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null	null	transformers	# HebEMO - Emotion Recognition Model for Modern Hebrew <img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250"> HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated. HebEMO yielded a high performance of weighted average F1-score = 0.96 for polarity classification. Emotion detection reached an F1-score of 0.78-0.97, with the exception of surprise, which the model failed to capture (F1 = 0.41). These results are better than the best-reported performance, even when compared to the English language. ## Emotion UGC Data Description Our UGC data includes comments posted on news articles collected from 3 major Israeli news sites, between January 2020 to August 2020. The total size of the data is ~150 MB, including over 7 million words and 350K sentences. ~2000 sentences were annotated by crowd members (3-10 annotators per sentence) for overall sentiment (polarity) and [eight emotions](https://en.wikipedia.org/wiki/Robert_Plutchik#Plutchik's_wheel_of_emotions): anger, disgust, anticipation , fear, joy, sadness, surprise and trust. The percentage of sentences in which each emotion appeared is found in the table below. \| \| anger \| disgust \| expectation \| fear \| happy \| sadness \| surprise \| trust \| sentiment \| \|------:\|------:\|--------:\|------------:\|-----:\|------:\|--------:\|---------:\|------:\|-----------\| \| ratio \| 0.78 \| 0.83 \| 0.58 \| 0.45 \| 0.12 \| 0.59 \| 0.17 \| 0.11 \| 0.25 \| ## Performance ### Emotion Recognition \| emotion \| f1-score \| precision \| recall \| \|-------------\|----------\|-----------\|----------\| \| anger \| 0.96 \| 0.99 \| 0.93 \| \| disgust \| 0.97 \| 0.98 \| 0.96 \| \|anticipation \| 0.82 \| 0.80 \| 0.87 \| \| fear \| 0.79 \| 0.88 \| 0.72 \| \| joy \| 0.90 \| 0.97 \| 0.84 \| \| sadness \| 0.90 \| 0.86 \| 0.94 \| \| surprise \| 0.40 \| 0.44 \| 0.37 \| \| trust \| 0.83 \| 0.86 \| 0.80 \| The above metrics is for positive class (meaning, the emotion is reflected in the text). ### Sentiment (Polarity) Analysis \| \| precision \| recall \| f1-score \| \|--------------\|-----------\|--------\|----------\| \| neutral \| 0.83 \| 0.56 \| 0.67 \| \| positive \| 0.96 \| 0.92 \| 0.94 \| \| negative \| 0.97 \| 0.99 \| 0.98 \| \| accuracy \| \| \| 0.97 \| \| macro avg \| 0.92 \| 0.82 \| 0.86 \| \| weighted avg \| 0.96 \| 0.97 \| 0.96 \| Sentiment (polarity) analysis model is also available on AWS! for more information visit [AWS' git](https://github.com/aws-samples/aws-lambda-docker-serverless-inference/tree/main/hebert-sentiment-analysis-inference-docker-lambda) ## How to use ### Emotion Recognition Model An online model can be found at [huggingface spaces](https://huggingface.co/spaces/avichr/HebEMO_demo) or as [colab notebook](https://colab.research.google.com/drive/1Jw3gOWjwVMcZslu-ttXoNeD17lms1-ff?usp=sharing) ``` # !pip install pyplutchik==0.0.7 # !pip install transformers==4.14.1 !git clone https://github.com/avichaychriqui/HeBERT.git from HeBERT.src.HebEMO import * HebEMO_model = HebEMO() HebEMO_model.hebemo(input_path = 'data/text_example.txt') # return analyzed pandas.DataFrame hebEMO_df = HebEMO_model.hebemo(text='החיים יפים ומאושרים', plot=True) ``` <img src="https://github.com/avichaychriqui/HeBERT/blob/main/data/hebEMO1.png?raw=true" width="300" height="300" /> ### For sentiment classification model (polarity ONLY): from transformers import AutoTokenizer, AutoModel, pipeline tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT_sentiment_analysis") #same as 'avichr/heBERT' tokenizer model = AutoModel.from_pretrained("avichr/heBERT_sentiment_analysis") # how to use? sentiment_analysis = pipeline( "sentiment-analysis", model="avichr/heBERT_sentiment_analysis", tokenizer="avichr/heBERT_sentiment_analysis", return_all_scores = True ) sentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים') >>> [[{'label': 'neutral', 'score': 0.9978172183036804}, >>> {'label': 'positive', 'score': 0.0014792329166084528}, >>> {'label': 'negative', 'score': 0.0007035882445052266}]] sentiment_analysis('קפה זה טעים') >>> [[{'label': 'neutral', 'score': 0.00047328314394690096}, >>> {'label': 'possitive', 'score': 0.9994067549705505}, >>> {'label': 'negetive', 'score': 0.00011996887042187154}]] sentiment_analysis('אני לא אוהב את העולם') >>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, >>> {'label': 'possitive', 'score': 8.876807987689972e-05}, >>> {'label': 'negetive', 'score': 0.9998190999031067}]] ## Contact us [Avichay Chriqui](mailto:[email protected]) <br> [Inbal yahav](mailto:[email protected]) <br> The Coller Semitic Languages AI Lab <br> Thank you, תודה, شكرا <br> ## If you used this model please cite us as : Chriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming. ``` @article{chriqui2021hebert, title={HeBERT \& HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition}, author={Chriqui, Avihay and Yahav, Inbal}, journal={INFORMS Journal on Data Science}, year={2022} } ```	{}	text-classification	avichr/hebEMO_disgust	[ "transformers", "pytorch", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us	HebEMO - Emotion Recognition Model for Modern Hebrew ==================================================== <img align="right" src="URL width="250"> HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated. HebEMO yielded a high performance of weighted average F1-score = 0.96 for polarity classification. Emotion detection reached an F1-score of 0.78-0.97, with the exception of surprise, which the model failed to capture (F1 = 0.41). These results are better than the best-reported performance, even when compared to the English language. Emotion UGC Data Description ---------------------------- Our UGC data includes comments posted on news articles collected from 3 major Israeli news sites, between January 2020 to August 2020. The total size of the data is ~150 MB, including over 7 million words and 350K sentences. ~2000 sentences were annotated by crowd members (3-10 annotators per sentence) for overall sentiment (polarity) and eight emotions: anger, disgust, anticipation , fear, joy, sadness, surprise and trust. The percentage of sentences in which each emotion appeared is found in the table below. Performance ----------- ### Emotion Recognition The above metrics is for positive class (meaning, the emotion is reflected in the text). ### Sentiment (Polarity) Analysis Sentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git How to use ---------- ### Emotion Recognition Model An online model can be found at huggingface spaces or as colab notebook <img src="URL width="300" height="300" /> ### For sentiment classification model (polarity ONLY): ``` from transformers import AutoTokenizer, AutoModel, pipeline tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT_sentiment_analysis") #same as 'avichr/heBERT' tokenizer model = AutoModel.from_pretrained("avichr/heBERT_sentiment_analysis") # how to use? sentiment_analysis = pipeline( "sentiment-analysis", model="avichr/heBERT_sentiment_analysis", tokenizer="avichr/heBERT_sentiment_analysis", return_all_scores = True ) sentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים') >>> [[{'label': 'neutral', 'score': 0.9978172183036804}, >>> {'label': 'positive', 'score': 0.0014792329166084528}, >>> {'label': 'negative', 'score': 0.0007035882445052266}]] sentiment_analysis('קפה זה טעים') >>> [[{'label': 'neutral', 'score': 0.00047328314394690096}, >>> {'label': 'possitive', 'score': 0.9994067549705505}, >>> {'label': 'negetive', 'score': 0.00011996887042187154}]] sentiment_analysis('אני לא אוהב את העולם') >>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, >>> {'label': 'possitive', 'score': 8.876807987689972e-05}, >>> {'label': 'negetive', 'score': 0.9998190999031067}]] ``` Contact us ---------- Avichay Chriqui Inbal yahav The Coller Semitic Languages AI Lab Thank you, תודה, شكرا If you used this model please cite us as : ------------------------------------------ Chriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming.	[ "### Emotion Recognition\n\n\n\nThe above metrics is for positive class (meaning, the emotion is reflected in the text).", "### Sentiment (Polarity) Analysis\n\n\n\nSentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git\n\n\nHow to use\n----------", "### Emotion Recognition Model\n\n\nAn online model can be found at huggingface spaces or as colab notebook\n\n\n<img src=\"URL width=\"300\" height=\"300\" />", "### For sentiment classification model (polarity ONLY):\n\n\n\n```\nfrom transformers import AutoTokenizer, AutoModel, pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"avichr/heBERT_sentiment_analysis\") #same as 'avichr/heBERT' tokenizer\nmodel = AutoModel.from_pretrained(\"avichr/heBERT_sentiment_analysis\")", "# how to use?\nsentiment_analysis = pipeline(\n \"sentiment-analysis\",\n model=\"avichr/heBERT_sentiment_analysis\",\n tokenizer=\"avichr/heBERT_sentiment_analysis\",\n return_all_scores = True\n)\n\nsentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים')\t\n>>> [[{'label': 'neutral', 'score': 0.9978172183036804},\n>>> {'label': 'positive', 'score': 0.0014792329166084528},\n>>> {'label': 'negative', 'score': 0.0007035882445052266}]]\n\nsentiment_analysis('קפה זה טעים')\n>>> [[{'label': 'neutral', 'score': 0.00047328314394690096},\n>>> {'label': 'possitive', 'score': 0.9994067549705505},\n>>> {'label': 'negetive', 'score': 0.00011996887042187154}]]\n\nsentiment_analysis('אני לא אוהב את העולם')\n>>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, \n>>> {'label': 'possitive', 'score': 8.876807987689972e-05}, \n>>> {'label': 'negetive', 'score': 0.9998190999031067}]]\n\n```\n\nContact us\n----------\n\n\nAvichay Chriqui \n\nInbal yahav \n\nThe Coller Semitic Languages AI Lab \n\nThank you, תודה, شكرا \n\n\n\nIf you used this model please cite us as :\n------------------------------------------\n\n\nChriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming." ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n", "### Emotion Recognition\n\n\n\nThe above metrics is for positive class (meaning, the emotion is reflected in the text).", "### Sentiment (Polarity) Analysis\n\n\n\nSentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git\n\n\nHow to use\n----------", "### Emotion Recognition Model\n\n\nAn online model can be found at huggingface spaces or as colab notebook\n\n\n<img src=\"URL width=\"300\" height=\"300\" />", "### For sentiment classification model (polarity ONLY):\n\n\n\n```\nfrom transformers import AutoTokenizer, AutoModel, pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"avichr/heBERT_sentiment_analysis\") #same as 'avichr/heBERT' tokenizer\nmodel = AutoModel.from_pretrained(\"avichr/heBERT_sentiment_analysis\")", "# how to use?\nsentiment_analysis = pipeline(\n \"sentiment-analysis\",\n model=\"avichr/heBERT_sentiment_analysis\",\n tokenizer=\"avichr/heBERT_sentiment_analysis\",\n return_all_scores = True\n)\n\nsentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים')\t\n>>> [[{'label': 'neutral', 'score': 0.9978172183036804},\n>>> {'label': 'positive', 'score': 0.0014792329166084528},\n>>> {'label': 'negative', 'score': 0.0007035882445052266}]]\n\nsentiment_analysis('קפה זה טעים')\n>>> [[{'label': 'neutral', 'score': 0.00047328314394690096},\n>>> {'label': 'possitive', 'score': 0.9994067549705505},\n>>> {'label': 'negetive', 'score': 0.00011996887042187154}]]\n\nsentiment_analysis('אני לא אוהב את העולם')\n>>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, \n>>> {'label': 'possitive', 'score': 8.876807987689972e-05}, \n>>> {'label': 'negetive', 'score': 0.9998190999031067}]]\n\n```\n\nContact us\n----------\n\n\nAvichay Chriqui \n\nInbal yahav \n\nThe Coller Semitic Languages AI Lab \n\nThank you, תודה, شكرا \n\n\n\nIf you used this model please cite us as :\n------------------------------------------\n\n\nChriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. 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null	null	transformers	# HebEMO - Emotion Recognition Model for Modern Hebrew <img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250"> HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated. HebEMO yielded a high performance of weighted average F1-score = 0.96 for polarity classification. Emotion detection reached an F1-score of 0.78-0.97, with the exception of surprise, which the model failed to capture (F1 = 0.41). These results are better than the best-reported performance, even when compared to the English language. ## Emotion UGC Data Description Our UGC data includes comments posted on news articles collected from 3 major Israeli news sites, between January 2020 to August 2020. The total size of the data is ~150 MB, including over 7 million words and 350K sentences. ~2000 sentences were annotated by crowd members (3-10 annotators per sentence) for overall sentiment (polarity) and [eight emotions](https://en.wikipedia.org/wiki/Robert_Plutchik#Plutchik's_wheel_of_emotions): anger, disgust, anticipation , fear, joy, sadness, surprise and trust. The percentage of sentences in which each emotion appeared is found in the table below. \| \| anger \| disgust \| expectation \| fear \| happy \| sadness \| surprise \| trust \| sentiment \| \|------:\|------:\|--------:\|------------:\|-----:\|------:\|--------:\|---------:\|------:\|-----------\| \| ratio \| 0.78 \| 0.83 \| 0.58 \| 0.45 \| 0.12 \| 0.59 \| 0.17 \| 0.11 \| 0.25 \| ## Performance ### Emotion Recognition \| emotion \| f1-score \| precision \| recall \| \|-------------\|----------\|-----------\|----------\| \| anger \| 0.96 \| 0.99 \| 0.93 \| \| disgust \| 0.97 \| 0.98 \| 0.96 \| \|anticipation \| 0.82 \| 0.80 \| 0.87 \| \| fear \| 0.79 \| 0.88 \| 0.72 \| \| joy \| 0.90 \| 0.97 \| 0.84 \| \| sadness \| 0.90 \| 0.86 \| 0.94 \| \| surprise \| 0.40 \| 0.44 \| 0.37 \| \| trust \| 0.83 \| 0.86 \| 0.80 \| The above metrics is for positive class (meaning, the emotion is reflected in the text). ### Sentiment (Polarity) Analysis \| \| precision \| recall \| f1-score \| \|--------------\|-----------\|--------\|----------\| \| neutral \| 0.83 \| 0.56 \| 0.67 \| \| positive \| 0.96 \| 0.92 \| 0.94 \| \| negative \| 0.97 \| 0.99 \| 0.98 \| \| accuracy \| \| \| 0.97 \| \| macro avg \| 0.92 \| 0.82 \| 0.86 \| \| weighted avg \| 0.96 \| 0.97 \| 0.96 \| Sentiment (polarity) analysis model is also available on AWS! for more information visit [AWS' git](https://github.com/aws-samples/aws-lambda-docker-serverless-inference/tree/main/hebert-sentiment-analysis-inference-docker-lambda) ## How to use ### Emotion Recognition Model An online model can be found at [huggingface spaces](https://huggingface.co/spaces/avichr/HebEMO_demo) or as [colab notebook](https://colab.research.google.com/drive/1Jw3gOWjwVMcZslu-ttXoNeD17lms1-ff?usp=sharing) ``` # !pip install pyplutchik==0.0.7 # !pip install transformers==4.14.1 !git clone https://github.com/avichaychriqui/HeBERT.git from HeBERT.src.HebEMO import * HebEMO_model = HebEMO() HebEMO_model.hebemo(input_path = 'data/text_example.txt') # return analyzed pandas.DataFrame hebEMO_df = HebEMO_model.hebemo(text='החיים יפים ומאושרים', plot=True) ``` <img src="https://github.com/avichaychriqui/HeBERT/blob/main/data/hebEMO1.png?raw=true" width="300" height="300" /> ### For sentiment classification model (polarity ONLY): from transformers import AutoTokenizer, AutoModel, pipeline tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT_sentiment_analysis") #same as 'avichr/heBERT' tokenizer model = AutoModel.from_pretrained("avichr/heBERT_sentiment_analysis") # how to use? sentiment_analysis = pipeline( "sentiment-analysis", model="avichr/heBERT_sentiment_analysis", tokenizer="avichr/heBERT_sentiment_analysis", return_all_scores = True ) sentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים') >>> [[{'label': 'neutral', 'score': 0.9978172183036804}, >>> {'label': 'positive', 'score': 0.0014792329166084528}, >>> {'label': 'negative', 'score': 0.0007035882445052266}]] sentiment_analysis('קפה זה טעים') >>> [[{'label': 'neutral', 'score': 0.00047328314394690096}, >>> {'label': 'possitive', 'score': 0.9994067549705505}, >>> {'label': 'negetive', 'score': 0.00011996887042187154}]] sentiment_analysis('אני לא אוהב את העולם') >>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, >>> {'label': 'possitive', 'score': 8.876807987689972e-05}, >>> {'label': 'negetive', 'score': 0.9998190999031067}]] ## Contact us [Avichay Chriqui](mailto:[email protected]) <br> [Inbal yahav](mailto:[email protected]) <br> The Coller Semitic Languages AI Lab <br> Thank you, תודה, شكرا <br> ## If you used this model please cite us as : Chriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming. ``` @article{chriqui2021hebert, title={HeBERT \& HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition}, author={Chriqui, Avihay and Yahav, Inbal}, journal={INFORMS Journal on Data Science}, year={2022} } ```	{}	text-classification	avichr/hebEMO_fear	[ "transformers", "pytorch", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us	HebEMO - Emotion Recognition Model for Modern Hebrew ==================================================== <img align="right" src="URL width="250"> HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated. HebEMO yielded a high performance of weighted average F1-score = 0.96 for polarity classification. Emotion detection reached an F1-score of 0.78-0.97, with the exception of surprise, which the model failed to capture (F1 = 0.41). These results are better than the best-reported performance, even when compared to the English language. Emotion UGC Data Description ---------------------------- Our UGC data includes comments posted on news articles collected from 3 major Israeli news sites, between January 2020 to August 2020. The total size of the data is ~150 MB, including over 7 million words and 350K sentences. ~2000 sentences were annotated by crowd members (3-10 annotators per sentence) for overall sentiment (polarity) and eight emotions: anger, disgust, anticipation , fear, joy, sadness, surprise and trust. The percentage of sentences in which each emotion appeared is found in the table below. Performance ----------- ### Emotion Recognition The above metrics is for positive class (meaning, the emotion is reflected in the text). ### Sentiment (Polarity) Analysis Sentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git How to use ---------- ### Emotion Recognition Model An online model can be found at huggingface spaces or as colab notebook <img src="URL width="300" height="300" /> ### For sentiment classification model (polarity ONLY): ``` from transformers import AutoTokenizer, AutoModel, pipeline tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT_sentiment_analysis") #same as 'avichr/heBERT' tokenizer model = AutoModel.from_pretrained("avichr/heBERT_sentiment_analysis") # how to use? sentiment_analysis = pipeline( "sentiment-analysis", model="avichr/heBERT_sentiment_analysis", tokenizer="avichr/heBERT_sentiment_analysis", return_all_scores = True ) sentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים') >>> [[{'label': 'neutral', 'score': 0.9978172183036804}, >>> {'label': 'positive', 'score': 0.0014792329166084528}, >>> {'label': 'negative', 'score': 0.0007035882445052266}]] sentiment_analysis('קפה זה טעים') >>> [[{'label': 'neutral', 'score': 0.00047328314394690096}, >>> {'label': 'possitive', 'score': 0.9994067549705505}, >>> {'label': 'negetive', 'score': 0.00011996887042187154}]] sentiment_analysis('אני לא אוהב את העולם') >>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, >>> {'label': 'possitive', 'score': 8.876807987689972e-05}, >>> {'label': 'negetive', 'score': 0.9998190999031067}]] ``` Contact us ---------- Avichay Chriqui Inbal yahav The Coller Semitic Languages AI Lab Thank you, תודה, شكرا If you used this model please cite us as : ------------------------------------------ Chriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming.	[ "### Emotion Recognition\n\n\n\nThe above metrics is for positive class (meaning, the emotion is reflected in the text).", "### Sentiment (Polarity) Analysis\n\n\n\nSentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git\n\n\nHow to use\n----------", "### Emotion Recognition Model\n\n\nAn online model can be found at huggingface spaces or as colab notebook\n\n\n<img src=\"URL width=\"300\" height=\"300\" />", "### For sentiment classification model (polarity ONLY):\n\n\n\n```\nfrom transformers import AutoTokenizer, AutoModel, pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"avichr/heBERT_sentiment_analysis\") #same as 'avichr/heBERT' tokenizer\nmodel = AutoModel.from_pretrained(\"avichr/heBERT_sentiment_analysis\")", "# how to use?\nsentiment_analysis = pipeline(\n \"sentiment-analysis\",\n model=\"avichr/heBERT_sentiment_analysis\",\n tokenizer=\"avichr/heBERT_sentiment_analysis\",\n return_all_scores = True\n)\n\nsentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים')\t\n>>> [[{'label': 'neutral', 'score': 0.9978172183036804},\n>>> {'label': 'positive', 'score': 0.0014792329166084528},\n>>> {'label': 'negative', 'score': 0.0007035882445052266}]]\n\nsentiment_analysis('קפה זה טעים')\n>>> [[{'label': 'neutral', 'score': 0.00047328314394690096},\n>>> {'label': 'possitive', 'score': 0.9994067549705505},\n>>> {'label': 'negetive', 'score': 0.00011996887042187154}]]\n\nsentiment_analysis('אני לא אוהב את העולם')\n>>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, \n>>> {'label': 'possitive', 'score': 8.876807987689972e-05}, \n>>> {'label': 'negetive', 'score': 0.9998190999031067}]]\n\n```\n\nContact us\n----------\n\n\nAvichay Chriqui \n\nInbal yahav \n\nThe Coller Semitic Languages AI Lab \n\nThank you, תודה, شكرا \n\n\n\nIf you used this model please cite us as :\n------------------------------------------\n\n\nChriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming." ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n", "### Emotion Recognition\n\n\n\nThe above metrics is for positive class (meaning, the emotion is reflected in the text).", "### Sentiment (Polarity) Analysis\n\n\n\nSentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git\n\n\nHow to use\n----------", "### Emotion Recognition Model\n\n\nAn online model can be found at huggingface spaces or as colab notebook\n\n\n<img src=\"URL width=\"300\" height=\"300\" />", "### For sentiment classification model (polarity ONLY):\n\n\n\n```\nfrom transformers import AutoTokenizer, AutoModel, pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"avichr/heBERT_sentiment_analysis\") #same as 'avichr/heBERT' tokenizer\nmodel = AutoModel.from_pretrained(\"avichr/heBERT_sentiment_analysis\")", "# how to use?\nsentiment_analysis = pipeline(\n \"sentiment-analysis\",\n model=\"avichr/heBERT_sentiment_analysis\",\n tokenizer=\"avichr/heBERT_sentiment_analysis\",\n return_all_scores = True\n)\n\nsentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים')\t\n>>> [[{'label': 'neutral', 'score': 0.9978172183036804},\n>>> {'label': 'positive', 'score': 0.0014792329166084528},\n>>> {'label': 'negative', 'score': 0.0007035882445052266}]]\n\nsentiment_analysis('קפה זה טעים')\n>>> [[{'label': 'neutral', 'score': 0.00047328314394690096},\n>>> {'label': 'possitive', 'score': 0.9994067549705505},\n>>> {'label': 'negetive', 'score': 0.00011996887042187154}]]\n\nsentiment_analysis('אני לא אוהב את העולם')\n>>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, \n>>> {'label': 'possitive', 'score': 8.876807987689972e-05}, \n>>> {'label': 'negetive', 'score': 0.9998190999031067}]]\n\n```\n\nContact us\n----------\n\n\nAvichay Chriqui \n\nInbal yahav \n\nThe Coller Semitic Languages AI Lab \n\nThank you, תודה, شكرا \n\n\n\nIf you used this model please cite us as :\n------------------------------------------\n\n\nChriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming." ]	[ 36, 30, 43, 42, 101, 454 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n### Emotion Recognition\n\n\n\nThe above metrics is for positive class (meaning, the emotion is reflected in the text).### Sentiment (Polarity) Analysis\n\n\n\nSentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git\n\n\nHow to use\n----------### Emotion Recognition Model\n\n\nAn online model can be found at huggingface spaces or as colab notebook\n\n\n<img src=\"URL width=\"300\" height=\"300\" />### For sentiment classification model (polarity ONLY):\n\n\n\n```\nfrom transformers import AutoTokenizer, AutoModel, pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"avichr/heBERT_sentiment_analysis\") #same as 'avichr/heBERT' tokenizer\nmodel = AutoModel.from_pretrained(\"avichr/heBERT_sentiment_analysis\")" ]	[ -0.05714952573180199, 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null	null	transformers	# HebEMO - Emotion Recognition Model for Modern Hebrew <img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250"> HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated. HebEMO yielded a high performance of weighted average F1-score = 0.96 for polarity classification. Emotion detection reached an F1-score of 0.78-0.97, with the exception of surprise, which the model failed to capture (F1 = 0.41). These results are better than the best-reported performance, even when compared to the English language. ## Emotion UGC Data Description Our UGC data includes comments posted on news articles collected from 3 major Israeli news sites, between January 2020 to August 2020. The total size of the data is ~150 MB, including over 7 million words and 350K sentences. ~2000 sentences were annotated by crowd members (3-10 annotators per sentence) for overall sentiment (polarity) and [eight emotions](https://en.wikipedia.org/wiki/Robert_Plutchik#Plutchik's_wheel_of_emotions): anger, disgust, anticipation , fear, joy, sadness, surprise and trust. The percentage of sentences in which each emotion appeared is found in the table below. \| \| anger \| disgust \| expectation \| fear \| happy \| sadness \| surprise \| trust \| sentiment \| \|------:\|------:\|--------:\|------------:\|-----:\|------:\|--------:\|---------:\|------:\|-----------\| \| ratio \| 0.78 \| 0.83 \| 0.58 \| 0.45 \| 0.12 \| 0.59 \| 0.17 \| 0.11 \| 0.25 \| ## Performance ### Emotion Recognition \| emotion \| f1-score \| precision \| recall \| \|-------------\|----------\|-----------\|----------\| \| anger \| 0.96 \| 0.99 \| 0.93 \| \| disgust \| 0.97 \| 0.98 \| 0.96 \| \|anticipation \| 0.82 \| 0.80 \| 0.87 \| \| fear \| 0.79 \| 0.88 \| 0.72 \| \| joy \| 0.90 \| 0.97 \| 0.84 \| \| sadness \| 0.90 \| 0.86 \| 0.94 \| \| surprise \| 0.40 \| 0.44 \| 0.37 \| \| trust \| 0.83 \| 0.86 \| 0.80 \| The above metrics is for positive class (meaning, the emotion is reflected in the text). ### Sentiment (Polarity) Analysis \| \| precision \| recall \| f1-score \| \|--------------\|-----------\|--------\|----------\| \| neutral \| 0.83 \| 0.56 \| 0.67 \| \| positive \| 0.96 \| 0.92 \| 0.94 \| \| negative \| 0.97 \| 0.99 \| 0.98 \| \| accuracy \| \| \| 0.97 \| \| macro avg \| 0.92 \| 0.82 \| 0.86 \| \| weighted avg \| 0.96 \| 0.97 \| 0.96 \| Sentiment (polarity) analysis model is also available on AWS! for more information visit [AWS' git](https://github.com/aws-samples/aws-lambda-docker-serverless-inference/tree/main/hebert-sentiment-analysis-inference-docker-lambda) ## How to use ### Emotion Recognition Model An online model can be found at [huggingface spaces](https://huggingface.co/spaces/avichr/HebEMO_demo) or as [colab notebook](https://colab.research.google.com/drive/1Jw3gOWjwVMcZslu-ttXoNeD17lms1-ff?usp=sharing) ``` # !pip install pyplutchik==0.0.7 # !pip install transformers==4.14.1 !git clone https://github.com/avichaychriqui/HeBERT.git from HeBERT.src.HebEMO import * HebEMO_model = HebEMO() HebEMO_model.hebemo(input_path = 'data/text_example.txt') # return analyzed pandas.DataFrame hebEMO_df = HebEMO_model.hebemo(text='החיים יפים ומאושרים', plot=True) ``` <img src="https://github.com/avichaychriqui/HeBERT/blob/main/data/hebEMO1.png?raw=true" width="300" height="300" /> ### For sentiment classification model (polarity ONLY): from transformers import AutoTokenizer, AutoModel, pipeline tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT_sentiment_analysis") #same as 'avichr/heBERT' tokenizer model = AutoModel.from_pretrained("avichr/heBERT_sentiment_analysis") # how to use? sentiment_analysis = pipeline( "sentiment-analysis", model="avichr/heBERT_sentiment_analysis", tokenizer="avichr/heBERT_sentiment_analysis", return_all_scores = True ) sentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים') >>> [[{'label': 'neutral', 'score': 0.9978172183036804}, >>> {'label': 'positive', 'score': 0.0014792329166084528}, >>> {'label': 'negative', 'score': 0.0007035882445052266}]] sentiment_analysis('קפה זה טעים') >>> [[{'label': 'neutral', 'score': 0.00047328314394690096}, >>> {'label': 'possitive', 'score': 0.9994067549705505}, >>> {'label': 'negetive', 'score': 0.00011996887042187154}]] sentiment_analysis('אני לא אוהב את העולם') >>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, >>> {'label': 'possitive', 'score': 8.876807987689972e-05}, >>> {'label': 'negetive', 'score': 0.9998190999031067}]] ## Contact us [Avichay Chriqui](mailto:[email protected]) <br> [Inbal yahav](mailto:[email protected]) <br> The Coller Semitic Languages AI Lab <br> Thank you, תודה, شكرا <br> ## If you used this model please cite us as : Chriqui, A., & Yahav, I. (2021). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. arXiv preprint arXiv:2102.01909. ``` @article{chriqui2021hebert, title={HeBERT \& HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition}, author={Chriqui, Avihay and Yahav, Inbal}, journal={arXiv preprint arXiv:2102.01909}, year={2021} } ```	{}	text-classification	avichr/hebEMO_joy	[ "transformers", "pytorch", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us	HebEMO - Emotion Recognition Model for Modern Hebrew ==================================================== <img align="right" src="URL width="250"> HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated. HebEMO yielded a high performance of weighted average F1-score = 0.96 for polarity classification. Emotion detection reached an F1-score of 0.78-0.97, with the exception of surprise, which the model failed to capture (F1 = 0.41). These results are better than the best-reported performance, even when compared to the English language. Emotion UGC Data Description ---------------------------- Our UGC data includes comments posted on news articles collected from 3 major Israeli news sites, between January 2020 to August 2020. The total size of the data is ~150 MB, including over 7 million words and 350K sentences. ~2000 sentences were annotated by crowd members (3-10 annotators per sentence) for overall sentiment (polarity) and eight emotions: anger, disgust, anticipation , fear, joy, sadness, surprise and trust. The percentage of sentences in which each emotion appeared is found in the table below. Performance ----------- ### Emotion Recognition The above metrics is for positive class (meaning, the emotion is reflected in the text). ### Sentiment (Polarity) Analysis Sentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git How to use ---------- ### Emotion Recognition Model An online model can be found at huggingface spaces or as colab notebook <img src="URL width="300" height="300" /> ### For sentiment classification model (polarity ONLY): ``` from transformers import AutoTokenizer, AutoModel, pipeline tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT_sentiment_analysis") #same as 'avichr/heBERT' tokenizer model = AutoModel.from_pretrained("avichr/heBERT_sentiment_analysis") # how to use? sentiment_analysis = pipeline( "sentiment-analysis", model="avichr/heBERT_sentiment_analysis", tokenizer="avichr/heBERT_sentiment_analysis", return_all_scores = True ) sentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים') >>> [[{'label': 'neutral', 'score': 0.9978172183036804}, >>> {'label': 'positive', 'score': 0.0014792329166084528}, >>> {'label': 'negative', 'score': 0.0007035882445052266}]] sentiment_analysis('קפה זה טעים') >>> [[{'label': 'neutral', 'score': 0.00047328314394690096}, >>> {'label': 'possitive', 'score': 0.9994067549705505}, >>> {'label': 'negetive', 'score': 0.00011996887042187154}]] sentiment_analysis('אני לא אוהב את העולם') >>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, >>> {'label': 'possitive', 'score': 8.876807987689972e-05}, >>> {'label': 'negetive', 'score': 0.9998190999031067}]] ``` Contact us ---------- Avichay Chriqui Inbal yahav The Coller Semitic Languages AI Lab Thank you, תודה, شكرا If you used this model please cite us as : ------------------------------------------ Chriqui, A., & Yahav, I. (2021). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. arXiv preprint arXiv:2102.01909.	[ "### Emotion Recognition\n\n\n\nThe above metrics is for positive class (meaning, the emotion is reflected in the text).", "### Sentiment (Polarity) Analysis\n\n\n\nSentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git\n\n\nHow to use\n----------", "### Emotion Recognition Model\n\n\nAn online model can be found at huggingface spaces or as colab notebook\n\n\n<img src=\"URL width=\"300\" height=\"300\" />", "### For sentiment classification model (polarity ONLY):\n\n\n\n```\nfrom transformers import AutoTokenizer, AutoModel, pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"avichr/heBERT_sentiment_analysis\") #same as 'avichr/heBERT' tokenizer\nmodel = AutoModel.from_pretrained(\"avichr/heBERT_sentiment_analysis\")", "# how to use?\nsentiment_analysis = pipeline(\n \"sentiment-analysis\",\n model=\"avichr/heBERT_sentiment_analysis\",\n tokenizer=\"avichr/heBERT_sentiment_analysis\",\n return_all_scores = True\n)\n\nsentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים')\t\n>>> [[{'label': 'neutral', 'score': 0.9978172183036804},\n>>> {'label': 'positive', 'score': 0.0014792329166084528},\n>>> {'label': 'negative', 'score': 0.0007035882445052266}]]\n\nsentiment_analysis('קפה זה טעים')\n>>> [[{'label': 'neutral', 'score': 0.00047328314394690096},\n>>> {'label': 'possitive', 'score': 0.9994067549705505},\n>>> {'label': 'negetive', 'score': 0.00011996887042187154}]]\n\nsentiment_analysis('אני לא אוהב את העולם')\n>>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, \n>>> {'label': 'possitive', 'score': 8.876807987689972e-05}, \n>>> {'label': 'negetive', 'score': 0.9998190999031067}]]\n\n```\n\nContact us\n----------\n\n\nAvichay Chriqui \n\nInbal yahav \n\nThe Coller Semitic Languages AI Lab \n\nThank you, תודה, شكرا \n\n\n\nIf you used this model please cite us as :\n------------------------------------------\n\n\nChriqui, A., & Yahav, I. (2021). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. arXiv preprint arXiv:2102.01909." ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n", "### Emotion Recognition\n\n\n\nThe above metrics is for positive class (meaning, the emotion is reflected in the text).", "### Sentiment (Polarity) Analysis\n\n\n\nSentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git\n\n\nHow to use\n----------", "### Emotion Recognition Model\n\n\nAn online model can be found at huggingface spaces or as colab notebook\n\n\n<img src=\"URL width=\"300\" height=\"300\" />", "### For sentiment classification model (polarity ONLY):\n\n\n\n```\nfrom transformers import AutoTokenizer, AutoModel, pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"avichr/heBERT_sentiment_analysis\") #same as 'avichr/heBERT' tokenizer\nmodel = AutoModel.from_pretrained(\"avichr/heBERT_sentiment_analysis\")", "# how to use?\nsentiment_analysis = pipeline(\n \"sentiment-analysis\",\n model=\"avichr/heBERT_sentiment_analysis\",\n tokenizer=\"avichr/heBERT_sentiment_analysis\",\n return_all_scores = True\n)\n\nsentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים')\t\n>>> [[{'label': 'neutral', 'score': 0.9978172183036804},\n>>> {'label': 'positive', 'score': 0.0014792329166084528},\n>>> {'label': 'negative', 'score': 0.0007035882445052266}]]\n\nsentiment_analysis('קפה זה טעים')\n>>> [[{'label': 'neutral', 'score': 0.00047328314394690096},\n>>> {'label': 'possitive', 'score': 0.9994067549705505},\n>>> {'label': 'negetive', 'score': 0.00011996887042187154}]]\n\nsentiment_analysis('אני לא אוהב את העולם')\n>>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, \n>>> {'label': 'possitive', 'score': 8.876807987689972e-05}, \n>>> {'label': 'negetive', 'score': 0.9998190999031067}]]\n\n```\n\nContact us\n----------\n\n\nAvichay Chriqui \n\nInbal yahav \n\nThe Coller Semitic Languages AI Lab \n\nThank you, תודה, شكرا \n\n\n\nIf you used this model please cite us as :\n------------------------------------------\n\n\nChriqui, A., & Yahav, I. (2021). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. arXiv preprint arXiv:2102.01909." ]	[ 36, 30, 43, 42, 101, 454 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n### Emotion Recognition\n\n\n\nThe above metrics is for positive class (meaning, the emotion is reflected in the text).### Sentiment (Polarity) Analysis\n\n\n\nSentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git\n\n\nHow to use\n----------### Emotion Recognition Model\n\n\nAn online model can be found at huggingface spaces or as colab notebook\n\n\n<img src=\"URL width=\"300\" height=\"300\" />### For sentiment classification model (polarity ONLY):\n\n\n\n```\nfrom transformers import AutoTokenizer, AutoModel, pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"avichr/heBERT_sentiment_analysis\") #same as 'avichr/heBERT' tokenizer\nmodel = 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null	null	transformers	# HebEMO - Emotion Recognition Model for Modern Hebrew <img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250"> HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated. HebEMO yielded a high performance of weighted average F1-score = 0.96 for polarity classification. Emotion detection reached an F1-score of 0.78-0.97, with the exception of surprise, which the model failed to capture (F1 = 0.41). These results are better than the best-reported performance, even when compared to the English language. ## Emotion UGC Data Description Our UGC data includes comments posted on news articles collected from 3 major Israeli news sites, between January 2020 to August 2020. The total size of the data is ~150 MB, including over 7 million words and 350K sentences. ~2000 sentences were annotated by crowd members (3-10 annotators per sentence) for overall sentiment (polarity) and [eight emotions](https://en.wikipedia.org/wiki/Robert_Plutchik#Plutchik's_wheel_of_emotions): anger, disgust, anticipation , fear, joy, sadness, surprise and trust. The percentage of sentences in which each emotion appeared is found in the table below. \| \| anger \| disgust \| expectation \| fear \| happy \| sadness \| surprise \| trust \| sentiment \| \|------:\|------:\|--------:\|------------:\|-----:\|------:\|--------:\|---------:\|------:\|-----------\| \| ratio \| 0.78 \| 0.83 \| 0.58 \| 0.45 \| 0.12 \| 0.59 \| 0.17 \| 0.11 \| 0.25 \| ## Performance ### Emotion Recognition \| emotion \| f1-score \| precision \| recall \| \|-------------\|----------\|-----------\|----------\| \| anger \| 0.96 \| 0.99 \| 0.93 \| \| disgust \| 0.97 \| 0.98 \| 0.96 \| \|anticipation \| 0.82 \| 0.80 \| 0.87 \| \| fear \| 0.79 \| 0.88 \| 0.72 \| \| joy \| 0.90 \| 0.97 \| 0.84 \| \| sadness \| 0.90 \| 0.86 \| 0.94 \| \| surprise \| 0.40 \| 0.44 \| 0.37 \| \| trust \| 0.83 \| 0.86 \| 0.80 \| The above metrics is for positive class (meaning, the emotion is reflected in the text). ### Sentiment (Polarity) Analysis \| \| precision \| recall \| f1-score \| \|--------------\|-----------\|--------\|----------\| \| neutral \| 0.83 \| 0.56 \| 0.67 \| \| positive \| 0.96 \| 0.92 \| 0.94 \| \| negative \| 0.97 \| 0.99 \| 0.98 \| \| accuracy \| \| \| 0.97 \| \| macro avg \| 0.92 \| 0.82 \| 0.86 \| \| weighted avg \| 0.96 \| 0.97 \| 0.96 \| Sentiment (polarity) analysis model is also available on AWS! for more information visit [AWS' git](https://github.com/aws-samples/aws-lambda-docker-serverless-inference/tree/main/hebert-sentiment-analysis-inference-docker-lambda) ## How to use ### Emotion Recognition Model An online model can be found at [huggingface spaces](https://huggingface.co/spaces/avichr/HebEMO_demo) or as [colab notebook](https://colab.research.google.com/drive/1Jw3gOWjwVMcZslu-ttXoNeD17lms1-ff?usp=sharing) ``` # !pip install pyplutchik==0.0.7 # !pip install transformers==4.14.1 !git clone https://github.com/avichaychriqui/HeBERT.git from HeBERT.src.HebEMO import * HebEMO_model = HebEMO() HebEMO_model.hebemo(input_path = 'data/text_example.txt') # return analyzed pandas.DataFrame hebEMO_df = HebEMO_model.hebemo(text='החיים יפים ומאושרים', plot=True) ``` <img src="https://github.com/avichaychriqui/HeBERT/blob/main/data/hebEMO1.png?raw=true" width="300" height="300" /> ### For sentiment classification model (polarity ONLY): from transformers import AutoTokenizer, AutoModel, pipeline tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT_sentiment_analysis") #same as 'avichr/heBERT' tokenizer model = AutoModel.from_pretrained("avichr/heBERT_sentiment_analysis") # how to use? sentiment_analysis = pipeline( "sentiment-analysis", model="avichr/heBERT_sentiment_analysis", tokenizer="avichr/heBERT_sentiment_analysis", return_all_scores = True ) sentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים') >>> [[{'label': 'neutral', 'score': 0.9978172183036804}, >>> {'label': 'positive', 'score': 0.0014792329166084528}, >>> {'label': 'negative', 'score': 0.0007035882445052266}]] sentiment_analysis('קפה זה טעים') >>> [[{'label': 'neutral', 'score': 0.00047328314394690096}, >>> {'label': 'possitive', 'score': 0.9994067549705505}, >>> {'label': 'negetive', 'score': 0.00011996887042187154}]] sentiment_analysis('אני לא אוהב את העולם') >>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, >>> {'label': 'possitive', 'score': 8.876807987689972e-05}, >>> {'label': 'negetive', 'score': 0.9998190999031067}]] ## Contact us [Avichay Chriqui](mailto:[email protected]) <br> [Inbal yahav](mailto:[email protected]) <br> The Coller Semitic Languages AI Lab <br> Thank you, תודה, شكرا <br> ## If you used this model please cite us as : Chriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming. ``` @article{chriqui2021hebert, title={HeBERT \& HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition}, author={Chriqui, Avihay and Yahav, Inbal}, journal={INFORMS Journal on Data Science}, year={2022} } ```	{}	text-classification	avichr/hebEMO_sadness	[ "transformers", "pytorch", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us	HebEMO - Emotion Recognition Model for Modern Hebrew ==================================================== <img align="right" src="URL width="250"> HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated. HebEMO yielded a high performance of weighted average F1-score = 0.96 for polarity classification. Emotion detection reached an F1-score of 0.78-0.97, with the exception of surprise, which the model failed to capture (F1 = 0.41). These results are better than the best-reported performance, even when compared to the English language. Emotion UGC Data Description ---------------------------- Our UGC data includes comments posted on news articles collected from 3 major Israeli news sites, between January 2020 to August 2020. The total size of the data is ~150 MB, including over 7 million words and 350K sentences. ~2000 sentences were annotated by crowd members (3-10 annotators per sentence) for overall sentiment (polarity) and eight emotions: anger, disgust, anticipation , fear, joy, sadness, surprise and trust. The percentage of sentences in which each emotion appeared is found in the table below. Performance ----------- ### Emotion Recognition The above metrics is for positive class (meaning, the emotion is reflected in the text). ### Sentiment (Polarity) Analysis Sentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git How to use ---------- ### Emotion Recognition Model An online model can be found at huggingface spaces or as colab notebook <img src="URL width="300" height="300" /> ### For sentiment classification model (polarity ONLY): ``` from transformers import AutoTokenizer, AutoModel, pipeline tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT_sentiment_analysis") #same as 'avichr/heBERT' tokenizer model = AutoModel.from_pretrained("avichr/heBERT_sentiment_analysis") # how to use? sentiment_analysis = pipeline( "sentiment-analysis", model="avichr/heBERT_sentiment_analysis", tokenizer="avichr/heBERT_sentiment_analysis", return_all_scores = True ) sentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים') >>> [[{'label': 'neutral', 'score': 0.9978172183036804}, >>> {'label': 'positive', 'score': 0.0014792329166084528}, >>> {'label': 'negative', 'score': 0.0007035882445052266}]] sentiment_analysis('קפה זה טעים') >>> [[{'label': 'neutral', 'score': 0.00047328314394690096}, >>> {'label': 'possitive', 'score': 0.9994067549705505}, >>> {'label': 'negetive', 'score': 0.00011996887042187154}]] sentiment_analysis('אני לא אוהב את העולם') >>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, >>> {'label': 'possitive', 'score': 8.876807987689972e-05}, >>> {'label': 'negetive', 'score': 0.9998190999031067}]] ``` Contact us ---------- Avichay Chriqui Inbal yahav The Coller Semitic Languages AI Lab Thank you, תודה, شكرا If you used this model please cite us as : ------------------------------------------ Chriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming.	[ "### Emotion Recognition\n\n\n\nThe above metrics is for positive class (meaning, the emotion is reflected in the text).", "### Sentiment (Polarity) Analysis\n\n\n\nSentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git\n\n\nHow to use\n----------", "### Emotion Recognition Model\n\n\nAn online model can be found at huggingface spaces or as colab notebook\n\n\n<img src=\"URL width=\"300\" height=\"300\" />", "### For sentiment classification model (polarity ONLY):\n\n\n\n```\nfrom transformers import AutoTokenizer, AutoModel, pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"avichr/heBERT_sentiment_analysis\") #same as 'avichr/heBERT' tokenizer\nmodel = AutoModel.from_pretrained(\"avichr/heBERT_sentiment_analysis\")", "# how to use?\nsentiment_analysis = pipeline(\n \"sentiment-analysis\",\n model=\"avichr/heBERT_sentiment_analysis\",\n tokenizer=\"avichr/heBERT_sentiment_analysis\",\n return_all_scores = True\n)\n\nsentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים')\t\n>>> [[{'label': 'neutral', 'score': 0.9978172183036804},\n>>> {'label': 'positive', 'score': 0.0014792329166084528},\n>>> {'label': 'negative', 'score': 0.0007035882445052266}]]\n\nsentiment_analysis('קפה זה טעים')\n>>> [[{'label': 'neutral', 'score': 0.00047328314394690096},\n>>> {'label': 'possitive', 'score': 0.9994067549705505},\n>>> {'label': 'negetive', 'score': 0.00011996887042187154}]]\n\nsentiment_analysis('אני לא אוהב את העולם')\n>>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, \n>>> {'label': 'possitive', 'score': 8.876807987689972e-05}, \n>>> {'label': 'negetive', 'score': 0.9998190999031067}]]\n\n```\n\nContact us\n----------\n\n\nAvichay Chriqui \n\nInbal yahav \n\nThe Coller Semitic Languages AI Lab \n\nThank you, תודה, شكرا \n\n\n\nIf you used this model please cite us as :\n------------------------------------------\n\n\nChriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming." ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n", "### Emotion Recognition\n\n\n\nThe above metrics is for positive class (meaning, the emotion is reflected in the text).", "### Sentiment (Polarity) Analysis\n\n\n\nSentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git\n\n\nHow to use\n----------", "### Emotion Recognition Model\n\n\nAn online model can be found at huggingface spaces or as colab notebook\n\n\n<img src=\"URL width=\"300\" height=\"300\" />", "### For sentiment classification model (polarity ONLY):\n\n\n\n```\nfrom transformers import AutoTokenizer, AutoModel, pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"avichr/heBERT_sentiment_analysis\") #same as 'avichr/heBERT' tokenizer\nmodel = AutoModel.from_pretrained(\"avichr/heBERT_sentiment_analysis\")", "# how to use?\nsentiment_analysis = pipeline(\n \"sentiment-analysis\",\n model=\"avichr/heBERT_sentiment_analysis\",\n tokenizer=\"avichr/heBERT_sentiment_analysis\",\n return_all_scores = True\n)\n\nsentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים')\t\n>>> [[{'label': 'neutral', 'score': 0.9978172183036804},\n>>> {'label': 'positive', 'score': 0.0014792329166084528},\n>>> {'label': 'negative', 'score': 0.0007035882445052266}]]\n\nsentiment_analysis('קפה זה טעים')\n>>> [[{'label': 'neutral', 'score': 0.00047328314394690096},\n>>> {'label': 'possitive', 'score': 0.9994067549705505},\n>>> {'label': 'negetive', 'score': 0.00011996887042187154}]]\n\nsentiment_analysis('אני לא אוהב את העולם')\n>>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, \n>>> {'label': 'possitive', 'score': 8.876807987689972e-05}, \n>>> {'label': 'negetive', 'score': 0.9998190999031067}]]\n\n```\n\nContact us\n----------\n\n\nAvichay Chriqui \n\nInbal yahav \n\nThe Coller Semitic Languages AI Lab \n\nThank you, תודה, شكرا \n\n\n\nIf you used this model please cite us as :\n------------------------------------------\n\n\nChriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming." ]	[ 36, 30, 43, 42, 101, 454 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n### Emotion Recognition\n\n\n\nThe above metrics is for positive class (meaning, the emotion is reflected in the text).