 1600 | 0.1103 | 0.0807 |
| 0.0604 | 3.81 | 1700 | 0.1088 | 0.0809 |
| 0.0586 | 4.04 | 1800 | 0.1113 | 0.0804 |
| 0.0516 | 4.26 | 1900 | 0.1123 | 0.0808 |
| 0.055 | 4.48 | 2000 | 0.1130 | 0.0764 |
| 0.0568 | 4.71 | 2100 | 0.1128 | 0.0807 |
| 0.0529 | 4.93 | 2200 | 0.1009 | 0.0727 |
| 0.0455 | 5.16 | 2300 | 0.1050 | 0.0726 |
| 0.0443 | 5.38 | 2400 | 0.1078 | 0.0720 |
| 0.0434 | 5.61 | 2500 | 0.1027 | 0.0702 |
| 0.0418 | 5.83 | 2600 | 0.1009 | 0.0693 |
| 0.0381 | 6.05 | 2700 | 0.1079 | 0.0689 |
| 0.0344 | 6.28 | 2800 | 0.1062 | 0.0678 |
| 0.0353 | 6.5 | 2900 | 0.1054 | 0.0682 |
| 0.0342 | 6.73 | 3000 | 0.1030 | 0.0661 |
| 0.0329 | 6.95 | 3100 | 0.1021 | 0.0659 |
| 0.0316 | 7.17 | 3200 | 0.1085 | 0.0667 |
| 0.0275 | 7.4 | 3300 | 0.1089 | 0.0645 |
| 0.0275 | 7.62 | 3400 | 0.1064 | 0.0645 |
| 0.0268 | 7.85 | 3500 | 0.1109 | 0.0639 |
| 0.0259 | 8.07 | 3600 | 0.1123 | 0.0636 |
| 0.024 | 8.3 | 3700 | 0.1169 | 0.0631 |
| 0.0225 | 8.52 | 3800 | 0.1170 | 0.0617 |
| 0.0229 | 8.74 | 3900 | 0.1153 | 0.0614 |
| 0.0214 | 8.97 | 4000 | 0.1143 | 0.0610 |
| 0.02 | 9.19 | 4100 | 0.1162 | 0.0606 |
| 0.0194 | 9.42 | 4200 | 0.1173 | 0.0603 |
| 0.0193 | 9.64 | 4300 | 0.1184 | 0.0601 |
| 0.0177 | 9.87 | 4400 | 0.1169 | 0.0597 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
MrYiRen/DialoGPT-small-harrypotter | 15cca8d96fae801f913700cd9bc79fb8cd7faf72 | 2022-04-04T07:39:25.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | MrYiRen | null | MrYiRen/DialoGPT-small-harrypotter | 1 | null | transformers | 31,083 | ---
tags:
- conversational
---
# Harry Potter DialoGPT Model |
microsoft/cvt-21-384-22k | 813100c6a0cf8157243eac067667eb3a96564c09 | 2022-05-18T16:16:59.000Z | [
"pytorch",
"cvt",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2103.15808",
"transformers",
"vision",
"license:apache-2.0"
] | image-classification | false | microsoft | null | microsoft/cvt-21-384-22k | 1 | null | transformers | 31,084 | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# Convolutional Vision Transformer (CvT)
CvT-21 model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Wu et al. and first released in [this repository](https://github.com/microsoft/CvT).