### Sentiment (Polarity) Analysis\n\n\n\nSentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git\n\n\nHow to use\n----------### Emotion Recognition Model\n\n\nAn online model can be found at huggingface spaces or as colab notebook\n\n\n<img src=\"URL width=\"300\" height=\"300\" />### For sentiment classification model (polarity ONLY):\n\n\n\n```\nfrom transformers import AutoTokenizer, AutoModel, pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"avichr/heBERT_sentiment_analysis\") #same as 'avichr/heBERT' tokenizer\nmodel = AutoModel.from_pretrained(\"avichr/heBERT_sentiment_analysis\")" ]	[ -0.05714952573180199, 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null	null	transformers	# HebEMO - Emotion Recognition Model for Modern Hebrew <img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250"> HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated. HebEMO yielded a high performance of weighted average F1-score = 0.96 for polarity classification. Emotion detection reached an F1-score of 0.78-0.97, with the exception of surprise, which the model failed to capture (F1 = 0.41). These results are better than the best-reported performance, even when compared to the English language. ## Emotion UGC Data Description Our UGC data includes comments posted on news articles collected from 3 major Israeli news sites, between January 2020 to August 2020. The total size of the data is ~150 MB, including over 7 million words and 350K sentences. ~2000 sentences were annotated by crowd members (3-10 annotators per sentence) for overall sentiment (polarity) and [eight emotions](https://en.wikipedia.org/wiki/Robert_Plutchik#Plutchik's_wheel_of_emotions): anger, disgust, anticipation , fear, joy, sadness, surprise and trust. The percentage of sentences in which each emotion appeared is found in the table below. \| \| anger \| disgust \| expectation \| fear \| happy \| sadness \| surprise \| trust \| sentiment \| \|------:\|------:\|--------:\|------------:\|-----:\|------:\|--------:\|---------:\|------:\|-----------\| \| ratio \| 0.78 \| 0.83 \| 0.58 \| 0.45 \| 0.12 \| 0.59 \| 0.17 \| 0.11 \| 0.25 \| ## Performance ### Emotion Recognition \| emotion \| f1-score \| precision \| recall \| \|-------------\|----------\|-----------\|----------\| \| anger \| 0.96 \| 0.99 \| 0.93 \| \| disgust \| 0.97 \| 0.98 \| 0.96 \| \|anticipation \| 0.82 \| 0.80 \| 0.87 \| \| fear \| 0.79 \| 0.88 \| 0.72 \| \| joy \| 0.90 \| 0.97 \| 0.84 \| \| sadness \| 0.90 \| 0.86 \| 0.94 \| \| surprise \| 0.40 \| 0.44 \| 0.37 \| \| trust \| 0.83 \| 0.86 \| 0.80 \| The above metrics is for positive class (meaning, the emotion is reflected in the text). ### Sentiment (Polarity) Analysis \| \| precision \| recall \| f1-score \| \|--------------\|-----------\|--------\|----------\| \| neutral \| 0.83 \| 0.56 \| 0.67 \| \| positive \| 0.96 \| 0.92 \| 0.94 \| \| negative \| 0.97 \| 0.99 \| 0.98 \| \| accuracy \| \| \| 0.97 \| \| macro avg \| 0.92 \| 0.82 \| 0.86 \| \| weighted avg \| 0.96 \| 0.97 \| 0.96 \| Sentiment (polarity) analysis model is also available on AWS! for more information visit [AWS' git](https://github.com/aws-samples/aws-lambda-docker-serverless-inference/tree/main/hebert-sentiment-analysis-inference-docker-lambda) ## How to use ### Emotion Recognition Model An online model can be found at [huggingface spaces](https://huggingface.co/spaces/avichr/HebEMO_demo) or as [colab notebook](https://colab.research.google.com/drive/1Jw3gOWjwVMcZslu-ttXoNeD17lms1-ff?usp=sharing) ``` # !pip install pyplutchik==0.0.7 # !pip install transformers==4.14.1 !git clone https://github.com/avichaychriqui/HeBERT.git from HeBERT.src.HebEMO import * HebEMO_model = HebEMO() HebEMO_model.hebemo(input_path = 'data/text_example.txt') # return analyzed pandas.DataFrame hebEMO_df = HebEMO_model.hebemo(text='החיים יפים ומאושרים', plot=True) ``` <img src="https://github.com/avichaychriqui/HeBERT/blob/main/data/hebEMO1.png?raw=true" width="300" height="300" /> ### For sentiment classification model (polarity ONLY): from transformers import AutoTokenizer, AutoModel, pipeline tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT_sentiment_analysis") #same as 'avichr/heBERT' tokenizer model = AutoModel.from_pretrained("avichr/heBERT_sentiment_analysis") # how to use? sentiment_analysis = pipeline( "sentiment-analysis", model="avichr/heBERT_sentiment_analysis", tokenizer="avichr/heBERT_sentiment_analysis", return_all_scores = True ) sentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים') >>> [[{'label': 'neutral', 'score': 0.9978172183036804}, >>> {'label': 'positive', 'score': 0.0014792329166084528}, >>> {'label': 'negative', 'score': 0.0007035882445052266}]] sentiment_analysis('קפה זה טעים') >>> [[{'label': 'neutral', 'score': 0.00047328314394690096}, >>> {'label': 'possitive', 'score': 0.9994067549705505}, >>> {'label': 'negetive', 'score': 0.00011996887042187154}]] sentiment_analysis('אני לא אוהב את העולם') >>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, >>> {'label': 'possitive', 'score': 8.876807987689972e-05}, >>> {'label': 'negetive', 'score': 0.9998190999031067}]] ## Contact us [Avichay Chriqui](mailto:[email protected]) <br> [Inbal yahav](mailto:[email protected]) <br> The Coller Semitic Languages AI Lab <br> Thank you, תודה, شكرا <br> ## If you used this model please cite us as : Chriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming. ``` @article{chriqui2021hebert, title={HeBERT \& HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition}, author={Chriqui, Avihay and Yahav, Inbal}, journal={INFORMS Journal on Data Science}, year={2022} } ```	{}	text-classification	avichr/hebEMO_surprise	[ "transformers", "pytorch", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us	HebEMO - Emotion Recognition Model for Modern Hebrew ==================================================== <img align="right" src="URL width="250"> HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated. HebEMO yielded a high performance of weighted average F1-score = 0.96 for polarity classification. Emotion detection reached an F1-score of 0.78-0.97, with the exception of surprise, which the model failed to capture (F1 = 0.41). These results are better than the best-reported performance, even when compared to the English language. Emotion UGC Data Description ---------------------------- Our UGC data includes comments posted on news articles collected from 3 major Israeli news sites, between January 2020 to August 2020. The total size of the data is ~150 MB, including over 7 million words and 350K sentences. ~2000 sentences were annotated by crowd members (3-10 annotators per sentence) for overall sentiment (polarity) and eight emotions: anger, disgust, anticipation , fear, joy, sadness, surprise and trust. The percentage of sentences in which each emotion appeared is found in the table below. Performance ----------- ### Emotion Recognition The above metrics is for positive class (meaning, the emotion is reflected in the text). ### Sentiment (Polarity) Analysis Sentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git How to use ---------- ### Emotion Recognition Model An online model can be found at huggingface spaces or as colab notebook <img src="URL width="300" height="300" /> ### For sentiment classification model (polarity ONLY): ``` from transformers import AutoTokenizer, AutoModel, pipeline tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT_sentiment_analysis") #same as 'avichr/heBERT' tokenizer model = AutoModel.from_pretrained("avichr/heBERT_sentiment_analysis") # how to use? sentiment_analysis = pipeline( "sentiment-analysis", model="avichr/heBERT_sentiment_analysis", tokenizer="avichr/heBERT_sentiment_analysis", return_all_scores = True ) sentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים') >>> [[{'label': 'neutral', 'score': 0.9978172183036804}, >>> {'label': 'positive', 'score': 0.0014792329166084528}, >>> {'label': 'negative', 'score': 0.0007035882445052266}]] sentiment_analysis('קפה זה טעים') >>> [[{'label': 'neutral', 'score': 0.00047328314394690096}, >>> {'label': 'possitive', 'score': 0.9994067549705505}, >>> {'label': 'negetive', 'score': 0.00011996887042187154}]] sentiment_analysis('אני לא אוהב את העולם') >>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, >>> {'label': 'possitive', 'score': 8.876807987689972e-05}, >>> {'label': 'negetive', 'score': 0.9998190999031067}]] ``` Contact us ---------- Avichay Chriqui Inbal yahav The Coller Semitic Languages AI Lab Thank you, תודה, شكرا If you used this model please cite us as : ------------------------------------------ Chriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming.	[ "### Emotion Recognition\n\n\n\nThe above metrics is for positive class (meaning, the emotion is reflected in the text).", "### Sentiment (Polarity) Analysis\n\n\n\nSentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git\n\n\nHow to use\n----------", "### Emotion Recognition Model\n\n\nAn online model can be found at huggingface spaces or as colab notebook\n\n\n<img src=\"URL width=\"300\" height=\"300\" />", "### For sentiment classification model (polarity ONLY):\n\n\n\n```\nfrom transformers import AutoTokenizer, AutoModel, pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"avichr/heBERT_sentiment_analysis\") #same as 'avichr/heBERT' tokenizer\nmodel = AutoModel.from_pretrained(\"avichr/heBERT_sentiment_analysis\")", "# how to use?\nsentiment_analysis = pipeline(\n \"sentiment-analysis\",\n model=\"avichr/heBERT_sentiment_analysis\",\n tokenizer=\"avichr/heBERT_sentiment_analysis\",\n return_all_scores = True\n)\n\nsentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים')\t\n>>> [[{'label': 'neutral', 'score': 0.9978172183036804},\n>>> {'label': 'positive', 'score': 0.0014792329166084528},\n>>> {'label': 'negative', 'score': 0.0007035882445052266}]]\n\nsentiment_analysis('קפה זה טעים')\n>>> [[{'label': 'neutral', 'score': 0.00047328314394690096},\n>>> {'label': 'possitive', 'score': 0.9994067549705505},\n>>> {'label': 'negetive', 'score': 0.00011996887042187154}]]\n\nsentiment_analysis('אני לא אוהב את העולם')\n>>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, \n>>> {'label': 'possitive', 'score': 8.876807987689972e-05}, \n>>> {'label': 'negetive', 'score': 0.9998190999031067}]]\n\n```\n\nContact us\n----------\n\n\nAvichay Chriqui \n\nInbal yahav \n\nThe Coller Semitic Languages AI Lab \n\nThank you, תודה, شكرا \n\n\n\nIf you used this model please cite us as :\n------------------------------------------\n\n\nChriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming." ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n", "### Emotion Recognition\n\n\n\nThe above metrics is for positive class (meaning, the emotion is reflected in the text).", "### Sentiment (Polarity) Analysis\n\n\n\nSentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git\n\n\nHow to use\n----------", "### Emotion Recognition Model\n\n\nAn online model can be found at huggingface spaces or as colab notebook\n\n\n<img src=\"URL width=\"300\" height=\"300\" />", "### For sentiment classification model (polarity ONLY):\n\n\n\n```\nfrom transformers import AutoTokenizer, AutoModel, pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"avichr/heBERT_sentiment_analysis\") #same as 'avichr/heBERT' tokenizer\nmodel = AutoModel.from_pretrained(\"avichr/heBERT_sentiment_analysis\")", "# how to use?\nsentiment_analysis = pipeline(\n \"sentiment-analysis\",\n model=\"avichr/heBERT_sentiment_analysis\",\n tokenizer=\"avichr/heBERT_sentiment_analysis\",\n return_all_scores = True\n)\n\nsentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים')\t\n>>> [[{'label': 'neutral', 'score': 0.9978172183036804},\n>>> {'label': 'positive', 'score': 0.0014792329166084528},\n>>> {'label': 'negative', 'score': 0.0007035882445052266}]]\n\nsentiment_analysis('קפה זה טעים')\n>>> [[{'label': 'neutral', 'score': 0.00047328314394690096},\n>>> {'label': 'possitive', 'score': 0.9994067549705505},\n>>> {'label': 'negetive', 'score': 0.00011996887042187154}]]\n\nsentiment_analysis('אני לא אוהב את העולם')\n>>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, \n>>> {'label': 'possitive', 'score': 8.876807987689972e-05}, \n>>> {'label': 'negetive', 'score': 0.9998190999031067}]]\n\n```\n\nContact us\n----------\n\n\nAvichay Chriqui \n\nInbal yahav \n\nThe Coller Semitic Languages AI Lab \n\nThank you, תודה, شكرا \n\n\n\nIf you used this model please cite us as :\n------------------------------------------\n\n\nChriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming." ]	[ 36, 30, 43, 42, 101, 454 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n### Emotion Recognition\n\n\n\nThe above metrics is for positive class (meaning, the emotion is reflected in the text).### Sentiment (Polarity) Analysis\n\n\n\nSentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git\n\n\nHow to use\n----------### Emotion Recognition Model\n\n\nAn online model can be found at huggingface spaces or as colab notebook\n\n\n<img src=\"URL width=\"300\" height=\"300\" />### For sentiment classification model (polarity ONLY):\n\n\n\n```\nfrom transformers import AutoTokenizer, AutoModel, pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"avichr/heBERT_sentiment_analysis\") #same as 'avichr/heBERT' tokenizer\nmodel = AutoModel.from_pretrained(\"avichr/heBERT_sentiment_analysis\")" ]	[ -0.05714952573180199, 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null	null	transformers	# HebEMO - Emotion Recognition Model for Modern Hebrew <img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250"> HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated. HebEMO yielded a high performance of weighted average F1-score = 0.96 for polarity classification. Emotion detection reached an F1-score of 0.78-0.97, with the exception of surprise, which the model failed to capture (F1 = 0.41). These results are better than the best-reported performance, even when compared to the English language. ## Emotion UGC Data Description Our UGC data includes comments posted on news articles collected from 3 major Israeli news sites, between January 2020 to August 2020. The total size of the data is ~150 MB, including over 7 million words and 350K sentences. ~2000 sentences were annotated by crowd members (3-10 annotators per sentence) for overall sentiment (polarity) and [eight emotions](https://en.wikipedia.org/wiki/Robert_Plutchik#Plutchik's_wheel_of_emotions): anger, disgust, anticipation , fear, joy, sadness, surprise and trust. The percentage of sentences in which each emotion appeared is found in the table below. \| \| anger \| disgust \| expectation \| fear \| happy \| sadness \| surprise \| trust \| sentiment \| \|------:\|------:\|--------:\|------------:\|-----:\|------:\|--------:\|---------:\|------:\|-----------\| \| ratio \| 0.78 \| 0.83 \| 0.58 \| 0.45 \| 0.12 \| 0.59 \| 0.17 \| 0.11 \| 0.25 \| ## Performance ### Emotion Recognition \| emotion \| f1-score \| precision \| recall \| \|-------------\|----------\|-----------\|----------\| \| anger \| 0.96 \| 0.99 \| 0.93 \| \| disgust \| 0.97 \| 0.98 \| 0.96 \| \|anticipation \| 0.82 \| 0.80 \| 0.87 \| \| fear \| 0.79 \| 0.88 \| 0.72 \| \| joy \| 0.90 \| 0.97 \| 0.84 \| \| sadness \| 0.90 \| 0.86 \| 0.94 \| \| surprise \| 0.40 \| 0.44 \| 0.37 \| \| trust \| 0.83 \| 0.86 \| 0.80 \| The above metrics is for positive class (meaning, the emotion is reflected in the text). ### Sentiment (Polarity) Analysis \| \| precision \| recall \| f1-score \| \|--------------\|-----------\|--------\|----------\| \| neutral \| 0.83 \| 0.56 \| 0.67 \| \| positive \| 0.96 \| 0.92 \| 0.94 \| \| negative \| 0.97 \| 0.99 \| 0.98 \| \| accuracy \| \| \| 0.97 \| \| macro avg \| 0.92 \| 0.82 \| 0.86 \| \| weighted avg \| 0.96 \| 0.97 \| 0.96 \| Sentiment (polarity) analysis model is also available on AWS! for more information visit [AWS' git](https://github.com/aws-samples/aws-lambda-docker-serverless-inference/tree/main/hebert-sentiment-analysis-inference-docker-lambda) ## How to use ### Emotion Recognition Model An online model can be found at [huggingface spaces](https://huggingface.co/spaces/avichr/HebEMO_demo) or as [colab notebook](https://colab.research.google.com/drive/1Jw3gOWjwVMcZslu-ttXoNeD17lms1-ff?usp=sharing) ``` # !pip install pyplutchik==0.0.7 # !pip install transformers==4.14.1 !git clone https://github.com/avichaychriqui/HeBERT.git from HeBERT.src.HebEMO import * HebEMO_model = HebEMO() HebEMO_model.hebemo(input_path = 'data/text_example.txt') # return analyzed pandas.DataFrame hebEMO_df = HebEMO_model.hebemo(text='החיים יפים ומאושרים', plot=True) ``` <img src="https://github.com/avichaychriqui/HeBERT/blob/main/data/hebEMO1.png?raw=true" width="300" height="300" /> ### For sentiment classification model (polarity ONLY): from transformers import AutoTokenizer, AutoModel, pipeline tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT_sentiment_analysis") #same as 'avichr/heBERT' tokenizer model = AutoModel.from_pretrained("avichr/heBERT_sentiment_analysis") # how to use? sentiment_analysis = pipeline( "sentiment-analysis", model="avichr/heBERT_sentiment_analysis", tokenizer="avichr/heBERT_sentiment_analysis", return_all_scores = True ) sentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים') >>> [[{'label': 'neutral', 'score': 0.9978172183036804}, >>> {'label': 'positive', 'score': 0.0014792329166084528}, >>> {'label': 'negative', 'score': 0.0007035882445052266}]] sentiment_analysis('קפה זה טעים') >>> [[{'label': 'neutral', 'score': 0.00047328314394690096}, >>> {'label': 'possitive', 'score': 0.9994067549705505}, >>> {'label': 'negetive', 'score': 0.00011996887042187154}]] sentiment_analysis('אני לא אוהב את העולם') >>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, >>> {'label': 'possitive', 'score': 8.876807987689972e-05}, >>> {'label': 'negetive', 'score': 0.9998190999031067}]] ## Contact us [Avichay Chriqui](mailto:[email protected]) <br> [Inbal yahav](mailto:[email protected]) <br> The Coller Semitic Languages AI Lab <br> Thank you, תודה, شكرا <br> ## If you used this model please cite us as : Chriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming. ``` @article{chriqui2021hebert, title={HeBERT \& HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition}, author={Chriqui, Avihay and Yahav, Inbal}, journal={INFORMS Journal on Data Science}, year={2022} } ```	{}	text-classification	avichr/hebEMO_trust	[ "transformers", "pytorch", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us	HebEMO - Emotion Recognition Model for Modern Hebrew ==================================================== <img align="right" src="URL width="250"> HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated. HebEMO yielded a high performance of weighted average F1-score = 0.96 for polarity classification. Emotion detection reached an F1-score of 0.78-0.97, with the exception of surprise, which the model failed to capture (F1 = 0.41). These results are better than the best-reported performance, even when compared to the English language. Emotion UGC Data Description ---------------------------- Our UGC data includes comments posted on news articles collected from 3 major Israeli news sites, between January 2020 to August 2020. The total size of the data is ~150 MB, including over 7 million words and 350K sentences. ~2000 sentences were annotated by crowd members (3-10 annotators per sentence) for overall sentiment (polarity) and eight emotions: anger, disgust, anticipation , fear, joy, sadness, surprise and trust. The percentage of sentences in which each emotion appeared is found in the table below. Performance ----------- ### Emotion Recognition The above metrics is for positive class (meaning, the emotion is reflected in the text). ### Sentiment (Polarity) Analysis Sentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git How to use ---------- ### Emotion Recognition Model An online model can be found at huggingface spaces or as colab notebook <img src="URL width="300" height="300" /> ### For sentiment classification model (polarity ONLY): ``` from transformers import AutoTokenizer, AutoModel, pipeline tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT_sentiment_analysis") #same as 'avichr/heBERT' tokenizer model = AutoModel.from_pretrained("avichr/heBERT_sentiment_analysis") # how to use? sentiment_analysis = pipeline( "sentiment-analysis", model="avichr/heBERT_sentiment_analysis", tokenizer="avichr/heBERT_sentiment_analysis", return_all_scores = True ) sentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים') >>> [[{'label': 'neutral', 'score': 0.9978172183036804}, >>> {'label': 'positive', 'score': 0.0014792329166084528}, >>> {'label': 'negative', 'score': 0.0007035882445052266}]] sentiment_analysis('קפה זה טעים') >>> [[{'label': 'neutral', 'score': 0.00047328314394690096}, >>> {'label': 'possitive', 'score': 0.9994067549705505}, >>> {'label': 'negetive', 'score': 0.00011996887042187154}]] sentiment_analysis('אני לא אוהב את העולם') >>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, >>> {'label': 'possitive', 'score': 8.876807987689972e-05}, >>> {'label': 'negetive', 'score': 0.9998190999031067}]] ``` Contact us ---------- Avichay Chriqui Inbal yahav The Coller Semitic Languages AI Lab Thank you, תודה, شكرا If you used this model please cite us as : ------------------------------------------ Chriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming.	[ "### Emotion Recognition\n\n\n\nThe above metrics is for positive class (meaning, the emotion is reflected in the text).", "### Sentiment (Polarity) Analysis\n\n\n\nSentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git\n\n\nHow to use\n----------", "### Emotion Recognition Model\n\n\nAn online model can be found at huggingface spaces or as colab notebook\n\n\n<img src=\"URL width=\"300\" height=\"300\" />", "### For sentiment classification model (polarity ONLY):\n\n\n\n```\nfrom transformers import AutoTokenizer, AutoModel, pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"avichr/heBERT_sentiment_analysis\") #same as 'avichr/heBERT' tokenizer\nmodel = AutoModel.from_pretrained(\"avichr/heBERT_sentiment_analysis\")", "# how to use?\nsentiment_analysis = pipeline(\n \"sentiment-analysis\",\n model=\"avichr/heBERT_sentiment_analysis\",\n tokenizer=\"avichr/heBERT_sentiment_analysis\",\n return_all_scores = True\n)\n\nsentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים')\t\n>>> [[{'label': 'neutral', 'score': 0.9978172183036804},\n>>> {'label': 'positive', 'score': 0.0014792329166084528},\n>>> {'label': 'negative', 'score': 0.0007035882445052266}]]\n\nsentiment_analysis('קפה זה טעים')\n>>> [[{'label': 'neutral', 'score': 0.00047328314394690096},\n>>> {'label': 'possitive', 'score': 0.9994067549705505},\n>>> {'label': 'negetive', 'score': 0.00011996887042187154}]]\n\nsentiment_analysis('אני לא אוהב את העולם')\n>>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, \n>>> {'label': 'possitive', 'score': 8.876807987689972e-05}, \n>>> {'label': 'negetive', 'score': 0.9998190999031067}]]\n\n```\n\nContact us\n----------\n\n\nAvichay Chriqui \n\nInbal yahav \n\nThe Coller Semitic Languages AI Lab \n\nThank you, תודה, شكرا \n\n\n\nIf you used this model please cite us as :\n------------------------------------------\n\n\nChriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming." ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n", "### Emotion Recognition\n\n\n\nThe above metrics is for positive class (meaning, the emotion is reflected in the text).", "### Sentiment (Polarity) Analysis\n\n\n\nSentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git\n\n\nHow to use\n----------", "### Emotion Recognition Model\n\n\nAn online model can be found at huggingface spaces or as colab notebook\n\n\n<img src=\"URL width=\"300\" height=\"300\" />", "### For sentiment classification model (polarity ONLY):\n\n\n\n```\nfrom transformers import AutoTokenizer, AutoModel, pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"avichr/heBERT_sentiment_analysis\") #same as 'avichr/heBERT' tokenizer\nmodel = AutoModel.from_pretrained(\"avichr/heBERT_sentiment_analysis\")", "# how to use?\nsentiment_analysis = pipeline(\n \"sentiment-analysis\",\n model=\"avichr/heBERT_sentiment_analysis\",\n tokenizer=\"avichr/heBERT_sentiment_analysis\",\n return_all_scores = True\n)\n\nsentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים')\t\n>>> [[{'label': 'neutral', 'score': 0.9978172183036804},\n>>> {'label': 'positive', 'score': 0.0014792329166084528},\n>>> {'label': 'negative', 'score': 0.0007035882445052266}]]\n\nsentiment_analysis('קפה זה טעים')\n>>> [[{'label': 'neutral', 'score': 0.00047328314394690096},\n>>> {'label': 'possitive', 'score': 0.9994067549705505},\n>>> {'label': 'negetive', 'score': 0.00011996887042187154}]]\n\nsentiment_analysis('אני לא אוהב את העולם')\n>>> [[{'label': 'neutral', 'score': 9.214012970915064e-05}, \n>>> {'label': 'possitive', 'score': 8.876807987689972e-05}, \n>>> {'label': 'negetive', 'score': 0.9998190999031067}]]\n\n```\n\nContact us\n----------\n\n\nAvichay Chriqui \n\nInbal yahav \n\nThe Coller Semitic Languages AI Lab \n\nThank you, תודה, شكرا \n\n\n\nIf you used this model please cite us as :\n------------------------------------------\n\n\nChriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming." ]	[ 36, 30, 43, 42, 101, 454 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n### Emotion Recognition\n\n\n\nThe above metrics is for positive class (meaning, the emotion is reflected in the text).### Sentiment (Polarity) Analysis\n\n\n\nSentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git\n\n\nHow to use\n----------### Emotion Recognition Model\n\n\nAn online model can be found at huggingface spaces or as colab notebook\n\n\n<img src=\"URL width=\"300\" height=\"300\" />### For sentiment classification model (polarity ONLY):\n\n\n\n```\nfrom transformers import AutoTokenizer, AutoModel, pipeline\n\ntokenizer = AutoTokenizer.from_pretrained(\"avichr/heBERT_sentiment_analysis\") #same as 'avichr/heBERT' tokenizer\nmodel = AutoModel.from_pretrained(\"avichr/heBERT_sentiment_analysis\")" ]	[ -0.05714952573180199, 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null	null	transformers	# rickbot Dialo-GPT	{"tags": ["conversational"]}	text-generation	avinashshrangee/DialoGPT-small-Ricky	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# rickbot Dialo-GPT	[ "# rickbot Dialo-GPT" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# rickbot Dialo-GPT" ]	[ 51, 9 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# rickbot Dialo-GPT" ]	[ -0.00890159048140049, 0.0815073698759079, -0.0066546304151415825, 0.02727660909295082, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.2125 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.2637 \| 1.0 \| 5533 \| 1.2125 \| ### Framework versions - Transformers 4.10.2 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"]}	question-answering	avioo1/distilbert-base-uncased-finetuned-squad	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us	distilbert-base-uncased-finetuned-squad ======================================= This model is a fine-tuned version of distilbert-base-uncased on the squad dataset. It achieves the following results on the evaluation set: * Loss: 1.2125 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.10.2 * Pytorch 1.9.0+cu102 * Datasets 1.11.0 * Tokenizers 0.10.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]	[ 56, 98, 4, 34 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]	[ -0.1081218421459198, 0.08665481954813004, -0.0019734499510377645, 0.11980724334716797, 0.1639399528503418, 0.023396607488393784, 0.09885699301958084, 0.12226859480142593, -0.09512254595756531, 0.031825821846723557, 0.13206203281879425, 0.17120274901390076, 0.0016580113442614675, 0.06159606948494911, -0.04702337458729744, -0.21845468878746033, -0.0200874675065279, 0.05637078359723091, -0.0998520702123642, 0.14527656137943268, 0.08514146506786346, -0.14992168545722961, 0.07109404355287552, 0.004433309193700552, -0.21555066108703613, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-squad2-finetuned-squad This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.0220 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 74 \| 1.7148 \| \| No log \| 2.0 \| 148 \| 1.6994 \| \| No log \| 3.0 \| 222 \| 1.7922 \| \| No log \| 4.0 \| 296 \| 1.9947 \| \| No log \| 5.0 \| 370 \| 2.0753 \| \| No log \| 6.0 \| 444 \| 2.2096 \| \| 0.9547 \| 7.0 \| 518 \| 2.3070 \| \| 0.9547 \| 8.0 \| 592 \| 2.6947 \| \| 0.9547 \| 9.0 \| 666 \| 2.7169 \| \| 0.9547 \| 10.0 \| 740 \| 2.8503 \| \| 0.9547 \| 11.0 \| 814 \| 3.1990 \| \| 0.9547 \| 12.0 \| 888 \| 3.4931 \| \| 0.9547 \| 13.0 \| 962 \| 3.6575 \| \| 0.3191 \| 14.0 \| 1036 \| 3.1863 \| \| 0.3191 \| 15.0 \| 1110 \| 3.7922 \| \| 0.3191 \| 16.0 \| 1184 \| 3.6336 \| \| 0.3191 \| 17.0 \| 1258 \| 4.1156 \| \| 0.3191 \| 18.0 \| 1332 \| 4.1353 \| \| 0.3191 \| 19.0 \| 1406 \| 3.9888 \| \| 0.3191 \| 20.0 \| 1480 \| 4.4290 \| \| 0.1904 \| 21.0 \| 1554 \| 4.0473 \| \| 0.1904 \| 22.0 \| 1628 \| 4.5048 \| \| 0.1904 \| 23.0 \| 1702 \| 4.4026 \| \| 0.1904 \| 24.0 \| 1776 \| 4.2864 \| \| 0.1904 \| 25.0 \| 1850 \| 4.3941 \| \| 0.1904 \| 26.0 \| 1924 \| 4.4921 \| \| 0.1904 \| 27.0 \| 1998 \| 4.9139 \| \| 0.1342 \| 28.0 \| 2072 \| 4.8914 \| \| 0.1342 \| 29.0 \| 2146 \| 5.0148 \| \| 0.1342 \| 30.0 \| 2220 \| 5.0220 \| ### Framework versions - Transformers 4.11.0 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3	{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-squad2-finetuned-squad", "results": []}]}	question-answering	avioo1/roberta-base-squad2-finetuned-squad	[ "transformers", "pytorch", "tensorboard", "roberta", "question-answering", "generated_from_trainer", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #license-cc-by-4.0 #endpoints_compatible #region-us	roberta-base-squad2-finetuned-squad =================================== This model is a fine-tuned version of deepset/roberta-base-squad2 on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 5.0220 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0001 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 30 ### Training results ### Framework versions * Transformers 4.11.0 * Pytorch 1.9.0+cu102 * Datasets 1.12.1 * Tokenizers 0.10.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.0\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #license-cc-by-4.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.0\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ 50, 97, 4, 34 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #license-cc-by-4.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30### Training results### Framework versions\n\n\n* Transformers 4.11.0\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]	[ -0.09406963735818863, 0.04242470860481262, -0.0017767370445653796, 0.11381895840167999, 0.17902567982673645, 0.028026478365063667, 0.11019888520240784, 0.09754886478185654, -0.10873712599277496, 0.043134111911058426, 0.12573936581611633, 0.16602644324302673, -0.00579972006380558, 0.048085927963256836, -0.05289237201213837, -0.23571202158927917, -0.036072321236133575, 0.04895747825503349, -0.10335659980773926, 0.13871072232723236, 0.07473985105752945, -0.16192837059497833, 0.06590133160352707, -0.0017634426476433873, -0.26464876532554626, 0.01761934719979763, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4981 - Matthews Correlation: 0.4218 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5248 \| 1.0 \| 535 \| 0.4981 \| 0.4218 \| ### Framework versions - Transformers 4.9.1 - Pytorch 1.9.0+cu102 - Datasets 1.10.2 - Tokenizers 0.10.3	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model_index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metric": {"name": "Matthews Correlation", "type": "matthews_correlation", "value": 0.42176824452830747}}]}]}	text-classification	avneet/distilbert-base-uncased-finetuned-cola	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	distilbert-base-uncased-finetuned-cola ====================================== This model is a fine-tuned version of distilbert-base-uncased on the glue dataset. It achieves the following results on the evaluation set: * Loss: 0.4981 * Matthews Correlation: 0.4218 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.9.1 * Pytorch 1.9.0+cu102 * Datasets 1.10.2 * Tokenizers 0.10.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.9.1\n* Pytorch 1.9.0+cu102\n* Datasets 1.10.2\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.9.1\n* Pytorch 1.9.0+cu102\n* Datasets 1.10.2\n* Tokenizers 0.10.3" ]	[ 63, 98, 4, 35 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.9.1\n* Pytorch 1.9.0+cu102\n* Datasets 1.10.2\n* Tokenizers 0.10.3" ]	[ -0.10041800886392593, 0.09187408536672592, -0.0017998735420405865, 0.12316817045211792, 0.17497141659259796, 0.030917715281248093, 0.11766374111175537, 0.12837880849838257, -0.09793481975793839, 0.021454423666000366, 0.12565848231315613, 0.17039784789085388, 0.010765883140265942, 0.11671925336122513, -0.05836978927254677, -0.26020652055740356, -0.01399797573685646, 0.047568097710609436, -0.07857032865285873, 0.1329323947429657, 0.09647627174854279, -0.12376619130373001, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-sst2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3651 - Accuracy: 0.9151 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.1902 \| 1.0 \| 4210 \| 0.3102 \| 0.9117 \| \| 0.1293 \| 2.0 \| 8420 \| 0.3672 \| 0.9048 \| \| 0.084 \| 3.0 \| 12630 \| 0.3651 \| 0.9151 \| \| 0.0682 \| 4.0 \| 16840 \| 0.3971 \| 0.9037 \| \| 0.0438 \| 5.0 \| 21050 \| 0.4720 \| 0.9117 \| ### Framework versions - Transformers 4.9.1 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model_index": [{"name": "distilbert-base-uncased-finetuned-sst2", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9151376146788991}}]}]}	text-classification	avneet/distilbert-base-uncased-finetuned-sst2	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	distilbert-base-uncased-finetuned-sst2 ====================================== This model is a fine-tuned version of distilbert-base-uncased on the glue dataset. It achieves the following results on the evaluation set: * Loss: 0.3651 * Accuracy: 0.9151 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.9.1 * Pytorch 1.9.0+cu102 * Datasets 1.11.0 * Tokenizers 0.10.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.9.1\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.9.1\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]	[ 63, 98, 4, 35 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.9.1\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]	[ -0.10138363391160965, 0.09181274473667145, -0.0017480016686022282, 0.12251314520835876, 0.17487236857414246, 0.030555732548236847, 0.11795240640640259, 0.1284196972846985, -0.0991346538066864, 0.021271787583827972, 0.12584805488586426, 0.17002835869789124, 0.010741710662841797, 0.11769048124551773, -0.05881334841251373, -0.2584119737148285, -0.014017499051988125, 0.04791738837957382, -0.07845775783061981, 0.13301406800746918, 0.09648574143648148, -0.12414977699518204, 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null	null	transformers	---- tags: - conversational --- #Rick DialoGPT model	{}	text-generation	avnish100/DialoGPT-small-rick	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	---- tags: - conversational --- #Rick DialoGPT model	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 47 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.027653997763991356, 0.02414041943848133, -0.0068230400793254375, 0.010564634576439857, 0.18164798617362976, 0.033704131841659546, 0.08821956068277359, 0.13570955395698547, -0.0068973456509411335, 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null	null	transformers	## Описание модели Этот чатбот - дипломная работа студента Андрея Ворожко в УИИ (Университет Искусственного Интеллекта). Окончание обучения - март 2022 года. Чатбот сделан на основе модели [Kirili4ik/ruDialoGpt3-medium-finetuned-telegram](https://huggingface.co/Kirili4ik/ruDialoGpt3-medium-finetuned-telegram) Теперь модель дообучена на основе 27000 анекдотов (14 эпох, скорость обучения в колабе 2-6 часов на эпоху) и умеет понимать контекст разговора. Однако контекст приходится ограничивать несколькими последними сообщениями потому что чем больше контекста тем медленнее модель работает, а контекст растет как снежный ком в процессе разговора. Инференс находится в [spaces](https://huggingface.co/spaces/avorozhko/funbot): Там с ботом можно поговорить. Контекст ограничен 10 последними сообщениями. Шутки бот выдает, но пока скорее случайно, чем намеренно. Однако разговор поддержать способен и даже немного развлечь. Так как это генерация текста, то на одну и ту же фразу бот всегда будет выдавать разные ответы. Также для определения качества данной модели использовалась кастомная метрика - угловое расстояния между эмбеддингами y_train и предикта. То есть мы взяли первый слой эмбеддинга модели и прогоняли предикты и лейблы, получили вектора слов. Потом вектора слов суммировали и получили общие (суммарные) вектора лейблов и предиктов. Чем меньше угол между ними, тем лучше. При рассчетах ориентировались на косинус этого угла, так как cos 0 = 1, то это очень удобно - чем ближе показатель к 1, тем лучше. Вот такое распределение этих значений получилось по эпохам на ПРОВЕРОЧНОЙ выборке (1406 анекдотов): ``` {1: tensor(0.9357, device='cuda:0', grad_fn=<DivBackward0>), 2: tensor(0.9390, device='cuda:0', grad_fn=<DivBackward0>), 3: tensor(0.9417, device='cuda:0', grad_fn=<DivBackward0>), 4: tensor(0.9439, device='cuda:0', grad_fn=<DivBackward0>), 5: tensor(0.9470, device='cuda:0', grad_fn=<DivBackward0>), 6: tensor(0.9537, device='cuda:0', grad_fn=<DivBackward0>), 7: tensor(0.9568, device='cuda:0', grad_fn=<DivBackward0>), 8: tensor(0.9592, device='cuda:0', grad_fn=<DivBackward0>), 9: tensor(0.9610, device='cuda:0', grad_fn=<DivBackward0>), 10: tensor(0.9622, device='cuda:0', grad_fn=<DivBackward0>), 11: tensor(0.9628, device='cuda:0', grad_fn=<DivBackward0>), 12: tensor(0.9632, device='cuda:0', grad_fn=<DivBackward0>), 13: tensor(0.9630, device='cuda:0', grad_fn=<DivBackward0>), 14: tensor(0.9634, device='cuda:0', grad_fn=<DivBackward0>), 15: tensor(0.9634, device='cuda:0', grad_fn=<DivBackward0>)} ``` Для инференса выбрана 14-я эпоха с точностью 0.9634. Далее, судя по всему идет уже переобучение.	{}	text-generation	avorozhko/ruDialoGpt3-medium-finetuned-context	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us	## Описание модели Этот чатбот - дипломная работа студента Андрея Ворожко в УИИ (Университет Искусственного Интеллекта). Окончание обучения - март 2022 года. Чатбот сделан на основе модели Kirili4ik/ruDialoGpt3-medium-finetuned-telegram Теперь модель дообучена на основе 27000 анекдотов (14 эпох, скорость обучения в колабе 2-6 часов на эпоху) и умеет понимать контекст разговора. Однако контекст приходится ограничивать несколькими последними сообщениями потому что чем больше контекста тем медленнее модель работает, а контекст растет как снежный ком в процессе разговора. Инференс находится в spaces: Там с ботом можно поговорить. Контекст ограничен 10 последними сообщениями. Шутки бот выдает, но пока скорее случайно, чем намеренно. Однако разговор поддержать способен и даже немного развлечь. Так как это генерация текста, то на одну и ту же фразу бот всегда будет выдавать разные ответы. Также для определения качества данной модели использовалась кастомная метрика - угловое расстояния между эмбеддингами y_train и предикта. То есть мы взяли первый слой эмбеддинга модели и прогоняли предикты и лейблы, получили вектора слов. Потом вектора слов суммировали и получили общие (суммарные) вектора лейблов и предиктов. Чем меньше угол между ними, тем лучше. При рассчетах ориентировались на косинус этого угла, так как cos 0 = 1, то это очень удобно - чем ближе показатель к 1, тем лучше. Вот такое распределение этих значений получилось по эпохам на ПРОВЕРОЧНОЙ выборке (1406 анекдотов): Для инференса выбрана 14-я эпоха с точностью 0.9634. Далее, судя по всему идет уже переобучение.	[ "## Описание модели\n\nЭтот чатбот - дипломная работа студента Андрея Ворожко в УИИ (Университет Искусственного Интеллекта).\n\nОкончание обучения - март 2022 года.\n\nЧатбот сделан на основе модели Kirili4ik/ruDialoGpt3-medium-finetuned-telegram\n\nТеперь модель дообучена на основе 27000 анекдотов (14 эпох, скорость обучения в колабе 2-6 часов на эпоху) и умеет понимать контекст разговора. Однако контекст приходится ограничивать несколькими последними сообщениями потому что чем больше контекста тем медленнее модель работает, а контекст растет как снежный ком в процессе разговора.\n\nИнференс находится в spaces:\n\nТам с ботом можно поговорить. Контекст ограничен 10 последними сообщениями.\n\nШутки бот выдает, но пока скорее случайно, чем намеренно. Однако разговор поддержать способен и даже немного развлечь.\n\nТак как это генерация текста, то на одну и ту же фразу бот всегда будет выдавать разные ответы.\n\nТакже для определения качества данной модели использовалась кастомная метрика - угловое расстояния между эмбеддингами y_train и предикта.\n\nТо есть мы взяли первый слой эмбеддинга модели и прогоняли предикты и лейблы, получили вектора слов. Потом вектора слов суммировали и получили общие (суммарные) вектора лейблов и предиктов. Чем меньше угол между ними, тем лучше. При рассчетах ориентировались на косинус этого угла, так как cos 0 = 1, то это очень удобно - чем ближе показатель к 1, тем лучше.\n\nВот такое распределение этих значений получилось по эпохам на ПРОВЕРОЧНОЙ выборке (1406 анекдотов):\n\nДля инференса выбрана 14-я эпоха с точностью 0.9634. Далее, судя по всему идет уже переобучение." ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "## Описание модели\n\nЭтот чатбот - дипломная работа студента Андрея Ворожко в УИИ (Университет Искусственного Интеллекта).\n\nОкончание обучения - март 2022 года.\n\nЧатбот сделан на основе модели Kirili4ik/ruDialoGpt3-medium-finetuned-telegram\n\nТеперь модель дообучена на основе 27000 анекдотов (14 эпох, скорость обучения в колабе 2-6 часов на эпоху) и умеет понимать контекст разговора. Однако контекст приходится ограничивать несколькими последними сообщениями потому что чем больше контекста тем медленнее модель работает, а контекст растет как снежный ком в процессе разговора.\n\nИнференс находится в spaces:\n\nТам с ботом можно поговорить. Контекст ограничен 10 последними сообщениями.\n\nШутки бот выдает, но пока скорее случайно, чем намеренно. Однако разговор поддержать способен и даже немного развлечь.\n\nТак как это генерация текста, то на одну и ту же фразу бот всегда будет выдавать разные ответы.\n\nТакже для определения качества данной модели использовалась кастомная метрика - угловое расстояния между эмбеддингами y_train и предикта.\n\nТо есть мы взяли первый слой эмбеддинга модели и прогоняли предикты и лейблы, получили вектора слов. Потом вектора слов суммировали и получили общие (суммарные) вектора лейблов и предиктов. Чем меньше угол между ними, тем лучше. При рассчетах ориентировались на косинус этого угла, так как cos 0 = 1, то это очень удобно - чем ближе показатель к 1, тем лучше.\n\nВот такое распределение этих значений получилось по эпохам на ПРОВЕРОЧНОЙ выборке (1406 анекдотов):\n\nДля инференса выбрана 14-я эпоха с точностью 0.9634. Далее, судя по всему идет уже переобучение." ]	[ 51, 418 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n## Описание модели\n\nЭтот чатбот - дипломная работа студента Андрея Ворожко в УИИ (Университет Искусственного Интеллекта).\n\nОкончание обучения - март 2022 года.\n\nЧатбот сделан на основе модели Kirili4ik/ruDialoGpt3-medium-finetuned-telegram\n\nТеперь модель дообучена на основе 27000 анекдотов (14 эпох, скорость обучения в колабе 2-6 часов на эпоху) и умеет понимать контекст разговора. Однако контекст приходится ограничивать несколькими последними сообщениями потому что чем больше контекста тем медленнее модель работает, а контекст растет как снежный ком в процессе разговора.\n\nИнференс находится в spaces:\n\nТам с ботом можно поговорить. Контекст ограничен 10 последними сообщениями.\n\nШутки бот выдает, но пока скорее случайно, чем намеренно. Однако разговор поддержать способен и даже немного развлечь.\n\nТак как это генерация текста, то на одну и ту же фразу бот всегда будет выдавать разные ответы.\n\nТакже для определения качества данной модели использовалась кастомная метрика - угловое расстояния между эмбеддингами y_train и предикта.\n\nТо есть мы взяли первый слой эмбеддинга модели и прогоняли предикты и лейблы, получили вектора слов. Потом вектора слов суммировали и получили общие (суммарные) вектора лейблов и предиктов. Чем меньше угол между ними, тем лучше. При рассчетах ориентировались на косинус этого угла, так как cos 0 = 1, то это очень удобно - чем ближе показатель к 1, тем лучше.\n\nВот такое распределение этих значений получилось по эпохам на ПРОВЕРОЧНОЙ выборке (1406 анекдотов):\n\nДля инференса выбрана 14-я эпоха с точностью 0.9634. Далее, судя по всему идет уже переобучение." ]	[ 0.015265676192939281, 0.009312869980931282, -0.01757386326789856, 0.02179829776287079, 0.04184107482433319, -0.011989162303507328, 0.09330148994922638, 0.03334183618426323, 0.02116091549396515, 0.06668468564748764, 0.05700088292360306, 0.08391701430082321, 0.0359375923871994, 0.07228323817253113, 0.0005198165308684111, -0.13924869894981384, 0.05801243335008621, -0.045891325920820236, -0.009293947368860245, 0.061273910105228424, 0.07978726923465729, -0.07229633629322052, 0.04845258593559265, 0.028760069981217384, -0.0370023138821125, 0.00015626236563548446, 0.003514431416988373, -0.018103569746017456, 0.06412799656391144, 0.04671066999435425, 0.07415737956762314, 0.06519564241170883, -0.009020816534757614, -0.08950625360012054, 0.028430592268705368, 0.04296473413705826, 0.01185925304889679, 0.028158338740468025, -0.017735863104462624, -0.007076045498251915, 0.06915900111198425, -0.03425327315926552, 0.02715950645506382, 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null	null	keras	# [Deep Chimpact](https://www.drivendata.org/competitions/82/competition-wildlife-video-depth-estimation/page/390/) > Depth Estimation for Wildlife Conservation (1st place solution) <div align=center> <img src="https://user-images.githubusercontent.com/36858976/138281204-c3cbcb77-11ca-448b-a693-cb3cfa3c5181.png" width=800> ## Overview Healthy natural ecosystems have wide-ranging benefits from public health to the economy to agriculture. In order to protect the Earth's natural resources, conservationists need to be able to monitor species population sizes and population change. Camera traps are widely used in conservation research to capture images and videos of wildlife without human interference. Using statistical models for distance sampling, the frequency of animal sightings can be combined with the distance of each animal from the camera to estimate a species' full population size. However, getting distances from camera trap footage currently entails an extremely manual, time-intensive process. It takes a researcher more than 10 minutes on average to label distance for every 1 minute of video - that’s a lot of time when you have a million videos! This also creates a bottleneck for critical information that conservationists can use to monitor wildlife populations. > Your goal in this challenge is to use machine learning to automatically estimate the distance between a camera trap and an animal in a series of camera trap videos. You will be given a series of timestamps indicating when animals are visible in each camera trap video. To complete the challenge, you will predict the distance between the animal and the camera at each point in time. Along the way, keep an eye out for some sneaky leopards hunting at night, baby chimpanzees getting piggy-back rides, and diva elephants that can't get enough of the limelight. By contributing to this challenge, you can help advance cutting-edge methods for keeping these animal populations (and humans) healthy and safe!	{}	null	awsaf49/deep-chimpact	[ "keras", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #keras #region-us	# Deep Chimpact > Depth Estimation for Wildlife Conservation (1st place solution) <div align=center> <img src="URL width=800> ## Overview Healthy natural ecosystems have wide-ranging benefits from public health to the economy to agriculture. In order to protect the Earth's natural resources, conservationists need to be able to monitor species population sizes and population change. Camera traps are widely used in conservation research to capture images and videos of wildlife without human interference. Using statistical models for distance sampling, the frequency of animal sightings can be combined with the distance of each animal from the camera to estimate a species' full population size. However, getting distances from camera trap footage currently entails an extremely manual, time-intensive process. It takes a researcher more than 10 minutes on average to label distance for every 1 minute of video - that’s a lot of time when you have a million videos! This also creates a bottleneck for critical information that conservationists can use to monitor wildlife populations. > Your goal in this challenge is to use machine learning to automatically estimate the distance between a camera trap and an animal in a series of camera trap videos. You will be given a series of timestamps indicating when animals are visible in each camera trap video. To complete the challenge, you will predict the distance between the animal and the camera at each point in time. Along the way, keep an eye out for some sneaky leopards hunting at night, baby chimpanzees getting piggy-back rides, and diva elephants that can't get enough of the limelight. By contributing to this challenge, you can help advance cutting-edge methods for keeping these animal populations (and humans) healthy and safe!	[ "# Deep Chimpact\n> Depth Estimation for Wildlife Conservation (1st place solution)\n\n<div align=center> <img src=\"URL width=800>", "## Overview\n\nHealthy natural ecosystems have wide-ranging benefits from public health to the economy to agriculture. In order to protect the Earth's natural resources, conservationists need to be able to monitor species population sizes and population change. Camera traps are widely used in conservation research to capture images and videos of wildlife without human interference. Using statistical models for distance sampling, the frequency of animal sightings can be combined with the distance of each animal from the camera to estimate a species' full population size.\n\nHowever, getting distances from camera trap footage currently entails an extremely manual, time-intensive process. It takes a researcher more than 10 minutes on average to label distance for every 1 minute of video - that’s a lot of time when you have a million videos! This also creates a bottleneck for critical information that conservationists can use to monitor wildlife populations.\n\n> Your goal in this challenge is to use machine learning to automatically estimate the distance between a camera trap and an animal in a series of camera trap videos. You will be given a series of timestamps indicating when animals are visible in each camera trap video. To complete the challenge, you will predict the distance between the animal and the camera at each point in time.\n\nAlong the way, keep an eye out for some sneaky leopards hunting at night, baby chimpanzees getting piggy-back rides, and diva elephants that can't get enough of the limelight. By contributing to this challenge, you can help advance cutting-edge methods for keeping these animal populations (and humans) healthy and safe!" ]	[ "TAGS\n#keras #region-us \n", "# Deep Chimpact\n> Depth Estimation for Wildlife Conservation (1st place solution)\n\n<div align=center> <img src=\"URL width=800>", "## Overview\n\nHealthy natural ecosystems have wide-ranging benefits from public health to the economy to agriculture. In order to protect the Earth's natural resources, conservationists need to be able to monitor species population sizes and population change. Camera traps are widely used in conservation research to capture images and videos of wildlife without human interference. Using statistical models for distance sampling, the frequency of animal sightings can be combined with the distance of each animal from the camera to estimate a species' full population size.\n\nHowever, getting distances from camera trap footage currently entails an extremely manual, time-intensive process. It takes a researcher more than 10 minutes on average to label distance for every 1 minute of video - that’s a lot of time when you have a million videos! This also creates a bottleneck for critical information that conservationists can use to monitor wildlife populations.\n\n> Your goal in this challenge is to use machine learning to automatically estimate the distance between a camera trap and an animal in a series of camera trap videos. You will be given a series of timestamps indicating when animals are visible in each camera trap video. To complete the challenge, you will predict the distance between the animal and the camera at each point in time.\n\nAlong the way, keep an eye out for some sneaky leopards hunting at night, baby chimpanzees getting piggy-back rides, and diva elephants that can't get enough of the limelight. By contributing to this challenge, you can help advance cutting-edge methods for keeping these animal populations (and humans) healthy and safe!" ]	[ 9, 40, 360 ]	[ "passage: TAGS\n#keras #region-us \n# Deep Chimpact\n> Depth Estimation for Wildlife Conservation (1st place solution)\n\n<div align=center> <img src=\"URL width=800>## Overview\n\nHealthy natural ecosystems have wide-ranging benefits from public health to the economy to agriculture. In order to protect the Earth's natural resources, conservationists need to be able to monitor species population sizes and population change. Camera traps are widely used in conservation research to capture images and videos of wildlife without human interference. Using statistical models for distance sampling, the frequency of animal sightings can be combined with the distance of each animal from the camera to estimate a species' full population size.\n\nHowever, getting distances from camera trap footage currently entails an extremely manual, time-intensive process. It takes a researcher more than 10 minutes on average to label distance for every 1 minute of video - that’s a lot of time when you have a million videos! This also creates a bottleneck for critical information that conservationists can use to monitor wildlife populations.\n\n> Your goal in this challenge is to use machine learning to automatically estimate the distance between a camera trap and an animal in a series of camera trap videos. You will be given a series of timestamps indicating when animals are visible in each camera trap video. To complete the challenge, you will predict the distance between the animal and the camera at each point in time.\n\nAlong the way, keep an eye out for some sneaky leopards hunting at night, baby chimpanzees getting piggy-back rides, and diva elephants that can't get enough of the limelight. By contributing to this challenge, you can help advance cutting-edge methods for keeping these animal populations (and humans) healthy and safe!" ]	[ -0.016787000000476837, 0.06410630792379379, -0.00021983768965583295, 0.015950320288538933, 0.061920613050460815, -0.08633604645729065, -0.12440649420022964, 0.0878363698720932, 0.000058415862440597266, 0.07305929064750671, -0.006569799035787582, -0.0006446929182857275, 0.1260526180267334, 0.10245458036661148, 0.0798720195889473, -0.24286556243896484, 0.0632309541106224, -0.02801370434463024, 0.038139380514621735, 0.07311864197254181, 0.059961218386888504, -0.13233241438865662, 0.09182637184858322, 0.022851303219795227, -0.12588991224765778, -0.05699533224105835, -0.08954137563705444, -0.015820061787962914, 0.11693160235881805, -0.10445405542850494, -0.07274587452411652, -0.062149468809366226, 0.11709266901016235, -0.04429467022418976, -0.014020344242453575, 0.09782461822032928, 0.019928807392716408, -0.027295416221022606, 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null	null	transformers	# My Awesome Model	{"tags": ["conversational"]}	text-generation	awvik360/DialoGPT-medium-plemons	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# My Awesome Model	[ "# My Awesome Model" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# My Awesome Model" ]	[ 51, 4 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# My Awesome Model" ]	[ -0.05259015038609505, 0.05521034821867943, -0.005910294596105814, 0.017722278833389282, 0.15250112116336823, 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null	null	null	# My Awesome Model	{"tags": ["conversational"]}	text-generation	awvik360/DialoGPT-medium-plemons2	[ "conversational", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #conversational #region-us	# My Awesome Model	[ "# My Awesome Model" ]	[ "TAGS\n#conversational #region-us \n", "# My Awesome Model" ]	[ 10, 4 ]	[ "passage: TAGS\n#conversational #region-us \n# My Awesome Model" ]	[ -0.03546040877699852, 0.10198262333869934, -0.009167643263936043, -0.06873539090156555, 0.09720280766487122, 0.08721881359815598, 0.05205019563436508, -0.007754879537969828, 0.1307058483362198, -0.07418710738420486, 0.15552383661270142, 0.09356602281332016, -0.055710893124341965, 0.054602161049842834, 0.023362739011645317, -0.24776877462863922, 0.05416174605488777, -0.018358204513788223, 0.038026146590709686, 0.059432677924633026, -0.008917242288589478, -0.06420617550611496, 0.032797642052173615, -0.054550930857658386, -0.047955937683582306, 0.05154015123844147, -0.015393521636724472, 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null	null	transformers	# My Awesome Model	{"tags": ["conversational"]}	text-generation	awvik360/DialoGPT-small-plemons	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# My Awesome Model	[ "# My Awesome Model" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# My Awesome Model" ]	[ 51, 4 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# My Awesome Model" ]	[ -0.05259015038609505, 0.05521034821867943, -0.005910294596105814, 0.017722278833389282, 0.15250112116336823, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-indonesian-1.5G-finetuned-sentiment-analysis-smsa This model is a fine-tuned version of [cahya/bert-base-indonesian-1.5G](https://huggingface.co/cahya/bert-base-indonesian-1.5G) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.3390 - Accuracy: 0.9373 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2864 \| 1.0 \| 688 \| 0.2154 \| 0.9286 \| \| 0.1648 \| 2.0 \| 1376 \| 0.2238 \| 0.9357 \| \| 0.0759 \| 3.0 \| 2064 \| 0.3351 \| 0.9365 \| \| 0.044 \| 4.0 \| 2752 \| 0.3390 \| 0.9373 \| \| 0.0308 \| 5.0 \| 3440 \| 0.4346 \| 0.9365 \| \| 0.0113 \| 6.0 \| 4128 \| 0.4708 \| 0.9365 \| \| 0.006 \| 7.0 \| 4816 \| 0.5533 \| 0.9325 \| \| 0.0047 \| 8.0 \| 5504 \| 0.5888 \| 0.9310 \| \| 0.0001 \| 9.0 \| 6192 \| 0.5961 \| 0.9333 \| \| 0.0 \| 10.0 \| 6880 \| 0.5992 \| 0.9357 \| ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3	{"language": "id", "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["indonlu"], "metrics": ["accuracy"], "widget": [{"text": "Saya mengapresiasi usaha anda"}], "model-index": [{"name": "bert-base-indonesian-1.5G-finetuned-sentiment-analysis-smsa", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "indonlu", "type": "indonlu", "args": "smsa"}, "metrics": [{"type": "accuracy", "value": 0.9373015873015873, "name": "Accuracy"}]}]}]}	text-classification	ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "id", "dataset:indonlu", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "id" ]	TAGS #transformers #pytorch #bert #text-classification #generated_from_trainer #id #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us	bert-base-indonesian-1.5G-finetuned-sentiment-analysis-smsa =========================================================== This model is a fine-tuned version of cahya/bert-base-indonesian-1.5G on the indonlu dataset. It achieves the following results on the evaluation set: * Loss: 0.3390 * Accuracy: 0.9373 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.14.1 * Pytorch 1.10.0+cu111 * Datasets 1.16.1 * Tokenizers 0.10.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.14.1\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #id #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.14.1\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]	[ 61, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #id #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.14.1\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]	[ -0.11437919735908508, 0.06797140091657639, -0.0017803250811994076, 0.1313408762216568, 0.18969780206680298, 0.045225195586681366, 0.1362580507993698, 0.10920530557632446, -0.07014871388673782, 0.009777862578630447, 0.12498773634433746, 0.17389145493507385, 0.007851325906813145, 0.09938061982393265, -0.0523936003446579, -0.2635502815246582, -0.01372514944523573, 0.04697141796350479, -0.05414706468582153, 0.13845610618591309, 0.09473682940006256, -0.1464952826499939, 0.09721025824546814, 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null	null	transformers	# Indonesian GPT-2-medium finetuned on Indonesian poems This is the [Indonesian gpt2-medium model](https://huggingface.co/flax-community/gpt2-medium-indonesian) fine-tuned to Indonesian poems. The dataset can be found in [here](https://huggingface.co/datasets/id_puisi) All training was done on Google Colab Jupyter Notebook (soon). The dataset is splitted into two subset with details belows: \| split \| count (examples) \| percentage \| \| ---------- \| ---------- \| -------------- \| \| train \| 7,358 \| 80% \| \| validation \| 1,890 \| 20% \| ### Evaluation results The model evaluation results after 10 epochs are as follows: \| dataset \| train/loss \| eval/loss \| eval perplexity \| \| ---------- \| ---------- \| -------------- \| ---------- \| \| [id puisi](https://huggingface.co/datasets/id_puisi) \| 3.104 \| 3.384 \| 29.4884 \| The logs can be found in [wandb page here](https://wandb.ai/ayamerushia/gpt-2_poem/runs/3jsu1orj/overview?workspace=user-ayamerushia)	{"language": "id", "widget": [{"text": "Wahai rembulan yang tertutup awan hujan"}]}	text-generation	ayameRushia/gpt2-medium-fine-tuning-indonesia-poem	[ "transformers", "pytorch", "gpt2", "text-generation", "id", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "id" ]	TAGS #transformers #pytorch #gpt2 #text-generation #id #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Indonesian GPT-2-medium finetuned on Indonesian poems ===================================================== This is the Indonesian gpt2-medium model fine-tuned to Indonesian poems. The dataset can be found in here All training was done on Google Colab Jupyter Notebook (soon). The dataset is splitted into two subset with details belows: split: train, count (examples): 7,358, percentage: 80% split: validation, count (examples): 1,890, percentage: 20% ### Evaluation results The model evaluation results after 10 epochs are as follows: The logs can be found in wandb page here	[ "### Evaluation results\n\n\nThe model evaluation results after 10 epochs are as follows:\n\n\n\nThe logs can be found in wandb page here" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #id #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Evaluation results\n\n\nThe model evaluation results after 10 epochs are as follows:\n\n\n\nThe logs can be found in wandb page here" ]	[ 49, 31 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #id #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Evaluation results\n\n\nThe model evaluation results after 10 epochs are as follows:\n\n\n\nThe logs can be found in wandb page here" ]	[ -0.10117746889591217, 0.06658052653074265, -0.0006968708476051688, -0.00990425143390894, 0.13880082964897156, 0.023944832384586334, 0.12929512560367584, 0.09361468255519867, 0.013800856657326221, -0.038545507937669754, 0.14305365085601807, 0.12181886285543442, 0.006422610487788916, 0.12987573444843292, -0.09492801129817963, -0.2591094374656677, 0.0633668377995491, 0.0642278790473938, -0.0007437833119183779, 0.15105338394641876, 0.09026111662387848, -0.09822893142700195, 0.08229352533817291, 0.03585956618189812, -0.16685311496257782, -0.009849673137068748, -0.02075161412358284, -0.0877496749162674, 0.12647491693496704, 0.03605896607041359, 0.05977118760347366, 0.05191203951835632, -0.003026457503437996, -0.08407635241746902, 0.026113109663128853, -0.011937840841710567, 0.016784612089395523, 0.10102831572294235, 0.07623087614774704, 0.03851056471467018, 0.2029859870672226, 0.167047381401062, -0.017159054055809975, 0.04097798466682434, 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null	null	transformers	# Indonesian GPT-2 finetuned on Indonesian poems This is the [Indonesian gpt2-small model](https://huggingface.co/flax-community/gpt2-small-indonesian) fine-tuned to Indonesian poems. The dataset can be found in [here](https://huggingface.co/datasets/id_puisi) All training was done on Google Colab Jupyter Notebook (soon). The dataset is splitted into two subset with details belows: \| split \| count (examples) \| percentage \| \| ---------- \| ---------- \| -------------- \| \| train \| 7,358 \| 80% \| \| validation \| 1,890 \| 20% \| ### Evaluation results The model evaluation results after 10 epochs are as follows: \| dataset \| train/loss \| eval/loss \| eval perplexity \| \| ---------- \| ---------- \| -------------- \| ---------- \| \| [id puisi](https://huggingface.co/datasets/id_puisi) \| 3.324700 \| 3.502665 \| 33.20 \| The logs can be found in [wandb page here](https://wandb.ai/ayamerushia/gpt-2_poem/runs/36ymudz9/overview?workspace=user-ayamerushia) or tensorboard [here](https://huggingface.co/ayameRushia/gpt2-small-indonesia-fine-tuning-poem/tensorboard)	{"language": "id", "widget": [{"text": "Wahai rembulan yang tertutup awan hujan"}]}	text-generation	ayameRushia/gpt2-small-indonesia-fine-tuning-poem	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "id", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "id" ]	TAGS #transformers #pytorch #tensorboard #gpt2 #text-generation #id #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Indonesian GPT-2 finetuned on Indonesian poems ============================================== This is the Indonesian gpt2-small model fine-tuned to Indonesian poems. The dataset can be found in here All training was done on Google Colab Jupyter Notebook (soon). The dataset is splitted into two subset with details belows: split: train, count (examples): 7,358, percentage: 80% split: validation, count (examples): 1,890, percentage: 20% ### Evaluation results The model evaluation results after 10 epochs are as follows: The logs can be found in wandb page here or tensorboard here	[ "### Evaluation results\n\n\nThe model evaluation results after 10 epochs are as follows:\n\n\n\nThe logs can be found in wandb page here or tensorboard here" ]	[ "TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #id #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Evaluation results\n\n\nThe model evaluation results after 10 epochs are as follows:\n\n\n\nThe logs can be found in wandb page here or tensorboard here" ]	[ 53, 36 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #id #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Evaluation results\n\n\nThe model evaluation results after 10 epochs are as follows:\n\n\n\nThe logs can be found in wandb page here or tensorboard here" ]	[ -0.11473763734102249, 0.08347636461257935, -0.0009562394698150456, 0.015419983305037022, 0.13231243193149567, 0.011825288645923138, 0.1853383332490921, 0.11607883870601654, 0.025238260626792908, -0.0005190070951357484, 0.14725059270858765, 0.13851012289524078, 0.01770622283220291, 0.024345161393284798, -0.074005626142025, -0.26009440422058105, -0.0018005919409915805, 0.0835559070110321, -0.02480950951576233, 0.12923933565616608, 0.063873291015625, -0.11781938374042511, 0.06971319019794464, 0.05647486075758934, -0.18709667026996613, 0.003834386356174946, 0.0031435759738087654, -0.08123831450939178, 0.11660989373922348, 0.055093683302402496, 0.09720900654792786, 0.07001786679029465, 0.0037217866629362106, -0.06008148565888405, 0.03942427411675453, 0.02151079662144184, 0.002953430637717247, 0.13061802089214325, 0.11674090474843979, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # indobert-base-uncased-finetuned-indonlu-smsa This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.2277 - Accuracy: 0.9302 - F1: 0.9066 - Precision: 0.8992 - Recall: 0.9147 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1500 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 344 \| 0.3831 \| 0.8476 \| 0.7715 \| 0.7817 \| 0.7627 \| \| 0.4167 \| 2.0 \| 688 \| 0.2809 \| 0.8905 \| 0.8406 \| 0.8699 \| 0.8185 \| \| 0.2624 \| 3.0 \| 1032 \| 0.2254 \| 0.9230 \| 0.8842 \| 0.9004 \| 0.8714 \| \| 0.2624 \| 4.0 \| 1376 \| 0.2378 \| 0.9238 \| 0.8797 \| 0.9180 \| 0.8594 \| \| 0.1865 \| 5.0 \| 1720 \| 0.2277 \| 0.9302 \| 0.9066 \| 0.8992 \| 0.9147 \| \| 0.1217 \| 6.0 \| 2064 \| 0.2444 \| 0.9262 \| 0.8981 \| 0.9013 \| 0.8957 \| \| 0.1217 \| 7.0 \| 2408 \| 0.2985 \| 0.9286 \| 0.8999 \| 0.9035 \| 0.8971 \| \| 0.0847 \| 8.0 \| 2752 \| 0.3397 \| 0.9278 \| 0.8969 \| 0.9090 \| 0.8871 \| \| 0.0551 \| 9.0 \| 3096 \| 0.3542 \| 0.9270 \| 0.8961 \| 0.9010 \| 0.8924 \| \| 0.0551 \| 10.0 \| 3440 \| 0.3862 \| 0.9222 \| 0.8895 \| 0.8970 \| 0.8846 \| ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3	{"language": "id", "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["indonlu"], "metrics": ["accuracy", "f1", "precision", "recall"], "widget": [{"text": "Entah mengapa saya merasakan ada sesuatu yang janggal di produk ini"}], "model-index": [{"name": "indobert-base-uncased-finetuned-indonlu-smsa", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "indonlu", "type": "indonlu", "args": "smsa"}, "metrics": [{"type": "accuracy", "value": 0.9301587301587302, "name": "Accuracy"}, {"type": "f1", "value": 0.9066105299178986, "name": "F1"}, {"type": "precision", "value": 0.8992078788375845, "name": "Precision"}, {"type": "recall", "value": 0.9147307323234121, "name": "Recall"}]}]}]}	text-classification	ayameRushia/indobert-base-uncased-finetuned-indonlu-smsa	[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "generated_from_trainer", "id", "dataset:indonlu", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "id" ]	TAGS #transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #id #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us	indobert-base-uncased-finetuned-indonlu-smsa ============================================ This model is a fine-tuned version of indolem/indobert-base-uncased on the indonlu dataset. It achieves the following results on the evaluation set: * Loss: 0.2277 * Accuracy: 0.9302 * F1: 0.9066 * Precision: 0.8992 * Recall: 0.9147 ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1500 * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.14.1 * Pytorch 1.10.0+cu111 * Datasets 1.17.0 * Tokenizers 0.10.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1500\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.14.1\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #id #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1500\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.14.1\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]	[ 66, 116, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #id #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1500\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.14.1\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3" ]	[ -0.1200881227850914, 0.0961245745420456, -0.0029513067565858364, 0.1186232790350914, 0.14128491282463074, 0.018396200612187386, 0.13863709568977356, 0.13996286690235138, -0.06858107447624207, 0.04150970280170441, 0.12430985271930695, 0.14602310955524445, 0.018594758585095406, 0.14098167419433594, -0.06697011739015579, -0.2752443552017212, 0.006323852110654116, 0.02066919021308422, -0.06385838240385056, 0.13124071061611176, 0.07773225009441376, -0.13538268208503723, 0.10055918246507645, -0.001933935796841979, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-indonesian-1.5G-sentiment-analysis-smsa This model is a fine-tuned version of [cahya/roberta-base-indonesian-1.5G](https://huggingface.co/cahya/roberta-base-indonesian-1.5G) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.4294 - Accuracy: 0.9262 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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 - lr_scheduler_warmup_steps: 1500 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6461 \| 1.0 \| 688 \| 0.2620 \| 0.9087 \| \| 0.2627 \| 2.0 \| 1376 \| 0.2291 \| 0.9151 \| \| 0.1784 \| 3.0 \| 2064 \| 0.2891 \| 0.9167 \| \| 0.1099 \| 4.0 \| 2752 \| 0.3317 \| 0.9230 \| \| 0.0857 \| 5.0 \| 3440 \| 0.4294 \| 0.9262 \| \| 0.0346 \| 6.0 \| 4128 \| 0.4759 \| 0.9246 \| \| 0.0221 \| 7.0 \| 4816 \| 0.4946 \| 0.9206 \| \| 0.006 \| 8.0 \| 5504 \| 0.5823 \| 0.9175 \| \| 0.0047 \| 9.0 \| 6192 \| 0.5777 \| 0.9159 \| \| 0.004 \| 10.0 \| 6880 \| 0.5800 \| 0.9175 \| ### How to use this model in Transformers Library ```python from transformers import pipeline pipe = pipeline( "text-classification", model="ayameRushia/roberta-base-indonesian-1.5G-sentiment-analysis-smsa" ) pipe("Terima kasih atas bantuannya ya!") ``` ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3	{"language": ["id"], "tags": ["generated_from_trainer"], "datasets": ["indonlp/indonlu"], "metrics": ["accuracy"], "widget": [{"text": "Entah mengapa saya merasakan ada sesuatu yang janggal di produk ini"}], "model-index": [{"name": "roberta-base-indonesian-1.5G-sentiment-analysis-smsa", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "indonlu", "type": "indonlu", "args": "smsa"}, "metrics": [{"type": "accuracy", "value": 0.9261904761904762, "name": "Accuracy"}]}]}]}	text-classification	ayameRushia/roberta-base-indonesian-1.5G-sentiment-analysis-smsa	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "id", "dataset:indonlp/indonlu", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "id" ]	TAGS #transformers #pytorch #roberta #text-classification #generated_from_trainer #id #dataset-indonlp/indonlu #model-index #autotrain_compatible #endpoints_compatible #region-us	roberta-base-indonesian-1.5G-sentiment-analysis-smsa ==================================================== This model is a fine-tuned version of cahya/roberta-base-indonesian-1.5G on the indonlu dataset. It achieves the following results on the evaluation set: * Loss: 0.4294 * Accuracy: 0.9262 ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-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 * lr\_scheduler\_warmup\_steps: 1500 * num\_epochs: 10 ### Training results ### How to use this model in Transformers Library ### Framework versions * Transformers 4.14.1 * Pytorch 1.10.0+cu111 * Datasets 1.16.1 * Tokenizers 0.10.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1500\n* num\\_epochs: 10", "### Training results", "### How to use this model in Transformers Library", "### Framework versions\n\n\n* Transformers 4.14.1\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #id #dataset-indonlp/indonlu #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1500\n* num\\_epochs: 10", "### Training results", "### How to use this model in Transformers Library", "### Framework versions\n\n\n* Transformers 4.14.1\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]	[ 61, 116, 4, 12, 33 ]	[ "passage: TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #id #dataset-indonlp/indonlu #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1500\n* num\\_epochs: 10### Training results### How to use this model in Transformers Library### Framework versions\n\n\n* Transformers 4.14.1\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]	[ -0.09014276415109634, 0.1129119023680687, -0.003086252138018608, 0.10822094976902008, 0.16209177672863007, 0.01805664412677288, 0.13475678861141205, 0.14819595217704773, -0.12067179381847382, 0.04961404576897621, 0.13248412311077118, 0.17654989659786224, 0.04191618412733078, 0.14752599596977234, -0.06949098408222198, -0.2752723693847656, 0.016668250784277916, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-indonesian-sentiment-analysis-smsa This model is a fine-tuned version of [flax-community/indonesian-roberta-base](https://huggingface.co/flax-community/indonesian-roberta-base) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.4252 - Accuracy: 0.9349 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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 - lr_scheduler_warmup_steps: 2000 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7582 \| 1.0 \| 688 \| 0.3280 \| 0.8786 \| \| 0.3225 \| 2.0 \| 1376 \| 0.2398 \| 0.9206 \| \| 0.2057 \| 3.0 \| 2064 \| 0.2574 \| 0.9230 \| \| 0.1642 \| 4.0 \| 2752 \| 0.2820 \| 0.9302 \| \| 0.1266 \| 5.0 \| 3440 \| 0.3344 \| 0.9317 \| \| 0.0608 \| 6.0 \| 4128 \| 0.3543 \| 0.9341 \| \| 0.058 \| 7.0 \| 4816 \| 0.4252 \| 0.9349 \| \| 0.0315 \| 8.0 \| 5504 \| 0.4736 \| 0.9310 \| \| 0.0166 \| 9.0 \| 6192 \| 0.4649 \| 0.9349 \| \| 0.0143 \| 10.0 \| 6880 \| 0.4648 \| 0.9341 \| ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3	{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["indonlu"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-indonesian-sentiment-analysis-smsa", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "indonlu", "type": "indonlu", "args": "smsa"}, "metrics": [{"type": "accuracy", "value": 0.9349206349206349, "name": "Accuracy"}]}]}]}	text-classification	ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:indonlu", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us	roberta-base-indonesian-sentiment-analysis-smsa =============================================== This model is a fine-tuned version of flax-community/indonesian-roberta-base on the indonlu dataset. It achieves the following results on the evaluation set: * Loss: 0.4252 * Accuracy: 0.9349 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-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 * lr\_scheduler\_warmup\_steps: 2000 * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.14.1 * Pytorch 1.10.0+cu111 * Datasets 1.16.1 * Tokenizers 0.10.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.14.1\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.14.1\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]	[ 60, 116, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.14.1\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]	[ -0.09738113731145859, 0.08291201293468475, -0.002051000948995352, 0.12836791574954987, 0.17542676627635956, 0.036752812564373016, 0.12750540673732758, 0.13863885402679443, -0.078737773001194, 0.020604539662599564, 0.12331770360469818, 0.1622711569070816, 0.014937324449419975, 0.14365322887897491, -0.05351385101675987, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-ar 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: 0.4819 - Wer: 0.4244 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 4 - 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: 400 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 11.0435 \| 0.67 \| 400 \| 4.3104 \| 1.0 \| \| 3.4451 \| 1.34 \| 800 \| 3.1566 \| 1.0 \| \| 3.1399 \| 2.01 \| 1200 \| 3.0532 \| 0.9990 \| \| 2.8538 \| 2.68 \| 1600 \| 1.6994 \| 0.9238 \| \| 1.7195 \| 3.35 \| 2000 \| 0.8867 \| 0.6727 \| \| 1.326 \| 4.02 \| 2400 \| 0.6603 \| 0.5834 \| \| 1.1561 \| 4.69 \| 2800 \| 0.5809 \| 0.5479 \| \| 1.0764 \| 5.36 \| 3200 \| 0.5943 \| 0.5495 \| \| 1.0144 \| 6.03 \| 3600 \| 0.5344 \| 0.5251 \| \| 0.965 \| 6.7 \| 4000 \| 0.4844 \| 0.4936 \| \| 0.927 \| 7.37 \| 4400 \| 0.5048 \| 0.5019 \| \| 0.8985 \| 8.04 \| 4800 \| 0.5809 \| 0.5267 \| \| 0.8684 \| 8.71 \| 5200 \| 0.4740 \| 0.4753 \| \| 0.8581 \| 9.38 \| 5600 \| 0.4813 \| 0.4834 \| \| 0.8334 \| 10.05 \| 6000 \| 0.4515 \| 0.4545 \| \| 0.8134 \| 10.72 \| 6400 \| 0.4370 \| 0.4543 \| \| 0.8002 \| 11.39 \| 6800 \| 0.4225 \| 0.4384 \| \| 0.7884 \| 12.06 \| 7200 \| 0.4593 \| 0.4565 \| \| 0.7675 \| 12.73 \| 7600 \| 0.4752 \| 0.4680 \| \| 0.7607 \| 13.4 \| 8000 \| 0.4950 \| 0.4771 \| \| 0.7475 \| 14.07 \| 8400 \| 0.4373 \| 0.4391 \| \| 0.7397 \| 14.74 \| 8800 \| 0.4506 \| 0.4541 \| \| 0.7289 \| 15.41 \| 9200 \| 0.4840 \| 0.4691 \| \| 0.722 \| 16.08 \| 9600 \| 0.4701 \| 0.4571 \| \| 0.7067 \| 16.75 \| 10000 \| 0.4561 \| 0.4461 \| \| 0.7033 \| 17.42 \| 10400 \| 0.4384 \| 0.4347 \| \| 0.6915 \| 18.09 \| 10800 \| 0.4424 \| 0.4290 \| \| 0.6854 \| 18.76 \| 11200 \| 0.4635 \| 0.4360 \| \| 0.6813 \| 19.43 \| 11600 \| 0.4280 \| 0.4147 \| \| 0.6776 \| 20.1 \| 12000 \| 0.4610 \| 0.4344 \| \| 0.67 \| 20.77 \| 12400 \| 0.4540 \| 0.4367 \| \| 0.6653 \| 21.44 \| 12800 \| 0.4509 \| 0.4234 \| \| 0.6609 \| 22.11 \| 13200 \| 0.4874 \| 0.4444 \| \| 0.6541 \| 22.78 \| 13600 \| 0.4542 \| 0.4230 \| \| 0.6528 \| 23.45 \| 14000 \| 0.4732 \| 0.4373 \| \| 0.6463 \| 24.12 \| 14400 \| 0.4483 \| 0.4188 \| \| 0.6399 \| 24.79 \| 14800 \| 0.4731 \| 0.4341 \| \| 0.6353 \| 25.46 \| 15200 \| 0.5031 \| 0.4412 \| \| 0.6358 \| 26.13 \| 15600 \| 0.4986 \| 0.4397 \| \| 0.6317 \| 26.8 \| 16000 \| 0.5000 \| 0.4360 \| \| 0.6262 \| 27.47 \| 16400 \| 0.4958 \| 0.4318 \| \| 0.6317 \| 28.14 \| 16800 \| 0.4738 \| 0.4234 \| \| 0.6205 \| 28.81 \| 17200 \| 0.4853 \| 0.4262 \| \| 0.6205 \| 29.48 \| 17600 \| 0.4819 \| 0.4244 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-ar", "results": []}]}	automatic-speech-recognition	ayameRushia/wav2vec2-large-xls-r-300m-ar	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us	wav2vec2-large-xls-r-300m-ar ============================ This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset. It achieves the following results on the evaluation set: * Loss: 0.4819 * Wer: 0.4244 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 3e-05 * train\_batch\_size: 32 * eval\_batch\_size: 4 * 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: 400 * num\_epochs: 30 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.2+cu102 * Datasets 1.18.3 * Tokenizers 0.11.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 400\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 400\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ 61, 159, 4, 38 ]	[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 400\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - EL dataset. It achieves the following results on the evaluation set: - Loss: 0.3218 - Wer: 0.3095 ## Training and evaluation data Evaluation is conducted in Notebook, you can see within the repo "notebook_evaluation_wav2vec2_el.ipynb" Test WER without LM wer = 31.1294 % cer = 7.9509 % Test WER using LM wer = 20.7340 % cer = 6.0466 % How to use eval.py ``` huggingface-cli login #login to huggingface for getting auth token to access the common voice v8 #running with LM !python eval.py --model_id ayameRushia/wav2vec2-large-xls-r-300m-el --dataset mozilla-foundation/common_voice_8_0 --config el --split test # running without LM !python eval.py --model_id ayameRushia/wav2vec2-large-xls-r-300m-el --dataset mozilla-foundation/common_voice_8_0 --config el --split test --greedy ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - 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: 400 - num_epochs: 80.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 6.3683 \| 8.77 \| 500 \| 3.1280 \| 1.0 \| \| 1.9915 \| 17.54 \| 1000 \| 0.6600 \| 0.6444 \| \| 0.6565 \| 26.32 \| 1500 \| 0.4208 \| 0.4486 \| \| 0.4484 \| 35.09 \| 2000 \| 0.3885 \| 0.4006 \| \| 0.3573 \| 43.86 \| 2500 \| 0.3548 \| 0.3626 \| \| 0.3063 \| 52.63 \| 3000 \| 0.3375 \| 0.3430 \| \| 0.2751 \| 61.4 \| 3500 \| 0.3359 \| 0.3241 \| \| 0.2511 \| 70.18 \| 4000 \| 0.3222 \| 0.3108 \| \| 0.2361 \| 78.95 \| 4500 \| 0.3205 \| 0.3084 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0	{"language": ["el"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-el", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "el"}, "metrics": [{"type": "wer", "value": 20.9, "name": "Test WER using LM"}, {"type": "cer", "value": 6.0466, "name": "Test CER using LM"}]}]}]}	automatic-speech-recognition	ayameRushia/wav2vec2-large-xls-r-300m-el	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "el", "dataset:mozilla-foundation/common_voice_8_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "el" ]	TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #el #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us	This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - EL dataset. It achieves the following results on the evaluation set: * Loss: 0.3218 * Wer: 0.3095 Training and evaluation data ---------------------------- Evaluation is conducted in Notebook, you can see within the repo "notebook\_evaluation\_wav2vec2\_el.ipynb" Test WER without LM wer = 31.1294 % cer = 7.9509 % Test WER using LM wer = 20.7340 % cer = 6.0466 % How to use URL Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 32 * eval\_batch\_size: 8 * 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: 400 * num\_epochs: 80.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.1+cu102 * Datasets 1.18.3 * Tokenizers 0.11.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 400\n* num\\_epochs: 80.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #el #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 400\n* num\\_epochs: 80.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ 111, 160, 4, 38 ]	[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #el #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 400\n* num\\_epochs: 80.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ -0.12867338955402374, 0.11383134126663208, -0.0053503867238759995, 0.05088794603943825, 0.12288543581962585, 0.015738720074295998, 0.09791316837072372, 0.14653952419757843, -0.08275110274553299, 0.08956418931484222, 0.07430341839790344, 0.08870711922645569, 0.0839126706123352, 0.08867949992418289, -0.005079356022179127, -0.29594382643699646, 0.01487681083381176, -0.021777601912617683, -0.11331191658973694, 0.10684893280267715, 0.09687558561563492, -0.09079272300004959, 0.033751241862773895, 0.032316554337739944, -0.08320451527833939, -0.00004341223757364787, -0.046322938054800034, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-ia 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: 0.1452 - Wer: 0.1253 ## Training Procedure Training is conducted in Google Colab, the training notebook provided in the repo ## Training and evaluation data Language Model Created from texts from processed sentence in train + validation split of dataset (common voice 8.0 for Interlingua) Evaluation is conducted in Notebook, you can see within the repo "notebook_evaluation_wav2vec2_ia.ipynb" Test WER without LM wer = 20.1776 % cer = 4.7205 % Test WER using wer = 8.6074 % cer = 2.4147 % evaluation using eval.py ``` huggingface-cli login #login to huggingface for getting auth token to access the common voice v8 #running with LM python eval.py --model_id ayameRushia/wav2vec2-large-xls-r-300m-ia --dataset mozilla-foundation/common_voice_8_0 --config ia --split test # running without LM python eval.py --model_id ayameRushia/wav2vec2-large-xls-r-300m-ia --dataset mozilla-foundation/common_voice_8_0 --config ia --split test --greedy ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 4 - 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: 400 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 7.432 \| 1.87 \| 400 \| 2.9636 \| 1.0 \| \| 2.6922 \| 3.74 \| 800 \| 2.2111 \| 0.9977 \| \| 1.2581 \| 5.61 \| 1200 \| 0.4864 \| 0.4028 \| \| 0.6232 \| 7.48 \| 1600 \| 0.2807 \| 0.2413 \| \| 0.4479 \| 9.35 \| 2000 \| 0.2219 \| 0.1885 \| \| 0.3654 \| 11.21 \| 2400 \| 0.1886 \| 0.1606 \| \| 0.323 \| 13.08 \| 2800 \| 0.1716 \| 0.1444 \| \| 0.2935 \| 14.95 \| 3200 \| 0.1687 \| 0.1443 \| \| 0.2707 \| 16.82 \| 3600 \| 0.1632 \| 0.1382 \| \| 0.2559 \| 18.69 \| 4000 \| 0.1507 \| 0.1337 \| \| 0.2433 \| 20.56 \| 4400 \| 0.1572 \| 0.1358 \| \| 0.2338 \| 22.43 \| 4800 \| 0.1489 \| 0.1305 \| \| 0.2258 \| 24.3 \| 5200 \| 0.1485 \| 0.1278 \| \| 0.2218 \| 26.17 \| 5600 \| 0.1470 \| 0.1272 \| \| 0.2169 \| 28.04 \| 6000 \| 0.1470 \| 0.1270 \| \| 0.2117 \| 29.91 \| 6400 \| 0.1452 \| 0.1253 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0	{"language": ["ia"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-ia", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "ia"}, "metrics": [{"type": "wer", "value": 8.6074, "name": "Test WER using LM"}, {"type": "cer", "value": 2.4147, "name": "Test CER using LM"}]}]}]}	automatic-speech-recognition	ayameRushia/wav2vec2-large-xls-r-300m-ia	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "mozilla-foundation/common_voice_8_0", "ia", "dataset:mozilla-foundation/common_voice_8_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "ia" ]	TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #mozilla-foundation/common_voice_8_0 #ia #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us	wav2vec2-large-xls-r-300m-ia ============================ This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset. It achieves the following results on the evaluation set: * Loss: 0.1452 * Wer: 0.1253 Training Procedure ------------------ Training is conducted in Google Colab, the training notebook provided in the repo Training and evaluation data ---------------------------- Language Model Created from texts from processed sentence in train + validation split of dataset (common voice 8.0 for Interlingua) Evaluation is conducted in Notebook, you can see within the repo "notebook\_evaluation\_wav2vec2\_ia.ipynb" Test WER without LM wer = 20.1776 % cer = 4.7205 % Test WER using wer = 8.6074 % cer = 2.4147 % evaluation using URL ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 3e-05 * train\_batch\_size: 16 * eval\_batch\_size: 4 * 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: 400 * num\_epochs: 30 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 400\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #mozilla-foundation/common_voice_8_0 #ia #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 400\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ 111, 159, 4, 38 ]	[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #mozilla-foundation/common_voice_8_0 #ia #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 400\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ -0.13417483866214752, 0.10810285061597824, -0.00513931829482317, 0.046122509986162186, 0.11306290328502655, 0.009446452371776104, 0.10716062784194946, 0.1428627222776413, -0.08867528289556503, 0.10069473087787628, 0.08064551651477814, 0.08290591835975647, 0.07936400920152664, 0.0947943851351738, -0.007655202876776457, -0.2972838878631592, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ID dataset. It achieves the following results on the evaluation set: - Loss: 0.3975 - Wer: 0.2633 ## 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: 8 - 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: 30.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 0.78 \| 100 \| 4.5645 \| 1.0 \| \| No log \| 1.55 \| 200 \| 2.9016 \| 1.0 \| \| No log \| 2.33 \| 300 \| 2.2666 \| 1.0982 \| \| No log \| 3.1 \| 400 \| 0.6079 \| 0.6376 \| \| 3.2188 \| 3.88 \| 500 \| 0.4985 \| 0.5008 \| \| 3.2188 \| 4.65 \| 600 \| 0.4477 \| 0.4469 \| \| 3.2188 \| 5.43 \| 700 \| 0.3953 \| 0.3915 \| \| 3.2188 \| 6.2 \| 800 \| 0.4319 \| 0.3921 \| \| 3.2188 \| 6.98 \| 900 \| 0.4171 \| 0.3698 \| \| 0.2193 \| 7.75 \| 1000 \| 0.3957 \| 0.3600 \| \| 0.2193 \| 8.53 \| 1100 \| 0.3730 \| 0.3493 \| \| 0.2193 \| 9.3 \| 1200 \| 0.3780 \| 0.3348 \| \| 0.2193 \| 10.08 \| 1300 \| 0.4133 \| 0.3568 \| \| 0.2193 \| 10.85 \| 1400 \| 0.3984 \| 0.3193 \| \| 0.1129 \| 11.63 \| 1500 \| 0.3845 \| 0.3174 \| \| 0.1129 \| 12.4 \| 1600 \| 0.3882 \| 0.3162 \| \| 0.1129 \| 13.18 \| 1700 \| 0.3982 \| 0.3008 \| \| 0.1129 \| 13.95 \| 1800 \| 0.3902 \| 0.3198 \| \| 0.1129 \| 14.73 \| 1900 \| 0.4082 \| 0.3237 \| \| 0.0765 \| 15.5 \| 2000 \| 0.3732 \| 0.3126 \| \| 0.0765 \| 16.28 \| 2100 \| 0.3893 \| 0.3001 \| \| 0.0765 \| 17.05 \| 2200 \| 0.4168 \| 0.3083 \| \| 0.0765 \| 17.83 \| 2300 \| 0.4193 \| 0.3044 \| \| 0.0765 \| 18.6 \| 2400 \| 0.4006 \| 0.3013 \| \| 0.0588 \| 19.38 \| 2500 \| 0.3836 \| 0.2892 \| \| 0.0588 \| 20.16 \| 2600 \| 0.3761 \| 0.2903 \| \| 0.0588 \| 20.93 \| 2700 \| 0.3895 \| 0.2930 \| \| 0.0588 \| 21.71 \| 2800 \| 0.3885 \| 0.2791 \| \| 0.0588 \| 22.48 \| 2900 \| 0.3902 \| 0.2891 \| \| 0.0448 \| 23.26 \| 3000 \| 0.4200 \| 0.2849 \| \| 0.0448 \| 24.03 \| 3100 \| 0.4013 \| 0.2799 \| \| 0.0448 \| 24.81 \| 3200 \| 0.4039 \| 0.2731 \| \| 0.0448 \| 25.58 \| 3300 \| 0.3970 \| 0.2647 \| \| 0.0448 \| 26.36 \| 3400 \| 0.4081 \| 0.2690 \| \| 0.0351 \| 27.13 \| 3500 \| 0.4090 \| 0.2674 \| \| 0.0351 \| 27.91 \| 3600 \| 0.3953 \| 0.2663 \| \| 0.0351 \| 28.68 \| 3700 \| 0.4044 \| 0.2650 \| \| 0.0351 \| 29.46 \| 3800 \| 0.3969 \| 0.2646 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0	{"language": ["id"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "XLS-R-300M - Indonesia", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "sv-SE"}, "metrics": [{"type": "wer", "value": 38.098, "name": "Test WER"}, {"type": "cer", "value": 14.261, "name": "Test CER"}]}]}]}	automatic-speech-recognition	ayameRushia/wav2vec2-large-xls-r-300m-id	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "id", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "id" ]	TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us	This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - ID dataset. It achieves the following results on the evaluation set: * Loss: 0.3975 * Wer: 0.2633 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: 8 * 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: 30.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.1+cu102 * Datasets 1.17.1.dev0 * Tokenizers 0.11.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 30.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.1.dev0\n* Tokenizers 0.11.0" ]	[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 30.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.1.dev0\n* Tokenizers 0.11.0" ]	[ 91, 159, 4, 39 ]	[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 30.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.1.dev0\n* Tokenizers 0.11.0" ]	[ -0.13737544417381287, 0.16891443729400635, -0.005101142916828394, 0.045729830861091614, 0.09399552643299103, 0.011626206338405609, 0.08394631743431091, 0.15368600189685822, -0.05164054036140442, 0.1362622231245041, 0.1067264974117279, 0.08647013455629349, 0.0886663943529129, 0.14896075427532196, -0.008808729238808155, -0.2902718186378479, 0.02684761770069599, -0.033335331827402115, -0.08800826966762543, 0.10708009451627731, 0.08333712071180344, -0.09986496716737747, 0.020965948700904846, 0.00825074315071106, -0.08601880818605423, -0.0330517441034317, -0.058746837079524994, -0.04807676002383232, 0.09836218506097794, 0.01737651601433754, 0.026231342926621437, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MN dataset. It achieves the following results on the evaluation set: - Loss: 0.5502 - Wer: 0.4042 ## Training and evaluation data Evaluation is conducted in Notebook, you can see within the repo "notebook_evaluation_wav2vec2_mn.ipynb" Test WER without LM wer = 58.2171 % cer = 16.0670 % Test WER using wer = 31.3919 % cer = 10.2565 % How to use eval.py ``` huggingface-cli login #login to huggingface for getting auth token to access the common voice v8 #running with LM python eval.py --model_id ayameRushia/wav2vec2-large-xls-r-300m-mn --dataset mozilla-foundation/common_voice_8_0 --config mn --split test # running without LM python eval.py --model_id ayameRushia/wav2vec2-large-xls-r-300m-mn --dataset mozilla-foundation/common_voice_8_0 --config mn --split test --greedy ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 8 - 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: 40.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 6.35 \| 400 \| 0.9380 \| 0.7902 \| \| 3.2674 \| 12.7 \| 800 \| 0.5794 \| 0.5309 \| \| 0.7531 \| 19.05 \| 1200 \| 0.5749 \| 0.4815 \| \| 0.5382 \| 25.4 \| 1600 \| 0.5530 \| 0.4447 \| \| 0.4293 \| 31.75 \| 2000 \| 0.5709 \| 0.4237 \| \| 0.4293 \| 38.1 \| 2400 \| 0.5476 \| 0.4059 \| ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0	{"language": ["mn"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-mn", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "mn"}, "metrics": [{"type": "wer", "value": 31.3919, "name": "Test WER using LM"}, {"type": "cer", "value": 10.2565, "name": "Test CER using LM"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "mn"}, "metrics": [{"type": "wer", "value": 65.26, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "mn"}, "metrics": [{"type": "wer", "value": 63.09, "name": "Test WER"}]}]}]}	automatic-speech-recognition	ayameRushia/wav2vec2-large-xls-r-300m-mn	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "mozilla-foundation/common_voice_8_0", "mn", "dataset:mozilla-foundation/common_voice_8_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "mn" ]	TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #mozilla-foundation/common_voice_8_0 #mn #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us	This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - MN dataset. It achieves the following results on the evaluation set: * Loss: 0.5502 * Wer: 0.4042 Training and evaluation data ---------------------------- Evaluation is conducted in Notebook, you can see within the repo "notebook\_evaluation\_wav2vec2\_mn.ipynb" Test WER without LM wer = 58.2171 % cer = 16.0670 % Test WER using wer = 31.3919 % cer = 10.2565 % How to use URL Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0003 * train\_batch\_size: 32 * eval\_batch\_size: 8 * 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: 40.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.1+cu102 * Datasets 1.18.3 * Tokenizers 0.11.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 40.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #mozilla-foundation/common_voice_8_0 #mn #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 40.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ 111, 159, 4, 38 ]	[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #mozilla-foundation/common_voice_8_0 #mn #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 40.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ -0.13510292768478394, 0.12448471039533615, -0.005370603874325752, 0.04649150371551514, 0.1182214617729187, 0.005783065687865019, 0.10255559533834457, 0.1448727399110794, -0.09206388890743256, 0.10490849614143372, 0.07884195446968079, 0.08272586762905121, 0.08366817981004715, 0.09909352660179138, -0.01032936479896307, -0.3011786937713623, 0.015171813778579235, -0.02831410802900791, -0.12334714084863663, 0.10375779867172241, 0.09769953042268753, -0.08943444490432739, 0.03392615169286728, 0.0308302640914917, -0.0931830033659935, -0.0016571036539971828, -0.04625971242785454, 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null	null	transformers	# Wav2Vec2-Large-XLSR-53-Indonesia Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Indonesia using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "id", split="test[:2%]"). processor = Wav2Vec2Processor.from_pretrained("ayameRushia/wav2vec2-large-xlsr-indonesia-demo") model = Wav2Vec2ForCTC.from_pretrained("ayameRushia/wav2vec2-large-xlsr-indonesia-demo") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the {language} test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "id", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("ayameRushia/wav2vec2-large-xlsr-indonesia-demo") model = Wav2Vec2ForCTC.from_pretrained("ayameRushia/wav2vec2-large-xlsr-indonesia-demo") model.to("cuda") chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` Test Result: WER = 20.072720 % ## Training Training using common voice dataset	{"language": "id", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Indonesia by Ayame Rushia", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice id", "type": "common_voice", "args": "id"}, "metrics": [{"type": "wer", "value": "???", "name": "Test WER"}]}]}]}	automatic-speech-recognition	ayameRushia/wav2vec2-large-xlsr-indo-base	[ "transformers", "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "xlsr-fine-tuning-week", "id", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "id" ]	TAGS #transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us	# Wav2Vec2-Large-XLSR-53-Indonesia Fine-tuned facebook/wav2vec2-large-xlsr-53 in Indonesia using the Common Voice When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ## Evaluation The model can be evaluated as follows on the {language} test data of Common Voice. Test Result: WER = 20.072720 % ## Training Training using common voice dataset	[ "# Wav2Vec2-Large-XLSR-53-Indonesia\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Indonesia using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.", "## Usage\nThe model can be used directly (without a language model) as follows:", "## Evaluation\nThe model can be evaluated as follows on the {language} test data of Common Voice.\n\nTest Result: \nWER = 20.072720 %", "## Training\nTraining using common voice dataset" ]	[ "TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "# Wav2Vec2-Large-XLSR-53-Indonesia\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Indonesia using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.", "## Usage\nThe model can be used directly (without a language model) as follows:", "## Evaluation\nThe model can be evaluated as follows on the {language} test data of Common Voice.\n\nTest Result: \nWER = 20.072720 %", "## Training\nTraining using common voice dataset" ]	[ 80, 60, 20, 34, 8 ]	[ "passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-53-Indonesia\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Indonesia using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:## Evaluation\nThe model can be evaluated as follows on the {language} test data of Common Voice.\n\nTest Result: \nWER = 20.072720 %## Training\nTraining using common voice dataset" ]	[ -0.12025351077318192, 0.009708316996693611, -0.0012697172351181507, 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null	null	transformers	# Wav2Vec2-Large-XLSR-53-Indonesia Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Indonesia using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "id", split="test[:2%]"). processor = Wav2Vec2Processor.from_pretrained("ayameRushia/wav2vec2-large-xlsr-indonesia-demo") model = Wav2Vec2ForCTC.from_pretrained("ayameRushia/wav2vec2-large-xlsr-indonesia-demo") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the {language} test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "id", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("ayameRushia/wav2vec2-large-xlsr-indonesia-demo") model = Wav2Vec2ForCTC.from_pretrained("ayameRushia/wav2vec2-large-xlsr-indonesia-demo") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` Test Result: WER = 19.830319 % ## Training Training using common voice dataset	{"language": "id", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Indonesia by Ayame Rushia", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice id", "type": "common_voice", "args": "id"}, "metrics": [{"type": "wer", "value": 19.830319, "name": "Test WER"}]}]}]}	automatic-speech-recognition	ayameRushia/wav2vec2-large-xlsr-indonesia	[ "transformers", "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "xlsr-fine-tuning-week", "id", "dataset:common_voice", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "id" ]	TAGS #transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us	# Wav2Vec2-Large-XLSR-53-Indonesia Fine-tuned facebook/wav2vec2-large-xlsr-53 in Indonesia using the Common Voice When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ## Evaluation The model can be evaluated as follows on the {language} test data of Common Voice. Test Result: WER = 19.830319 % ## Training Training using common voice dataset	[ "# Wav2Vec2-Large-XLSR-53-Indonesia\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Indonesia using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.", "## Usage\nThe model can be used directly (without a language model) as follows:", "## Evaluation\nThe model can be evaluated as follows on the {language} test data of Common Voice.\n\nTest Result: \nWER = 19.830319 %", "## Training\nTraining using common voice dataset" ]	[ "TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "# Wav2Vec2-Large-XLSR-53-Indonesia\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Indonesia using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.", "## Usage\nThe model can be used directly (without a language model) as follows:", "## Evaluation\nThe model can be evaluated as follows on the {language} test data of Common Voice.\n\nTest Result: \nWER = 19.830319 %", "## Training\nTraining using common voice dataset" ]	[ 80, 60, 20, 34, 8 ]	[ "passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-53-Indonesia\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Indonesia using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:## Evaluation\nThe model can be evaluated as follows on the {language} test data of Common Voice.\n\nTest Result: \nWER = 19.830319 %## Training\nTraining using common voice dataset" ]	[ -0.11943415552377701, 0.007512805983424187, -0.0013085979735478759, 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null	null	transformers	# `false-positives-scancode-bert-base-uncased-L8-1` ## Intended Use This model is intended to be used for Sentence Classification which is used for results analysis in [`scancode-results-analyzer`](https://github.com/nexB/scancode-results-analyzer). `scancode-results-analyzer` helps detect faulty scans in [`scancode-toolkit`](https://github.com/nexB/scancode-results-analyzer) by using statistics and nlp modeling, among other tools, to make Scancode better. #### How to use Refer [quickstart](https://github.com/nexB/scancode-results-analyzer#quickstart---local-machine) section in `scancode-results-analyzer` documentation, for installing and getting started. - [Link to Code](https://github.com/nexB/scancode-results-analyzer/blob/master/src/results_analyze/nlp_models.py) Then in `NLPModelsPredict` class, function `predict_basic_false_positive` uses this classifier to predict sentances as either valid license tags or false positives. #### Limitations and bias As this model is a fine-tuned version of the [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) model, it has the same biases, but as the task it is fine-tuned to is a very specific field (license tags vs false positives) without those intended biases, it's safe to assume those don't apply at all here. ## Training and Fine-Tuning Data The BERT model was pretrained on BookCorpus, a dataset consisting of 11,038 unpublished books and English Wikipedia (excluding lists, tables and headers). Then this `bert-base-uncased` model was fine-tuned on Scancode Rule texts, specifically trained in the context of sentence classification, where the two classes are - License Tags - False Positives of License Tags ## Training procedure For fine-tuning procedure and training, refer `scancode-results-analyzer` code. - [Link to Code](https://github.com/nexB/scancode-results-analyzer/blob/master/src/results_analyze/nlp_models.py) In `NLPModelsTrain` class, function `prepare_input_data_false_positive` prepares the training data. In `NLPModelsTrain` class, function `train_basic_false_positive_classifier` fine-tunes this classifier. 1. Model - [BertBaseUncased](https://huggingface.co/bert-base-uncased) (Weights 0.5 GB) 2. Sentence Length - 8 3. Labels - 2 (False Positive/License Tag) 4. After 4-6 Epochs of Fine-Tuning with learning rate 2e-5 (6 secs each on an RTX 2060) Note: The classes aren't balanced. ## Eval results - Accuracy on the training data (90%) : 0.99 (+- 0.005) - Accuracy on the validation data (10%) : 0.96 (+- 0.015) The errors have lower confidence scores using thresholds on confidence scores almost makes it a perfect classifier as the classification task is comparatively easier. Results are stable, in the sence fine-tuning accuracy is very easily achieved every time, though more learning epochs makes the data overfit, i.e. the training loss decreases, but the validation loss increases, even though accuracies are very stable even on overfitting.	{"language": "en", "license": "apache-2.0", "tags": ["license", "sentence-classification", "scancode", "license-compliance"], "datasets": ["bookcorpus", "wikipedia", "scancode-rules"], "version": 1.0}	fill-mask	ayansinha/false-positives-scancode-bert-base-uncased-L8-1	[ "transformers", "tf", "bert", "fill-mask", "license", "sentence-classification", "scancode", "license-compliance", "en", "dataset:bookcorpus", "dataset:wikipedia", "dataset:scancode-rules", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #tf #bert #fill-mask #license #sentence-classification #scancode #license-compliance #en #dataset-bookcorpus #dataset-wikipedia #dataset-scancode-rules #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# 'false-positives-scancode-bert-base-uncased-L8-1' ## Intended Use This model is intended to be used for Sentence Classification which is used for results analysis in 'scancode-results-analyzer'. 