Disclaimer: The team releasing CvT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Usage
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoFeatureExtractor, CvtForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained('microsoft/cvt-21-384-22k')
model = CvtForImageClassification.from_pretrained('microsoft/cvt-21-384-22k')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
``` |
gao-huggingface/T5-IDX-Parent | 0bc3846a2141fa90bb8657e616989583815d14c2 | 2022-04-04T15:57:54.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | gao-huggingface | null | gao-huggingface/T5-IDX-Parent | 1 | null | transformers | 31,085 | Entry not found |
birgermoell/psst-common-voice | ca88d7f1e5c536a0bf1e937ebcef254bb43ed323 | 2022-04-04T18:06:17.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | birgermoell | null | birgermoell/psst-common-voice | 1 | null | transformers | 31,086 | Entry not found |
Erfan/Test_model0 | cd6fc5f6d239ce2bf47d97876f07c00ab95da9de | 2022-04-04T21:36:06.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | false | Erfan | null | Erfan/Test_model0 | 1 | null | transformers | 31,087 | Entry not found |
deepspeechvision/wav2vec2hindiasr | f96326e9bcb8abc02bb49effb4eaee0964e0877c | 2022-04-05T16:16:09.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | false | deepspeechvision | null | deepspeechvision/wav2vec2hindiasr | 1 | null | transformers | 31,088 | Entry not found |
Diya-999/fdBart-FNS | c7fb007b56b0338c837e53f4ec4d11afdbcad23a | 2022-04-17T16:01:17.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"license:afl-3.0",
"autotrain_compatible"
] | text2text-generation | false | Diya-999 | null | Diya-999/fdBart-FNS | 1 | null | transformers | 31,089 | ---
license: afl-3.0
---
|
inigopm/bert-finetuned-squad | efc7b0d872d807cd090b37219a9c31d963cfb35c | 2022-05-11T15:10:08.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | inigopm | null | inigopm/bert-finetuned-squad | 1 | null | transformers | 31,090 | Entry not found |
MrYiRen/DialoGPT-small-harrypotter2 | 2aa7c4bd32875dbf127b502015e93dfe42a638ad | 2022-04-05T14:04:02.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | false | MrYiRen | null | MrYiRen/DialoGPT-small-harrypotter2 | 1 | null | transformers | 31,091 | ---
tags:
- conversational
---
# Harry Potter2 DialoGPT Model |
AnonymousSub/fpdm_bert_FT_new_newsqa | dae2aeaf6b8d6195a6090f52a5fe156b0f45eabd | 2022-04-05T14:41:52.000Z | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | AnonymousSub | null | AnonymousSub/fpdm_bert_FT_new_newsqa | 1 | null | transformers | 31,092 | Entry not found |
AnonymousSub/fpdm_hier_roberta_FT_new_newsqa | ca5f9cd4298d286683efe44f454f5c7bab690a4c | 2022-04-05T15:01:01.000Z | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | AnonymousSub | null | AnonymousSub/fpdm_hier_roberta_FT_new_newsqa | 1 | null | transformers | 31,093 | Entry not found |
quincyqiang/bert-base-uncased-finetuned-swag | 32a60650393d93ad713e40d2970e880608d66d49 | 2022-04-05T15:59:49.000Z | [
"pytorch",
"tensorboard",
"bert",
"multiple-choice",
"dataset:swag",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | multiple-choice | false | quincyqiang | null | quincyqiang/bert-base-uncased-finetuned-swag | 1 | null | transformers | 31,094 | ---
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: 1.0397
- Accuracy: 0.7892
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.756 | 1.0 | 4597 | 0.6021 | 0.7646 |
| 0.3978 | 2.0 | 9194 | 0.6617 | 0.7783 |
| 0.1468 | 3.0 | 13791 | 1.0397 | 0.7892 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.8.0+cu111
- Datasets 1.17.0
- Tokenizers 0.11.6
|
rowan1224/distilbert-slp | fd6827bf85a2a62c861aeaf5fa10bd3f02c3c579 | 2022-04-05T16:46:16.000Z | [
"pytorch",
"distilbert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | rowan1224 | null | rowan1224/distilbert-slp | 1 | null | transformers | 31,095 | Entry not found |
rowan1224/electra-squad-slp | 0a56022ba06428731690941aae39776e19481eb7 | 2022-04-05T16:47:59.000Z | [
"pytorch",
"electra",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | rowan1224 | null | rowan1224/electra-squad-slp | 1 | null | transformers | 31,096 | Entry not found |
novarac23/xlm-roberta-base-finetuned-panx-de | 9d0a8dd0db9ad9881c7c6e7068708394984d846a | 2022-04-05T18:26:07.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"dataset:xtreme",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
] | token-classification | false | novarac23 | null | novarac23/xlm-roberta-base-finetuned-panx-de | 1 | null | transformers | 31,097 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.862669465085938
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1374
- F1: 0.8627
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2596 | 1.0 | 525 | 0.1571 | 0.8302 |
| 0.1292 | 2.0 | 1050 | 0.1416 | 0.8455 |
| 0.0809 | 3.0 | 1575 | 0.1374 | 0.8627 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
miesnerjacob/marian-finetuned-kde4-en-to-fr | 25e0a2b6fdb6a2dc83bad9ac07a4a57db6db5412 | 2022-04-05T20:28:41.000Z | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:kde4",
"transformers",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | translation | false | miesnerjacob | null | miesnerjacob/marian-finetuned-kde4-en-to-fr | 1 | null | transformers | 31,098 | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.94560734092563
---
<!-- 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8559
- Bleu: 52.9456
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
CenIA/albert-xlarge-spanish-finetuned-qa-tar | fc04b4aa4070e495d95b17acb846f3d4804061c2 | 2022-04-05T18:55:50.000Z | [
"pytorch",
"albert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | false | CenIA | null | CenIA/albert-xlarge-spanish-finetuned-qa-tar | 1 | null | transformers | 31,099 | Entry not found |
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