'scancode-results-analyzer' helps detect faulty scans in 'scancode-toolkit' by using statistics and nlp modeling, among other tools, to make Scancode better. #### How to use Refer quickstart section in 'scancode-results-analyzer' documentation, for installing and getting started. - Link to Code Then in 'NLPModelsPredict' class, function 'predict_basic_false_positive' uses this classifier to predict sentances as either valid license tags or false positives. #### Limitations and bias As this model is a fine-tuned version of the 'bert-base-uncased' model, it has the same biases, but as the task it is fine-tuned to is a very specific field (license tags vs false positives) without those intended biases, it's safe to assume those don't apply at all here. ## Training and Fine-Tuning Data The BERT model was pretrained on BookCorpus, a dataset consisting of 11,038 unpublished books and English Wikipedia (excluding lists, tables and headers). Then this 'bert-base-uncased' model was fine-tuned on Scancode Rule texts, specifically trained in the context of sentence classification, where the two classes are - License Tags - False Positives of License Tags ## Training procedure For fine-tuning procedure and training, refer 'scancode-results-analyzer' code. - Link to Code In 'NLPModelsTrain' class, function 'prepare_input_data_false_positive' prepares the training data. In 'NLPModelsTrain' class, function 'train_basic_false_positive_classifier' fine-tunes this classifier. 1. Model - BertBaseUncased (Weights 0.5 GB) 2. Sentence Length - 8 3. Labels - 2 (False Positive/License Tag) 4. After 4-6 Epochs of Fine-Tuning with learning rate 2e-5 (6 secs each on an RTX 2060) Note: The classes aren't balanced. ## Eval results - Accuracy on the training data (90%) : 0.99 (+- 0.005) - Accuracy on the validation data (10%) : 0.96 (+- 0.015) The errors have lower confidence scores using thresholds on confidence scores almost makes it a perfect classifier as the classification task is comparatively easier. Results are stable, in the sence fine-tuning accuracy is very easily achieved every time, though more learning epochs makes the data overfit, i.e. the training loss decreases, but the validation loss increases, even though accuracies are very stable even on overfitting.	[ "# 'false-positives-scancode-bert-base-uncased-L8-1'", "## Intended Use\n\nThis model is intended to be used for Sentence Classification which is used for results\nanalysis in 'scancode-results-analyzer'.\n\n'scancode-results-analyzer' helps detect faulty scans in 'scancode-toolkit' by using statistics and nlp modeling, among other tools,\nto make Scancode better.", "#### How to use\n\nRefer quickstart section in 'scancode-results-analyzer' documentation, for installing and getting started.\n\n- Link to Code\n\nThen in 'NLPModelsPredict' class, function 'predict_basic_false_positive' uses this classifier to\npredict sentances as either valid license tags or false positives.", "#### Limitations and bias\n\nAs this model is a fine-tuned version of the 'bert-base-uncased' model,\nit has the same biases, but as the task it is fine-tuned to is a very specific field\n(license tags vs false positives) without those intended biases, it's safe to assume\nthose don't apply at all here.", "## Training and Fine-Tuning Data\n\nThe BERT model was pretrained on BookCorpus, a dataset consisting of 11,038 unpublished books and English Wikipedia (excluding lists, tables and headers).\n\nThen this 'bert-base-uncased' model was fine-tuned on Scancode Rule texts, specifically\ntrained in the context of sentence classification, where the two classes are\n\n\t- License Tags \n\t- False Positives of License Tags", "## Training procedure\n\nFor fine-tuning procedure and training, refer 'scancode-results-analyzer' code.\n\n- Link to Code\n\nIn 'NLPModelsTrain' class, function 'prepare_input_data_false_positive' prepares the\ntraining data.\n\nIn 'NLPModelsTrain' class, function 'train_basic_false_positive_classifier' fine-tunes\nthis classifier.\n\n1. Model - BertBaseUncased (Weights 0.5 GB)\n2. Sentence Length - 8\n3. Labels - 2 (False Positive/License Tag)\n4. After 4-6 Epochs of Fine-Tuning with learning rate 2e-5 (6 secs each on an RTX 2060)\n\nNote: The classes aren't balanced.", "## Eval results\n\n- Accuracy on the training data (90%) : 0.99 (+- 0.005) \n- Accuracy on the validation data (10%) : 0.96 (+- 0.015)\n\nThe errors have lower confidence scores using thresholds on confidence scores almost\nmakes it a perfect classifier as the classification task is comparatively easier.\n\nResults are stable, in the sence fine-tuning accuracy is very easily achieved every\ntime, though more learning epochs makes the data overfit, i.e. the training loss \ndecreases, but the validation loss increases, even though accuracies are very stable\neven on overfitting." ]	[ "TAGS\n#transformers #tf #bert #fill-mask #license #sentence-classification #scancode #license-compliance #en #dataset-bookcorpus #dataset-wikipedia #dataset-scancode-rules #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# 'false-positives-scancode-bert-base-uncased-L8-1'", "## Intended Use\n\nThis model is intended to be used for Sentence Classification which is used for results\nanalysis in 'scancode-results-analyzer'.\n\n'scancode-results-analyzer' helps detect faulty scans in 'scancode-toolkit' by using statistics and nlp modeling, among other tools,\nto make Scancode better.", "#### How to use\n\nRefer quickstart section in 'scancode-results-analyzer' documentation, for installing and getting started.\n\n- Link to Code\n\nThen in 'NLPModelsPredict' class, function 'predict_basic_false_positive' uses this classifier to\npredict sentances as either valid license tags or false positives.", "#### Limitations and bias\n\nAs this model is a fine-tuned version of the 'bert-base-uncased' model,\nit has the same biases, but as the task it is fine-tuned to is a very specific field\n(license tags vs false positives) without those intended biases, it's safe to assume\nthose don't apply at all here.", "## Training and Fine-Tuning Data\n\nThe BERT model was pretrained on BookCorpus, a dataset consisting of 11,038 unpublished books and English Wikipedia (excluding lists, tables and headers).\n\nThen this 'bert-base-uncased' model was fine-tuned on Scancode Rule texts, specifically\ntrained in the context of sentence classification, where the two classes are\n\n\t- License Tags \n\t- False Positives of License Tags", "## Training procedure\n\nFor fine-tuning procedure and training, refer 'scancode-results-analyzer' code.\n\n- Link to Code\n\nIn 'NLPModelsTrain' class, function 'prepare_input_data_false_positive' prepares the\ntraining data.\n\nIn 'NLPModelsTrain' class, function 'train_basic_false_positive_classifier' fine-tunes\nthis classifier.\n\n1. Model - BertBaseUncased (Weights 0.5 GB)\n2. Sentence Length - 8\n3. Labels - 2 (False Positive/License Tag)\n4. After 4-6 Epochs of Fine-Tuning with learning rate 2e-5 (6 secs each on an RTX 2060)\n\nNote: The classes aren't balanced.", "## Eval results\n\n- Accuracy on the training data (90%) : 0.99 (+- 0.005) \n- Accuracy on the validation data (10%) : 0.96 (+- 0.015)\n\nThe errors have lower confidence scores using thresholds on confidence scores almost\nmakes it a perfect classifier as the classification task is comparatively easier.\n\nResults are stable, in the sence fine-tuning accuracy is very easily achieved every\ntime, though more learning epochs makes the data overfit, i.e. the training loss \ndecreases, but the validation loss increases, even though accuracies are very stable\neven on overfitting." ]	[ 86, 24, 86, 81, 85, 102, 175, 144 ]	[ "passage: TAGS\n#transformers #tf #bert #fill-mask #license #sentence-classification #scancode #license-compliance #en #dataset-bookcorpus #dataset-wikipedia #dataset-scancode-rules #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# 'false-positives-scancode-bert-base-uncased-L8-1'## Intended Use\n\nThis model is intended to be used for Sentence Classification which is used for results\nanalysis in 'scancode-results-analyzer'.\n\n'scancode-results-analyzer' helps detect faulty scans in 'scancode-toolkit' by using statistics and nlp modeling, among other tools,\nto make Scancode better.#### How to use\n\nRefer quickstart section in 'scancode-results-analyzer' documentation, for installing and getting started.\n\n- Link to Code\n\nThen in 'NLPModelsPredict' class, function 'predict_basic_false_positive' uses this classifier to\npredict sentances as either valid license tags or false positives.#### Limitations and bias\n\nAs this model is a fine-tuned version of the 'bert-base-uncased' model,\nit has the same biases, but as the task it is fine-tuned to is a very specific field\n(license tags vs false positives) without those intended biases, it's safe to assume\nthose don't apply at all here.## Training and Fine-Tuning Data\n\nThe BERT model was pretrained on BookCorpus, a dataset consisting of 11,038 unpublished books and English Wikipedia (excluding lists, tables and headers).\n\nThen this 'bert-base-uncased' model was fine-tuned on Scancode Rule texts, specifically\ntrained in the context of sentence classification, where the two classes are\n\n\t- License Tags \n\t- False Positives of License Tags" ]	[ -0.04031326249241829, 0.13766920566558838, -0.006441679783165455, 0.053930316120386124, 0.0382624976336956, -0.014384493231773376, 0.06086663901805878, 0.09505443274974823, 0.011709480546414852, 0.07558996230363846, -0.01341312937438488, 0.053697358816862106, 0.08797045052051544, 0.052468325942754745, -0.004049098584800959, -0.15762414038181305, 0.045229848474264145, -0.04340432211756706, 0.1012033000588417, 0.077041856944561, 0.12379249930381775, -0.04752998426556587, 0.05568401888012886, 0.04806157201528549, -0.05417029187083244, -0.0010619147215038538, 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null	null	transformers	# `lic-class-scancode-bert-base-cased-L32-1` ## Intended Use This model is intended to be used for Sentence Classification which is used for results analysis in [`scancode-results-analyzer`](https://github.com/nexB/scancode-results-analyzer). `scancode-results-analyzer` helps detect faulty scans in [`scancode-toolkit`](https://github.com/nexB/scancode-results-analyzer) by using statistics and nlp modeling, among other tools, to make Scancode better. ## How to Use Refer [quickstart](https://github.com/nexB/scancode-results-analyzer#quickstart---local-machine) section in `scancode-results-analyzer` documentation, for installing and getting started. - [Link to Code](https://github.com/nexB/scancode-results-analyzer/blob/master/src/results_analyze/nlp_models.py) Then in `NLPModelsPredict` class, function `predict_basic_lic_class` uses this classifier to predict sentances as either valid license tags or false positives. ## Limitations and Bias As this model is a fine-tuned version of the [`bert-base-cased`](https://huggingface.co/bert-base-cased) model, it has the same biases, but as the task it is fine-tuned to is a very specific task (license text/notice/tag/referance) without those intended biases, it's safe to assume those don't apply at all here. ## Training and Fine-Tuning Data The BERT model was pretrained on BookCorpus, a dataset consisting of 11,038 unpublished books and English Wikipedia (excluding lists, tables and headers). Then this `bert-base-cased` model was fine-tuned on Scancode Rule texts, specifically trained in the context of sentence classification, where the four classes are - License Text - License Notice - License Tag - License Referance ## Training Procedure For fine-tuning procedure and training, refer `scancode-results-analyzer` code. - [Link to Code](https://github.com/nexB/scancode-results-analyzer/blob/master/src/results_analyze/nlp_models.py) In `NLPModelsTrain` class, function `prepare_input_data_false_positive` prepares the training data. In `NLPModelsTrain` class, function `train_basic_false_positive_classifier` fine-tunes this classifier. 1. Model - [BertBaseCased](https://huggingface.co/bert-base-cased) (Weights 0.5 GB) 2. Sentence Length - 32 3. Labels - 4 (License Text/Notice/Tag/Referance) 4. After 4 Epochs of Fine-Tuning with learning rate 2e-5 (60 secs each on an RTX 2060) Note: The classes aren't balanced. ## Eval Results - Accuracy on the training data (90%) : 0.98 (+- 0.01) - Accuracy on the validation data (10%) : 0.84 (+- 0.01) ## Further Work 1. Apllying Splitting/Aggregation Strategies 2. Data Augmentation according to Vaalidation Errors 3. Bigger/Better Suited Models	{"language": "en", "license": "apache-2.0", "tags": ["license", "sentence-classification", "scancode", "license-compliance"], "datasets": ["bookcorpus", "wikipedia", "scancode-rules"], "version": 1.0}	fill-mask	ayansinha/lic-class-scancode-bert-base-cased-L32-1	[ "transformers", "tf", "bert", "fill-mask", "license", "sentence-classification", "scancode", "license-compliance", "en", "dataset:bookcorpus", "dataset:wikipedia", "dataset:scancode-rules", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #tf #bert #fill-mask #license #sentence-classification #scancode #license-compliance #en #dataset-bookcorpus #dataset-wikipedia #dataset-scancode-rules #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# 'lic-class-scancode-bert-base-cased-L32-1' ## Intended Use This model is intended to be used for Sentence Classification which is used for results analysis in 'scancode-results-analyzer'. 'scancode-results-analyzer' helps detect faulty scans in 'scancode-toolkit' by using statistics and nlp modeling, among other tools, to make Scancode better. ## How to Use Refer quickstart section in 'scancode-results-analyzer' documentation, for installing and getting started. - Link to Code Then in 'NLPModelsPredict' class, function 'predict_basic_lic_class' uses this classifier to predict sentances as either valid license tags or false positives. ## Limitations and Bias As this model is a fine-tuned version of the 'bert-base-cased' model, it has the same biases, but as the task it is fine-tuned to is a very specific task (license text/notice/tag/referance) without those intended biases, it's safe to assume those don't apply at all here. ## Training and Fine-Tuning Data The BERT model was pretrained on BookCorpus, a dataset consisting of 11,038 unpublished books and English Wikipedia (excluding lists, tables and headers). Then this 'bert-base-cased' model was fine-tuned on Scancode Rule texts, specifically trained in the context of sentence classification, where the four classes are - License Text - License Notice - License Tag - License Referance ## Training Procedure For fine-tuning procedure and training, refer 'scancode-results-analyzer' code. - Link to Code In 'NLPModelsTrain' class, function 'prepare_input_data_false_positive' prepares the training data. In 'NLPModelsTrain' class, function 'train_basic_false_positive_classifier' fine-tunes this classifier. 1. Model - BertBaseCased (Weights 0.5 GB) 2. Sentence Length - 32 3. Labels - 4 (License Text/Notice/Tag/Referance) 4. After 4 Epochs of Fine-Tuning with learning rate 2e-5 (60 secs each on an RTX 2060) Note: The classes aren't balanced. ## Eval Results - Accuracy on the training data (90%) : 0.98 (+- 0.01) - Accuracy on the validation data (10%) : 0.84 (+- 0.01) ## Further Work 1. Apllying Splitting/Aggregation Strategies 2. Data Augmentation according to Vaalidation Errors 3. Bigger/Better Suited Models	[ "# 'lic-class-scancode-bert-base-cased-L32-1'", "## Intended Use\n\nThis model is intended to be used for Sentence Classification which is used for results\nanalysis in 'scancode-results-analyzer'.\n\n'scancode-results-analyzer' helps detect faulty scans in 'scancode-toolkit' by using statistics and nlp modeling, among other tools,\nto make Scancode better.", "## How to Use\n\nRefer quickstart section in 'scancode-results-analyzer' documentation, for installing and getting started.\n\n- Link to Code\n\nThen in 'NLPModelsPredict' class, function 'predict_basic_lic_class' uses this classifier to\npredict sentances as either valid license tags or false positives.", "## Limitations and Bias\n\nAs this model is a fine-tuned version of the 'bert-base-cased' model,\nit has the same biases, but as the task it is fine-tuned to is a very specific task\n(license text/notice/tag/referance) without those intended biases, it's safe to assume\nthose don't apply at all here.", "## Training and Fine-Tuning Data\n\nThe BERT model was pretrained on BookCorpus, a dataset consisting of 11,038 unpublished books and English Wikipedia (excluding lists, tables and headers).\n\nThen this 'bert-base-cased' model was fine-tuned on Scancode Rule texts, specifically\ntrained in the context of sentence classification, where the four classes are\n\n\t- License Text\n\t- License Notice\n\t- License Tag\n\t- License Referance", "## Training Procedure\n\nFor fine-tuning procedure and training, refer 'scancode-results-analyzer' code.\n\n- Link to Code\n\nIn 'NLPModelsTrain' class, function 'prepare_input_data_false_positive' prepares the\ntraining data.\n\nIn 'NLPModelsTrain' class, function 'train_basic_false_positive_classifier' fine-tunes\nthis classifier.\n\n1. Model - BertBaseCased (Weights 0.5 GB)\n2. Sentence Length - 32\n3. Labels - 4 (License Text/Notice/Tag/Referance)\n4. After 4 Epochs of Fine-Tuning with learning rate 2e-5 (60 secs each on an RTX 2060)\n\nNote: The classes aren't balanced.", "## Eval Results\n\n- Accuracy on the training data (90%) : 0.98 (+- 0.01) \n- Accuracy on the validation data (10%) : 0.84 (+- 0.01)", "## Further Work\n\n1. Apllying Splitting/Aggregation Strategies\n2. Data Augmentation according to Vaalidation Errors\n3. Bigger/Better Suited Models" ]	[ "TAGS\n#transformers #tf #bert #fill-mask #license #sentence-classification #scancode #license-compliance #en #dataset-bookcorpus #dataset-wikipedia #dataset-scancode-rules #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# 'lic-class-scancode-bert-base-cased-L32-1'", "## Intended Use\n\nThis model is intended to be used for Sentence Classification which is used for results\nanalysis in 'scancode-results-analyzer'.\n\n'scancode-results-analyzer' helps detect faulty scans in 'scancode-toolkit' by using statistics and nlp modeling, among other tools,\nto make Scancode better.", "## How to Use\n\nRefer quickstart section in 'scancode-results-analyzer' documentation, for installing and getting started.\n\n- Link to Code\n\nThen in 'NLPModelsPredict' class, function 'predict_basic_lic_class' uses this classifier to\npredict sentances as either valid license tags or false positives.", "## Limitations and Bias\n\nAs this model is a fine-tuned version of the 'bert-base-cased' model,\nit has the same biases, but as the task it is fine-tuned to is a very specific task\n(license text/notice/tag/referance) without those intended biases, it's safe to assume\nthose don't apply at all here.", "## Training and Fine-Tuning Data\n\nThe BERT model was pretrained on BookCorpus, a dataset consisting of 11,038 unpublished books and English Wikipedia (excluding lists, tables and headers).\n\nThen this 'bert-base-cased' model was fine-tuned on Scancode Rule texts, specifically\ntrained in the context of sentence classification, where the four classes are\n\n\t- License Text\n\t- License Notice\n\t- License Tag\n\t- License Referance", "## Training Procedure\n\nFor fine-tuning procedure and training, refer 'scancode-results-analyzer' code.\n\n- Link to Code\n\nIn 'NLPModelsTrain' class, function 'prepare_input_data_false_positive' prepares the\ntraining data.\n\nIn 'NLPModelsTrain' class, function 'train_basic_false_positive_classifier' fine-tunes\nthis classifier.\n\n1. Model - BertBaseCased (Weights 0.5 GB)\n2. Sentence Length - 32\n3. Labels - 4 (License Text/Notice/Tag/Referance)\n4. After 4 Epochs of Fine-Tuning with learning rate 2e-5 (60 secs each on an RTX 2060)\n\nNote: The classes aren't balanced.", "## Eval Results\n\n- Accuracy on the training data (90%) : 0.98 (+- 0.01) \n- Accuracy on the validation data (10%) : 0.84 (+- 0.01)", "## Further Work\n\n1. Apllying Splitting/Aggregation Strategies\n2. Data Augmentation according to Vaalidation Errors\n3. Bigger/Better Suited Models" ]	[ 86, 21, 86, 78, 88, 104, 179, 44, 38 ]	[ "passage: TAGS\n#transformers #tf #bert #fill-mask #license #sentence-classification #scancode #license-compliance #en #dataset-bookcorpus #dataset-wikipedia #dataset-scancode-rules #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# 'lic-class-scancode-bert-base-cased-L32-1'## Intended Use\n\nThis model is intended to be used for Sentence Classification which is used for results\nanalysis in 'scancode-results-analyzer'.\n\n'scancode-results-analyzer' helps detect faulty scans in 'scancode-toolkit' by using statistics and nlp modeling, among other tools,\nto make Scancode better.## How to Use\n\nRefer quickstart section in 'scancode-results-analyzer' documentation, for installing and getting started.\n\n- Link to Code\n\nThen in 'NLPModelsPredict' class, function 'predict_basic_lic_class' uses this classifier to\npredict sentances as either valid license tags or false positives.## Limitations and Bias\n\nAs this model is a fine-tuned version of the 'bert-base-cased' model,\nit has the same biases, but as the task it is fine-tuned to is a very specific task\n(license text/notice/tag/referance) without those intended biases, it's safe to assume\nthose don't apply at all here.## Training and Fine-Tuning Data\n\nThe BERT model was pretrained on BookCorpus, a dataset consisting of 11,038 unpublished books and English Wikipedia (excluding lists, tables and headers).\n\nThen this 'bert-base-cased' model was fine-tuned on Scancode Rule texts, specifically\ntrained in the context of sentence classification, where the four classes are\n\n\t- License Text\n\t- License Notice\n\t- License Tag\n\t- License Referance" ]	[ -0.04472174495458603, 0.15657564997673035, -0.00612260028719902, 0.04665049538016319, 0.04596731439232826, -0.0027946175541728735, 0.07285820692777634, 0.09863945096731186, 0.03382302448153496, 0.07375029474496841, -0.011146560311317444, 0.07966703921556473, 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null	null	transformers	# bert-base-cased trained on TREC 6-class task ## Model description A simple base BERT model trained on the "trec" dataset. ## Intended uses & limitations #### How to use ##### Transformers ```python # Load model and tokenizer from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) # Use pipeline from transformers import pipeline model_name = "aychang/bert-base-cased-trec-coarse" nlp = pipeline("sentiment-analysis", model=model_name, tokenizer=model_name) results = nlp(["Where did the queen go?", "Why did the Queen hire 1000 ML Engineers?"]) ``` ##### AdaptNLP ```python from adaptnlp import EasySequenceClassifier model_name = "aychang/bert-base-cased-trec-coarse" texts = ["Where did the queen go?", "Why did the Queen hire 1000 ML Engineers?"] classifer = EasySequenceClassifier results = classifier.tag_text(text=texts, model_name_or_path=model_name, mini_batch_size=2) ``` #### Limitations and bias This is minimal language model trained on a benchmark dataset. ## Training data TREC https://huggingface.co/datasets/trec ## Training procedure Preprocessing, hardware used, hyperparameters... #### Hardware One V100 #### Hyperparameters and Training Args ```python from transformers import TrainingArguments training_args = TrainingArguments( output_dir='./models', num_train_epochs=2, per_device_train_batch_size=16, per_device_eval_batch_size=16, warmup_steps=500, weight_decay=0.01, evaluation_strategy="steps", logging_dir='./logs', save_steps=3000 ) ``` ## Eval results ``` {'epoch': 2.0, 'eval_accuracy': 0.974, 'eval_f1': array([0.98181818, 0.94444444, 1. , 0.99236641, 0.96995708, 0.98159509]), 'eval_loss': 0.138086199760437, 'eval_precision': array([0.98540146, 0.98837209, 1. , 0.98484848, 0.94166667, 0.97560976]), 'eval_recall': array([0.97826087, 0.90425532, 1. , 1. , 1. , 0.98765432]), 'eval_runtime': 1.6132, 'eval_samples_per_second': 309.943} ```	{"language": ["en"], "license": "mit", "tags": ["text-classification"], "datasets": ["trec"], "model-index": [{"name": "aychang/bert-base-cased-trec-coarse", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "trec", "type": "trec", "config": "default", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.974, "name": "Accuracy", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTUwZTU1ZGU5YTRiMzNhNmQyMjNlY2M5YjAwN2RlMmYxODI2MjFkY2Q3NWFjZDg3Zjg5ZDk1Y2I1MTUxYjFhMCIsInZlcnNpb24iOjF9.GJkxJOFhsO4UaoHpHH1136Qj_fu9UQ9o3DThtT46hvMduswkgobl9iz6ICYQ7IdYKFbh3zRTlsZzjnAlzGqdBA"}, {"type": "precision", "value": 0.9793164100816639, "name": "Precision Macro", "verified": true, "verifyToken": 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{"type": "loss", "value": 0.13812002539634705, "name": "loss", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjk4MDQ5NGRiNTExYmE3NGU1ZmQ1YjUzMTQ4NzUwNWViYzFiODEzMjc2MDA2MzYyOGNjNjYxYzliNDM4Y2U0ZSIsInZlcnNpb24iOjF9.u68ogPOH6-_pb6ZVulzMVfHIfFlLwBeDp8H4iqgfBadjwj2h-aO0jzc4umWFWtzWespsZvnlDjklbhhgrd1vCQ"}]}]}]}	text-classification	aychang/bert-base-cased-trec-coarse	[ "transformers", "pytorch", "jax", "bert", "text-classification", "en", "dataset:trec", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #bert #text-classification #en #dataset-trec #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us	# bert-base-cased trained on TREC 6-class task ## Model description A simple base BERT model trained on the "trec" dataset. ## Intended uses & limitations #### How to use ##### Transformers ##### AdaptNLP #### Limitations and bias This is minimal language model trained on a benchmark dataset. ## Training data TREC URL ## Training procedure Preprocessing, hardware used, hyperparameters... #### Hardware One V100 #### Hyperparameters and Training Args ## Eval results	[ "# bert-base-cased trained on TREC 6-class task", "## Model description\n\nA simple base BERT model trained on the \"trec\" dataset.", "## Intended uses & limitations", "#### How to use", "##### Transformers", "##### AdaptNLP", "#### Limitations and bias\n\nThis is minimal language model trained on a benchmark dataset.", "## Training data\n\nTREC URL", "## Training procedure\n\nPreprocessing, hardware used, hyperparameters...", "#### Hardware\nOne V100", "#### Hyperparameters and Training Args", "## Eval results" ]	[ "TAGS\n#transformers #pytorch #jax #bert #text-classification #en #dataset-trec #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-base-cased trained on TREC 6-class task", "## Model description\n\nA simple base BERT model trained on the \"trec\" dataset.", "## Intended uses & limitations", "#### How to use", "##### Transformers", "##### AdaptNLP", "#### Limitations and bias\n\nThis is minimal language model trained on a benchmark dataset.", "## Training data\n\nTREC URL", "## Training procedure\n\nPreprocessing, hardware used, hyperparameters...", "#### Hardware\nOne V100", "#### Hyperparameters and Training Args", "## Eval results" ]	[ 56, 16, 20, 9, 5, 5, 5, 20, 6, 15, 6, 10, 4 ]	[ "passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #en #dataset-trec #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n# bert-base-cased trained on TREC 6-class task## Model description\n\nA simple base BERT model trained on the \"trec\" dataset.## Intended uses & limitations#### How to use##### Transformers##### AdaptNLP#### Limitations and bias\n\nThis is minimal language model trained on a benchmark dataset.## Training data\n\nTREC URL## Training procedure\n\nPreprocessing, hardware used, hyperparameters...#### Hardware\nOne V100#### Hyperparameters and Training Args## Eval results" ]	[ -0.042306482791900635, 0.039718471467494965, -0.0007646337617188692, 0.09010499715805054, 0.17626409232616425, -0.007981636561453342, 0.09881993383169174, 0.0875883623957634, -0.08996979892253876, -0.03754369914531708, 0.11623118817806244, 0.1697741448879242, 0.023455839604139328, 0.10696275532245636, -0.04159223660826683, -0.25405964255332947, 0.01328955590724945, 0.04140638932585716, -0.04673474282026291, 0.12127429991960526, 0.15842010080814362, -0.11825437098741531, 0.10437395423650742, -0.02754095569252968, -0.1353091448545456, 0.022339612245559692, 0.018069662153720856, -0.05202494189143181, 0.11859090626239777, 0.04269130155444145, 0.14209075272083282, -0.0030178732704371214, 0.11476544290781021, -0.20650531351566315, 0.019610660150647163, 0.054352063685655594, 0.00643192557618022, 0.08554814755916595, 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null	null	null	# TorchScript model of bert-large-cased-whole-word-masking-finetuned-squad ## Model description A serialized torchscript model of bert-large-cased-whole-word-masking-finetuned-squad with a config.pbtxt for deployment using NVIDIA Triton Inference Server.	{"language": ["en"], "license": "mit", "tags": ["question-answering", "torchscript", "FastNN"], "datasets": ["squad"]}	question-answering	aychang/bert-large-cased-whole-word-masking-finetuned-squad	[ "question-answering", "torchscript", "FastNN", "en", "dataset:squad", "license:mit", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #question-answering #torchscript #FastNN #en #dataset-squad #license-mit #region-us	# TorchScript model of bert-large-cased-whole-word-masking-finetuned-squad ## Model description A serialized torchscript model of bert-large-cased-whole-word-masking-finetuned-squad with a URL for deployment using NVIDIA Triton Inference Server.	[ "# TorchScript model of bert-large-cased-whole-word-masking-finetuned-squad", "## Model description\n\nA serialized torchscript model of bert-large-cased-whole-word-masking-finetuned-squad with a URL for deployment using NVIDIA Triton Inference Server." ]	[ "TAGS\n#question-answering #torchscript #FastNN #en #dataset-squad #license-mit #region-us \n", "# TorchScript model of bert-large-cased-whole-word-masking-finetuned-squad", "## Model description\n\nA serialized torchscript model of bert-large-cased-whole-word-masking-finetuned-squad with a URL for deployment using NVIDIA Triton Inference Server." ]	[ 33, 29, 51 ]	[ "passage: TAGS\n#question-answering #torchscript #FastNN #en #dataset-squad #license-mit #region-us \n# TorchScript model of bert-large-cased-whole-word-masking-finetuned-squad## Model description\n\nA serialized torchscript model of bert-large-cased-whole-word-masking-finetuned-squad with a URL for deployment using NVIDIA Triton Inference Server." ]	[ 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null	null	transformers	# TREC 6-class Task: distilbert-base-cased ## Model description A simple base distilBERT model trained on the "trec" dataset. ## Intended uses & limitations #### How to use ##### Transformers ```python # Load model and tokenizer from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) # Use pipeline from transformers import pipeline model_name = "aychang/distilbert-base-cased-trec-coarse" nlp = pipeline("sentiment-analysis", model=model_name, tokenizer=model_name) results = nlp(["Where did the queen go?", "Why did the Queen hire 1000 ML Engineers?"]) ``` ##### AdaptNLP ```python from adaptnlp import EasySequenceClassifier model_name = "aychang/distilbert-base-cased-trec-coarse" texts = ["Where did the queen go?", "Why did the Queen hire 1000 ML Engineers?"] classifer = EasySequenceClassifier results = classifier.tag_text(text=texts, model_name_or_path=model_name, mini_batch_size=2) ``` #### Limitations and bias This is minimal language model trained on a benchmark dataset. ## Training data TREC https://huggingface.co/datasets/trec ## Training procedure Preprocessing, hardware used, hyperparameters... #### Hardware One V100 #### Hyperparameters and Training Args ```python from transformers import TrainingArguments training_args = TrainingArguments( output_dir='./models', overwrite_output_dir=False, num_train_epochs=2, per_device_train_batch_size=16, per_device_eval_batch_size=16, warmup_steps=500, weight_decay=0.01, evaluation_strategy="steps", logging_dir='./logs', fp16=False, eval_steps=500, save_steps=300000 ) ``` ## Eval results ``` {'epoch': 2.0, 'eval_accuracy': 0.97, 'eval_f1': array([0.98220641, 0.91620112, 1. , 0.97709924, 0.98678414, 0.97560976]), 'eval_loss': 0.14275787770748138, 'eval_precision': array([0.96503497, 0.96470588, 1. , 0.96969697, 0.98245614, 0.96385542]), 'eval_recall': array([1. , 0.87234043, 1. , 0.98461538, 0.99115044, 0.98765432]), 'eval_runtime': 0.9731, 'eval_samples_per_second': 513.798} ```	{"language": ["en"], "license": "mit", "tags": ["text-classification"], "datasets": ["trec"], "model-index": [{"name": "aychang/distilbert-base-cased-trec-coarse", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "trec", "type": "trec", "config": "default", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.97, "name": "Accuracy", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGNmZTQ1Mjk3YTQ0NTdiZmY2NGM2NDM2Yzc2OTI4NGNiZDg4MmViN2I0ZGZiYWJlMTg1ZDU0MTc2ZTg1NjcwZiIsInZlcnNpb24iOjF9.4x_Ze9S5MbAeIHZ4p1EFmWev8RLkAIYWKqouAzYOxTNqdfFN0HnqULiM19EMP42v658vl_fR3-Ig0xG45DioCA"}, {"type": "precision", "value": 0.9742915631870833, "name": "Precision Macro", "verified": true, "verifyToken": 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"loss", "value": 0.14272506535053253, "name": "loss", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODU3NGFiMzIxYWI4NzYxMzUxZGE5ZTZkYTlkN2U5MTI1NzA5NTBiNGM3Y2Q5YmVmZjU0MmU5MjJlZThkZTllMCIsInZlcnNpb24iOjF9.3QeWbECpJ0MHV5gC0_ES6PpwplLsCHPKuToErB1MSG69xNWVyMjKu1-1YEWZOU6dGfwKGh_HvwucY5kC9qwWBQ"}]}]}]}	text-classification	aychang/distilbert-base-cased-trec-coarse	[ "transformers", "pytorch", "distilbert", "text-classification", "en", "dataset:trec", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #distilbert #text-classification #en #dataset-trec #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us	# TREC 6-class Task: distilbert-base-cased ## Model description A simple base distilBERT model trained on the "trec" dataset. ## Intended uses & limitations #### How to use ##### Transformers ##### AdaptNLP #### Limitations and bias This is minimal language model trained on a benchmark dataset. ## Training data TREC URL ## Training procedure Preprocessing, hardware used, hyperparameters... #### Hardware One V100 #### Hyperparameters and Training Args ## Eval results	[ "# TREC 6-class Task: distilbert-base-cased", "## Model description\n\nA simple base distilBERT model trained on the \"trec\" dataset.", "## Intended uses & limitations", "#### How to use", "##### Transformers", "##### AdaptNLP", "#### Limitations and bias\n\nThis is minimal language model trained on a benchmark dataset.", "## Training data\n\nTREC URL", "## Training procedure\n\nPreprocessing, hardware used, hyperparameters...", "#### Hardware\nOne V100", "#### Hyperparameters and Training Args", "## Eval results" ]	[ "TAGS\n#transformers #pytorch #distilbert #text-classification #en #dataset-trec #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "# TREC 6-class Task: distilbert-base-cased", "## Model description\n\nA simple base distilBERT model trained on the \"trec\" dataset.", "## Intended uses & limitations", "#### How to use", "##### Transformers", "##### AdaptNLP", "#### Limitations and bias\n\nThis is minimal language model trained on a benchmark dataset.", "## Training data\n\nTREC URL", "## Training procedure\n\nPreprocessing, hardware used, hyperparameters...", "#### Hardware\nOne V100", "#### Hyperparameters and Training Args", "## Eval results" ]	[ 55, 16, 22, 9, 5, 5, 5, 20, 6, 15, 6, 10, 4 ]	[ "passage: TAGS\n#transformers #pytorch #distilbert #text-classification #en #dataset-trec #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n# TREC 6-class Task: distilbert-base-cased## Model description\n\nA simple base distilBERT model trained on the \"trec\" dataset.## Intended uses & limitations#### How to use##### Transformers##### AdaptNLP#### Limitations and bias\n\nThis is minimal language model trained on a benchmark dataset.## Training data\n\nTREC URL## Training procedure\n\nPreprocessing, hardware used, hyperparameters...#### Hardware\nOne V100#### Hyperparameters and Training Args## Eval results" ]	[ -0.05427967384457588, 0.015722500160336494, -0.0009245880646631122, 0.08824535459280014, 0.18027552962303162, 0.01509677805006504, 0.12258879840373993, 0.07990962266921997, -0.0790855884552002, -0.03062337078154087, 0.10461676865816116, 0.18650265038013458, 0.023496853187680244, 0.11970707774162292, -0.045831818133592606, -0.26709020137786865, 0.01514724362641573, 0.023050542920827866, -0.034245822578668594, 0.1327408403158188, 0.16867993772029877, -0.10593067109584808, 0.10082412511110306, -0.029874417930841446, -0.14412373304367065, -0.0010079317726194859, 0.02255718596279621, -0.053271062672138214, 0.11776119470596313, 0.03248246759176254, 0.13130001723766327, 0.0026545117143541574, 0.1140175461769104, -0.2170119285583496, 0.020970460027456284, 0.05926140397787094, 0.004029375966638327, 0.07428277283906937, 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null	null	null	# TorchScript model of distilbert-squad ## Model description A serialized torchscript model of distilbert-squad with a config.pbtxt for deployment using NVIDIA Triton Inference Server.	{"language": ["en"], "license": "mit", "tags": ["question-answering", "torchscript", "FastNN"], "datasets": ["squad"]}	question-answering	aychang/distilbert-squad	[ "question-answering", "torchscript", "FastNN", "en", "dataset:squad", "license:mit", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #question-answering #torchscript #FastNN #en #dataset-squad #license-mit #region-us	# TorchScript model of distilbert-squad ## Model description A serialized torchscript model of distilbert-squad with a URL for deployment using NVIDIA Triton Inference Server.	[ "# TorchScript model of distilbert-squad", "## Model description\n\nA serialized torchscript model of distilbert-squad with a URL for deployment using NVIDIA Triton Inference Server." ]	[ "TAGS\n#question-answering #torchscript #FastNN #en #dataset-squad #license-mit #region-us \n", "# TorchScript model of distilbert-squad", "## Model description\n\nA serialized torchscript model of distilbert-squad with a URL for deployment using NVIDIA Triton Inference Server." ]	[ 33, 12, 34 ]	[ "passage: TAGS\n#question-answering #torchscript #FastNN #en #dataset-squad #license-mit #region-us \n# TorchScript model of distilbert-squad## Model description\n\nA serialized torchscript model of distilbert-squad with a URL for deployment using NVIDIA Triton Inference Server." ]	[ -0.027889307588338852, -0.14914438128471375, -0.003942803014069796, 0.10985821485519409, 0.07756263762712479, 0.09827492386102676, 0.26733341813087463, 0.001415330683812499, 0.12559135258197784, -0.01405874639749527, 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null	null	null	# TorchScript model of faster-rcnn ## Model description A serialized torchscript model of [faster-rcnn](https://pytorch.org/vision/stable/models.html#faster-r-cnn) with a config.pbtxt for deployment using NVIDIA Triton Inference Server.	{"language": ["en"], "license": "mit", "tags": ["object-detection", "torchscript", "FastNN"], "datasets": ["coco"]}	object-detection	aychang/fasterrcnn-resnet50-cpu	[ "object-detection", "torchscript", "FastNN", "en", "dataset:coco", "license:mit", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #object-detection #torchscript #FastNN #en #dataset-coco #license-mit #region-us	# TorchScript model of faster-rcnn ## Model description A serialized torchscript model of faster-rcnn with a URL for deployment using NVIDIA Triton Inference Server.	[ "# TorchScript model of faster-rcnn", "## Model description\n\nA serialized torchscript model of faster-rcnn with a URL for deployment using NVIDIA Triton Inference Server." ]	[ "TAGS\n#object-detection #torchscript #FastNN #en #dataset-coco #license-mit #region-us \n", "# TorchScript model of faster-rcnn", "## Model description\n\nA serialized torchscript model of faster-rcnn with a URL for deployment using NVIDIA Triton Inference Server." ]	[ 32, 11, 33 ]	[ "passage: TAGS\n#object-detection #torchscript #FastNN #en #dataset-coco #license-mit #region-us \n# TorchScript model of faster-rcnn## Model description\n\nA serialized torchscript model of faster-rcnn with a URL for deployment using NVIDIA Triton Inference Server." ]	[ -0.0022977953776717186, -0.09428861737251282, -0.0034128765109926462, 0.12409916520118713, 0.03179033100605011, 0.023235918954014778, 0.0858667865395546, 0.03513389825820923, 0.051206931471824646, 0.0022325559984892607, 0.15303246676921844, 0.21949857473373413, 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null	null	transformers	# IMDB Sentiment Task: roberta-base ## Model description A simple base roBERTa model trained on the "imdb" dataset. ## Intended uses & limitations #### How to use ##### Transformers ```python # Load model and tokenizer from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) # Use pipeline from transformers import pipeline model_name = "aychang/roberta-base-imdb" nlp = pipeline("sentiment-analysis", model=model_name, tokenizer=model_name) results = nlp(["I didn't really like it because it was so terrible.", "I love how easy it is to watch and get good results."]) ``` ##### AdaptNLP ```python from adaptnlp import EasySequenceClassifier model_name = "aychang/roberta-base-imdb" texts = ["I didn't really like it because it was so terrible.", "I love how easy it is to watch and get good results."] classifer = EasySequenceClassifier results = classifier.tag_text(text=texts, model_name_or_path=model_name, mini_batch_size=2) ``` #### Limitations and bias This is minimal language model trained on a benchmark dataset. ## Training data IMDB https://huggingface.co/datasets/imdb ## Training procedure #### Hardware One V100 #### Hyperparameters and Training Args ```python from transformers import TrainingArguments training_args = TrainingArguments( output_dir='./models', overwrite_output_dir=False, num_train_epochs=2, per_device_train_batch_size=8, per_device_eval_batch_size=8, warmup_steps=500, weight_decay=0.01, evaluation_strategy="steps", logging_dir='./logs', fp16=False, eval_steps=800, save_steps=300000 ) ``` ## Eval results ``` {'epoch': 2.0, 'eval_accuracy': 0.94668, 'eval_f1': array([0.94603457, 0.94731017]), 'eval_loss': 0.2578844428062439, 'eval_precision': array([0.95762642, 0.93624502]), 'eval_recall': array([0.93472, 0.95864]), 'eval_runtime': 244.7522, 'eval_samples_per_second': 102.144} ```	{"language": ["en"], "license": "mit", "tags": ["text-classification"], "datasets": ["imdb"]}	text-classification	aychang/roberta-base-imdb	[ "transformers", "pytorch", "jax", "roberta", "text-classification", "en", "dataset:imdb", "license:mit", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #roberta #text-classification #en #dataset-imdb #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us	# IMDB Sentiment Task: roberta-base ## Model description A simple base roBERTa model trained on the "imdb" dataset. ## Intended uses & limitations #### How to use ##### Transformers ##### AdaptNLP #### Limitations and bias This is minimal language model trained on a benchmark dataset. ## Training data IMDB URL ## Training procedure #### Hardware One V100 #### Hyperparameters and Training Args ## Eval results	[ "# IMDB Sentiment Task: roberta-base", "## Model description\n\nA simple base roBERTa model trained on the \"imdb\" dataset.", "## Intended uses & limitations", "#### How to use", "##### Transformers", "##### AdaptNLP", "#### Limitations and bias\n\nThis is minimal language model trained on a benchmark dataset.", "## Training data\n\nIMDB URL", "## Training procedure", "#### Hardware\nOne V100", "#### Hyperparameters and Training Args", "## Eval results" ]	[ "TAGS\n#transformers #pytorch #jax #roberta #text-classification #en #dataset-imdb #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# IMDB Sentiment Task: roberta-base", "## Model description\n\nA simple base roBERTa model trained on the \"imdb\" dataset.", "## Intended uses & limitations", "#### How to use", "##### Transformers", "##### AdaptNLP", "#### Limitations and bias\n\nThis is minimal language model trained on a benchmark dataset.", "## Training data\n\nIMDB URL", "## Training procedure", "#### Hardware\nOne V100", "#### Hyperparameters and Training Args", "## Eval results" ]	[ 57, 12, 21, 9, 5, 5, 5, 20, 6, 3, 6, 10, 4 ]	[ "passage: TAGS\n#transformers #pytorch #jax #roberta #text-classification #en #dataset-imdb #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n# IMDB Sentiment Task: roberta-base## Model description\n\nA simple base roBERTa model trained on the \"imdb\" dataset.## Intended uses & limitations#### How to use##### Transformers##### AdaptNLP#### Limitations and bias\n\nThis is minimal language model trained on a benchmark dataset.## Training data\n\nIMDB URL## Training procedure#### Hardware\nOne V100#### Hyperparameters and Training Args## Eval results" ]	[ -0.08453140407800674, 0.07960882037878036, -0.0009968917584046721, 0.06482906639575958, 0.16175708174705505, 0.02732716128230095, 0.16545242071151733, 0.13388845324516296, -0.005205670837312937, -0.013927618972957134, 0.07558269053697586, 0.1376921832561493, 0.043163374066352844, 0.12276215106248856, -0.06805366277694702, 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null	null	transformers	# My Awesome Model	{"tags": ["conversational"]}	text-generation	aydin/DialoGPT-medium-michael	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# My Awesome Model	[ "# My Awesome Model" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# My Awesome Model" ]	[ 51, 4 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# My Awesome Model" ]	[ -0.05259015038609505, 0.05521034821867943, -0.005910294596105814, 0.017722278833389282, 0.15250112116336823, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-imdb This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the [imdb](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews) 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: 5e-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 ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2 - Datasets 1.18.3 - Tokenizers 0.11.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-imdb", "results": []}]}	text-generation	aypan17/distilgpt2-imdb	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# distilgpt2-imdb This model is a fine-tuned version of distilgpt2 on the imdb 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: 5e-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 ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2 - Datasets 1.18.3 - Tokenizers 0.11.0	[ "# distilgpt2-imdb\n\nThis model is a fine-tuned version of distilgpt2 on the imdb dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0", "### Training results", "### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.2\n- Datasets 1.18.3\n- Tokenizers 0.11.0" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# distilgpt2-imdb\n\nThis model is a fine-tuned version of distilgpt2 on the imdb dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0", "### Training results", "### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.2\n- Datasets 1.18.3\n- Tokenizers 0.11.0" ]	[ 62, 30, 6, 12, 8, 3, 90, 4, 35 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# distilgpt2-imdb\n\nThis model is a fine-tuned version of distilgpt2 on the imdb dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0### Training results### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.2\n- Datasets 1.18.3\n- Tokenizers 0.11.0" ]	[ -0.09858903288841248, 0.05785796418786049, -0.0016759686404839158, 0.07638204842805862, 0.1661156713962555, 0.028764519840478897, 0.11697712540626526, 0.13459038734436035, -0.1260804682970047, 0.024657925590872765, 0.06175536662340164, 0.08885443955659866, 0.04200506582856178, 0.11280221492052078, -0.027482978999614716, -0.2789631485939026, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-med-imdb This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown 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: 5e-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 ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2 - Datasets 1.18.3 - Tokenizers 0.11.0	{"tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-med-imdb", "results": []}]}	text-generation	aypan17/gpt2-med-imdb	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# gpt2-med-imdb This model is a fine-tuned version of gpt2-medium on an unknown 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: 5e-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 ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2 - Datasets 1.18.3 - Tokenizers 0.11.0	[ "# gpt2-med-imdb\n\nThis model is a fine-tuned version of gpt2-medium on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0", "### Training results", "### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.2\n- Datasets 1.18.3\n- Tokenizers 0.11.0" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# gpt2-med-imdb\n\nThis model is a fine-tuned version of gpt2-medium on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0", "### Training results", "### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.2\n- Datasets 1.18.3\n- Tokenizers 0.11.0" ]	[ 54, 31, 6, 12, 8, 3, 90, 4, 35 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# gpt2-med-imdb\n\nThis model is a fine-tuned version of gpt2-medium on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0### Training results### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.2\n- Datasets 1.18.3\n- Tokenizers 0.11.0" ]	[ -0.11191119998693466, 0.10264981538057327, -0.002660356694832444, 0.07640868425369263, 0.172215074300766, 0.03170574828982353, 0.10284847766160965, 0.13874346017837524, -0.13081935048103333, 0.05207156389951706, 0.10994459688663483, 0.08905108273029327, 0.05286567285656929, 0.1266162097454071, -0.020493952557444572, -0.27640533447265625, 0.01229099277406931, 0.028435014188289642, -0.0575854666531086, 0.10903692990541458, 0.11520075798034668, -0.09608583897352219, 0.07221675664186478, 0.02928522787988186, -0.19467467069625854, 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null	null	transformers	TrainingArgs: lr=2e-5, train-batch-size=16, eval-batch-size=16, num-train-epochs=5, weight-decay=0.01,	{"license": "mit"}	text-classification	aypan17/roberta-base-imdb	[ "transformers", "pytorch", "roberta", "text-classification", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #roberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us	TrainingArgs: lr=2e-5, train-batch-size=16, eval-batch-size=16, num-train-epochs=5, weight-decay=0.01,	[]	[ "TAGS\n#transformers #pytorch #roberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n" ]	[ 42 ]	[ "passage: TAGS\n#transformers #pytorch #roberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n" ]	[ -0.015270475298166275, 0.07784062623977661, -0.006913014221936464, 0.03031000867486, 0.17507308721542358, 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null	null	transformers	# RudeRick discord bot	{"tags": ["conversational"]}	text-generation	ayush19/rick-sanchez	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# RudeRick discord bot	[ "# RudeRick discord bot" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# RudeRick discord bot" ]	[ 51, 7 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# RudeRick discord bot" ]	[ -0.004051297437399626, -0.019657569006085396, -0.007262144237756729, 0.033118169754743576, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mbert-finetuned-azerbaijani-ner This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.1385 - Precision: 0.8899 - Recall: 0.9154 - F1: 0.9025 - Accuracy: 0.9669 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.2928 \| 1.0 \| 625 \| 0.1415 \| 0.8584 \| 0.8918 \| 0.8748 \| 0.9595 \| \| 0.1254 \| 2.0 \| 1250 \| 0.1335 \| 0.8875 \| 0.9119 \| 0.8996 \| 0.9637 \| \| 0.077 \| 3.0 \| 1875 \| 0.1385 \| 0.8899 \| 0.9154 \| 0.9025 \| 0.9669 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wikiann"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "mbert-finetuned-azerbaijani-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann", "type": "wikiann", "args": "az"}, "metrics": [{"type": "precision", "value": 0.8898541731306236, "name": "Precision"}, {"type": "recall", "value": 0.915416533673795, "name": "Recall"}, {"type": "f1", "value": 0.9024543738200126, "name": "F1"}, {"type": "accuracy", "value": 0.966948310139165, "name": "Accuracy"}]}]}]}	token-classification	azizbarank/mbert-finetuned-azerbaijani-ner	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:wikiann", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	mbert-finetuned-azerbaijani-ner =============================== This model is a fine-tuned version of bert-base-multilingual-cased on the wikiann dataset. It achieves the following results on the evaluation set: * Loss: 0.1385 * Precision: 0.8899 * Recall: 0.9154 * F1: 0.9025 * Accuracy: 0.9669 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.6	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.6" ]	[ "TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.6" ]	[ 66, 98, 4, 35 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.6" ]	[ -0.10958761721849442, 0.0958002582192421, -0.0018517913995310664, 0.12605558335781097, 0.15620048344135284, 0.03152143210172653, 0.11130799353122711, 0.12501031160354614, -0.08756159245967865, 0.018895640969276428, 0.1338922679424286, 0.1655699610710144, 0.014896656386554241, 0.10021331906318665, -0.043103691190481186, -0.2634996473789215, 0.0001310136285610497, 0.046963080763816833, -0.06226341798901558, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-gn-demo 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: 0.7426 - Wer: 0.7256 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 50 - num_epochs: 60 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 4.0 \| 100 \| 0.7045 \| 0.7409 \| \| No log \| 8.0 \| 200 \| 0.7200 \| 0.75 \| \| No log \| 12.0 \| 300 \| 0.7400 \| 0.7439 \| \| No log \| 16.0 \| 400 \| 0.7677 \| 0.7515 \| \| 0.0846 \| 20.0 \| 500 \| 0.7765 \| 0.7271 \| \| 0.0846 \| 24.0 \| 600 \| 0.7821 \| 0.7287 \| \| 0.0846 \| 28.0 \| 700 \| 0.7671 \| 0.7180 \| \| 0.0846 \| 32.0 \| 800 \| 0.7594 \| 0.7180 \| \| 0.0846 \| 36.0 \| 900 \| 0.7500 \| 0.7165 \| \| 0.0713 \| 40.0 \| 1000 \| 0.7351 \| 0.7287 \| \| 0.0713 \| 44.0 \| 1100 \| 0.7361 \| 0.7241 \| \| 0.0713 \| 48.0 \| 1200 \| 0.7389 \| 0.7378 \| \| 0.0713 \| 52.0 \| 1300 \| 0.7424 \| 0.7210 \| \| 0.0713 \| 56.0 \| 1400 \| 0.7425 \| 0.7256 \| \| 0.0669 \| 60.0 \| 1500 \| 0.7426 \| 0.7256 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.10.3	{"language": ["gn"], "license": "apache-2.0", "tags": ["generated_from_trainer", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["common_voice", "mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-base-gn-demo", "results": []}]}	automatic-speech-recognition	azuur/wav2vec2-base-gn-demo	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "hf-asr-leaderboard", "gn", "dataset:common_voice", "dataset:mozilla-foundation/common_voice_8_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "gn" ]	TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #mozilla-foundation/common_voice_8_0 #robust-speech-event #hf-asr-leaderboard #gn #dataset-common_voice #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us	wav2vec2-base-gn-demo ===================== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset. It achieves the following results on the evaluation set: * Loss: 0.7426 * Wer: 0.7256 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine\_with\_restarts * lr\_scheduler\_warmup\_steps: 50 * num\_epochs: 60 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.11.3 * Pytorch 1.10.2+cu102 * Datasets 1.18.3 * Tokenizers 0.10.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 60\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #mozilla-foundation/common_voice_8_0 #robust-speech-event #hf-asr-leaderboard #gn #dataset-common_voice #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 60\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.10.3" ]	[ 121, 140, 4, 35 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #mozilla-foundation/common_voice_8_0 #robust-speech-event #hf-asr-leaderboard #gn #dataset-common_voice #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 60\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.10.3" ]	[ -0.10688646882772446, 0.09505119919776917, -0.00515039311721921, 0.0431925430893898, 0.11434032022953033, 0.018510842695832253, 0.1408039927482605, 0.1513698697090149, -0.0821479931473732, 0.08104146271944046, 0.04119870066642761, 0.0706731453537941, 0.08011022955179214, 0.09863688796758652, -0.026617703959345818, -0.2775384783744812, 0.02913520485162735, -0.0200668778270483, -0.11188948154449463, 0.10311473160982132, 0.08301793783903122, -0.09621796011924744, 0.03699545934796333, 0.016232989728450775, -0.10856426507234573, 0.012577686458826065, -0.021295646205544472, -0.047551099210977554, 0.11430887877941132, 0.03668620064854622, 0.057457104325294495, 0.03963867202401161, 0.10990285873413086, -0.25250566005706787, 0.014764181338250637, 0.06509692221879959, 0.026960985735058784, 0.05432897061109543, 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null	null	transformers	#Ragnar Lothbrok DialoGPT Model	{"tags": ["conversational"]}	text-generation	b0shakk/DialoGPT-small-Ragnar	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	#Ragnar Lothbrok DialoGPT Model	[]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 51 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.009697278961539268, 0.03208012506365776, -0.007204889785498381, 0.004809224978089333, 0.16726240515708923, 0.014898733235895634, 0.09765533357858658, 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null	null	transformers	# shirt_identifier Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### Big Check shirt ![Big Check shirt](images/Big_Check_shirt.jpg) #### Formal Shirt ![Formal Shirt](images/Formal_Shirt.jpg) #### casual shirt ![casual shirt](images/casual_shirt.jpg) #### denim shirt ![denim shirt](images/denim_shirt.jpg)	{"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]}	image-classification	b25mayank3/shirt_identifier	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us	# shirt_identifier Autogenerated by HuggingPics️ Create your own image classifier for anything by running the demo on Google Colab. Report any issues with the demo at the github repo. ## Example Images #### Big Check shirt !Big Check shirt #### Formal Shirt !Formal Shirt #### casual shirt !casual shirt #### denim shirt !denim shirt	[ "# shirt_identifier\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.", "## Example Images", "#### Big Check shirt\n\n!Big Check shirt", "#### Formal Shirt\n\n!Formal Shirt", "#### casual shirt\n\n!casual shirt", "#### denim shirt\n\n!denim shirt" ]	[ "TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "# shirt_identifier\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.", "## Example Images", "#### Big Check shirt\n\n!Big Check shirt", "#### Formal Shirt\n\n!Formal Shirt", "#### casual shirt\n\n!casual shirt", "#### denim shirt\n\n!denim shirt" ]	[ 49, 44, 4, 11, 11, 10, 11 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n# shirt_identifier\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.## Example Images#### Big Check shirt\n\n!Big Check shirt#### Formal Shirt\n\n!Formal Shirt#### casual shirt\n\n!casual shirt#### denim shirt\n\n!denim shirt" ]	[ -0.03138252720236778, 0.05613218992948532, -0.0012805734295397997, 0.0988122746348381, 0.11850672960281372, 0.03656696528196335, 0.11040925979614258, 0.1275201439857483, 0.08940932899713516, -0.04565230756998062, 0.14869830012321472, 0.16167841851711273, 0.05534561350941658, 0.20792777836322784, 0.03007795661687851, 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null	null	transformers	# GPT-Neo 125M finetuned with beer recipes ## Model Description GPT-Neo 125M is a transformer model based on EleutherAI's replication of the GPT-3 architecture https://huggingface.co/EleutherAI/gpt-neo-125M. It generates recipes for brewing beer in a YAML-like format which can be easily used for different purposes. ## Training data This model was trained on a custom dataset of ~ 76,800 beer recipes from the internet. It includes recipes for the following styles of beer: * Strong American Ale * Pale American Ale * India Pale Ale (IPA) * Standard American Beer * Stout * English Pale Ale * IPA * American Porter and Stout * Sour Ale * Irish Beer * Strong British Ale * Belgian and French Ale * German Wheat and Rye Beer * Czech Lager * Spice/Herb/Vegetable Beer * Specialty Beer * American Ale * Pilsner * Belgian Ale * Strong Belgian Ale * Bock * Brown British Beer * German Wheat Beer * Fruit Beer * Amber Malty European Lager * Pale Malty European Lager * British Bitter * Amber and Brown American Beer * Light Hybrid Beer * Pale Commonwealth Beer * American Wild Ale * European Amber Lager * Belgian Strong Ale * International Lager * Amber Bitter European Lager * Light Lager * Scottish and Irish Ale * European Sour Ale * Trappist Ale * Strong European Beer * Porter * Historical Beer * Pale Bitter European Beer * Amber Hybrid Beer * Smoke Flavored/Wood-Aged Beer * Spiced Beer * Dark European Lager * Alternative Fermentables Beer * Mead * Strong Ale * Dark British Beer * Scottish Ale * Smoked Beer * English Brown Ale * Dark Lager * Cider or Perry * Wood Beer ### How to use You can use this model directly with a pipeline for text generation. This example generates a different recipe each time it's run: ```py >>> from transformers import pipeline >>> generator = pipeline('text-generation', model='b3ck1/gpt-neo-125M-finetuned-beer-recipes') >>> generator("style: Pilsner\nbatch_size: 20\nefficiency: 75\nboil_size:", do_sample=True, min_length=50, max_length=500) >>> print(output[0]['generated_text']) style: Pilsner batch_size: 20 efficiency: 70 boil_size: 24 boil_time: 60 fermentables: - name: Pale Ale type: Grain amount: 6.5 hops: - name: Saaz alpha: 3.5 use: Boil time: 60 amount: 0.06 - name: Saaz alpha: 3.5 use: Boil time: 30 amount: 0.06 - name: Saaz alpha: 3.5 use: Boil time: 10 amount: 0.06 - name: Saaz alpha: 3.5 use: Boil time: 0 amount: 0.06 yeasts: - name: Safale - American Ale Yeast US-05 amount: 0.11 min_temperature: 12 max_temperature: 25 primary_temp: null mash_steps: - step_temp: 65 step_time: 60 miscs: [] ``` ### See this model in action This model was used to build https://beerai.net.	{"language": ["en"], "license": "apache-2.0", "tags": ["text generation", "pytorch", "causal-lm"], "datasets": ["custom"], "widget": [{"text": "style: Pilsner\nbatch_size: 20\nefficiency: 75\nboil_size:", "example_title": "Pilsener"}, {"text": "style: IPA\nbatch_size: 20\nefficiency: 75\nboil_size:", "example_title": "IPA"}, {"text": "style: Scottish Ale\nbatch_size: 20\nefficiency: 75\nboil_size:", "example_title": "Scottish Ale"}], "inference": {"parameters": {"do_sample": true, "top_k": 10, "top_p": 0.99, "max_length": 500}}}	text-generation	b3ck1/gpt-neo-125M-finetuned-beer-recipes	[ "transformers", "pytorch", "gpt_neo", "text-generation", "text generation", "causal-lm", "en", "dataset:custom", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt_neo #text-generation #text generation #causal-lm #en #dataset-custom #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# GPT-Neo 125M finetuned with beer recipes ## Model Description GPT-Neo 125M is a transformer model based on EleutherAI's replication of the GPT-3 architecture URL It generates recipes for brewing beer in a YAML-like format which can be easily used for different purposes. ## Training data This model was trained on a custom dataset of ~ 76,800 beer recipes from the internet. It includes recipes for the following styles of beer: * Strong American Ale * Pale American Ale * India Pale Ale (IPA) * Standard American Beer * Stout * English Pale Ale * IPA * American Porter and Stout * Sour Ale * Irish Beer * Strong British Ale * Belgian and French Ale * German Wheat and Rye Beer * Czech Lager * Spice/Herb/Vegetable Beer * Specialty Beer * American Ale * Pilsner * Belgian Ale * Strong Belgian Ale * Bock * Brown British Beer * German Wheat Beer * Fruit Beer * Amber Malty European Lager * Pale Malty European Lager * British Bitter * Amber and Brown American Beer * Light Hybrid Beer * Pale Commonwealth Beer * American Wild Ale * European Amber Lager * Belgian Strong Ale * International Lager * Amber Bitter European Lager * Light Lager * Scottish and Irish Ale * European Sour Ale * Trappist Ale * Strong European Beer * Porter * Historical Beer * Pale Bitter European Beer * Amber Hybrid Beer * Smoke Flavored/Wood-Aged Beer * Spiced Beer * Dark European Lager * Alternative Fermentables Beer * Mead * Strong Ale * Dark British Beer * Scottish Ale * Smoked Beer * English Brown Ale * Dark Lager * Cider or Perry * Wood Beer ### How to use You can use this model directly with a pipeline for text generation. This example generates a different recipe each time it's run: ### See this model in action This model was used to build URL.	[ "# GPT-Neo 125M finetuned with beer recipes", "## Model Description\n\nGPT-Neo 125M is a transformer model based on EleutherAI's replication of the GPT-3 architecture URL\nIt generates recipes for brewing beer in a YAML-like format which can be easily used for different purposes.", "## Training data\n\nThis model was trained on a custom dataset of ~ 76,800 beer recipes from the internet. It includes recipes for the following \nstyles of beer:\n\n* Strong American Ale \n* Pale American Ale\n* India Pale Ale (IPA)\n* Standard American Beer\n* Stout\n* English Pale Ale\n* IPA\n* American Porter and Stout\n* Sour Ale\n* Irish Beer\n* Strong British Ale\n* Belgian and French Ale\n* German Wheat and Rye Beer\n* Czech Lager\n* Spice/Herb/Vegetable Beer\n* Specialty Beer\n* American Ale\n* Pilsner\n* Belgian Ale\n* Strong Belgian Ale\n* Bock\n* Brown British Beer\n* German Wheat Beer\n* Fruit Beer\n* Amber Malty European Lager\n* Pale Malty European Lager\n* British Bitter\n* Amber and Brown American Beer\n* Light Hybrid Beer\n* Pale Commonwealth Beer\n* American Wild Ale\n* European Amber Lager\n* Belgian Strong Ale\n* International Lager\n* Amber Bitter European Lager\n* Light Lager\n* Scottish and Irish Ale\n* European Sour Ale\n* Trappist Ale\n* Strong European Beer\n* Porter\n* Historical Beer\n* Pale Bitter European Beer\n* Amber Hybrid Beer\n* Smoke Flavored/Wood-Aged Beer\n* Spiced Beer\n* Dark European Lager\n* Alternative Fermentables Beer\n* Mead\n* Strong Ale\n* Dark British Beer\n* Scottish Ale\n* Smoked Beer\n* English Brown Ale\n* Dark Lager\n* Cider or Perry\n* Wood Beer", "### How to use\n\nYou can use this model directly with a pipeline for text generation. This example generates a different recipe each time it's run:", "### See this model in action\n\nThis model was used to build URL." ]	[ "TAGS\n#transformers #pytorch #gpt_neo #text-generation #text generation #causal-lm #en #dataset-custom #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# GPT-Neo 125M finetuned with beer recipes", "## Model Description\n\nGPT-Neo 125M is a transformer model based on EleutherAI's replication of the GPT-3 architecture URL\nIt generates recipes for brewing beer in a YAML-like format which can be easily used for different purposes.", "## Training data\n\nThis model was trained on a custom dataset of ~ 76,800 beer recipes from the internet. It includes recipes for the following \nstyles of beer:\n\n* Strong American Ale \n* Pale American Ale\n* India Pale Ale (IPA)\n* Standard American Beer\n* Stout\n* English Pale Ale\n* IPA\n* American Porter and Stout\n* Sour Ale\n* Irish Beer\n* Strong British Ale\n* Belgian and French Ale\n* German Wheat and Rye Beer\n* Czech Lager\n* Spice/Herb/Vegetable Beer\n* Specialty Beer\n* American Ale\n* Pilsner\n* Belgian Ale\n* Strong Belgian Ale\n* Bock\n* Brown British Beer\n* German Wheat Beer\n* Fruit Beer\n* Amber Malty European Lager\n* Pale Malty European Lager\n* British Bitter\n* Amber and Brown American Beer\n* Light Hybrid Beer\n* Pale Commonwealth Beer\n* American Wild Ale\n* European Amber Lager\n* Belgian Strong Ale\n* International Lager\n* Amber Bitter European Lager\n* Light Lager\n* Scottish and Irish Ale\n* European Sour Ale\n* Trappist Ale\n* Strong European Beer\n* Porter\n* Historical Beer\n* Pale Bitter European Beer\n* Amber Hybrid Beer\n* Smoke Flavored/Wood-Aged Beer\n* Spiced Beer\n* Dark European Lager\n* Alternative Fermentables Beer\n* Mead\n* Strong Ale\n* Dark British Beer\n* Scottish Ale\n* Smoked Beer\n* English Brown Ale\n* Dark Lager\n* Cider or Perry\n* Wood Beer", "### How to use\n\nYou can use this model directly with a pipeline for text generation. This example generates a different recipe each time it's run:", "### See this model in action\n\nThis model was used to build URL." ]	[ 64, 15, 59, 307, 33, 15 ]	[ "passage: TAGS\n#transformers #pytorch #gpt_neo #text-generation #text generation #causal-lm #en #dataset-custom #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# GPT-Neo 125M finetuned with beer recipes## Model Description\n\nGPT-Neo 125M is a transformer model based on EleutherAI's replication of the GPT-3 architecture URL\nIt generates recipes for brewing beer in a YAML-like format which can be easily used for different purposes.## Training data\n\nThis model was trained on a custom dataset of ~ 76,800 beer recipes from the internet. It includes recipes for the following \nstyles of beer:\n\n* Strong American Ale \n* Pale American Ale\n* India Pale Ale (IPA)\n* Standard American Beer\n* Stout\n* English Pale Ale\n* IPA\n* American Porter and Stout\n* Sour Ale\n* Irish Beer\n* Strong British Ale\n* Belgian and French Ale\n* German Wheat and Rye Beer\n* Czech Lager\n* Spice/Herb/Vegetable Beer\n* Specialty Beer\n* American Ale\n* Pilsner\n* Belgian Ale\n* Strong Belgian Ale\n* Bock\n* Brown British Beer\n* German Wheat Beer\n* Fruit Beer\n* Amber Malty European Lager\n* Pale Malty European Lager\n* British Bitter\n* Amber and Brown American Beer\n* Light Hybrid Beer\n* Pale Commonwealth Beer\n* American Wild Ale\n* European Amber Lager\n* Belgian Strong Ale\n* International Lager\n* Amber Bitter European Lager\n* Light Lager\n* Scottish and Irish Ale\n* European Sour Ale\n* Trappist Ale\n* Strong European Beer\n* Porter\n* Historical Beer\n* Pale Bitter European Beer\n* Amber Hybrid Beer\n* Smoke Flavored/Wood-Aged Beer\n* Spiced Beer\n* Dark European Lager\n* Alternative Fermentables Beer\n* Mead\n* Strong Ale\n* Dark British Beer\n* Scottish Ale\n* Smoked Beer\n* English Brown Ale\n* Dark Lager\n* Cider or Perry\n* Wood Beer### How to use\n\nYou can use this model directly with a pipeline for text generation. This example generates a different recipe each time it's run:### See this model in action\n\nThis model was used to build URL." ]	[ -0.05138006806373596, 0.09788857400417328, -0.0036381310783326626, 0.035594966262578964, 0.042012784630060196, 0.05534058436751366, 0.09942968934774399, 0.04458291083574295, 0.13875900208950043, 0.11461103707551956, -0.029648706316947937, -0.016917986795306206, 0.0849643275141716, 0.12265969812870026, 0.10826718807220459, -0.1674015372991562, 0.036027371883392334, -0.10553622990846634, 0.02785572223365307, 0.06676670163869858, 0.08552197366952896, -0.043842945247888565, 0.04774772748351097, -0.06090201064944267, -0.0243307463824749, 0.049756694585084915, -0.06490012258291245, -0.024885213002562523, 0.005882767029106617, -0.0002262345515191555, 0.06463160365819931, -0.0376150943338871, -0.008549576625227928, -0.2509196996688843, 0.019711634144186974, 0.011664032004773617, -0.011394962668418884, -0.0006757593364454806, 0.0736897811293602, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the COMMON_VOICE - AB dataset. It achieves the following results on the evaluation set: - Loss: 133.5167 - Wer: 18.9286 ## 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: 2.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0	{"language": ["ab"], "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]}	automatic-speech-recognition	baaastien/xls-r-ab-test	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "common_voice", "generated_from_trainer", "ab", "dataset:common_voice", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "ab" ]	TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us	# This model is a fine-tuned version of hf-test/xls-r-dummy on the COMMON_VOICE - AB dataset. It achieves the following results on the evaluation set: - Loss: 133.5167 - Wer: 18.9286 ## 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: 2.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0	[ "# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the COMMON_VOICE - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 133.5167\n- Wer: 18.9286", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 2.0\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.17.1.dev0\n- Tokenizers 0.11.0" ]	[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us \n", "# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the COMMON_VOICE - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 133.5167\n- Wer: 18.9286", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 2.0\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.17.1.dev0\n- Tokenizers 0.11.0" ]	[ 61, 59, 6, 12, 8, 3, 140, 4, 41 ]	[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us \n# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the COMMON_VOICE - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 133.5167\n- Wer: 18.9286## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 2.0\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.17.1.dev0\n- Tokenizers 0.11.0" ]	[ -0.0865170955657959, 0.1464160829782486, -0.0035482191015034914, 0.020073749125003815, 0.1262628734111786, 0.03187305107712746, 0.06765814870595932, 0.14301787316799164, -0.06566157191991806, 0.10688281804323196, 0.05168229341506958, -0.006196088157594204, 0.08671777695417404, 0.06779796630144119, 0.012509878724813461, -0.2680591344833374, 0.006310082972049713, -0.021381428465247154, -0.057771217077970505, 0.07868572324514389, 0.1128728985786438, -0.08253269642591476, 0.014997917227447033, 0.022388679906725883, -0.10981163382530212, 0.029735377058386803, -0.060549765825271606, -0.04700643941760063, 0.08475647121667862, 0.021587632596492767, 0.0461520180106163, 0.01983875036239624, 0.09625318646430969, -0.3003072440624237, 0.0011436209315434098, 0.0752488523721695, 0.054204463958740234, 0.07207559794187546, 0.05753417685627937, 0.012106821872293949, 0.15071320533752441, -0.15327666699886322, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-timit_asr-oogway This model is a fine-tuned version of [OthmaneJ/distil-wav2vec2](https://huggingface.co/OthmaneJ/distil-wav2vec2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-timit_asr-oogway", "results": []}]}	automatic-speech-recognition	baby-oogway/wav2vec2-timit_asr-oogway	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us	# wav2vec2-timit_asr-oogway This model is a fine-tuned version of OthmaneJ/distil-wav2vec2 on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3	[ "# wav2vec2-timit_asr-oogway\n\nThis model is a fine-tuned version of OthmaneJ/distil-wav2vec2 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 30\n- mixed_precision_training: Native AMP", "### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n", "# wav2vec2-timit_asr-oogway\n\nThis model is a fine-tuned version of OthmaneJ/distil-wav2vec2 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 30\n- mixed_precision_training: Native AMP", "### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3" ]	[ 56, 44, 6, 12, 8, 3, 117, 33 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n# wav2vec2-timit_asr-oogway\n\nThis model is a fine-tuned version of OthmaneJ/distil-wav2vec2 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 30\n- mixed_precision_training: Native AMP### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3" ]	[ -0.09546970576047897, 0.11961981654167175, -0.0022739695850759745, 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null	null	transformers	"hello"	{}	null	bada/test	[ "transformers", "pytorch", "jax", "bert", "pretraining", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #jax #bert #pretraining #endpoints_compatible #region-us	"hello"	[]	[ "TAGS\n#transformers #pytorch #jax #bert #pretraining #endpoints_compatible #region-us \n" ]	[ 29 ]	[ "passage: TAGS\n#transformers #pytorch #jax #bert #pretraining #endpoints_compatible #region-us \n" ]	[ -0.04758477956056595, 0.025376562029123306, -0.010041696019470692, -0.026890631765127182, 0.12169862538576126, 0.03283354640007019, 0.04852999001741409, 0.06603716313838959, 0.06981325894594193, -0.02207045815885067, 0.16288776695728302, 0.1839056760072708, -0.04884219914674759, -0.004254240542650223, -0.038344986736774445, -0.2528855800628662, 0.053164176642894745, 0.08647771924734116, -0.05832131579518318, 0.11299949139356613, 0.05624457448720932, -0.12078862637281418, 0.052798882126808167, 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null	null	transformers	# Genji-python 6B For example usage or to easily use the model you can check our colab notebook: [Notebook](https://colab.research.google.com/drive/1PnWpx02IEUkY8jhLKd_NewUGEXahAska?usp=sharing) ## Model Description Genji is a transformer model finetuned on EleutherAI's GPT-J 6B model. This particular model is trained on python only code approaching 4GB in size. Split model has the checkpoints splitted, which makes it use less system RAM while loading and makes it faster to load. This model needs more effort to set up as you need to install git-lfs and pull the repo. \| Hyperparameter \| Value \| \|-------------------\|--------\| \| n_parameters \| 6,053,381,344 \| \| n_layers \| 28* \| \| d_model \| 4,096 \| \| d_ff \| 16,384 \| \| n_heads \| 16 \| \| d_head \| 256 \| \| n_ctx \| 2,048 \| \| n_vocab \| 50,400 (same tokenizer as GPT-2/3) \| \| position encoding \| [Rotary position encodings (RoPE)](https://arxiv.org/abs/2104.09864) \| \| RoPE dimensions \| [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) \| `` each layer consists of one feedforward block and one self attention block The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model dimension is split into 16 heads, each with a dimension of 256. Rotary position encodings (RoPE) was applied to 64 dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as GPT-2/GPT-3. ## Training data GPT-J 6B was pretrained on the [Pile](pile.eleuther.ai), a large scale curated dataset created by EleutherAI for the purpose of training this model. After the pre-training, it's finetuned on the python code that was taken from the Pile. ## Training procedure Genji-python-6B is trained for 20k steps on around 655 million tokens with learning rate of 2e-06 ## Intended Use This model is trained for assistence on writing python code and having fun trying weird stuff with it. ### How to use This model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we will make this model easily loadable. For now, you need to use this fork: [Fork](https://github.com/finetuneanon/transformers) to install with pip: ```bash pip install git+https://github.com/finetuneanon/transformers@gpt-neo-localattention3-rp-b ``` git-lfs* also needs to be installed, on ubuntu: ```bash apt install git-lfs ``` after it's installed, initialize git-lfs: ```bash git lfs install ``` then clone this repo: ```bash git clone https://huggingface.co/NovelAI/genji-python-6B-split ``` Now we can load the model. We recommend the usage of the model as FP16. That way, it fits in 16GB VRAM cards. How to use: ```python from transformers import ( AutoTokenizer, AutoModelForCausalLM, GPTNeoForCausalLM, ) model = AutoModelForCausalLM.from_pretrained("genji-python-6B-split/model").half().eval().cuda() tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B") text = '''def print_customer_name''' tokens = tokenizer(text, return_tensors="pt").input_ids generated_tokens = model.generate(tokens.long().cuda(), use_cache=True, do_sample=True, top_k=50, temperature=0.3, top_p=0.9, repetition_penalty=1.125, min_length=1, max_length=len(tokens[0]) + 400, pad_token_id=tokenizer.eos_token_id) last_tokens = generated_tokens[0][len(tokens[0]):] generated_text = tokenizer.decode(last_tokens) print("Generation:\n" + generated_text) ``` When ran, this code generates: ```python Prompt: def print_customer_name Generation: (self, customer): """Print the name of a customer.""" if not self.is_valid(): return print("Customer: {}".format(customer)) ``` For example usage, you can see our colab notebook as well: [Notebook](https://colab.research.google.com/drive/1PnWpx02IEUkY8jhLKd_NewUGEXahAska?usp=sharing) ## Eval results TBD ## Acknowledgements This project was possible because of the compute provided by the [TPU Research Cloud](https://sites.research.google/trc/) and [EleutherAI](https://eleuther.ai/) for pretraining of the GPT-J 6B. Thanks to everyone who contributed to this project: - [Aero](https://github.com/AeroScripts) - [Finetune](https://github.com/finetuneanon) - [Kurumuz](https://github.com/kurumuz)	{"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "datasets": ["the Pile"]}	text-generation	baffo32/genji-python-6B-split	[ "transformers", "gpt_neo", "text-generation", "pytorch", "causal-lm", "en", "arxiv:2104.09864", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[ "2104.09864" ]	[ "en" ]	TAGS #transformers #gpt_neo #text-generation #pytorch #causal-lm #en #arxiv-2104.09864 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	Genji-python 6B =============== For example usage or to easily use the model you can check our colab notebook: Notebook Model Description ----------------- Genji is a transformer model finetuned on EleutherAI's GPT-J 6B model. This particular model is trained on python only code approaching 4GB in size. Split model has the checkpoints splitted, which makes it use less system RAM while loading and makes it faster to load. This model needs more effort to set up as you need to install git-lfs and pull the repo. '\' each layer consists of one feedforward block and one self attention block The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model dimension is split into 16 heads, each with a dimension of 256. Rotary position encodings (RoPE) was applied to 64 dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as GPT-2/GPT-3. Training data ------------- GPT-J 6B was pretrained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model. After the pre-training, it's finetuned on the python code that was taken from the Pile. Training procedure ------------------ Genji-python-6B is trained for 20k steps on around 655 million tokens with learning rate of 2e-06 Intended Use ------------ This model is trained for assistence on writing python code and having fun trying weird stuff with it. ### How to use This model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we will make this model easily loadable. For now, you need to use this fork: Fork to install with pip: git-lfs also needs to be installed, on ubuntu: after it's installed, initialize git-lfs: then clone this repo: Now we can load the model. We recommend the usage of the model as FP16. That way, it fits in 16GB VRAM cards. How to use: When ran, this code generates: For example usage, you can see our colab notebook as well: Notebook Eval results ------------ TBD Acknowledgements ---------------- This project was possible because of the compute provided by the TPU Research Cloud and EleutherAI for pretraining of the GPT-J 6B. Thanks to everyone who contributed to this project: Aero * Finetune * Kurumuz	[ "### How to use\n\n\nThis model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we will make this model easily loadable.\nFor now, you need to use this fork:\nFork\n\n\nto install with pip:\n\n\ngit-lfs also needs to be installed, on ubuntu:\n\n\nafter it's installed, initialize git-lfs:\n\n\nthen clone this repo:\n\n\nNow we can load the model.\n\n\nWe recommend the usage of the model as FP16. That way, it fits in 16GB VRAM cards.\n\n\nHow to use:\n\n\nWhen ran, this code generates:\n\n\nFor example usage, you can see our colab notebook as well:\nNotebook\n\n\nEval results\n------------\n\n\nTBD\n\n\nAcknowledgements\n----------------\n\n\nThis project was possible because of the compute provided by the\nTPU Research Cloud and EleutherAI for pretraining of the GPT-J 6B.\n\n\nThanks to everyone who contributed to this project:\n\n\n* Aero\n* Finetune\n* Kurumuz" ]	[ "TAGS\n#transformers #gpt_neo #text-generation #pytorch #causal-lm #en #arxiv-2104.09864 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### How to use\n\n\nThis model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we will make this model easily loadable.\nFor now, you need to use this fork:\nFork\n\n\nto install with pip:\n\n\ngit-lfs also needs to be installed, on ubuntu:\n\n\nafter it's installed, initialize git-lfs:\n\n\nthen clone this repo:\n\n\nNow we can load the model.\n\n\nWe recommend the usage of the model as FP16. That way, it fits in 16GB VRAM cards.\n\n\nHow to use:\n\n\nWhen ran, this code generates:\n\n\nFor example usage, you can see our colab notebook as well:\nNotebook\n\n\nEval results\n------------\n\n\nTBD\n\n\nAcknowledgements\n----------------\n\n\nThis project was possible because of the compute provided by the\nTPU Research Cloud and EleutherAI for pretraining of the GPT-J 6B.\n\n\nThanks to everyone who contributed to this project:\n\n\n* Aero\n* Finetune\n* Kurumuz" ]	[ 63, 225 ]	[ "passage: TAGS\n#transformers #gpt_neo #text-generation #pytorch #causal-lm #en #arxiv-2104.09864 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### How to use\n\n\nThis model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we will make this model easily loadable.\nFor now, you need to use this fork:\nFork\n\n\nto install with pip:\n\n\ngit-lfs also needs to be installed, on ubuntu:\n\n\nafter it's installed, initialize git-lfs:\n\n\nthen clone this repo:\n\n\nNow we can load the model.\n\n\nWe recommend the usage of the model as FP16. That way, it fits in 16GB VRAM cards.\n\n\nHow to use:\n\n\nWhen ran, this code generates:\n\n\nFor example usage, you can see our colab notebook as well:\nNotebook\n\n\nEval results\n------------\n\n\nTBD\n\n\nAcknowledgements\n----------------\n\n\nThis project was possible because of the compute provided by the\nTPU Research Cloud and EleutherAI for pretraining of the GPT-J 6B.\n\n\nThanks to everyone who contributed to this project:\n\n\n* Aero\n* Finetune\n* Kurumuz" ]	[ -0.058129772543907166, 0.004712838679552078, -0.0018231793073937297, 0.04361617565155029, 0.14815929532051086, 0.008804505690932274, 0.039787374436855316, 0.12351933866739273, -0.025011049583554268, 0.04911085218191147, 0.023150214925408363, 0.14145207405090332, 0.07067826390266418, 0.10403070598840714, 0.10522454231977463, -0.11920871585607529, -0.014792272821068764, -0.01801154389977455, -0.03711875155568123, 0.056727297604084015, 0.06602800637483597, -0.07601170986890793, 0.09321314096450806, 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null	null	transformers	# GPT-J 6B ## Model Description GPT-J 6B is a transformer model trained using Ben Wang's [Mesh Transformer JAX](https://github.com/kingoflolz/mesh-transformer-jax/). "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters. <figure> \| Hyperparameter \| Value \| \|----------------------\|------------\| \| \\(n_{parameters}\\) \| 6053381344 \| \| \\(n_{layers}\\) \| 28&ast; \| \| \\(d_{model}\\) \| 4096 \| \| \\(d_{ff}\\) \| 16384 \| \| \\(n_{heads}\\) \| 16 \| \| \\(d_{head}\\) \| 256 \| \| \\(n_{ctx}\\) \| 2048 \| \| \\(n_{vocab}\\) \| 50257/50400&dagger; (same tokenizer as GPT-2/3) \| \| Positional Encoding \| [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) \| \| RoPE Dimensions \| [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) \| <figcaption><p><strong>&ast;</strong> Each layer consists of one feedforward block and one self attention block.</p> <p><strong>&dagger;</strong> Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer.</p></figcaption></figure> The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model dimension is split into 16 heads, each with a dimension of 256. Rotary Position Embedding (RoPE) is applied to 64 dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as GPT-2/GPT-3. ## Training data GPT-J 6B was trained on [the Pile](https://pile.eleuther.ai), a large-scale curated dataset created by [EleutherAI](https://www.eleuther.ai). ## Training procedure This model was trained for 402 billion tokens over 383,500 steps on TPU v3-256 pod. It was trained as an autoregressive language model, using cross-entropy loss to maximize the likelihood of predicting the next token correctly. ## Intended Use and Limitations GPT-J learns an inner representation of the English language that can be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating text from a prompt. ### How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B") model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B") ``` ### Limitations and Biases The core functionality of GPT-J is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting GPT-J it is important to remember that the statistically most likely next token is often not the token that produces the most "accurate" text. Never depend upon GPT-J to produce factually accurate output. GPT-J was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending upon use case GPT-J may produce socially unacceptable text. See [Sections 5 and 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a more detailed analysis of the biases in the Pile. As with all language models, it is hard to predict in advance how GPT-J will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results. ## Evaluation results <figure> \| Model \| Public \| Training FLOPs \| LAMBADA PPL ↓ \| LAMBADA Acc ↑ \| Winogrande ↑ \| Hellaswag ↑ \| PIQA ↑ \| Dataset Size (GB) \| \|--------------------------\|-------------\|----------------\|--- \|--- \|--- \|--- \|--- \|-------------------\| \| Random Chance \| &check; \| 0 \| ~a lot \| ~0% \| 50% \| 25% \| 25% \| 0 \| \| GPT-3 Ada&ddagger; \| &cross; \| ----- \| 9.95 \| 51.6% \| 52.9% \| 43.4% \| 70.5% \| ----- \| \| GPT-2 1.5B \| &check; \| ----- \| 10.63 \| 51.21% \| 59.4% \| 50.9% \| 70.8% \| 40 \| \| GPT-Neo 1.3B&ddagger; \| &check; \| 3.0e21 \| 7.50 \| 57.2% \| 55.0% \| 48.9% \| 71.1% \| 825 \| \| Megatron-2.5B&ast; \| &cross; \| 2.4e21 \| ----- \| 61.7% \| ----- \| ----- \| ----- \| 174 \| \| GPT-Neo 2.7B&ddagger; \| &check; \| 6.8e21 \| 5.63 \| 62.2% \| 56.5% \| 55.8% \| 73.0% \| 825 \| \| GPT-3 1.3B&ast;&ddagger; \| &cross; \| 2.4e21 \| 5.44 \| 63.6% \| 58.7% \| 54.7% \| 75.1% \| ~800 \| \| GPT-3 Babbage&ddagger; \| &cross; \| ----- \| 5.58 \| 62.4% \| 59.0% \| 54.5% \| 75.5% \| ----- \| \| Megatron-8.3B&ast; \| &cross; \| 7.8e21 \| ----- \| 66.5% \| ----- \| ----- \| ----- \| 174 \| \| GPT-3 2.7B&ast;&ddagger; \| &cross; \| 4.8e21 \| 4.60 \| 67.1% \| 62.3% \| 62.8% \| 75.6% \| ~800 \| \| Megatron-11B&dagger; \| &check; \| 1.0e22 \| ----- \| ----- \| ----- \| ----- \| ----- \| 161 \| \| GPT-J 6B&ddagger; \| &check; \| 1.5e22 \| 3.99 \| 69.7% \| 65.3% \| 66.1% \| 76.5% \| 825 \| \| GPT-3 6.7B&ast;&ddagger; \| &cross; \| 1.2e22 \| 4.00 \| 70.3% \| 64.5% \| 67.4% \| 78.0% \| ~800 \| \| GPT-3 Curie&ddagger; \| &cross; \| ----- \| 4.00 \| 69.3% \| 65.6% \| 68.5% \| 77.9% \| ----- \| \| GPT-3 13B&ast;&ddagger; \| &cross; \| 2.3e22 \| 3.56 \| 72.5% \| 67.9% \| 70.9% \| 78.5% \| ~800 \| \| GPT-3 175B&ast;&ddagger; \| &cross; \| 3.1e23 \| 3.00 \| 76.2% \| 70.2% \| 78.9% \| 81.0% \| ~800 \| \| GPT-3 Davinci&ddagger; \| &cross; \| ----- \| 3.0 \| 75% \| 72% \| 78% \| 80% \| ----- \| <figcaption><p>Models roughly sorted by performance, or by FLOPs if not available.</p> <p><strong>&ast;</strong> Evaluation numbers reported by their respective authors. All other numbers are provided by running <a href="https://github.com/EleutherAI/lm-evaluation-harness/"><code>lm-evaluation-harness</code></a> either with released weights or with API access. Due to subtle implementation differences as well as different zero shot task framing, these might not be directly comparable. See <a href="https://blog.eleuther.ai/gpt3-model-sizes/">this blog post</a> for more details.</p> <p><strong>†</strong> Megatron-11B provides no comparable metrics, and several implementations using the released weights do not reproduce the generation quality and evaluations. (see <a href="https://github.com/huggingface/transformers/pull/10301">1</a> <a href="https://github.com/pytorch/fairseq/issues/2358">2</a> <a href="https://github.com/pytorch/fairseq/issues/2719">3</a>) Thus, evaluation was not attempted.</p> <p><strong>‡</strong> These models have been trained with data which contains possible test set contamination. The OpenAI GPT-3 models failed to deduplicate training data for certain test sets, while the GPT-Neo models as well as this one is trained on the Pile, which has not been deduplicated against any test sets.</p></figcaption></figure> ## Citation and Related Information ### BibTeX entry To cite this model: ```bibtex @misc{gpt-j, author = {Wang, Ben and Komatsuzaki, Aran}, title = {{GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model}}, howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}}, year = 2021, month = May } ``` To cite the codebase that trained this model: ```bibtex @misc{mesh-transformer-jax, author = {Wang, Ben}, title = {{Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX}}, howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}}, year = 2021, month = May } ``` If you use this model, we would love to hear about it! Reach out on [GitHub](https://github.com/kingoflolz/mesh-transformer-jax), Discord, or shoot Ben an email. ## Acknowledgements This project would not have been possible without compute generously provided by Google through the [TPU Research Cloud](https://sites.research.google/trc/), as well as the Cloud TPU team for providing early access to the [Cloud TPU VM](https://cloud.google.com/blog/products/compute/introducing-cloud-tpu-vms) Alpha. Thanks to everyone who have helped out one way or another (listed alphabetically): - [James Bradbury](https://twitter.com/jekbradbury) for valuable assistance with debugging JAX issues. - [Stella Biderman](https://www.stellabiderman.com), [Eric Hallahan](https://twitter.com/erichallahan), [Kurumuz](https://github.com/kurumuz/), and [Finetune](https://github.com/finetuneanon/) for converting the model to be compatible with the `transformers` package. - [Leo Gao](https://twitter.com/nabla_theta) for running zero shot evaluations for the baseline models for the table. - [Laurence Golding](https://github.com/researcher2/) for adding some features to the web demo. - [Aran Komatsuzaki](https://twitter.com/arankomatsuzaki) for advice with experiment design and writing the blog posts. - [Janko Prester](https://github.com/jprester/) for creating the web demo frontend.	{"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "datasets": ["The Pile"]}	text-generation	baffo32/gpt-j-6B-ptmap	[ "transformers", "pytorch", "gptj", "text-generation", "causal-lm", "en", "arxiv:2104.09864", "arxiv:2101.00027", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[ "2104.09864", "2101.00027" ]	[ "en" ]	TAGS #transformers #pytorch #gptj #text-generation #causal-lm #en #arxiv-2104.09864 #arxiv-2101.00027 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	GPT-J 6B ======== Model Description ----------------- GPT-J 6B is a transformer model trained using Ben Wang's Mesh Transformer JAX. "GPT-J" refers to the class of model, while "6B" represents the number of trainable parameters. \* Each layer consists of one feedforward block and one self attention block. † Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer. The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model dimension is split into 16 heads, each with a dimension of 256. Rotary Position Embedding (RoPE) is applied to 64 dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as GPT-2/GPT-3. Training data ------------- GPT-J 6B was trained on the Pile, a large-scale curated dataset created by EleutherAI. Training procedure ------------------ This model was trained for 402 billion tokens over 383,500 steps on TPU v3-256 pod. It was trained as an autoregressive language model, using cross-entropy loss to maximize the likelihood of predicting the next token correctly. Intended Use and Limitations ---------------------------- GPT-J learns an inner representation of the English language that can be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating text from a prompt. ### How to use This model can be easily loaded using the 'AutoModelForCausalLM' functionality: ### Limitations and Biases The core functionality of GPT-J is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting GPT-J it is important to remember that the statistically most likely next token is often not the token that produces the most "accurate" text. Never depend upon GPT-J to produce factually accurate output. GPT-J was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending upon use case GPT-J may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile. As with all language models, it is hard to predict in advance how GPT-J will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results. Evaluation results ------------------ Models roughly sorted by performance, or by FLOPs if not available. \* Evaluation numbers reported by their respective authors. All other numbers are provided by running [for more details.](URL either with released weights or with API access. Due to subtle implementation differences as well as different zero shot task framing, these might not be directly comparable. See <a href=) † Megatron-11B provides no comparable metrics, and several implementations using the released weights do not reproduce the generation quality and evaluations. (see <a href="URL <a href="URL <a href="URL Thus, evaluation was not attempted.</p> ‡ These models have been trained with data which contains possible test set contamination. The OpenAI GPT-3 models failed to deduplicate training data for certain test sets, while the GPT-Neo models as well as this one is trained on the Pile, which has not been deduplicated against any test sets. and Related Information ### BibTeX entry To cite this model: To cite the codebase that trained this model: If you use this model, we would love to hear about it! Reach out on GitHub, Discord, or shoot Ben an email. Acknowledgements ---------------- This project would not have been possible without compute generously provided by Google through the TPU Research Cloud, as well as the Cloud TPU team for providing early access to the Cloud TPU VM Alpha. Thanks to everyone who have helped out one way or another (listed alphabetically): * James Bradbury for valuable assistance with debugging JAX issues. * Stella Biderman, Eric Hallahan, Kurumuz, and Finetune for converting the model to be compatible with the 'transformers' package. * Leo Gao for running zero shot evaluations for the baseline models for the table. * Laurence Golding for adding some features to the web demo. * Aran Komatsuzaki for advice with experiment design and writing the blog posts. * Janko Prester for creating the web demo frontend.	[ "### How to use\n\n\nThis model can be easily loaded using the 'AutoModelForCausalLM' functionality:", "### Limitations and Biases\n\n\nThe core functionality of GPT-J is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting GPT-J it is important to remember that the statistically most likely next token is often not the token that produces the most \"accurate\" text. Never depend upon GPT-J to produce factually accurate output.\n\n\nGPT-J was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending upon use case GPT-J may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile.\n\n\nAs with all language models, it is hard to predict in advance how GPT-J will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.\n\n\nEvaluation results\n------------------\n\n\n\n\nModels roughly sorted by performance, or by FLOPs if not available.\n\n\n\\* Evaluation numbers reported by their respective authors. All other numbers are provided by\nrunning [for more\ndetails.](URL either with released\nweights or with API access. Due to subtle implementation differences as well as different zero shot task framing, these\nmight not be directly comparable. See <a href=)\n\n\n† Megatron-11B provides no comparable metrics, and several implementations using the released weights do not\nreproduce the generation quality and evaluations. (see <a href=\"URL\n<a href=\"URL <a href=\"URL\nThus, evaluation was not attempted.</p>\n‡ These models have been trained with data which contains possible test set contamination. The OpenAI GPT-3 models\nfailed to deduplicate training data for certain test sets, while the GPT-Neo models as well as this one is\ntrained on the Pile, which has not been deduplicated against any test sets.\n\n\n\n\nand Related Information", "### BibTeX entry\n\n\nTo cite this model:\n\n\nTo cite the codebase that trained this model:\n\n\nIf you use this model, we would love to hear about it! Reach out on GitHub, Discord, or shoot Ben an email.\n\n\nAcknowledgements\n----------------\n\n\nThis project would not have been possible without compute generously provided by Google through the\nTPU Research Cloud, as well as the Cloud TPU team for providing early access to the Cloud TPU VM Alpha.\n\n\nThanks to everyone who have helped out one way or another (listed alphabetically):\n\n\n* James Bradbury for valuable assistance with debugging JAX issues.\n* Stella Biderman, Eric Hallahan, Kurumuz, and Finetune for converting the model to be compatible with the 'transformers' package.\n* Leo Gao for running zero shot evaluations for the baseline models for the table.\n* Laurence Golding for adding some features to the web demo.\n* Aran Komatsuzaki for advice with experiment design and writing the blog posts.\n* Janko Prester for creating the web demo frontend." ]	[ "TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #en #arxiv-2104.09864 #arxiv-2101.00027 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### How to use\n\n\nThis model can be easily loaded using the 'AutoModelForCausalLM' functionality:", "### Limitations and Biases\n\n\nThe core functionality of GPT-J is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting GPT-J it is important to remember that the statistically most likely next token is often not the token that produces the most \"accurate\" text. Never depend upon GPT-J to produce factually accurate output.\n\n\nGPT-J was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending upon use case GPT-J may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile.\n\n\nAs with all language models, it is hard to predict in advance how GPT-J will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.\n\n\nEvaluation results\n------------------\n\n\n\n\nModels roughly sorted by performance, or by FLOPs if not available.\n\n\n\\* Evaluation numbers reported by their respective authors. All other numbers are provided by\nrunning [for more\ndetails.](URL either with released\nweights or with API access. Due to subtle implementation differences as well as different zero shot task framing, these\nmight not be directly comparable. See <a href=)\n\n\n† Megatron-11B provides no comparable metrics, and several implementations using the released weights do not\nreproduce the generation quality and evaluations. (see <a href=\"URL\n<a href=\"URL <a href=\"URL\nThus, evaluation was not attempted.</p>\n‡ These models have been trained with data which contains possible test set contamination. The OpenAI GPT-3 models\nfailed to deduplicate training data for certain test sets, while the GPT-Neo models as well as this one is\ntrained on the Pile, which has not been deduplicated against any test sets.\n\n\n\n\nand Related Information", "### BibTeX entry\n\n\nTo cite this model:\n\n\nTo cite the codebase that trained this model:\n\n\nIf you use this model, we would love to hear about it! Reach out on GitHub, Discord, or shoot Ben an email.\n\n\nAcknowledgements\n----------------\n\n\nThis project would not have been possible without compute generously provided by Google through the\nTPU Research Cloud, as well as the Cloud TPU team for providing early access to the Cloud TPU VM Alpha.\n\n\nThanks to everyone who have helped out one way or another (listed alphabetically):\n\n\n* James Bradbury for valuable assistance with debugging JAX issues.\n* Stella Biderman, Eric Hallahan, Kurumuz, and Finetune for converting the model to be compatible with the 'transformers' package.\n* Leo Gao for running zero shot evaluations for the baseline models for the table.\n* Laurence Golding for adding some features to the web demo.\n* Aran Komatsuzaki for advice with experiment design and writing the blog posts.\n* Janko Prester for creating the web demo frontend." ]	[ 70, 26, 493, 228 ]	[ "passage: TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #en #arxiv-2104.09864 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null	null	transformers	# GPT-2 Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and first released at [this page](https://openai.com/blog/better-language-models/). Disclaimer: The team releasing GPT-2 also wrote a [model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card has been written by the Hugging Face team to complete the information they provided and give specific examples of bias. ## Model description GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt. ## Intended uses & limitations You can use the raw model for text generation or fine-tune it to a downstream task. See the [model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you. ### How to use You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) [{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."}, {'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"}, {'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"}, {'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"}, {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2Model.from_pretrained('gpt2') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import GPT2Tokenizer, TFGPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = TFGPT2Model.from_pretrained('gpt2') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases): > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases > that require the generated text to be true. > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do > not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a > study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, > and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar > levels of caution around use cases that are sensitive to biases around human attributes. Here's an example of how the model can have biased predictions: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("The White man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The White man worked as a mannequin for'}, {'generated_text': 'The White man worked as a maniser of the'}, {'generated_text': 'The White man worked as a bus conductor by day'}, {'generated_text': 'The White man worked as a plumber at the'}, {'generated_text': 'The White man worked as a journalist. He had'}] >>> set_seed(42) >>> generator("The Black man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The Black man worked as a man at a restaurant'}, {'generated_text': 'The Black man worked as a car salesman in a'}, {'generated_text': 'The Black man worked as a police sergeant at the'}, {'generated_text': 'The Black man worked as a man-eating monster'}, {'generated_text': 'The Black man worked as a slave, and was'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights 40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText [here](https://github.com/openai/gpt-2/blob/master/domains.txt). ## Training procedure ### Preprocessing The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens. The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact details of training. ## Evaluation results The model achieves the following results without any fine-tuning (zero-shot): \| Dataset \| LAMBADA \| LAMBADA \| CBT-CN \| CBT-NE \| WikiText2 \| PTB \| enwiki8 \| text8 \| WikiText103 \| 1BW \| \|:--------:\|:-------:\|:-------:\|:------:\|:------:\|:---------:\|:------:\|:-------:\|:------:\|:-----------:\|:-----:\| \| (metric) \| (PPL) \| (ACC) \| (ACC) \| (ACC) \| (PPL) \| (PPL) \| (BPB) \| (BPC) \| (PPL) \| (PPL) \| \| \| 35.13 \| 45.99 \| 87.65 \| 83.4 \| 29.41 \| 65.85 \| 1.16 \| 1,17 \| 37.50 \| 75.20 \| ### BibTeX entry and citation info ```bibtex @article{radford2019language, title={Language Models are Unsupervised Multitask Learners}, author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, year={2019} } ``` <a href="https://huggingface.co/exbert/?model=gpt2"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>	{"language": "en", "license": "mit", "tags": ["exbert"]}	text-generation	baffo32/gpt2-ptmap	[ "transformers", "pytorch", "tf", "jax", "tflite", "rust", "gpt2", "text-generation", "exbert", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #tf #jax #tflite #rust #gpt2 #text-generation #exbert #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	GPT-2 ===== Test the whole generation capabilities here: URL Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in this paper and first released at this page. Disclaimer: The team releasing GPT-2 also wrote a model card for their model. Content from this model card has been written by the Hugging Face team to complete the information they provided and give specific examples of bias. Model description ----------------- GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token 'i' only uses the inputs from '1' to 'i' but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt. Intended uses & limitations --------------------------- You can use the raw model for text generation or fine-tune it to a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. ### How to use You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: Here is how to use this model to get the features of a given text in PyTorch: and in TensorFlow: ### Limitations and bias The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their model card: > > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases > that require the generated text to be true. > > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do > not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a > study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, > and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar > levels of caution around use cases that are sensitive to biases around human attributes. > > > Here's an example of how the model can have biased predictions: This bias will also affect all fine-tuned versions of this model. Training data ------------- The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights 40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText here. Training procedure ------------------ ### Preprocessing The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens. The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact details of training. Evaluation results ------------------ The model achieves the following results without any fine-tuning (zero-shot): ### BibTeX entry and citation info <a href="URL <img width="300px" src="URL	[ "### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:", "### Limitations and bias\n\n\nThe training data used for this model has not been released as a dataset one can browse. We know it contains a lot of\nunfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their\nmodel card:\n\n\n\n> \n> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases\n> that require the generated text to be true.\n> \n> \n> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do\n> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a\n> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,\n> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar\n> levels of caution around use cases that are sensitive to biases around human attributes.\n> \n> \n> \n\n\nHere's an example of how the model can have biased predictions:\n\n\nThis bias will also affect all fine-tuned versions of this model.\n\n\nTraining data\n-------------\n\n\nThe OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web\npages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from\nthis dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights\n40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText\nhere.\n\n\nTraining procedure\n------------------", "### Preprocessing\n\n\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a\nvocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.\n\n\nThe larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact\ndetails of training.\n\n\nEvaluation results\n------------------\n\n\nThe model achieves the following results without any fine-tuning (zero-shot):", "### BibTeX entry and citation info\n\n\n<a href=\"URL\n<img width=\"300px\" src=\"URL" ]	[ "TAGS\n#transformers #pytorch #tf #jax #tflite #rust #gpt2 #text-generation #exbert #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:", "### Limitations and bias\n\n\nThe training data used for this model has not been released as a dataset one can browse. We know it contains a lot of\nunfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their\nmodel card:\n\n\n\n> \n> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases\n> that require the generated text to be true.\n> \n> \n> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do\n> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a\n> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,\n> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar\n> levels of caution around use cases that are sensitive to biases around human attributes.\n> \n> \n> \n\n\nHere's an example of how the model can have biased predictions:\n\n\nThis bias will also affect all fine-tuned versions of this model.\n\n\nTraining data\n-------------\n\n\nThe OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web\npages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from\nthis dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights\n40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText\nhere.\n\n\nTraining procedure\n------------------", "### Preprocessing\n\n\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a\nvocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.\n\n\nThe larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact\ndetails of training.\n\n\nEvaluation results\n------------------\n\n\nThe model achieves the following results without any fine-tuning (zero-shot):", "### BibTeX entry and citation info\n\n\n<a href=\"URL\n<img width=\"300px\" src=\"URL" ]	[ 69, 66, 393, 118, 30 ]	[ "passage: TAGS\n#transformers #pytorch #tf #jax #tflite #rust #gpt2 #text-generation #exbert #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:" ]	[ -0.0456831268966198, -0.0020836980547755957, -0.0048690494149923325, 0.06944583356380463, 0.13297227025032043, -0.01378298457711935, 0.09417504072189331, 0.08420883119106293, 0.032513681799173355, -0.01342508289963007, 0.2069116234779358, 0.24624812602996826, 0.005484237801283598, 0.117665596306324, -0.035996753722429276, -0.25306788086891174, -0.028685953468084335, 0.048219941556453705, 0.06303611397743225, 0.12564444541931152, 0.08235784620046616, -0.09188155829906464, 0.09324703365564346, 0.021142445504665375, -0.1461545079946518, 0.0028205339331179857, 0.010892256163060665, -0.07841305434703827, 0.10377965122461319, 0.09575073421001434, 0.045376501977443695, 0.08390199393033981, 0.007298699580132961, -0.06534374505281448, 0.04631540924310684, 0.0005943984142504632, -0.03911111503839493, 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null	null	transformers	# ByT5 - Base ByT5 is a tokenizer-free version of [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) and generally follows the architecture of [MT5](https://huggingface.co/google/mt5-base). ByT5 was only pre-trained on [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task. ByT5 works especially well on noisy text data,e.g., `google/byt5-base` significantly outperforms [mt5-base](https://huggingface.co/google/mt5-base) on [TweetQA](https://arxiv.org/abs/1907.06292). Paper: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) Authors: Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel ## Example Inference ByT5 works on raw UTF-8 bytes and can be used without a tokenizer: ```python from transformers import T5ForConditionalGeneration import torch model = T5ForConditionalGeneration.from_pretrained('google/byt5-base') input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + 3 # add 3 for special tokens labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + 3 # add 3 for special tokens loss = model(input_ids, labels=labels).loss # forward pass ``` For batched inference & training it is however recommended using a tokenizer class for padding: ```python from transformers import T5ForConditionalGeneration, AutoTokenizer model = T5ForConditionalGeneration.from_pretrained('google/byt5-base') tokenizer = AutoTokenizer.from_pretrained('google/byt5-base') model_inputs = tokenizer(["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt") labels = tokenizer(["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt").input_ids loss = model(**model_inputs, labels=labels).loss # forward pass ``` ## Abstract Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments. ![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/ByT5.png)	{"language": "multilingual", "license": "apache-2.0", "datasets": ["mc4"]}	text2text-generation	baffo32/pyc2py_alpha2	[ "transformers", "jax", "t5", "text2text-generation", "multilingual", "dataset:mc4", "arxiv:1907.06292", "arxiv:2105.13626", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[ "1907.06292", "2105.13626" ]	[ "multilingual" ]	TAGS #transformers #jax #t5 #text2text-generation #multilingual #dataset-mc4 #arxiv-1907.06292 #arxiv-2105.13626 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# ByT5 - Base ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5. ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task. ByT5 works especially well on noisy text data,e.g., 'google/byt5-base' significantly outperforms mt5-base on TweetQA. Paper: ByT5: Towards a token-free future with pre-trained byte-to-byte models Authors: Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel ## Example Inference ByT5 works on raw UTF-8 bytes and can be used without a tokenizer: For batched inference & training it is however recommended using a tokenizer class for padding: ## Abstract Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments. !model image	[ "# ByT5 - Base\n\nByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.\n\nByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.\n\nByT5 works especially well on noisy text data,e.g., 'google/byt5-base' significantly outperforms mt5-base on TweetQA.\n\nPaper: ByT5: Towards a token-free future with pre-trained byte-to-byte models\n\nAuthors: Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel", "## Example Inference\n\nByT5 works on raw UTF-8 bytes and can be used without a tokenizer:\n\n\n\nFor batched inference & training it is however recommended using a tokenizer class for padding:", "## Abstract\n\nMost widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments.\n\n!model image" ]	[ "TAGS\n#transformers #jax #t5 #text2text-generation #multilingual #dataset-mc4 #arxiv-1907.06292 #arxiv-2105.13626 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# ByT5 - Base\n\nByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.\n\nByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.\n\nByT5 works especially well on noisy text data,e.g., 'google/byt5-base' significantly outperforms mt5-base on TweetQA.\n\nPaper: ByT5: Towards a token-free future with pre-trained byte-to-byte models\n\nAuthors: Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel", "## Example Inference\n\nByT5 works on raw UTF-8 bytes and can be used without a tokenizer:\n\n\n\nFor batched inference & training it is however recommended using a tokenizer class for padding:", "## Abstract\n\nMost widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments.\n\n!model image" ]	[ 84, 199, 48, 326 ]	[ "passage: TAGS\n#transformers #jax #t5 #text2text-generation #multilingual #dataset-mc4 #arxiv-1907.06292 #arxiv-2105.13626 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ByT5 - Base\n\nByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5.\n\nByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task.\n\nByT5 works especially well on noisy text data,e.g., 'google/byt5-base' significantly outperforms mt5-base on TweetQA.\n\nPaper: ByT5: Towards a token-free future with pre-trained byte-to-byte models\n\nAuthors: Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel## Example Inference\n\nByT5 works on raw UTF-8 bytes and can be used without a tokenizer:\n\n\n\nFor batched inference & training it is however recommended using a tokenizer class for padding:" ]	[ -0.048178281635046005, 0.012601710855960846, -0.0038410562556236982, 0.05272539332509041, 0.06025351956486702, -0.0038117929361760616, 0.14522899687290192, 0.10922418534755707, -0.10096712410449982, 0.06171932816505432, 0.12820644676685333, 0.02685486152768135, 0.06622815132141113, 0.1683131456375122, 0.006431092973798513, -0.13330243527889252, -0.014924544841051102, -0.05786117911338806, 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null	null	transformers	[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Pretraining Dataset: [C4](https://huggingface.co/datasets/c4) Other Community Checkpoints: [here](https://huggingface.co/models?search=t5) Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) Authors: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu ## Abstract Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code. ![model image](https://camo.githubusercontent.com/623b4dea0b653f2ad3f36c71ebfe749a677ac0a1/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f343030362f312a44304a31674e51663876727255704b657944387750412e706e67)	{"language": ["en", "fr", "ro", "de"], "license": "apache-2.0", "tags": ["summarization", "translation"], "datasets": ["c4"]}	translation	baffo32/t5-base-ptmap	[ "transformers", "pytorch", "tf", "jax", "rust", "t5", "text2text-generation", "summarization", "translation", "en", "fr", "ro", "de", "dataset:c4", "arxiv:1910.10683", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[ "1910.10683" ]	[ "en", "fr", "ro", "de" ]	TAGS #transformers #pytorch #tf #jax #rust #t5 #text2text-generation #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Google's T5 Pretraining Dataset: C4 Other Community Checkpoints: here Paper: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer Authors: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu ## Abstract Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code. !model image	[ "## Abstract\n\nTransfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.\n\n!model image" ]	[ "TAGS\n#transformers #pytorch #tf #jax #rust #t5 #text2text-generation #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## Abstract\n\nTransfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.\n\n!model image" ]	[ 94, 234 ]	[ "passage: TAGS\n#transformers #pytorch #tf #jax #rust #t5 #text2text-generation #summarization #translation #en #fr #ro #de #dataset-c4 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Abstract\n\nTransfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.\n\n!model image" ]	[ -0.0292768906801939, 0.18225382268428802, -0.0041877152398228645, 0.009821473620831966, 0.09184163808822632, 0.010078704915940762, 0.06612750142812729, 0.13024841248989105, -0.043179113417863846, 0.01671442575752735, -0.012918300926685333, 0.10580939799547195, 0.056272100657224655, 0.11034394800662994, 0.020778708159923553, -0.22784610092639923, 0.028818830847740173, -0.064539335668087, -0.0897923931479454, 0.0984521210193634, 0.09351509809494019, -0.06347410380840302, 0.017154835164546967, -0.02617882750928402, -0.03762077912688255, -0.009199926629662514, -0.0921127051115036, -0.06007753685116768, 0.0895027369260788, 0.03845744952559471, 0.0846492126584053, 0.04182503744959831, 0.010236766189336777, -0.21272006630897522, 0.02441113255918026, 0.08553260564804077, 0.006877186242491007, 0.07739976793527603, 0.012124686501920223, -0.012532035820186138, 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null	null	transformers	# Model name Indian Political Tweets LM ## Model description Note: This model is based on GPT2, if you want a bigger model based on GPT2-medium and finetuned on the same data please take a look at the [IndianPoliticalTweetsLMMedium](https://huggingface.co/bagdaebhishek/IndianPoliticalTweetsLMMedium) model. This is a GPT2 Language model with LM head fine-tuned on tweets crawled from handles which belong predominantly to Indian Politics. For more information about the crawled data, you can go through this [blog](https://bagdeabhishek.github.io/twitterAnalysis) post. ## Intended uses & limitations This finetuned model can be used to generate tweets which are related to Indian politics. #### How to use ```python from transformers import AutoTokenizer,AutoModelWithLMHead,pipeline tokenizer = AutoTokenizer.from_pretrained("bagdaebhishek/IndianPoliticalTweetsLM") model = AutoModelWithLMHead.from_pretrained("bagdaebhishek/IndianPoliticalTweetsLM") text_generator = pipeline("text-generation",model=model, tokenizer=tokenizer) init_sentence = "India will always be" print(text_generator(init_sentence)) ``` #### Limitations and bias 1. The tweets used to train the model were not manually labelled, so the generated text may not always be in English. I've cleaned the data to remove non-English tweets but the model may generate "Hinglish" text and hence no assumptions should be made about the language of the generated text. 2. I've taken enough care to remove tweets from twitter handles which are not very influential but since it's not curated by hand there might be some artefacts like "-sent via NamoApp" etc. 3. Like any language model trained on real-world data this model also exhibits some biases which unfortunately are a part of the political discourse on Twitter. Please keep this in mind while using the output from this model. ## Training data I used the pre-trained gpt2 model from Huggingface transformers repository and fine-tuned it on custom data set crawled from twitter. The method used to identify the political handles is mentioned in detail in a [blog](https://bagdeabhishek.github.io/twitterAnalysis) post. I used tweets from both the Pro-BJP and Anti-BJP clusters mentioned in the blog. ## Training procedure For pre-processing, I removed tweets from handles which are not very influential in their cluster. I removed them by calculating Eigenvector centrality on the twitter graph and pruning handles which have this measure below a certain threshold. This threshold was set manually after experimenting with different values. I then separated tweets by these handles based on their language. I trained the LM with English tweets from both handles. ### Hardware 1. GPU: GTX 1080Ti 2. CPU: Ryzen 3900x 3. RAM: 32GB This model took roughly 36 hours to fine-tune.	{"language": "en", "license": "apache-2.0", "tags": ["India", "politics", "tweets", "BJP", "Congress", "AAP", "pytorch", "gpt2", "lm-head", "text-generation"], "datasets": ["Twitter", "IndianPolitics"], "thumbnail": "https://bagdeabhishek.github.io/twitterAnalysis_files/networkfin.jpg"}	text-generation	bagdaebhishek/IndianPoliticalTweetsLM	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "India", "politics", "tweets", "BJP", "Congress", "AAP", "lm-head", "en", "dataset:Twitter", "dataset:IndianPolitics", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #India #politics #tweets #BJP #Congress #AAP #lm-head #en #dataset-Twitter #dataset-IndianPolitics #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model name Indian Political Tweets LM ## Model description Note: This model is based on GPT2, if you want a bigger model based on GPT2-medium and finetuned on the same data please take a look at the IndianPoliticalTweetsLMMedium model. This is a GPT2 Language model with LM head fine-tuned on tweets crawled from handles which belong predominantly to Indian Politics. For more information about the crawled data, you can go through this blog post. ## Intended uses & limitations This finetuned model can be used to generate tweets which are related to Indian politics. #### How to use #### Limitations and bias 1. The tweets used to train the model were not manually labelled, so the generated text may not always be in English. I've cleaned the data to remove non-English tweets but the model may generate "Hinglish" text and hence no assumptions should be made about the language of the generated text. 2. I've taken enough care to remove tweets from twitter handles which are not very influential but since it's not curated by hand there might be some artefacts like "-sent via NamoApp" etc. 3. Like any language model trained on real-world data this model also exhibits some biases which unfortunately are a part of the political discourse on Twitter. Please keep this in mind while using the output from this model. ## Training data I used the pre-trained gpt2 model from Huggingface transformers repository and fine-tuned it on custom data set crawled from twitter. The method used to identify the political handles is mentioned in detail in a blog post. I used tweets from both the Pro-BJP and Anti-BJP clusters mentioned in the blog. ## Training procedure For pre-processing, I removed tweets from handles which are not very influential in their cluster. I removed them by calculating Eigenvector centrality on the twitter graph and pruning handles which have this measure below a certain threshold. This threshold was set manually after experimenting with different values. I then separated tweets by these handles based on their language. I trained the LM with English tweets from both handles. ### Hardware 1. GPU: GTX 1080Ti 2. CPU: Ryzen 3900x 3. RAM: 32GB This model took roughly 36 hours to fine-tune.	[ "# Model name\nIndian Political Tweets LM", "## Model description\nNote: This model is based on GPT2, if you want a bigger model based on GPT2-medium and finetuned on the same data please take a look at the IndianPoliticalTweetsLMMedium model. \n\nThis is a GPT2 Language model with LM head fine-tuned on tweets crawled from handles which belong predominantly to Indian Politics. For more information about the crawled data, you can go through this blog post.", "## Intended uses & limitations\n This finetuned model can be used to generate tweets which are related to Indian politics.", "#### How to use", "#### Limitations and bias\n1. The tweets used to train the model were not manually labelled, so the generated text may not always be in English. I've cleaned the data to remove non-English tweets but the model may generate \"Hinglish\" text and hence no assumptions should be made about the language of the generated text.\n2. I've taken enough care to remove tweets from twitter handles which are not very influential but since it's not curated by hand there might be some artefacts like \"-sent via NamoApp\" etc.\n3. Like any language model trained on real-world data this model also exhibits some biases which unfortunately are a part of the political discourse on Twitter. Please keep this in mind while using the output from this model.", "## Training data\nI used the pre-trained gpt2 model from Huggingface transformers repository and fine-tuned it on custom data set crawled from twitter. The method used to identify the political handles is mentioned in detail in a blog post. I used tweets from both the Pro-BJP and Anti-BJP clusters mentioned in the blog.", "## Training procedure\n\nFor pre-processing, I removed tweets from handles which are not very influential in their cluster. I removed them by calculating Eigenvector centrality on the twitter graph and pruning handles which have this measure below a certain threshold. This threshold was set manually after experimenting with different values.\n\nI then separated tweets by these handles based on their language. I trained the LM with English tweets from both handles.", "### Hardware\n1. GPU: GTX 1080Ti\n2. CPU: Ryzen 3900x\n3. RAM: 32GB\n\nThis model took roughly 36 hours to fine-tune." ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #India #politics #tweets #BJP #Congress #AAP #lm-head #en #dataset-Twitter #dataset-IndianPolitics #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model name\nIndian Political Tweets LM", "## Model description\nNote: This model is based on GPT2, if you want a bigger model based on GPT2-medium and finetuned on the same data please take a look at the IndianPoliticalTweetsLMMedium model. \n\nThis is a GPT2 Language model with LM head fine-tuned on tweets crawled from handles which belong predominantly to Indian Politics. For more information about the crawled data, you can go through this blog post.", "## Intended uses & limitations\n This finetuned model can be used to generate tweets which are related to Indian politics.", "#### How to use", "#### Limitations and bias\n1. The tweets used to train the model were not manually labelled, so the generated text may not always be in English. I've cleaned the data to remove non-English tweets but the model may generate \"Hinglish\" text and hence no assumptions should be made about the language of the generated text.\n2. I've taken enough care to remove tweets from twitter handles which are not very influential but since it's not curated by hand there might be some artefacts like \"-sent via NamoApp\" etc.\n3. Like any language model trained on real-world data this model also exhibits some biases which unfortunately are a part of the political discourse on Twitter. Please keep this in mind while using the output from this model.", "## Training data\nI used the pre-trained gpt2 model from Huggingface transformers repository and fine-tuned it on custom data set crawled from twitter. The method used to identify the political handles is mentioned in detail in a blog post. I used tweets from both the Pro-BJP and Anti-BJP clusters mentioned in the blog.", "## Training procedure\n\nFor pre-processing, I removed tweets from handles which are not very influential in their cluster. I removed them by calculating Eigenvector centrality on the twitter graph and pruning handles which have this measure below a certain threshold. This threshold was set manually after experimenting with different values.\n\nI then separated tweets by these handles based on their language. I trained the LM with English tweets from both handles.", "### Hardware\n1. GPU: GTX 1080Ti\n2. CPU: Ryzen 3900x\n3. RAM: 32GB\n\nThis model took roughly 36 hours to fine-tune." ]	[ 94, 9, 104, 29, 5, 176, 79, 103, 34 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #India #politics #tweets #BJP #Congress #AAP #lm-head #en #dataset-Twitter #dataset-IndianPolitics #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model name\nIndian Political Tweets LM## Model description\nNote: This model is based on GPT2, if you want a bigger model based on GPT2-medium and finetuned on the same data please take a look at the IndianPoliticalTweetsLMMedium model. \n\nThis is a GPT2 Language model with LM head fine-tuned on tweets crawled from handles which belong predominantly to Indian Politics. For more information about the crawled data, you can go through this blog post.## Intended uses & limitations\n This finetuned model can be used to generate tweets which are related to Indian politics.#### How to use#### Limitations and bias\n1. The tweets used to train the model were not manually labelled, so the generated text may not always be in English. I've cleaned the data to remove non-English tweets but the model may generate \"Hinglish\" text and hence no assumptions should be made about the language of the generated text.\n2. I've taken enough care to remove tweets from twitter handles which are not very influential but since it's not curated by hand there might be some artefacts like \"-sent via NamoApp\" etc.\n3. Like any language model trained on real-world data this model also exhibits some biases which unfortunately are a part of the political discourse on Twitter. Please keep this in mind while using the output from this model.## Training data\nI used the pre-trained gpt2 model from Huggingface transformers repository and fine-tuned it on custom data set crawled from twitter. The method used to identify the political handles is mentioned in detail in a blog post. 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null	null	transformers	# Model name Indian Political Tweets LM Medium (Based on GPT2-Medium) ## Model description This is a GPT2 Language model with LM head fine-tuned on tweets crawled from handles which belong predominantly to Indian Politics. For more information about the crawled data, you can go through this [blog](https://bagdeabhishek.github.io/twitterAnalysis) post. This model is finetuned using GPT2-medium instead of the vanilla GPT2 implementation. This model has more parameters but it is able to model language slightly better. ## Intended uses & limitations This finetuned model can be used to generate tweets which are related to Indian politics. #### How to use ```python from transformers import AutoTokenizer,AutoModelWithLMHead,pipeline tokenizer = AutoTokenizer.from_pretrained("bagdaebhishek/IndianPoliticalTweetsLM") model = AutoModelWithLMHead.from_pretrained("bagdaebhishek/IndianPoliticalTweetsLM") text_generator = pipeline("text-generation",model=model, tokenizer=tokenizer) init_sentence = "India will always be" print(text_generator(init_sentence)) ``` #### Limitations and bias 1. The tweets used to train the model were not manually labelled, so the generated text may not always be in English. I've cleaned the data to remove non-English tweets but the model may generate "Hinglish" text and hence no assumptions should be made about the language of the generated text. 2. I've taken enough care to remove tweets from twitter handles which are not very influential but since it's not curated by hand there might be some artefacts like "-sent via NamoApp" etc. 3. Like any language model trained on real-world data this model also exhibits some biases which unfortunately are a part of the political discourse on Twitter. Please keep this in mind while using the output from this model. ## Training data I used the pre-trained gpt2-medium model from Huggingface transformers repository and fine-tuned it on custom data set crawled from twitter. The method used to identify the political handles is mentioned in detail in a [blog](https://bagdeabhishek.github.io/twitterAnalysis) post. I used tweets from both the Pro-BJP and Anti-BJP clusters mentioned in the blog. ## Training procedure For pre-processing, I removed tweets from handles which are not very influential in their cluster. I removed them by calculating Eigenvector centrality on the twitter graph and pruning handles which have this measure below a certain threshold. This threshold was set manually after experimenting with different values. I then separated tweets by these handles based on their language. I trained the LM with English tweets from both handles. ### Hardware 1. GPU: GTX 1080Ti 2. CPU: Ryzen 3900x 3. RAM: 32GB This model took roughly 36 hours to fine-tune.	{"language": "en", "license": "apache-2.0", "tags": ["India", "politics", "tweets", "BJP", "Congress", "AAP", "pytorch", "gpt2", "lm-head", "text-generation"], "datasets": ["Twitter", "IndianPolitics"], "thumbnail": "https://bagdeabhishek.github.io/twitterAnalysis_files/networkfin.jpg"}	text-generation	bagdaebhishek/IndianPoliticalTweetsLMMedium	[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "India", "politics", "tweets", "BJP", "Congress", "AAP", "lm-head", "en", "dataset:Twitter", "dataset:IndianPolitics", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #jax #gpt2 #text-generation #India #politics #tweets #BJP #Congress #AAP #lm-head #en #dataset-Twitter #dataset-IndianPolitics #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model name Indian Political Tweets LM Medium (Based on GPT2-Medium) ## Model description This is a GPT2 Language model with LM head fine-tuned on tweets crawled from handles which belong predominantly to Indian Politics. For more information about the crawled data, you can go through this blog post. This model is finetuned using GPT2-medium instead of the vanilla GPT2 implementation. This model has more parameters but it is able to model language slightly better. ## Intended uses & limitations This finetuned model can be used to generate tweets which are related to Indian politics. #### How to use #### Limitations and bias 1. The tweets used to train the model were not manually labelled, so the generated text may not always be in English. I've cleaned the data to remove non-English tweets but the model may generate "Hinglish" text and hence no assumptions should be made about the language of the generated text. 2. I've taken enough care to remove tweets from twitter handles which are not very influential but since it's not curated by hand there might be some artefacts like "-sent via NamoApp" etc. 3. Like any language model trained on real-world data this model also exhibits some biases which unfortunately are a part of the political discourse on Twitter. Please keep this in mind while using the output from this model. ## Training data I used the pre-trained gpt2-medium model from Huggingface transformers repository and fine-tuned it on custom data set crawled from twitter. The method used to identify the political handles is mentioned in detail in a blog post. I used tweets from both the Pro-BJP and Anti-BJP clusters mentioned in the blog. ## Training procedure For pre-processing, I removed tweets from handles which are not very influential in their cluster. I removed them by calculating Eigenvector centrality on the twitter graph and pruning handles which have this measure below a certain threshold. This threshold was set manually after experimenting with different values. I then separated tweets by these handles based on their language. I trained the LM with English tweets from both handles. ### Hardware 1. GPU: GTX 1080Ti 2. CPU: Ryzen 3900x 3. RAM: 32GB This model took roughly 36 hours to fine-tune.	[ "# Model name\nIndian Political Tweets LM Medium (Based on GPT2-Medium)", "## Model description\n\nThis is a GPT2 Language model with LM head fine-tuned on tweets crawled from handles which belong predominantly to Indian Politics. For more information about the crawled data, you can go through this blog post. \n\nThis model is finetuned using GPT2-medium instead of the vanilla GPT2 implementation. This model has more parameters but it is able to model language slightly better.", "## Intended uses & limitations\n This finetuned model can be used to generate tweets which are related to Indian politics.", "#### How to use", "#### Limitations and bias\n1. The tweets used to train the model were not manually labelled, so the generated text may not always be in English. I've cleaned the data to remove non-English tweets but the model may generate \"Hinglish\" text and hence no assumptions should be made about the language of the generated text.\n2. I've taken enough care to remove tweets from twitter handles which are not very influential but since it's not curated by hand there might be some artefacts like \"-sent via NamoApp\" etc.\n3. Like any language model trained on real-world data this model also exhibits some biases which unfortunately are a part of the political discourse on Twitter. Please keep this in mind while using the output from this model.", "## Training data\nI used the pre-trained gpt2-medium model from Huggingface transformers repository and fine-tuned it on custom data set crawled from twitter. The method used to identify the political handles is mentioned in detail in a blog post. I used tweets from both the Pro-BJP and Anti-BJP clusters mentioned in the blog.", "## Training procedure\n\nFor pre-processing, I removed tweets from handles which are not very influential in their cluster. I removed them by calculating Eigenvector centrality on the twitter graph and pruning handles which have this measure below a certain threshold. This threshold was set manually after experimenting with different values.\n\nI then separated tweets by these handles based on their language. I trained the LM with English tweets from both handles.", "### Hardware\n1. GPU: GTX 1080Ti\n2. CPU: Ryzen 3900x\n3. RAM: 32GB\n\nThis model took roughly 36 hours to fine-tune." ]	[ "TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #India #politics #tweets #BJP #Congress #AAP #lm-head #en #dataset-Twitter #dataset-IndianPolitics #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model name\nIndian Political Tweets LM Medium (Based on GPT2-Medium)", "## Model description\n\nThis is a GPT2 Language model with LM head fine-tuned on tweets crawled from handles which belong predominantly to Indian Politics. For more information about the crawled data, you can go through this blog post. \n\nThis model is finetuned using GPT2-medium instead of the vanilla GPT2 implementation. This model has more parameters but it is able to model language slightly better.", "## Intended uses & limitations\n This finetuned model can be used to generate tweets which are related to Indian politics.", "#### How to use", "#### Limitations and bias\n1. The tweets used to train the model were not manually labelled, so the generated text may not always be in English. I've cleaned the data to remove non-English tweets but the model may generate \"Hinglish\" text and hence no assumptions should be made about the language of the generated text.\n2. I've taken enough care to remove tweets from twitter handles which are not very influential but since it's not curated by hand there might be some artefacts like \"-sent via NamoApp\" etc.\n3. Like any language model trained on real-world data this model also exhibits some biases which unfortunately are a part of the political discourse on Twitter. Please keep this in mind while using the output from this model.", "## Training data\nI used the pre-trained gpt2-medium model from Huggingface transformers repository and fine-tuned it on custom data set crawled from twitter. The method used to identify the political handles is mentioned in detail in a blog post. I used tweets from both the Pro-BJP and Anti-BJP clusters mentioned in the blog.", "## Training procedure\n\nFor pre-processing, I removed tweets from handles which are not very influential in their cluster. I removed them by calculating Eigenvector centrality on the twitter graph and pruning handles which have this measure below a certain threshold. This threshold was set manually after experimenting with different values.\n\nI then separated tweets by these handles based on their language. I trained the LM with English tweets from both handles.", "### Hardware\n1. GPU: GTX 1080Ti\n2. CPU: Ryzen 3900x\n3. RAM: 32GB\n\nThis model took roughly 36 hours to fine-tune." ]	[ 94, 20, 93, 29, 5, 176, 81, 103, 34 ]	[ "passage: TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #India #politics #tweets #BJP #Congress #AAP #lm-head #en #dataset-Twitter #dataset-IndianPolitics #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model name\nIndian Political Tweets LM Medium (Based on GPT2-Medium)## Model description\n\nThis is a GPT2 Language model with LM head fine-tuned on tweets crawled from handles which belong predominantly to Indian Politics. For more information about the crawled data, you can go through this blog post. \n\nThis model is finetuned using GPT2-medium instead of the vanilla GPT2 implementation. This model has more parameters but it is able to model language slightly better.## Intended uses & limitations\n This finetuned model can be used to generate tweets which are related to Indian politics.#### How to use#### Limitations and bias\n1. The tweets used to train the model were not manually labelled, so the generated text may not always be in English. I've cleaned the data to remove non-English tweets but the model may generate \"Hinglish\" text and hence no assumptions should be made about the language of the generated text.\n2. I've taken enough care to remove tweets from twitter handles which are not very influential but since it's not curated by hand there might be some artefacts like \"-sent via NamoApp\" etc.\n3. Like any language model trained on real-world data this model also exhibits some biases which unfortunately are a part of the political discourse on Twitter. Please keep this in mind while using the output from this model.## Training data\nI used the pre-trained gpt2-medium model from Huggingface transformers repository and fine-tuned it on custom data set crawled from twitter. The method used to identify the political handles is mentioned in detail in a blog post. 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null	null	transformers	hello	{}	fill-mask	baicuya/bert_cn	[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us	hello	[]	[ "TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]	[ 36 ]	[ "passage: TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]	[ -0.06357412785291672, 0.00690077617764473, -0.008467365056276321, 0.020235946401953697, 0.12968459725379944, 0.03302915394306183, 0.09807441383600235, 0.07729126513004303, 0.10806342214345932, -0.009440856985747814, 0.15823203325271606, 0.20325462520122528, -0.03393663093447685, 0.18361465632915497, -0.065280981361866, -0.2617916762828827, 0.06820162385702133, 0.06229938939213753, -0.06546879559755325, 0.11254725605249405, 0.05687131732702255, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Sinai Voice Arabic Speech Recognition Model # نموذج صوت سيناء للتعرف على الأصوات العربية الفصحى و تحويلها إلى نصوص This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - AR dataset. It achieves the following results on the evaluation set: - Loss: 0.2141 - Wer: 0.1808 It achieves the following results on the evaluation set: - eval_loss = 0.2141 - eval_samples = 10388 - eval_wer = 0.181 - eval_cer = 0.049 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id bakrianoo/sinai-voice-ar-stt --dataset mozilla-foundation/common_voice_8_0 --config ar --split test ``` ### Inference Without LM ```python from transformers import (Wav2Vec2Processor, Wav2Vec2ForCTC) import torchaudio import torch def speech_file_to_array_fn(voice_path, resampling_to=16000): speech_array, sampling_rate = torchaudio.load(voice_path) resampler = torchaudio.transforms.Resample(sampling_rate, resampling_to) return resampler(speech_array)[0].numpy(), sampling_rate # load the model cp = "bakrianoo/sinai-voice-ar-stt" processor = Wav2Vec2Processor.from_pretrained(cp) model = Wav2Vec2ForCTC.from_pretrained(cp) # recognize the text in a sample sound file sound_path = './my_voice.mp3' sample, sr = speech_file_to_array_fn(sound_path) inputs = processor([sample], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values,).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 32 - eval_batch_size: 10 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 256 - total_eval_batch_size: 80 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 1.354 \| 0.64 \| 1000 \| 0.4109 \| 0.4493 \| \| 0.5886 \| 1.28 \| 2000 \| 0.2798 \| 0.3099 \| \| 0.4977 \| 1.92 \| 3000 \| 0.2387 \| 0.2673 \| \| 0.4253 \| 2.56 \| 4000 \| 0.2266 \| 0.2523 \| \| 0.3942 \| 3.2 \| 5000 \| 0.2171 \| 0.2437 \| \| 0.3619 \| 3.84 \| 6000 \| 0.2076 \| 0.2253 \| \| 0.3245 \| 4.48 \| 7000 \| 0.2088 \| 0.2186 \| \| 0.308 \| 5.12 \| 8000 \| 0.2086 \| 0.2206 \| \| 0.2881 \| 5.76 \| 9000 \| 0.2089 \| 0.2105 \| \| 0.2557 \| 6.4 \| 10000 \| 0.2015 \| 0.2004 \| \| 0.248 \| 7.04 \| 11000 \| 0.2044 \| 0.1953 \| \| 0.2251 \| 7.68 \| 12000 \| 0.2058 \| 0.1932 \| \| 0.2052 \| 8.32 \| 13000 \| 0.2117 \| 0.1878 \| \| 0.1976 \| 8.96 \| 14000 \| 0.2104 \| 0.1825 \| \| 0.1845 \| 9.6 \| 15000 \| 0.2156 \| 0.1821 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0	{"language": ["ar"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "widget": [{"example_title": "Example 1", "src": "https://huggingface.co/bakrianoo/sinai-voice-ar-stt/raw/main/examples/common_voice_ar_19077324.mp3"}, {"example_title": "Example 2", "src": "https://huggingface.co/bakrianoo/sinai-voice-ar-stt/raw/main/examples/common_voice_ar_19205138.mp3"}, {"example_title": "Example 3", "src": "https://huggingface.co/bakrianoo/sinai-voice-ar-stt/raw/main/examples/common_voice_ar_19331711.mp3"}], "model-index": [{"name": "Sinai Voice Arabic Speech Recognition Model", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice ar", "type": "mozilla-foundation/common_voice_8_0", "args": "ar"}, "metrics": [{"type": "wer", "value": 0.181, "name": "Test WER"}, {"type": "cer", "value": 0.049, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "ar"}, "metrics": [{"type": "wer", "value": 93.03, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "ar"}, "metrics": [{"type": "wer", "value": 90.79, "name": "Test WER"}]}]}]}	automatic-speech-recognition	bakrianoo/sinai-voice-ar-stt	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event", "ar", "dataset:mozilla-foundation/common_voice_8_0", "license:apache-2.0", "model-index", "endpoints_compatible", "has_space", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "ar" ]	TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #ar #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us	Sinai Voice Arabic Speech Recognition Model =========================================== نموذج صوت سيناء للتعرف على الأصوات العربية الفصحى و تحويلها إلى نصوص ==================================================================== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - AR dataset. It achieves the following results on the evaluation set: * Loss: 0.2141 * Wer: 0.1808 It achieves the following results on the evaluation set: * eval\_loss = 0.2141 * eval\_samples = 10388 * eval\_wer = 0.181 * eval\_cer = 0.049 #### Evaluation Commands 1. To evaluate on 'mozilla-foundation/common\_voice\_8\_0' with split 'test' ### Inference Without LM ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0002 * train\_batch\_size: 32 * eval\_batch\_size: 10 * seed: 42 * distributed\_type: multi-GPU * num\_devices: 8 * total\_train\_batch\_size: 256 * total\_eval\_batch\_size: 80 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 10 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.2+cu113 * Datasets 1.18.3 * Tokenizers 0.11.0	[ "#### Evaluation Commands\n\n\n1. To evaluate on 'mozilla-foundation/common\\_voice\\_8\\_0' with split 'test'", "### Inference Without LM", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 10\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 256\n* total\\_eval\\_batch\\_size: 80\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #ar #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n", "#### Evaluation Commands\n\n\n1. To evaluate on 'mozilla-foundation/common\\_voice\\_8\\_0' with split 'test'", "### Inference Without LM", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 10\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 256\n* total\\_eval\\_batch\\_size: 80\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ 92, 36, 8, 179, 4, 35 ]	[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #ar #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n#### Evaluation Commands\n\n\n1. To evaluate on 'mozilla-foundation/common\\_voice\\_8\\_0' with split 'test'### Inference Without LM### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 10\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* total\\_train\\_batch\\_size: 256\n* total\\_eval\\_batch\\_size: 80\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.2+cu113\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]	[ -0.07657581567764282, 0.09032603353261948, -0.0042309509590268135, 0.05508272349834442, 0.10443264991044998, 0.03557080402970314, 0.10543101280927658, 0.15268485248088837, -0.09164413809776306, 0.10575651377439499, 0.061715368181467056, 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null	null	transformers	## Arabic T5 Base Model A customized T5 Model for Arabic and English Task. It could be used as an alternative for `google/mt5-base` model, as it's much smaller and only targets Arabic and English based tasks. ### About T5 ``` T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format. The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. ``` [Read More](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)	{"language": "Arabic", "license": "apache-2.0", "datasets": ["mc4"]}	text2text-generation	bakrianoo/t5-arabic-base	[ "transformers", "pytorch", "t5", "text2text-generation", "dataset:mc4", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "Arabic" ]	TAGS #transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	## Arabic T5 Base Model A customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-base' model, as it's much smaller and only targets Arabic and English based tasks. ### About T5 Read More	[ "## Arabic T5 Base Model\n\nA customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-base' model, as it's much smaller and only targets Arabic and English based tasks.", "### About T5\n\n\n\nRead More" ]	[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## Arabic T5 Base Model\n\nA customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-base' model, as it's much smaller and only targets Arabic and English based tasks.", "### About T5\n\n\n\nRead More" ]	[ 63, 54, 7 ]	[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Arabic T5 Base Model\n\nA customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-base' model, as it's much smaller and only targets Arabic and English based tasks.### About T5\n\n\n\nRead More" ]	[ -0.011279740370810032, -0.0841737762093544, -0.001882073120214045, 0.08252676576375961, 0.08490898460149765, 0.05583036318421364, 0.2091376632452011, 0.09918879717588425, -0.05184024199843407, -0.11732640862464905, 0.12092927098274231, 0.06653232127428055, 0.0290500707924366, 0.16440564393997192, 0.007034891285002232, -0.2456861138343811, -0.027312979102134705, -0.01697174273431301, 0.05092895030975342, 0.13478289544582367, 0.17964215576648712, 0.036950353533029556, 0.13509266078472137, -0.04006176441907883, -0.06584754586219788, 0.09449739009141922, 0.025554824620485306, -0.15146677196025848, 0.09017748385667801, 0.15549218654632568, 0.03617245331406593, 0.08664398640394211, 0.003505234606564045, -0.06381615996360779, 0.04051463305950165, 0.02700125239789486, -0.1335991621017456, -0.002542640781030059, 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null	null	transformers	## Arabic T5 Large Model A customized T5 Model for Arabic and English Task. It could be used as an alternative for `google/mt5-large` model, as it's much smaller and only targets Arabic and English based tasks. ### About T5 ``` T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format. The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. ``` [Read More](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)	{"language": "Arabic", "license": "apache-2.0", "datasets": ["mc4"]}	text2text-generation	bakrianoo/t5-arabic-large	[ "transformers", "pytorch", "t5", "text2text-generation", "dataset:mc4", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "Arabic" ]	TAGS #transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	## Arabic T5 Large Model A customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-large' model, as it's much smaller and only targets Arabic and English based tasks. ### About T5 Read More	[ "## Arabic T5 Large Model\n\nA customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-large' model, as it's much smaller and only targets Arabic and English based tasks.", "### About T5\n\n\n\nRead More" ]	[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## Arabic T5 Large Model\n\nA customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-large' model, as it's much smaller and only targets Arabic and English based tasks.", "### About T5\n\n\n\nRead More" ]	[ 63, 55, 7 ]	[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Arabic T5 Large Model\n\nA customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-large' model, as it's much smaller and only targets Arabic and English based tasks.### About T5\n\n\n\nRead More" ]	[ -0.016571247950196266, -0.10427235066890717, -0.0018624559743329883, 0.09119723737239838, 0.0916118249297142, 0.05487321689724922, 0.19583851099014282, 0.09539815038442612, -0.043594200164079666, -0.09470641613006592, 0.12559238076210022, 0.060031723231077194, 0.02532513625919819, 0.16865073144435883, 0.014568627811968327, -0.25764137506484985, -0.03976505994796753, -0.022655755281448364, 0.01865382120013237, 0.1276499330997467, 0.17682893574237823, 0.014135772362351418, 0.14831644296646118, -0.03837936744093895, -0.07574829459190369, 0.07232680916786194, 0.017792342230677605, -0.15288423001766205, 0.0841435119509697, 0.1539250910282135, 0.020557589828968048, 0.08010245114564896, -0.0008064936846494675, -0.03646570444107056, 0.03764007240533829, 0.015116386115550995, -0.11355780065059662, 0.000513062346726656, 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null	null	transformers	## Arabic T5 Small Model A customized T5 Model for Arabic and English Task. It could be used as an alternative for `google/mt5-small` model, as it's much smaller and only targets Arabic and English based tasks. ### About T5 ``` T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format. The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. ``` [Read More](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html)	{"language": "Arabic", "license": "apache-2.0", "datasets": ["mc4"]}	text2text-generation	bakrianoo/t5-arabic-small	[ "transformers", "pytorch", "t5", "text2text-generation", "dataset:mc4", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "Arabic" ]	TAGS #transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	## Arabic T5 Small Model A customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-small' model, as it's much smaller and only targets Arabic and English based tasks. ### About T5 Read More	[ "## Arabic T5 Small Model\n\nA customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-small' model, as it's much smaller and only targets Arabic and English based tasks.", "### About T5\n\n\n\nRead More" ]	[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## Arabic T5 Small Model\n\nA customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-small' model, as it's much smaller and only targets Arabic and English based tasks.", "### About T5\n\n\n\nRead More" ]	[ 63, 55, 7 ]	[ "passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #dataset-mc4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Arabic T5 Small Model\n\nA customized T5 Model for Arabic and English Task. It could be used as an alternative for 'google/mt5-small' model, as it's much smaller and only targets Arabic and English based tasks.### About T5\n\n\n\nRead More" ]	[ -0.010144056752324104, -0.12374555319547653, -0.002215723041445017, 0.09161084145307541, 0.08470426499843597, 0.041951734572649, 0.22133511304855347, 0.11206141859292984, -0.0556921623647213, -0.10254856199026108, 0.11652994155883789, 0.06226419284939766, 0.03536907583475113, 0.15238969027996063, 0.0068307253532111645, -0.25131112337112427, -0.03969539329409599, -0.013507157564163208, 0.057580772787332535, 0.12617242336273193, 0.18993277847766876, 0.007314556743949652, 0.1412498503923416, -0.019390862435102463, -0.060649171471595764, 0.0683576911687851, 0.03047346882522106, -0.1548633575439453, 0.07651761919260025, 0.14861388504505157, 0.024730173870921135, 0.08602172136306763, -0.00391812901943922, -0.038619235157966614, 0.036564018577337265, 0.014162503182888031, -0.10407629609107971, -0.009580723941326141, 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null	null	null	The main card for Saturday’s Manny Pacquiao vs Yordenis Ugas fight gets underway at T-Mobile Arena in Las Vegas at 9 p.m. ET and the main event is expected to start sometime around 11:30 p.m. This is going to air on FOX Sports PPV and YouTube PPV. The card will cost https://web.sites.google.com/view/ppv-livemanny-pacquiao-vs-yord/home https://web.sites.google.com/view/freevasyl-manny-pacquiao-vs-yo/home https://web.sites.google.com/view/ppvlivestreammannypacquiaovsyo/home https://web.sites.google.com/view/watchtv-manny-pacquiao-vs-yord/home https://web.sites.google.com/view/heresmannypacquiaovsyordenisug/home https://web.sites.google.com/view/mannypacquiaovsyordenislive/home https://web.sites.google.com/view/free-2021-manny-pacquiao-vs-yo/home LIVE::Watch Full Fight Live Here LIVE::Watch Full Fight Live Here https://goodavail.com/boxing/ The most intriguing storyline for this fight is the belt itself that is on the line. Pacquiao won the Super version of the WBA’s welterweight title in July 2019 when he beat Keith Thurman in a split decision. The WBA stripped Pacquiao of the title this past January due to inactivity. The organizing body then promoted Ugas into the Super belt. Ugas won the WBA’s Regular title in September 2020 when he beat Abel Ramos in a split decision. That means Ugas won a title last held by Pacquiao without having to beat Pacquiao. Pacquiao-Spence would have been a significantly bigger fight for welterweight supremacy, but this is still interesting. Pacquiao was an underdog against Spence, but comes into this fight as a -360 favorite at DraftKings Sportsbook. How to watch Manny Pacquiao vs. Yordenis Ugas TV channel: FOX Sports PPV	{}	null	balalsahabi/fdgdfg	[ "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #region-us	The main card for Saturday’s Manny Pacquiao vs Yordenis Ugas fight gets underway at T-Mobile Arena in Las Vegas at 9 p.m. ET and the main event is expected to start sometime around 11:30 p.m. This is going to air on FOX Sports PPV and YouTube PPV. The card will cost URL URL URL URL URL URL URL LIVE::Watch Full Fight Live Here LIVE::Watch Full Fight Live Here URL The most intriguing storyline for this fight is the belt itself that is on the line. Pacquiao won the Super version of the WBA’s welterweight title in July 2019 when he beat Keith Thurman in a split decision. The WBA stripped Pacquiao of the title this past January due to inactivity. The organizing body then promoted Ugas into the Super belt. Ugas won the WBA’s Regular title in September 2020 when he beat Abel Ramos in a split decision. That means Ugas won a title last held by Pacquiao without having to beat Pacquiao. Pacquiao-Spence would have been a significantly bigger fight for welterweight supremacy, but this is still interesting. Pacquiao was an underdog against Spence, but comes into this fight as a -360 favorite at DraftKings Sportsbook. 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null	null	transformers	# Named Entity Recognition using Transformers This is a Fine-tuned version of BERT using HuggingFace transformers to perform Named Entity Recognition on Text data. BERT is a state-of-the-art model with attention mechanism as underlying architecture trained with masked-language-modeling and next-sentence-prediction objectives, used for various tasks including Question answering systems, Text Summarization, etc... which can also perform token classification tasks such as NER with great performance. # Dataset CoNLL-2003 : The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations, and names of miscellaneous entities that do not belong to the previous three groups.<br><br> Link : https://huggingface.co/datasets/conll2003 # Using this fine-tuned version From python, download the whole pipeline and use it instantly using the following code : ``` from transformers import pipeline # Loading the pipeline from hub # Pipeline handles the preprocessing and post processing steps model_checkpoint = "balamurugan1603/bert-finetuned-ner" namedEntityRecogniser = pipeline( "token-classification", model=model_checkpoint, aggregation_strategy="simple" ) ``` Reference for using this pipeline to find NER tags can be found in this <a href="https://github.com/balamurugan1603/Named-Entity-Recognition-using-Tranformers/blob/main/named-entity-recognition-using-transfer-learning.ipynb">notebook</a>.	{}	token-classification	balamurugan1603/bert-finetuned-ner	[ "transformers", "pytorch", "tf", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us	# Named Entity Recognition using Transformers This is a Fine-tuned version of BERT using HuggingFace transformers to perform Named Entity Recognition on Text data. BERT is a state-of-the-art model with attention mechanism as underlying architecture trained with masked-language-modeling and next-sentence-prediction objectives, used for various tasks including Question answering systems, Text Summarization, etc... which can also perform token classification tasks such as NER with great performance. # Dataset CoNLL-2003 : The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations, and names of miscellaneous entities that do not belong to the previous three groups.<br><br> Link : URL # Using this fine-tuned version From python, download the whole pipeline and use it instantly using the following code : Reference for using this pipeline to find NER tags can be found in this <a href="URL	[ "# Named Entity Recognition using Transformers\nThis is a Fine-tuned version of BERT using HuggingFace transformers to perform Named Entity Recognition on Text data. BERT is a state-of-the-art model with attention mechanism as underlying architecture trained with masked-language-modeling and next-sentence-prediction objectives, used for various tasks including Question answering systems, Text Summarization, etc... which can also perform token classification tasks such as NER with great performance.", "# Dataset\nCoNLL-2003 :\nThe shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations, and names of miscellaneous entities that do not belong to the previous three groups.<br><br>\nLink : URL", "# Using this fine-tuned version\n\nFrom python, download the whole pipeline and use it instantly using the following code :\n\n\nReference for using this pipeline to find NER tags can be found in this <a href=\"URL" ]	[ "TAGS\n#transformers #pytorch #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# Named Entity Recognition using Transformers\nThis is a Fine-tuned version of BERT using HuggingFace transformers to perform Named Entity Recognition on Text data. BERT is a state-of-the-art model with attention mechanism as underlying architecture trained with masked-language-modeling and next-sentence-prediction objectives, used for various tasks including Question answering systems, Text Summarization, etc... which can also perform token classification tasks such as NER with great performance.", "# Dataset\nCoNLL-2003 :\nThe shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations, and names of miscellaneous entities that do not belong to the previous three groups.<br><br>\nLink : URL", "# Using this fine-tuned version\n\nFrom python, download the whole pipeline and use it instantly using the following code :\n\n\nReference for using this pipeline to find NER tags can be found in this <a href=\"URL" ]	[ 44, 122, 79, 50 ]	[ "passage: TAGS\n#transformers #pytorch #tf #bert #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Named Entity Recognition using Transformers\nThis is a Fine-tuned version of BERT using HuggingFace transformers to perform Named Entity Recognition on Text data. BERT is a state-of-the-art model with attention mechanism as underlying architecture trained with masked-language-modeling and next-sentence-prediction objectives, used for various tasks including Question answering systems, Text Summarization, etc... which can also perform token classification tasks such as NER with great performance.# Dataset\nCoNLL-2003 :\nThe shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations, and names of miscellaneous entities that do not belong to the previous three groups.<br><br>\nLink : URL# Using this fine-tuned version\n\nFrom python, download the whole pipeline and use it instantly using the following code :\n\n\nReference for using this pipeline to find NER tags can be found in this <a href=\"URL" ]	[ 0.007528549060225487, 0.12225406616926193, -0.005482603795826435, 0.07834136486053467, 0.16907888650894165, -0.006162520498037338, 0.025740865617990494, 0.035751648247241974, 0.01377036515623331, 0.06663728505373001, -0.049724236130714417, 0.03469553589820862, -0.008502813056111336, 0.010965624824166298, 0.0228731669485569, -0.17571376264095306, 0.013792689889669418, -0.018684446811676025, 0.01417973730713129, 0.07645481824874878, 0.05029646307229996, -0.046168841421604156, 0.09513140469789505, 0.07975509762763977, -0.08415573090314865, 0.06610231846570969, -0.022023197263479233, 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null	null	transformers	# Test Bot DialoGTP Model	{"tags": ["conversational"]}	text-generation	balta/DialoGPT-small-TestBot	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Test Bot DialoGTP Model	[ "# Test Bot DialoGTP Model" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Test Bot DialoGTP Model" ]	[ 51, 8 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Test Bot DialoGTP Model" ]	[ -0.04349280893802643, 0.012079959735274315, -0.0047833239659667015, 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null	null	transformers	TRIGGER WARNING --------------- This model was created by training GPT2-medium on a custom dataset containing tens of thousands of blog posts about people's experiences living with mental illnesses. As such, the texts that this model generates may be triggering and/or NSFW. Please explore at your own discretion. The blog posts that were compiled were specifically about 6 different mental health conditions: depression, ptsd, cptsd, borderline personality disorder, bipolar (non-specific), and dissociation. These are very serious illnesses so please treat this with respect, and I encourage everyone to learn more about these conditions. Thank you, and enjoy!	{"language": "en", "widget": [{"text": "I feel "}, {"text": "I want "}, {"text": "I believe "}]}	text-generation	banalyst/wonder-egg	[ "transformers", "pytorch", "gpt2", "text-generation", "en", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	TRIGGER WARNING --------------- This model was created by training GPT2-medium on a custom dataset containing tens of thousands of blog posts about people's experiences living with mental illnesses. As such, the texts that this model generates may be triggering and/or NSFW. Please explore at your own discretion. The blog posts that were compiled were specifically about 6 different mental health conditions: depression, ptsd, cptsd, borderline personality disorder, bipolar (non-specific), and dissociation. These are very serious illnesses so please treat this with respect, and I encourage everyone to learn more about these conditions. Thank you, and enjoy!	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null	null	transformers	# Rick Sanchez DialoGPT Model	{"tags": ["conversational"]}	text-generation	banden/DialoGPT-medium-RickBot	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Rick Sanchez DialoGPT Model	[ "# Rick Sanchez DialoGPT Model" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Rick Sanchez DialoGPT Model" ]	[ 51, 8 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Rick Sanchez DialoGPT Model" ]	[ -0.05704520270228386, 0.1080707237124443, 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null	null	transformers	# Loki DialoGPT Model	{"tags": ["conversational"]}	text-generation	banden/DialoGPT-small-LokiBot	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Loki DialoGPT Model	[ "# Loki DialoGPT Model" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Loki DialoGPT Model" ]	[ 51, 8 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Loki DialoGPT Model" ]	[ 0.01486613042652607, 0.030066967010498047, -0.004986276384443045, 0.037759166210889816, 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null	null	transformers	## Overview This model was trained with data from https://registry.opendata.aws/helpful-sentences-from-reviews/ to predict how "helpful" a review is. The model was fine-tuned from the `distilbert-base-uncased` model ### Labels LABEL_0 - Not helpful LABEL_1 - Helpful ### How to use The following code shows how to make a prediction with this model ```python from transformers import ( AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline, ) tokenizer = AutoTokenizer.from_pretrained("banjtheman/distilbert-base-uncased-helpful-amazon") model = AutoModelForSequenceClassification.from_pretrained( "banjtheman/distilbert-base-uncased-helpful-amazon" ) pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer) result = pipe("This was a Christmas gift for my grandson.") print(result) #[{'label': 'LABEL_0', 'score': 0.998775064945221}] # This is NOT A HELPFUL comment ```	{"license": "apache-2.0"}	text-classification	banjtheman/distilbert-base-uncased-helpful-amazon	[ "transformers", "pytorch", "distilbert", "text-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #distilbert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	## Overview This model was trained with data from URL to predict how "helpful" a review is. The model was fine-tuned from the 'distilbert-base-uncased' model ### Labels LABEL_0 - Not helpful LABEL_1 - Helpful ### How to use The following code shows how to make a prediction with this model	[ "## Overview\r\n\r\nThis model was trained with data from URL to predict how \"helpful\" a review is.\r\n\r\nThe model was fine-tuned from the 'distilbert-base-uncased' model", "### Labels\r\nLABEL_0 - Not helpful \r\nLABEL_1 - Helpful", "### How to use\r\n\r\nThe following code shows how to make a prediction with this model" ]	[ "TAGS\n#transformers #pytorch #distilbert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "## Overview\r\n\r\nThis model was trained with data from URL to predict how \"helpful\" a review is.\r\n\r\nThe model was fine-tuned from the 'distilbert-base-uncased' model", "### Labels\r\nLABEL_0 - Not helpful \r\nLABEL_1 - Helpful", "### How to use\r\n\r\nThe following code shows how to make a prediction with this model" ]	[ 46, 44, 16, 18 ]	[ "passage: TAGS\n#transformers #pytorch #distilbert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n## Overview\r\n\r\nThis model was trained with data from URL to predict how \"helpful\" a review is.\r\n\r\nThe model was fine-tuned from the 'distilbert-base-uncased' model### Labels\r\nLABEL_0 - Not helpful \r\nLABEL_1 - Helpful### How to use\r\n\r\nThe following code shows how to make a prediction with this model" ]	[ -0.041265297681093216, 0.14266382157802582, -0.0013327760389074683, 0.04730002582073212, 0.11242728680372238, -0.03496745228767395, 0.07329621911048889, 0.07766947150230408, 0.004562120884656906, -0.03897024691104889, 0.14361701905727386, 0.15199263393878937, -0.007313192822039127, 0.06383156031370163, -0.06333646178245544, -0.2305273860692978, 0.06364639103412628, 0.02619621530175209, 0.06776201725006104, 0.14936892688274384, 0.12718719244003296, -0.0917428731918335, 0.07252329587936401, 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null	null	transformers	Model based on [ruGPT-3](https://huggingface.co/sberbank-ai/rugpt3small_based_on_gpt2) for generating songs. Tuned on lyrics collected from [genius](https://genius.com/). Examples of used artists: * [Oxxxymiron](https://genius.com/artists/Oxxxymiron) * [Моргенштерн](https://genius.com/artists/Morgenshtern) * [ЛСП](https://genius.com/artists/Lsp) * [Гражданская оборона](https://genius.com/artists/Civil-defense) * [Король и Шут](https://genius.com/artists/The-king-and-the-jester) * etc	{"language": ["ru"], "tags": ["PyTorch", "Transformers"], "widget": [{"text": "\u0411\u0430\u0442\u044f \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u0442\u0441\u044f \u0442\u0440\u0435\u0437\u0432\u044b\u0439, \u0432 \u0440\u0443\u043a\u0435 \u0431\u0443\u0445\u0430\u043d\u043a\u0430", "example_title": "Example 1"}, {"text": "\u041a\u0430\u043a \u0434\u0435\u043b\u0430? \u041a\u0430\u043a \u0434\u0435\u043b\u0430? \u042d\u0442\u043e \u043d\u043e\u0432\u044b\u0439 \u043a\u0430\u0434\u0438\u043b\u043b\u0430\u043a", "example_title": "Example 2"}, {"text": "4:20 \u043d\u0430 \u0447\u0430\u0441\u0430\u0445 \u0438 \u044f \u0434\u0440\u043e\u0447\u0443 \u043d\u0430 \u0442\u0432\u043e\u0451 \u0444\u043e\u0442\u043e", "example_title": "Example 3"}], "inference": {"parameters": {"temperature": 0.9, "k": 50, "p": 0.95, "length": 1500}}}	text-generation	bankholdup/rugpt3_song_writer	[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "PyTorch", "Transformers", "ru", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "ru" ]	TAGS #transformers #pytorch #safetensors #gpt2 #text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us	Model based on ruGPT-3 for generating songs. Tuned on lyrics collected from genius. Examples of used artists: * Oxxxymiron * Моргенштерн * ЛСП * Гражданская оборона * Король и Шут * etc	[]	[ "TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n" ]	[ 67 ]	[ "passage: TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n" ]	[ -0.008647638373076916, -0.03651204705238342, -0.0060465168207883835, 0.018718423321843147, 0.1250101923942566, -0.006818299647420645, 0.15762774646282196, 0.08865608274936676, -0.0364232175052166, 0.02058558538556099, 0.16555942595005035, 0.1380137801170349, -0.02212666906416416, 0.09638597071170807, -0.0534849688410759, -0.2700098156929016, 0.09471429884433746, 0.02237537130713463, -0.060780156403779984, 0.14116628468036652, 0.09550432115793228, -0.0799560472369194, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7523 - Matthews Correlation: 0.5259 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.533 \| 1.0 \| 535 \| 0.5318 \| 0.3887 \| \| 0.3562 \| 2.0 \| 1070 \| 0.5145 \| 0.5100 \| \| 0.2429 \| 3.0 \| 1605 \| 0.6558 \| 0.4888 \| \| 0.1831 \| 4.0 \| 2140 \| 0.7523 \| 0.5259 \| \| 0.1352 \| 5.0 \| 2675 \| 0.8406 \| 0.5182 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.5258663312307151, "name": "Matthews Correlation"}]}]}]}	text-classification	banri/distilbert-base-uncased-finetuned-cola	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	distilbert-base-uncased-finetuned-cola ====================================== This model is a fine-tuned version of distilbert-base-uncased on the glue dataset. It achieves the following results on the evaluation set: * Loss: 0.7523 * Matthews Correlation: 0.5259 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.12.3 * Pytorch 1.10.0+cu111 * Datasets 1.15.1 * Tokenizers 0.10.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3" ]	[ 67, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3" ]	[ -0.09953347593545914, 0.09874087572097778, -0.002511604456230998, 0.12091247737407684, 0.16499070823192596, 0.034331291913986206, 0.1324433982372284, 0.12642896175384521, -0.08521388471126556, 0.021414093673229218, 0.12043958902359009, 0.1608530431985855, 0.02341119386255741, 0.11020602285861969, -0.04826747626066208, -0.26533666253089905, -0.01393829844892025, 0.04971835017204285, -0.05632239580154419, 0.1354067325592041, 0.09114500880241394, 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null	null	transformers	# Multi-dialect-Arabic-BERT This is a repository of Multi-dialect Arabic BERT model. By [Mawdoo3-AI](https://ai.mawdoo3.com/). <p align="center"> <br> <img src="https://raw.githubusercontent.com/mawdoo3/Multi-dialect-Arabic-BERT/master/multidialct_arabic_bert.png" alt="Background reference: http://www.qfi.org/wp-content/uploads/2018/02/Qfi_Infographic_Mother-Language_Final.pdf" width="500"/> <br> <p> ### About our Multi-dialect-Arabic-BERT model Instead of training the Multi-dialect Arabic BERT model from scratch, we initialized the weights of the model using [Arabic-BERT](https://github.com/alisafaya/Arabic-BERT) and trained it on 10M arabic tweets from the unlabled data of [The Nuanced Arabic Dialect Identification (NADI) shared task](https://sites.google.com/view/nadi-shared-task). ### To cite this work ``` @misc{talafha2020multidialect, title={Multi-Dialect Arabic BERT for Country-Level Dialect Identification}, author={Bashar Talafha and Mohammad Ali and Muhy Eddin Za'ter and Haitham Seelawi and Ibraheem Tuffaha and Mostafa Samir and Wael Farhan and Hussein T. Al-Natsheh}, year={2020}, eprint={2007.05612}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Usage The model weights can be loaded using `transformers` library by HuggingFace. ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bashar-talafha/multi-dialect-bert-base-arabic") model = AutoModel.from_pretrained("bashar-talafha/multi-dialect-bert-base-arabic") ``` Example using `pipeline`: ```python from transformers import pipeline fill_mask = pipeline( "fill-mask", model="bashar-talafha/multi-dialect-bert-base-arabic ", tokenizer="bashar-talafha/multi-dialect-bert-base-arabic " ) fill_mask(" سافر الرحالة من مطار [MASK] ") ``` ``` [{'sequence': '[CLS] سافر الرحالة من مطار الكويت [SEP]', 'score': 0.08296813815832138, 'token': 3226}, {'sequence': '[CLS] سافر الرحالة من مطار دبي [SEP]', 'score': 0.05123933032155037, 'token': 4747}, {'sequence': '[CLS] سافر الرحالة من مطار مسقط [SEP]', 'score': 0.046838656067848206, 'token': 13205}, {'sequence': '[CLS] سافر الرحالة من مطار القاهرة [SEP]', 'score': 0.03234650194644928, 'token': 4003}, {'sequence': '[CLS] سافر الرحالة من مطار الرياض [SEP]', 'score': 0.02606341242790222, 'token': 2200}] ``` ### Repository Please check the [original repository](https://github.com/mawdoo3/Multi-dialect-Arabic-BERT) for more information.	{"language": "ar", "datasets": ["nadi"], "thumbnail": "https://raw.githubusercontent.com/mawdoo3/Multi-dialect-Arabic-BERT/master/multidialct_arabic_bert.png"}	fill-mask	bashar-talafha/multi-dialect-bert-base-arabic	[ "transformers", "pytorch", "jax", "bert", "fill-mask", "ar", "dataset:nadi", "arxiv:2007.05612", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]	2022-03-02T23:29:05+00:00	[ "2007.05612" ]	[ "ar" ]	TAGS #transformers #pytorch #jax #bert #fill-mask #ar #dataset-nadi #arxiv-2007.05612 #autotrain_compatible #endpoints_compatible #has_space #region-us	# Multi-dialect-Arabic-BERT This is a repository of Multi-dialect Arabic BERT model. By Mawdoo3-AI. <p align="center"> <br> <img src="URL alt="Background reference: URL width="500"/> <br> <p> ### About our Multi-dialect-Arabic-BERT model Instead of training the Multi-dialect Arabic BERT model from scratch, we initialized the weights of the model using Arabic-BERT and trained it on 10M arabic tweets from the unlabled data of The Nuanced Arabic Dialect Identification (NADI) shared task. ### To cite this work ### Usage The model weights can be loaded using 'transformers' library by HuggingFace. Example using 'pipeline': ### Repository Please check the original repository for more information.	[ "# Multi-dialect-Arabic-BERT\nThis is a repository of Multi-dialect Arabic BERT model.\n\nBy Mawdoo3-AI. \n\n<p align=\"center\">\n <br>\n <img src=\"URL alt=\"Background reference: URL width=\"500\"/>\n <br>\n<p>", "### About our Multi-dialect-Arabic-BERT model\nInstead of training the Multi-dialect Arabic BERT model from scratch, we initialized the weights of the model using Arabic-BERT and trained it on 10M arabic tweets from the unlabled data of The Nuanced Arabic Dialect Identification (NADI) shared task.", "### To cite this work", "### Usage\nThe model weights can be loaded using 'transformers' library by HuggingFace.\n\n\n\nExample using 'pipeline':", "### Repository\nPlease check the original repository for more information." ]	[ "TAGS\n#transformers #pytorch #jax #bert #fill-mask #ar #dataset-nadi #arxiv-2007.05612 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# Multi-dialect-Arabic-BERT\nThis is a repository of Multi-dialect Arabic BERT model.\n\nBy Mawdoo3-AI. \n\n<p align=\"center\">\n <br>\n <img src=\"URL alt=\"Background reference: URL width=\"500\"/>\n <br>\n<p>", "### About our Multi-dialect-Arabic-BERT model\nInstead of training the Multi-dialect Arabic BERT model from scratch, we initialized the weights of the model using Arabic-BERT and trained it on 10M arabic tweets from the unlabled data of The Nuanced Arabic Dialect Identification (NADI) shared task.", "### To cite this work", "### Usage\nThe model weights can be loaded using 'transformers' library by HuggingFace.\n\n\n\nExample using 'pipeline':", "### Repository\nPlease check the original repository for more information." ]	[ 59, 73, 81, 6, 34, 16 ]	[ "passage: TAGS\n#transformers #pytorch #jax #bert #fill-mask #ar #dataset-nadi #arxiv-2007.05612 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Multi-dialect-Arabic-BERT\nThis is a repository of Multi-dialect Arabic BERT model.\n\nBy Mawdoo3-AI. \n\n<p align=\"center\">\n <br>\n <img src=\"URL alt=\"Background reference: URL width=\"500\"/>\n <br>\n<p>### About our Multi-dialect-Arabic-BERT model\nInstead of training the Multi-dialect Arabic BERT model from scratch, we initialized the weights of the model using Arabic-BERT and trained it on 10M arabic tweets from the unlabled data of The Nuanced Arabic Dialect Identification (NADI) shared task.### To cite this work### Usage\nThe model weights can be loaded using 'transformers' library by HuggingFace.\n\n\n\nExample using 'pipeline':### Repository\nPlease check the original repository for more information." ]	[ -0.04786442220211029, 0.14846716821193695, -0.0008238328737206757, 0.05153226852416992, 0.13231772184371948, 0.03876212239265442, 0.2706740200519562, 0.07417917996644974, 0.07446876913309097, -0.025244159623980522, 0.061297301203012466, 0.019946692511439323, 0.09599395841360092, 0.10672583431005478, 0.06938017159700394, -0.22578655183315277, -0.0013659228570759296, -0.002580532804131508, 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null	null	transformers	# BatteryBERT-cased for Battery Abstract Classification Language model: batterybert-cased Language: English Downstream-task: Text Classification Training data: training\_data.csv Eval data: val\_data.csv Code: See [example](https://github.com/ShuHuang/batterybert) Infrastructure: 8x DGX A100 ## Hyperparameters ``` batch_size = 32 n_epochs = 11 base_LM_model = "batterybert-cased" learning_rate = 2e-5 ``` ## Performance ``` "Validation accuracy": 97.29, "Test accuracy": 96.85, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline model_name = "batterydata/batterybert-cased-abstract" # a) Get predictions nlp = pipeline('text-classification', model=model_name, tokenizer=model_name) input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'} res = nlp(input) # b) Load model & tokenizer model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": "Text Classification", "datasets": ["batterydata/paper-abstracts"], "metrics": "glue"}	text-classification	batterydata/batterybert-cased-abstract	[ "transformers", "pytorch", "bert", "text-classification", "Text Classification", "en", "dataset:batterydata/paper-abstracts", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# BatteryBERT-cased for Battery Abstract Classification Language model: batterybert-cased Language: English Downstream-task: Text Classification Training data: training\_data.csv Eval data: val\_data.csv Code: See example Infrastructure: 8x DGX A100 ## Hyperparameters ## Performance ## Usage ### In Transformers ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BatteryBERT-cased for Battery Abstract Classification \r\nLanguage model: batterybert-cased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BatteryBERT-cased for Battery Abstract Classification \r\nLanguage model: batterybert-cased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 64, 69, 5, 2, 3, 6, 50 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# BatteryBERT-cased for Battery Abstract Classification \r\nLanguage model: batterybert-cased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100## Hyperparameters## Performance## Usage### In Transformers## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ -0.06621818244457245, 0.047621406614780426, -0.0034733200445771217, 0.04851040616631508, 0.25448092818260193, 0.029042290523648262, 0.17969052493572235, 0.03427138552069664, -0.10801959037780762, -0.038759876042604446, 0.042869798839092255, 0.03317273035645485, 0.04895966127514839, 0.06076286733150482, -0.03576920926570892, -0.18589478731155396, 0.03891358897089958, 0.004405754152685404, 0.026488736271858215, 0.12215378880500793, 0.14108961820602417, -0.09149077534675598, 0.11846058815717697, 0.021844904869794846, -0.11352993547916412, 0.02782781422138214, 0.02233487367630005, 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null	null	transformers	# BatteryBERT-cased for QA Language model: batterybert-cased Language: English Downstream-task: Extractive QA Training data: SQuAD v1 Eval data: SQuAD v1 Code: See [example](https://github.com/ShuHuang/batterybert) Infrastructure: 8x DGX A100 ## Hyperparameters ``` batch_size = 16 n_epochs = 4 base_LM_model = "batterybert-cased" max_seq_len = 386 learning_rate = 2e-5 doc_stride=128 max_query_length=64 ``` ## Performance Evaluated on the SQuAD v1.0 dev set. ``` "exact": 81.54, "f1": 89.16, ``` Evaluated on the battery device dataset. ``` "precision": 70.74, "recall": 84.19, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "batterydata/batterybert-cased-squad-v1" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'What is the electrolyte?', 'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": "question answering", "datasets": ["squad", "batterydata/battery-device-data-qa"], "metrics": "squad"}	question-answering	batterydata/batterybert-cased-squad-v1	[ "transformers", "pytorch", "bert", "question-answering", "question answering", "en", "dataset:squad", "dataset:batterydata/battery-device-data-qa", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us	# BatteryBERT-cased for QA Language model: batterybert-cased Language: English Downstream-task: Extractive QA Training data: SQuAD v1 Eval data: SQuAD v1 Code: See example Infrastructure: 8x DGX A100 ## Hyperparameters ## Performance Evaluated on the SQuAD v1.0 dev set. Evaluated on the battery device dataset. ## Usage ### In Transformers ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BatteryBERT-cased for QA \r\nLanguage model: batterybert-cased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n", "# BatteryBERT-cased for QA \r\nLanguage model: batterybert-cased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 67, 64, 5, 23, 3, 6, 50 ]	[ "passage: TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n# BatteryBERT-cased for QA \r\nLanguage model: batterybert-cased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100## Hyperparameters## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.## Usage### In Transformers## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ -0.07348915934562683, 0.02780606970191002, -0.004550732672214508, 0.05600939318537712, 0.22088462114334106, 0.04112272337079048, 0.11025670915842056, 0.04638279229402542, -0.1021728366613388, -0.02056519314646721, 0.04531377553939819, -0.033153459429740906, 0.077457956969738, 0.061577584594488144, 0.008907143026590347, -0.08559592068195343, 0.03550928458571434, -0.02996065653860569, 0.006684481166303158, 0.14341962337493896, 0.11783464252948761, -0.09675288200378418, 0.12642501294612885, 0.016437826678156853, -0.09183673560619354, 0.07271864265203476, -0.0068285297602415085, -0.07543203234672546, 0.10607361793518066, -0.08078379184007645, 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null	null	transformers	# BatteryBERT-uncased model Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the [bert-base-cased](https://huggingface.co/bert-base-cased) weights. It was introduced in [this paper](paper_link) and first released in [this repository](https://github.com/ShuHuang/batterybert). This model is case-sensitive: it makes a difference between english and English. ## Model description BatteryBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the [bert-base-cased](https://huggingface.co/bert-base-cased) weights. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Training data The BatteryBERT model was pretrained on the full text of battery papers only, after initialized from the [bert-base-cased](https://huggingface.co/bert-base-cased) weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at [Github](https://github.com/ShuHuang/batterybert/blob/main/corpus.txt). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 28,996. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=batterybert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='batterydata/batterybert-cased') >>> unmasker("Hello I'm a <mask> model.") ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('batterydata/batterybert-cased') model = BertModel.from_pretrained('batterydata/batterybert-cased') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('batterydata/batterybert-cased') model = TFBertModel.from_pretrained('batterydata/batterybert-cased') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results Final loss: 0.9609. ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["batterypapers"]}	fill-mask	batterydata/batterybert-cased	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "exbert", "en", "dataset:batterypapers", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# BatteryBERT-uncased model Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the bert-base-cased weights. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between english and English. ## Model description BatteryBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the bert-base-cased weights. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Training data The BatteryBERT model was pretrained on the full text of battery papers only, after initialized from the bert-base-cased weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at Github. ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 28,996. The inputs of the model are then of the form: The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by '[MASK]'. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: Here is how to use this model to get the features of a given text in PyTorch: and in TensorFlow: ## Evaluation results Final loss: 0.9609. ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BatteryBERT-uncased model\r\n\r\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the bert-base-cased weights. It was introduced in\r\nthis paper and first released in\r\nthis repository. This model is case-sensitive: it makes a difference between english and English.", "## Model description\r\n\r\nBatteryBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the bert-base-cased weights. This means\r\nit was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\r\npublicly available data) with an automatic process to generate inputs and labels from those texts. \r\n\r\nMore precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model\r\nrandomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict\r\nthe masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one\r\nafter the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to\r\nlearn a bidirectional representation of the sentence.\r\n\r\nThis way, the model learns an inner representation of the English language that can then be used to extract features\r\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\r\nclassifier using the features produced by the BERT model as inputs.", "## Training data\r\n\r\nThe BatteryBERT model was pretrained on the full text of battery papers only, after initialized from the bert-base-cased weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at Github.", "## Training procedure", "### Preprocessing\r\n\r\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 28,996. The inputs of the model are\r\nthen of the form:\r\n\r\n\r\n\r\nThe details of the masking procedure for each sentence are the following:\r\n- 15% of the tokens are masked.\r\n- In 80% of the cases, the masked tokens are replaced by '[MASK]'.\r\n- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.\r\n- In the 10% remaining cases, the masked tokens are left as is.", "### Pretraining\r\n\r\n\r\nThe model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\\\(\\beta_{1} = 0.9\\\\) and \\\\(\\beta_{2} = 0.999\\\\), a weight decay of 0.01,\r\nlearning rate warmup for 10,000 steps and linear decay of the learning rate after.", "## Intended uses & limitations\r\n\r\nYou can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.\r\nSee the model hub to look for fine-tuned versions on a task that\r\ninterests you.\r\n\r\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\r\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\r\ngeneration you should look at model like GPT2.", "### How to use\r\n\r\nYou can use this model directly with a pipeline for masked language modeling:\r\n\r\n\r\n\r\nHere is how to use this model to get the features of a given text in PyTorch:\r\n\r\n\r\n\r\nand in TensorFlow:", "## Evaluation results\r\n\r\nFinal loss: 0.9609.", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BatteryBERT-uncased model\r\n\r\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the bert-base-cased weights. It was introduced in\r\nthis paper and first released in\r\nthis repository. This model is case-sensitive: it makes a difference between english and English.", "## Model description\r\n\r\nBatteryBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the bert-base-cased weights. This means\r\nit was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\r\npublicly available data) with an automatic process to generate inputs and labels from those texts. \r\n\r\nMore precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model\r\nrandomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict\r\nthe masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one\r\nafter the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to\r\nlearn a bidirectional representation of the sentence.\r\n\r\nThis way, the model learns an inner representation of the English language that can then be used to extract features\r\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\r\nclassifier using the features produced by the BERT model as inputs.", "## Training data\r\n\r\nThe BatteryBERT model was pretrained on the full text of battery papers only, after initialized from the bert-base-cased weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at Github.", "## Training procedure", "### Preprocessing\r\n\r\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 28,996. The inputs of the model are\r\nthen of the form:\r\n\r\n\r\n\r\nThe details of the masking procedure for each sentence are the following:\r\n- 15% of the tokens are masked.\r\n- In 80% of the cases, the masked tokens are replaced by '[MASK]'.\r\n- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.\r\n- In the 10% remaining cases, the masked tokens are left as is.", "### Pretraining\r\n\r\n\r\nThe model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\\\(\\beta_{1} = 0.9\\\\) and \\\\(\\beta_{2} = 0.999\\\\), a weight decay of 0.01,\r\nlearning rate warmup for 10,000 steps and linear decay of the learning rate after.", "## Intended uses & limitations\r\n\r\nYou can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.\r\nSee the model hub to look for fine-tuned versions on a task that\r\ninterests you.\r\n\r\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\r\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\r\ngeneration you should look at model like GPT2.", "### How to use\r\n\r\nYou can use this model directly with a pipeline for masked language modeling:\r\n\r\n\r\n\r\nHere is how to use this model to get the features of a given text in PyTorch:\r\n\r\n\r\n\r\nand in TensorFlow:", "## Evaluation results\r\n\r\nFinal loss: 0.9609.", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 61, 82, 282, 96, 3, 135, 113, 128, 49, 11, 50 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# BatteryBERT-uncased model\r\n\r\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the bert-base-cased weights. It was introduced in\r\nthis paper and first released in\r\nthis repository. This model is case-sensitive: it makes a difference between english and English.## Model description\r\n\r\nBatteryBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the bert-base-cased weights. This means\r\nit was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\r\npublicly available data) with an automatic process to generate inputs and labels from those texts. \r\n\r\nMore precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model\r\nrandomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict\r\nthe masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one\r\nafter the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to\r\nlearn a bidirectional representation of the sentence.\r\n\r\nThis way, the model learns an inner representation of the English language that can then be used to extract features\r\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\r\nclassifier using the features produced by the BERT model as inputs." ]	[ -0.055391427129507065, 0.09050572663545609, -0.005469315219670534, 0.04962697997689247, 0.08549407869577408, 0.0047820089384913445, 0.14589671790599823, -0.003752774326130748, -0.026874249801039696, 0.039737846702337265, 0.03029802069067955, 0.0019992354791611433, 0.0649605393409729, 0.07026061415672302, 0.09658321738243103, -0.26704859733581543, 0.05833619460463524, -0.09446095675230026, 0.08288810402154922, 0.06383085995912552, 0.07149132341146469, -0.09972473978996277, 0.021886220201849937, 0.055596284568309784, 0.0523337796330452, -0.007386418990790844, -0.0169509407132864, -0.09314937889575958, 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null	null	transformers	# BatteryBERT-uncased for Battery Abstract Classification Language model: batterybert-uncased Language: English Downstream-task: Text Classification Training data: training\_data.csv Eval data: val\_data.csv Code: See [example](https://github.com/ShuHuang/batterybert) Infrastructure: 8x DGX A100 ## Hyperparameters ``` batch_size = 32 n_epochs = 11 base_LM_model = "batterybert-uncased" learning_rate = 2e-5 ``` ## Performance ``` "Validation accuracy": 97.10, "Test accuracy": 96.94, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline model_name = "batterydata/batterybert-uncased-abstract" # a) Get predictions nlp = pipeline('text-classification', model=model_name, tokenizer=model_name) input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'} res = nlp(input) # b) Load model & tokenizer model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": "Text Classification", "datasets": ["batterydata/paper-abstracts"], "metrics": "glue"}	text-classification	batterydata/batterybert-uncased-abstract	[ "transformers", "pytorch", "bert", "text-classification", "Text Classification", "en", "dataset:batterydata/paper-abstracts", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# BatteryBERT-uncased for Battery Abstract Classification Language model: batterybert-uncased Language: English Downstream-task: Text Classification Training data: training\_data.csv Eval data: val\_data.csv Code: See example Infrastructure: 8x DGX A100 ## Hyperparameters ## Performance ## Usage ### In Transformers ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BatteryBERT-uncased for Battery Abstract Classification \r\nLanguage model: batterybert-uncased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BatteryBERT-uncased for Battery Abstract Classification \r\nLanguage model: batterybert-uncased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 64, 71, 5, 2, 3, 6, 50 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# BatteryBERT-uncased for Battery Abstract Classification \r\nLanguage model: batterybert-uncased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100## Hyperparameters## Performance## Usage### In Transformers## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ -0.06678469479084015, 0.06125020235776901, -0.003756333375349641, 0.05189472436904907, 0.2590707540512085, 0.029930997639894485, 0.1844843327999115, 0.033447377383708954, -0.09817646443843842, -0.036899976432323456, 0.04206230491399765, 0.040243640542030334, 0.049198321998119354, 0.0659877136349678, -0.04858899861574173, -0.16907064616680145, 0.03671753779053688, -0.00459680613130331, 0.0334855280816555, 0.11696502566337585, 0.13804613053798676, -0.08840112388134003, 0.11608561873435974, 0.010011457838118076, -0.10004810988903046, 0.02469039335846901, 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null	null	transformers	# BatteryBERT-uncased for QA Language model: batterybert-uncased Language: English Downstream-task: Extractive QA Training data: SQuAD v1 Eval data: SQuAD v1 Code: See [example](https://github.com/ShuHuang/batterybert) Infrastructure: 8x DGX A100 ## Hyperparameters ``` batch_size = 32 n_epochs = 3 base_LM_model = "batterybert-uncased" max_seq_len = 386 learning_rate = 3e-5 doc_stride=128 max_query_length=64 ``` ## Performance Evaluated on the SQuAD v1.0 dev set. ``` "exact": 81.08, "f1": 88.41, ``` Evaluated on the battery device dataset. ``` "precision": 68.27, "recall": 80.88, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "batterydata/batterybert-uncased-squad-v1" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'What is the electrolyte?', 'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": "question answering", "datasets": ["squad", "batterydata/battery-device-data-qa"], "metrics": "squad"}	question-answering	batterydata/batterybert-uncased-squad-v1	[ "transformers", "pytorch", "bert", "question-answering", "question answering", "en", "dataset:squad", "dataset:batterydata/battery-device-data-qa", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us	# BatteryBERT-uncased for QA Language model: batterybert-uncased Language: English Downstream-task: Extractive QA Training data: SQuAD v1 Eval data: SQuAD v1 Code: See example Infrastructure: 8x DGX A100 ## Hyperparameters ## Performance Evaluated on the SQuAD v1.0 dev set. Evaluated on the battery device dataset. ## Usage ### In Transformers ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BatteryBERT-uncased for QA \r\nLanguage model: batterybert-uncased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n", "# BatteryBERT-uncased for QA \r\nLanguage model: batterybert-uncased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 67, 66, 5, 23, 3, 6, 50 ]	[ "passage: TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n# BatteryBERT-uncased for QA \r\nLanguage model: batterybert-uncased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100## Hyperparameters## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.## Usage### In Transformers## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ -0.08698596805334091, 0.03932749852538109, -0.004285785369575024, 0.05968068167567253, 0.23035301268100739, 0.03959587216377258, 0.11446557939052582, 0.04404348507523537, -0.10159076005220413, -0.02527792379260063, 0.036759987473487854, -0.032385025173425674, 0.07063262164592743, 0.07091840356588364, 0.006257476285099983, -0.09634119272232056, 0.04323998838663101, -0.034888606518507004, 0.02366548776626587, 0.14080674946308136, 0.11539973318576813, -0.09064141660928726, 0.11492826789617538, 0.011028829030692577, -0.09012522548437119, 0.061866819858551025, -0.006155235227197409, -0.07663898915052414, 0.10015404224395752, -0.08250439912080765, 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null	null	transformers	# BatteryBERT-uncased model Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the [bert-base-uncased](https://huggingface.co/bert-base-uncased) weights. It was introduced in [this paper](paper_link) and first released in [this repository](https://github.com/ShuHuang/batterybert). This model is uncased: it does not make a difference between english and English. ## Model description BatteryBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the [bert-base-uncased](https://huggingface.co/bert-base-uncased) weights. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Training data The BatteryBERT model was pretrained on the full text of battery papers only, after initialized from the [bert-base-uncased](https://huggingface.co/bert-base-uncased) weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at [Github](https://github.com/ShuHuang/batterybert/blob/main/corpus.txt). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,522. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=batterybert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='batterydata/batterybert-uncased') >>> unmasker("Hello I'm a <mask> model.") ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('batterydata/batterybert-uncased') model = BertModel.from_pretrained('batterydata/batterybert-uncased') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('batterydata/batterybert-uncased') model = TFBertModel.from_pretrained('batterydata/batterybert-uncased') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results Final loss: 1.0317. ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["batterypapers"]}	fill-mask	batterydata/batterybert-uncased	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "exbert", "en", "dataset:batterypapers", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# BatteryBERT-uncased model Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the bert-base-uncased weights. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English. ## Model description BatteryBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the bert-base-uncased weights. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Training data The BatteryBERT model was pretrained on the full text of battery papers only, after initialized from the bert-base-uncased weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at Github. ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,522. The inputs of the model are then of the form: The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by '[MASK]'. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: Here is how to use this model to get the features of a given text in PyTorch: and in TensorFlow: ## Evaluation results Final loss: 1.0317. ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BatteryBERT-uncased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the bert-base-uncased weights. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.", "## Model description\n\nBatteryBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the bert-base-uncased weights. This means\nit was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model\nrandomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict\nthe masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one\nafter the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to\nlearn a bidirectional representation of the sentence.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.", "## Training data\n\nThe BatteryBERT model was pretrained on the full text of battery papers only, after initialized from the bert-base-uncased weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at Github.", "## Training procedure", "### Preprocessing\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,522. The inputs of the model are\nthen of the form:\n\n\n\nThe details of the masking procedure for each sentence are the following:\n- 15% of the tokens are masked.\n- In 80% of the cases, the masked tokens are replaced by '[MASK]'.\n- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.\n- In the 10% remaining cases, the masked tokens are left as is.", "### Pretraining\n\n\nThe model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\\\(\\beta_{1} = 0.9\\\\) and \\\\(\\beta_{2} = 0.999\\\\), a weight decay of 0.01,\nlearning rate warmup for 10,000 steps and linear decay of the learning rate after.", "## Intended uses & limitations\n\nYou can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.\nSee the model hub to look for fine-tuned versions on a task that\ninterests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.", "### How to use\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:", "## Evaluation results\n\nFinal loss: 1.0317.", "## Authors\nShu Huang: 'sh2009 [at] URL'\n\nJacqueline Cole: 'jmc61 [at] URL'\n\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BatteryBERT-uncased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the bert-base-uncased weights. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.", "## Model description\n\nBatteryBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the bert-base-uncased weights. This means\nit was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model\nrandomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict\nthe masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one\nafter the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to\nlearn a bidirectional representation of the sentence.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.", "## Training data\n\nThe BatteryBERT model was pretrained on the full text of battery papers only, after initialized from the bert-base-uncased weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at Github.", "## Training procedure", "### Preprocessing\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,522. The inputs of the model are\nthen of the form:\n\n\n\nThe details of the masking procedure for each sentence are the following:\n- 15% of the tokens are masked.\n- In 80% of the cases, the masked tokens are replaced by '[MASK]'.\n- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.\n- In the 10% remaining cases, the masked tokens are left as is.", "### Pretraining\n\n\nThe model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\\\(\\beta_{1} = 0.9\\\\) and \\\\(\\beta_{2} = 0.999\\\\), a weight decay of 0.01,\nlearning rate warmup for 10,000 steps and linear decay of the learning rate after.", "## Intended uses & limitations\n\nYou can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.\nSee the model hub to look for fine-tuned versions on a task that\ninterests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.", "### How to use\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:", "## Evaluation results\n\nFinal loss: 1.0317.", "## Authors\nShu Huang: 'sh2009 [at] URL'\n\nJacqueline Cole: 'jmc61 [at] URL'\n\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 61, 84, 283, 97, 3, 136, 113, 128, 49, 11, 50 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# BatteryBERT-uncased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the bert-base-uncased weights. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.## Model description\n\nBatteryBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the bert-base-uncased weights. This means\nit was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model\nrandomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict\nthe masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one\nafter the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to\nlearn a bidirectional representation of the sentence.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs." ]	[ -0.05132001265883446, 0.08077171444892883, -0.006066843867301941, 0.04470290616154671, 0.09435369819402695, 0.01168136764317751, 0.13184002041816711, 0.006085169035941362, -0.02031603828072548, 0.043706148862838745, 0.04241371527314186, 0.0047059440985322, 0.05085068568587303, 0.05189827084541321, 0.10268843173980713, -0.25415903329849243, 0.0418178029358387, -0.10190104693174362, 0.09251967072486877, 0.0729428231716156, 0.05511574447154999, -0.09618941694498062, 0.02680739015340805, 0.03830705210566521, 0.04058275744318962, -0.01000748760998249, -0.020798856392502785, -0.0807870402932167, 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null	null	transformers	# BatteryOnlyBERT-cased for Battery Abstract Classification Language model: batteryonlybert-cased Language: English Downstream-task: Text Classification Training data: training\_data.csv Eval data: val\_data.csv Code: See [example](https://github.com/ShuHuang/batterybert) Infrastructure: 8x DGX A100 ## Hyperparameters ``` batch_size = 32 n_epochs = 14 base_LM_model = "batteryonlybert-cased" learning_rate = 2e-5 ``` ## Performance ``` "Validation accuracy": 97.33, "Test accuracy": 97.34, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline model_name = "batterydata/batteryonlybert-cased-abstract" # a) Get predictions nlp = pipeline('text-classification', model=model_name, tokenizer=model_name) input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'} res = nlp(input) # b) Load model & tokenizer model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": "Text Classification", "datasets": ["batterydata/paper-abstracts"], "metrics": "glue"}	text-classification	batterydata/batteryonlybert-cased-abstract	[ "transformers", "pytorch", "bert", "text-classification", "Text Classification", "en", "dataset:batterydata/paper-abstracts", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# BatteryOnlyBERT-cased for Battery Abstract Classification Language model: batteryonlybert-cased Language: English Downstream-task: Text Classification Training data: training\_data.csv Eval data: val\_data.csv Code: See example Infrastructure: 8x DGX A100 ## Hyperparameters ## Performance ## Usage ### In Transformers ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BatteryOnlyBERT-cased for Battery Abstract Classification \r\nLanguage model: batteryonlybert-cased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BatteryOnlyBERT-cased for Battery Abstract Classification \r\nLanguage model: batteryonlybert-cased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 64, 73, 5, 2, 3, 6, 50 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# BatteryOnlyBERT-cased for Battery Abstract Classification \r\nLanguage model: batteryonlybert-cased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100## Hyperparameters## Performance## Usage### In Transformers## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ -0.06501241773366928, 0.08365874737501144, -0.004337509628385305, 0.05261564254760742, 0.2405385822057724, 0.031636666506528854, 0.19674797356128693, 0.04219766706228256, -0.09023874253034592, -0.03372187167406082, 0.04984490945935249, 0.03683934360742569, 0.053219545632600784, 0.05905504152178764, -0.046300314366817474, -0.17478050291538239, 0.030460666865110397, -0.011528155766427517, 0.028184853494167328, 0.12500402331352234, 0.13912920653820038, -0.08421700447797775, 0.11795789003372192, 0.007920478470623493, -0.10449990630149841, 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null	null	transformers	# BatteryOnlyBERT-cased for QA Language model: batteryonlybert-cased Language: English Downstream-task: Extractive QA Training data: SQuAD v1 Eval data: SQuAD v1 Code: See [example](https://github.com/ShuHuang/batterybert) Infrastructure: 8x DGX A100 ## Hyperparameters ``` batch_size = 16 n_epochs = 3 base_LM_model = "batteryonlybert-cased" max_seq_len = 386 learning_rate = 2e-5 doc_stride=128 max_query_length=64 ``` ## Performance Evaluated on the SQuAD v1.0 dev set. ``` "exact": 79.61, "f1": 87.30, ``` Evaluated on the battery device dataset. ``` "precision": 64.28, "recall": 82.72, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "batterydata/batteryonlybert-cased-squad-v1" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'What is the electrolyte?', 'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": "question answering", "datasets": ["squad", "batterydata/battery-device-data-qa"], "metrics": "squad"}	question-answering	batterydata/batteryonlybert-cased-squad-v1	[ "transformers", "pytorch", "bert", "question-answering", "question answering", "en", "dataset:squad", "dataset:batterydata/battery-device-data-qa", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us	# BatteryOnlyBERT-cased for QA Language model: batteryonlybert-cased Language: English Downstream-task: Extractive QA Training data: SQuAD v1 Eval data: SQuAD v1 Code: See example Infrastructure: 8x DGX A100 ## Hyperparameters ## Performance Evaluated on the SQuAD v1.0 dev set. Evaluated on the battery device dataset. ## Usage ### In Transformers ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BatteryOnlyBERT-cased for QA \r\nLanguage model: batteryonlybert-cased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n", "# BatteryOnlyBERT-cased for QA \r\nLanguage model: batteryonlybert-cased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 67, 68, 5, 23, 3, 6, 50 ]	[ "passage: TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n# BatteryOnlyBERT-cased for QA \r\nLanguage model: batteryonlybert-cased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100## Hyperparameters## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.## Usage### In Transformers## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ -0.08079832792282104, 0.032594382762908936, -0.0042159222066402435, 0.06078297272324562, 0.23093898594379425, 0.0355365164577961, 0.13760748505592346, 0.04351189360022545, -0.10658019781112671, -0.027120541781187057, 0.03539686277508736, -0.03221694007515907, 0.07133567333221436, 0.06533680111169815, 0.012910266406834126, -0.11539989709854126, 0.03759542852640152, -0.0379268117249012, 0.019749969244003296, 0.1463412493467331, 0.11707369983196259, -0.0851849764585495, 0.11293014138936996, 0.019384296610951424, -0.10665177553892136, 0.06460337340831757, -0.01533759105950594, -0.07954446226358414, 0.09784703701734543, -0.08263854682445526, 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null	null	transformers	# BatteryOnlyBERT-uncased for Battery Abstract Classification Language model: batteryonlybert-uncased Language: English Downstream-task: Text Classification Training data: training\_data.csv Eval data: val\_data.csv Code: See [example](https://github.com/ShuHuang/batterybert) Infrastructure: 8x DGX A100 ## Hyperparameters ``` batch_size = 16 n_epochs = 13 base_LM_model = "batteryonlybert-uncased" learning_rate = 3e-5 ``` ## Performance ``` "Validation accuracy": 97.18, "Test accuracy": 97.08, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline model_name = "batterydata/batteryonlybert-uncased-abstract" # a) Get predictions nlp = pipeline('text-classification', model=model_name, tokenizer=model_name) input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'} res = nlp(input) # b) Load model & tokenizer model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": "Text Classification", "datasets": ["batterydata/paper-abstracts"], "metrics": "glue"}	text-classification	batterydata/batteryonlybert-uncased-abstract	[ "transformers", "pytorch", "bert", "text-classification", "Text Classification", "en", "dataset:batterydata/paper-abstracts", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# BatteryOnlyBERT-uncased for Battery Abstract Classification Language model: batteryonlybert-uncased Language: English Downstream-task: Text Classification Training data: training\_data.csv Eval data: val\_data.csv Code: See example Infrastructure: 8x DGX A100 ## Hyperparameters ## Performance ## Usage ### In Transformers ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BatteryOnlyBERT-uncased for Battery Abstract Classification \r\nLanguage model: batteryonlybert-uncased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BatteryOnlyBERT-uncased for Battery Abstract Classification \r\nLanguage model: batteryonlybert-uncased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 64, 75, 5, 2, 3, 6, 50 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# BatteryOnlyBERT-uncased for Battery Abstract Classification \r\nLanguage model: batteryonlybert-uncased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100## Hyperparameters## Performance## Usage### In Transformers## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ -0.07742828875780106, 0.10324563086032867, -0.004303965251892805, 0.06040967255830765, 0.23968106508255005, 0.04231724143028259, 0.19550156593322754, 0.04532476142048836, -0.0928277000784874, -0.03630809858441353, 0.0477912463247776, 0.04150407761335373, 0.050924938172101974, 0.05722937360405922, -0.04931613802909851, -0.1593005359172821, 0.03393811732530594, -0.020234057679772377, 0.03483179956674576, 0.11443459242582321, 0.13344796001911163, -0.07543669641017914, 0.11243012547492981, -0.0020331775303930044, -0.10889507830142975, 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null	null	transformers	# BatteryOnlyBERT-uncased for QA Language model: batteryonlybert-uncased Language: English Downstream-task: Extractive QA Training data: SQuAD v1 Eval data: SQuAD v1 Code: See [example](https://github.com/ShuHuang/batterybert) Infrastructure: 8x DGX A100 ## Hyperparameters ``` batch_size = 16 n_epochs = 2 base_LM_model = "batteryonlybert-uncased" max_seq_len = 386 learning_rate = 2e-5 doc_stride=128 max_query_length=64 ``` ## Performance Evaluated on the SQuAD v1.0 dev set. ``` "exact": 79.53, "f1": 87.22, ``` Evaluated on the battery device dataset. ``` "precision": 67.20, "recall": 83.82, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "batterydata/batteryonlybert-uncased-squad-v1" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'What is the electrolyte?', 'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": "question answering", "datasets": ["squad", "batterydata/battery-device-data-qa"], "metrics": "squad"}	question-answering	batterydata/batteryonlybert-uncased-squad-v1	[ "transformers", "pytorch", "bert", "question-answering", "question answering", "en", "dataset:squad", "dataset:batterydata/battery-device-data-qa", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us	# BatteryOnlyBERT-uncased for QA Language model: batteryonlybert-uncased Language: English Downstream-task: Extractive QA Training data: SQuAD v1 Eval data: SQuAD v1 Code: See example Infrastructure: 8x DGX A100 ## Hyperparameters ## Performance Evaluated on the SQuAD v1.0 dev set. Evaluated on the battery device dataset. ## Usage ### In Transformers ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BatteryOnlyBERT-uncased for QA \r\nLanguage model: batteryonlybert-uncased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n", "# BatteryOnlyBERT-uncased for QA \r\nLanguage model: batteryonlybert-uncased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 67, 70, 5, 23, 3, 6, 50 ]	[ "passage: TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n# BatteryOnlyBERT-uncased for QA \r\nLanguage model: batteryonlybert-uncased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100## Hyperparameters## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.## Usage### In Transformers## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ -0.07915658503770828, 0.04639317840337753, -0.004253112245351076, 0.0666617825627327, 0.22728046774864197, 0.04471142590045929, 0.14725103974342346, 0.043492503464221954, -0.09297074377536774, -0.027500653639435768, 0.03533560782670975, -0.040488943457603455, 0.07112124562263489, 0.06418238580226898, 0.015294237062335014, -0.10378307849168777, 0.03585332632064819, -0.040270086377859116, 0.05269744247198105, 0.13700202107429504, 0.11054771393537521, -0.08317040652036667, 0.10975746065378189, 0.014754255302250385, -0.09952733665704727, 0.07087180763483047, -0.030588248744606972, -0.06900492310523987, 0.09058455377817154, 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null	null	transformers	# BatterySciBERT-cased for Battery Abstract Classification Language model: batteryscibert-cased Language: English Downstream-task: Text Classification Training data: training\_data.csv Eval data: val\_data.csv Code: See [example](https://github.com/ShuHuang/batterybert) Infrastructure: 8x DGX A100 ## Hyperparameters ``` batch_size = 32 n_epochs = 11 base_LM_model = "batteryscibert-cased" learning_rate = 2e-5 ``` ## Performance ``` "Validation accuracy": 97.06, "Test accuracy": 97.19, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline model_name = "batterydata/batteryscibert-cased-abstract" # a) Get predictions nlp = pipeline('text-classification', model=model_name, tokenizer=model_name) input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'} res = nlp(input) # b) Load model & tokenizer model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": "Text Classification", "datasets": ["batterydata/paper-abstracts"], "metrics": "glue"}	text-classification	batterydata/batteryscibert-cased-abstract	[ "transformers", "pytorch", "bert", "text-classification", "Text Classification", "en", "dataset:batterydata/paper-abstracts", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# BatterySciBERT-cased for Battery Abstract Classification Language model: batteryscibert-cased Language: English Downstream-task: Text Classification Training data: training\_data.csv Eval data: val\_data.csv Code: See example Infrastructure: 8x DGX A100 ## Hyperparameters ## Performance ## Usage ### In Transformers ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BatterySciBERT-cased for Battery Abstract Classification \r\nLanguage model: batteryscibert-cased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BatterySciBERT-cased for Battery Abstract Classification \r\nLanguage model: batteryscibert-cased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 64, 72, 5, 2, 3, 6, 50 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# BatterySciBERT-cased for Battery Abstract Classification \r\nLanguage model: batteryscibert-cased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100## Hyperparameters## Performance## Usage### In Transformers## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ -0.06472767144441605, 0.05003158003091812, -0.004300367087125778, 0.0496634878218174, 0.2479923516511917, 0.03231930360198021, 0.19530346989631653, 0.030132675543427467, -0.09084128588438034, -0.029783716425299644, 0.04104619845747948, 0.04150325804948807, 0.05602128803730011, 0.05864802747964859, -0.04158348962664604, -0.17187824845314026, 0.03024134784936905, -0.004030480980873108, 0.029881933704018593, 0.1242157444357872, 0.1365336924791336, -0.09049676358699799, 0.12120754271745682, 0.0136170145124197, -0.10470312088727951, 0.028160136193037033, 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null	null	transformers	# BatterySciBERT-cased for QA Language model: batteryscibert-cased Language: English Downstream-task: Extractive QA Training data: SQuAD v1 Eval data: SQuAD v1 Code: See [example](https://github.com/ShuHuang/batterybert) Infrastructure: 8x DGX A100 ## Hyperparameters ``` batch_size = 32 n_epochs = 3 base_LM_model = "batteryscibert-cased" max_seq_len = 386 learning_rate = 2e-5 doc_stride=128 max_query_length=64 ``` ## Performance Evaluated on the SQuAD v1.0 dev set. ``` "exact": 79.66, "f1": 87.43, ``` Evaluated on the battery device dataset. ``` "precision": 65.09, "recall": 84.56, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "batterydata/batteryscibert-cased-squad-v1" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'What is the electrolyte?', 'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": "question answering", "datasets": ["squad", "batterydata/battery-device-data-qa"], "metrics": "squad"}	question-answering	batterydata/batteryscibert-cased-squad-v1	[ "transformers", "pytorch", "bert", "question-answering", "question answering", "en", "dataset:squad", "dataset:batterydata/battery-device-data-qa", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us	# BatterySciBERT-cased for QA Language model: batteryscibert-cased Language: English Downstream-task: Extractive QA Training data: SQuAD v1 Eval data: SQuAD v1 Code: See example Infrastructure: 8x DGX A100 ## Hyperparameters ## Performance Evaluated on the SQuAD v1.0 dev set. Evaluated on the battery device dataset. ## Usage ### In Transformers ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BatterySciBERT-cased for QA \r\nLanguage model: batteryscibert-cased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n", "# BatterySciBERT-cased for QA \r\nLanguage model: batteryscibert-cased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 67, 67, 5, 23, 3, 6, 50 ]	[ "passage: TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n# BatterySciBERT-cased for QA \r\nLanguage model: batteryscibert-cased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100## Hyperparameters## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.## Usage### In Transformers## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ -0.07918901741504669, 0.030719337984919548, -0.004270212724804878, 0.058000996708869934, 0.2310180813074112, 0.03763307258486748, 0.12892302870750427, 0.0430271290242672, -0.1080101728439331, -0.027432426810264587, 0.03357856348156929, -0.02925903908908367, 0.07349278777837753, 0.07380899786949158, 0.015072333626449108, -0.11344629526138306, 0.04173228144645691, -0.03555799275636673, 0.013595445081591606, 0.14523719251155853, 0.11850740760564804, -0.08880425244569778, 0.11594665050506592, 0.014074096456170082, -0.10229967534542084, 0.06594649702310562, -0.010987377725541592, -0.07650550454854965, 0.1011410653591156, -0.08119576424360275, 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null	null	transformers	# BatterySciBERT-cased model Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the [SciBERT-cased](https://huggingface.co/allenai/scibert_scivocab_cased) weights. It was introduced in [this paper](paper_link) and first released in [this repository](https://github.com/ShuHuang/batterybert). This model is case-sensitive: it makes a difference between english and English. ## Model description BatterySciBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the [SciBERT-cased](https://huggingface.co/allenai/scibert_scivocab_cased) weights. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Training data The BatterySciBERT model was pretrained on the full text of battery papers only, after initialized from the [SciBERT-cased](https://huggingface.co/allenai/scibert_scivocab_cased) weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at [Github](https://github.com/ShuHuang/batterybert/blob/main/corpus.txt). ## Training procedure ### Preprocessing The texts are tokenized using WordPiece and a vocabulary size of 31,116. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=batterybert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='batterydata/batteryscibert-cased') >>> unmasker("Hello I'm a <mask> model.") ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('batterydata/batteryscibert-cased') model = BertModel.from_pretrained('batterydata/batteryscibert-cased') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('batterydata/batteryscibert-cased') model = TFBertModel.from_pretrained('batterydata/batteryscibert-cased') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results Final loss: 1.0505. ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["batterypapers"]}	fill-mask	batterydata/batteryscibert-cased	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "exbert", "en", "dataset:batterypapers", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# BatterySciBERT-cased model Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the SciBERT-cased weights. It was introduced in this paper and first released in this repository. This model is case-sensitive: it makes a difference between english and English. ## Model description BatterySciBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the SciBERT-cased weights. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Training data The BatterySciBERT model was pretrained on the full text of battery papers only, after initialized from the SciBERT-cased weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at Github. ## Training procedure ### Preprocessing The texts are tokenized using WordPiece and a vocabulary size of 31,116. The inputs of the model are then of the form: The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by '[MASK]'. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: Here is how to use this model to get the features of a given text in PyTorch: and in TensorFlow: ## Evaluation results Final loss: 1.0505. ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BatterySciBERT-cased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the SciBERT-cased weights. It was introduced in\nthis paper and first released in\nthis repository. This model is case-sensitive: it makes a difference between english and English.", "## Model description\n\nBatterySciBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the SciBERT-cased weights. This means\nit was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model\nrandomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict\nthe masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one\nafter the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to\nlearn a bidirectional representation of the sentence.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.", "## Training data\n\nThe BatterySciBERT model was pretrained on the full text of battery papers only, after initialized from the SciBERT-cased weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at Github.", "## Training procedure", "### Preprocessing\n\nThe texts are tokenized using WordPiece and a vocabulary size of 31,116. The inputs of the model are\nthen of the form:\n\n\n\nThe details of the masking procedure for each sentence are the following:\n- 15% of the tokens are masked.\n- In 80% of the cases, the masked tokens are replaced by '[MASK]'.\n- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.\n- In the 10% remaining cases, the masked tokens are left as is.", "### Pretraining\n\n\nThe model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\\\(\\beta_{1} = 0.9\\\\) and \\\\(\\beta_{2} = 0.999\\\\), a weight decay of 0.01,\nlearning rate warmup for 10,000 steps and linear decay of the learning rate after.", "## Intended uses & limitations\n\nYou can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.\nSee the model hub to look for fine-tuned versions on a task that\ninterests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.", "### How to use\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:", "## Evaluation results\n\nFinal loss: 1.0505.", "## Authors\nShu Huang: 'sh2009 [at] URL'\n\nJacqueline Cole: 'jmc61 [at] URL'\n\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BatterySciBERT-cased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the SciBERT-cased weights. It was introduced in\nthis paper and first released in\nthis repository. This model is case-sensitive: it makes a difference between english and English.", "## Model description\n\nBatterySciBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the SciBERT-cased weights. This means\nit was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model\nrandomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict\nthe masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one\nafter the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to\nlearn a bidirectional representation of the sentence.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.", "## Training data\n\nThe BatterySciBERT model was pretrained on the full text of battery papers only, after initialized from the SciBERT-cased weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at Github.", "## Training procedure", "### Preprocessing\n\nThe texts are tokenized using WordPiece and a vocabulary size of 31,116. The inputs of the model are\nthen of the form:\n\n\n\nThe details of the masking procedure for each sentence are the following:\n- 15% of the tokens are masked.\n- In 80% of the cases, the masked tokens are replaced by '[MASK]'.\n- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.\n- In the 10% remaining cases, the masked tokens are left as is.", "### Pretraining\n\n\nThe model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\\\(\\beta_{1} = 0.9\\\\) and \\\\(\\beta_{2} = 0.999\\\\), a weight decay of 0.01,\nlearning rate warmup for 10,000 steps and linear decay of the learning rate after.", "## Intended uses & limitations\n\nYou can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.\nSee the model hub to look for fine-tuned versions on a task that\ninterests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.", "### How to use\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:", "## Evaluation results\n\nFinal loss: 1.0505.", "## Authors\nShu Huang: 'sh2009 [at] URL'\n\nJacqueline Cole: 'jmc61 [at] URL'\n\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 61, 82, 283, 97, 3, 131, 113, 128, 49, 11, 50 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# BatterySciBERT-cased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the SciBERT-cased weights. It was introduced in\nthis paper and first released in\nthis repository. This model is case-sensitive: it makes a difference between english and English.## Model description\n\nBatterySciBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the SciBERT-cased weights. This means\nit was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model\nrandomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict\nthe masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one\nafter the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to\nlearn a bidirectional representation of the sentence.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs." ]	[ -0.045523080974817276, 0.06799842417240143, -0.0057398974895477295, 0.047050315886735916, 0.07623998820781708, 0.0152371134608984, 0.1584051549434662, -0.005189898423850536, -0.008393499068915844, 0.05165661498904228, 0.03522811457514763, 0.0027335737831890583, 0.0664839968085289, 0.07607081532478333, 0.0996999740600586, -0.2664535641670227, 0.06323351711034775, -0.10992493480443954, 0.08061287552118301, 0.0682177022099495, 0.07505840808153152, -0.08828286081552505, 0.023560959845781326, 0.04012952744960785, 0.051219720393419266, -0.013542224653065205, -0.011629599146544933, -0.08223625272512436, 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null	null	transformers	# BatterySciBERT-uncased for Battery Abstract Classification Language model: batteryscibert-uncased Language: English Downstream-task: Text Classification Training data: training\_data.csv Eval data: val\_data.csv Code: See [example](https://github.com/ShuHuang/batterybert) Infrastructure: 8x DGX A100 ## Hyperparameters ``` batch_size = 32 n_epochs = 14 base_LM_model = "batteryscibert-uncased" learning_rate = 2e-5 ``` ## Performance ``` "Validation accuracy": 97.12, "Test accuracy": 97.47, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline model_name = "batterydata/batteryscibert-uncased-abstract" # a) Get predictions nlp = pipeline('text-classification', model=model_name, tokenizer=model_name) input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'} res = nlp(input) # b) Load model & tokenizer model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": "Text Classification", "datasets": ["batterydata/paper-abstracts"], "metrics": "glue"}	text-classification	batterydata/batteryscibert-uncased-abstract	[ "transformers", "pytorch", "bert", "text-classification", "Text Classification", "en", "dataset:batterydata/paper-abstracts", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# BatterySciBERT-uncased for Battery Abstract Classification Language model: batteryscibert-uncased Language: English Downstream-task: Text Classification Training data: training\_data.csv Eval data: val\_data.csv Code: See example Infrastructure: 8x DGX A100 ## Hyperparameters ## Performance ## Usage ### In Transformers ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BatterySciBERT-uncased for Battery Abstract Classification \r\nLanguage model: batteryscibert-uncased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BatterySciBERT-uncased for Battery Abstract Classification \r\nLanguage model: batteryscibert-uncased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 64, 74, 5, 2, 3, 6, 50 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# BatterySciBERT-uncased for Battery Abstract Classification \r\nLanguage model: batteryscibert-uncased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100## Hyperparameters## Performance## Usage### In Transformers## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ -0.0690784826874733, 0.10396218299865723, -0.004250640515238047, 0.05132226273417473, 0.24002067744731903, 0.040813639760017395, 0.20327043533325195, 0.043149519711732864, -0.08336584270000458, -0.033805962651968, 0.039766497910022736, 0.03456681966781616, 0.056017156690359116, 0.06823539733886719, -0.047821398824453354, -0.1662082076072693, 0.03689992427825928, -0.019848769530653954, 0.027264438569545746, 0.11987078934907913, 0.13373039662837982, -0.07764999568462372, 0.10805616527795792, -0.005728617776185274, -0.11437921226024628, 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null	null	transformers	# BatterySciBERT-uncased for QA Language model: batteryscibert-uncased Language: English Downstream-task: Extractive QA Training data: SQuAD v1 Eval data: SQuAD v1 Code: See [example](https://github.com/ShuHuang/batterybert) Infrastructure: 8x DGX A100 ## Hyperparameters ``` batch_size = 32 n_epochs = 3 base_LM_model = "batteryscibert-uncased" max_seq_len = 386 learning_rate = 2e-5 doc_stride=128 max_query_length=64 ``` ## Performance Evaluated on the SQuAD v1.0 dev set. ``` "exact": 79.81, "f1": 87.66, ``` Evaluated on the battery device dataset. ``` "precision": 66.65, "recall": 85.29, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "batterydata/batteryscibert-uncased-squad-v1" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'What is the electrolyte?', 'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": "question answering", "datasets": ["squad", "batterydata/battery-device-data-qa"], "metrics": "squad"}	question-answering	batterydata/batteryscibert-uncased-squad-v1	[ "transformers", "pytorch", "bert", "question-answering", "question answering", "en", "dataset:squad", "dataset:batterydata/battery-device-data-qa", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us	# BatterySciBERT-uncased for QA Language model: batteryscibert-uncased Language: English Downstream-task: Extractive QA Training data: SQuAD v1 Eval data: SQuAD v1 Code: See example Infrastructure: 8x DGX A100 ## Hyperparameters ## Performance Evaluated on the SQuAD v1.0 dev set. Evaluated on the battery device dataset. ## Usage ### In Transformers ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BatterySciBERT-uncased for QA \r\nLanguage model: batteryscibert-uncased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n", "# BatterySciBERT-uncased for QA \r\nLanguage model: batteryscibert-uncased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 67, 69, 5, 23, 3, 6, 50 ]	[ "passage: TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n# BatterySciBERT-uncased for QA \r\nLanguage model: batteryscibert-uncased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100## Hyperparameters## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.## Usage### In Transformers## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ -0.07842893153429031, 0.03512030467391014, -0.004168678540736437, 0.06044456735253334, 0.2351476401090622, 0.04356449469923973, 0.14292463660240173, 0.04534607008099556, -0.0868968665599823, -0.02720780298113823, 0.02862444333732128, -0.031762778759002686, 0.0724911093711853, 0.06883685290813446, 0.009129505604505539, -0.10524318367242813, 0.03885357454419136, -0.042949944734573364, 0.0426681824028492, 0.138103649020195, 0.11126072704792023, -0.08451574295759201, 0.11271123588085175, 0.013143558986485004, -0.10323674231767654, 0.06851489096879959, -0.023593880236148834, -0.07189091295003891, 0.08874589949846268, -0.09487683326005936, 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null	null	transformers	# BatterySciBERT-uncased model Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the [SciBERT-uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) weights. It was introduced in [this paper](paper_link) and first released in [this repository](https://github.com/ShuHuang/batterybert). This model is uncased: it does not make a difference between english and English. ## Model description BatterySciBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the [SciBERT-uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) weights. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Training data The BatterySciBERT model was pretrained on the full text of battery papers only, after initialized from the [SciBERT-uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at [Github](https://github.com/ShuHuang/batterybert/blob/main/corpus.txt). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 31,090. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=batterybert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='batterydata/batteryscibert-uncased') >>> unmasker("Hello I'm a <mask> model.") ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('batterydata/batteryscibert-uncased') model = BertModel.from_pretrained('batterydata/batteryscibert-uncased') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('batterydata/batteryscibert-uncased') model = TFBertModel.from_pretrained('batterydata/batteryscibert-uncased') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results Final loss: 1.095. ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["batterypapers"]}	fill-mask	batterydata/batteryscibert-uncased	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "exbert", "en", "dataset:batterypapers", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# BatterySciBERT-uncased model Pretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the SciBERT-uncased weights. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English. ## Model description BatterySciBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the SciBERT-uncased weights. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Training data The BatterySciBERT model was pretrained on the full text of battery papers only, after initialized from the SciBERT-uncased weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at Github. ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 31,090. The inputs of the model are then of the form: The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by '[MASK]'. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: Here is how to use this model to get the features of a given text in PyTorch: and in TensorFlow: ## Evaluation results Final loss: 1.095. ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BatterySciBERT-uncased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the SciBERT-uncased weights. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.", "## Model description\n\nBatterySciBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the SciBERT-uncased weights. This means\nit was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model\nrandomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict\nthe masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one\nafter the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to\nlearn a bidirectional representation of the sentence.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.", "## Training data\n\nThe BatterySciBERT model was pretrained on the full text of battery papers only, after initialized from the SciBERT-uncased weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at Github.", "## Training procedure", "### Preprocessing\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 31,090. The inputs of the model are\nthen of the form:\n\n\n\nThe details of the masking procedure for each sentence are the following:\n- 15% of the tokens are masked.\n- In 80% of the cases, the masked tokens are replaced by '[MASK]'.\n- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.\n- In the 10% remaining cases, the masked tokens are left as is.", "### Pretraining\n\n\nThe model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\\\(\\beta_{1} = 0.9\\\\) and \\\\(\\beta_{2} = 0.999\\\\), a weight decay of 0.01,\nlearning rate warmup for 10,000 steps and linear decay of the learning rate after.", "## Intended uses & limitations\n\nYou can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.\nSee the model hub to look for fine-tuned versions on a task that\ninterests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.", "### How to use\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:", "## Evaluation results\n\nFinal loss: 1.095.", "## Authors\nShu Huang: 'sh2009 [at] URL'\n\nJacqueline Cole: 'jmc61 [at] URL'\n\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BatterySciBERT-uncased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the SciBERT-uncased weights. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.", "## Model description\n\nBatterySciBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the SciBERT-uncased weights. This means\nit was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model\nrandomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict\nthe masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one\nafter the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to\nlearn a bidirectional representation of the sentence.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.", "## Training data\n\nThe BatterySciBERT model was pretrained on the full text of battery papers only, after initialized from the SciBERT-uncased weights. The paper corpus contains a total of 400,366 battery research papers that are published from 2000 to June 2021, from the publishers Royal Society of Chemistry (RSC), Elsevier, and Springer. The list of DOIs can be found at Github.", "## Training procedure", "### Preprocessing\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 31,090. The inputs of the model are\nthen of the form:\n\n\n\nThe details of the masking procedure for each sentence are the following:\n- 15% of the tokens are masked.\n- In 80% of the cases, the masked tokens are replaced by '[MASK]'.\n- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.\n- In the 10% remaining cases, the masked tokens are left as is.", "### Pretraining\n\n\nThe model was trained on 8 NVIDIA DGX A100 GPUs for 1,000,000 steps with a batch size of 256. The sequence length was limited to 512 tokens. The optimizer used is Adam with a learning rate of 2e-5, \\\\(\\beta_{1} = 0.9\\\\) and \\\\(\\beta_{2} = 0.999\\\\), a weight decay of 0.01,\nlearning rate warmup for 10,000 steps and linear decay of the learning rate after.", "## Intended uses & limitations\n\nYou can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.\nSee the model hub to look for fine-tuned versions on a task that\ninterests you.\n\nNote that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)\nto make decisions, such as sequence classification, token classification or question answering. For tasks such as text\ngeneration you should look at model like GPT2.", "### How to use\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\n\nand in TensorFlow:", "## Evaluation results\n\nFinal loss: 1.095.", "## Authors\nShu Huang: 'sh2009 [at] URL'\n\nJacqueline Cole: 'jmc61 [at] URL'\n\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 61, 85, 284, 98, 3, 135, 113, 128, 49, 10, 50 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #exbert #en #dataset-batterypapers #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# BatterySciBERT-uncased model\n\nPretrained model on a large corpus of battery research papers using a masked language modeling (MLM) objective, starting with the SciBERT-uncased weights. It was introduced in\nthis paper and first released in\nthis repository. This model is uncased: it does not make a difference\nbetween english and English.## Model description\n\nBatterySciBERT is a transformers model pretrained on a large corpus of battery research papers in a self-supervised fashion, starting with the SciBERT-uncased weights. This means\nit was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. \n\nMore precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model\nrandomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict\nthe masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one\nafter the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to\nlearn a bidirectional representation of the sentence.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs." ]	[ -0.050156813114881516, 0.06265944242477417, -0.006124672945588827, 0.04659106209874153, 0.09742653369903564, 0.022577708587050438, 0.13966883718967438, 0.006064188666641712, -0.0002157637500204146, 0.04666838422417641, 0.038157299160957336, 0.015551126562058926, 0.05739665403962135, 0.05689741298556328, 0.10000777244567871, -0.24663527309894562, 0.04260016977787018, -0.10623487830162048, 0.09856683015823364, 0.07739134877920151, 0.059567470103502274, -0.07976590842008591, 0.029639791697263718, 0.026280643418431282, 0.0224410742521286, -0.012751711532473564, -0.008154947310686111, -0.0638190507888794, 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null	null	transformers	# BERT-base-cased for Battery Abstract Classification Language model: bert-base-cased Language: English Downstream-task: Text Classification Training data: training\_data.csv Eval data: val\_data.csv Code: See [example](https://github.com/ShuHuang/batterybert) Infrastructure: 8x DGX A100 ## Hyperparameters ``` batch_size = 32 n_epochs = 15 base_LM_model = "bert-base-cased" learning_rate = 2e-5 ``` ## Performance ``` "Validation accuracy": 96.84, "Test accuracy": 96.83, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline model_name = "batterydata/bert-base-cased-abstract" # a) Get predictions nlp = pipeline('text-classification', model=model_name, tokenizer=model_name) input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'} res = nlp(input) # b) Load model & tokenizer model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": "Text Classification", "datasets": ["batterydata/paper-abstracts"], "metrics": "glue"}	text-classification	batterydata/bert-base-cased-abstract	[ "transformers", "pytorch", "bert", "text-classification", "Text Classification", "en", "dataset:batterydata/paper-abstracts", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# BERT-base-cased for Battery Abstract Classification Language model: bert-base-cased Language: English Downstream-task: Text Classification Training data: training\_data.csv Eval data: val\_data.csv Code: See example Infrastructure: 8x DGX A100 ## Hyperparameters ## Performance ## Usage ### In Transformers ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BERT-base-cased for Battery Abstract Classification \r\nLanguage model: bert-base-cased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BERT-base-cased for Battery Abstract Classification \r\nLanguage model: bert-base-cased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 64, 71, 5, 2, 3, 6, 50 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# BERT-base-cased for Battery Abstract Classification \r\nLanguage model: bert-base-cased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100## Hyperparameters## Performance## Usage### In Transformers## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ -0.0644843727350235, 0.08330554515123367, -0.0034968836698681116, 0.051429383456707, 0.24801141023635864, 0.016842391341924667, 0.18042923510074615, 0.041375622153282166, -0.10087358206510544, -0.04119560495018959, 0.048845257610082626, 0.03551759198307991, 0.05058338865637779, 0.0712677612900734, -0.05502569302916527, -0.18833637237548828, 0.036978013813495636, 0.016822529956698418, 0.000913276569917798, 0.10959042608737946, 0.13697457313537598, -0.09884942322969437, 0.11652502417564392, 0.014170125126838684, -0.10319194197654724, 0.028500162065029144, 0.014743178151547909, -0.14727835357189178, 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null	null	transformers	# BERT-base-cased for QA Language model: bert-base-cased Language: English Downstream-task: Extractive QA Training data: SQuAD v1 Eval data: SQuAD v1 Code: See [example](https://github.com/ShuHuang/batterybert) Infrastructure: 8x DGX A100 ## Hyperparameters ``` batch_size = 32 n_epochs = 2 base_LM_model = "bert-base-cased" max_seq_len = 386 learning_rate = 5e-5 doc_stride=128 max_query_length=64 ``` ## Performance Evaluated on the SQuAD v1.0 dev set. ``` "exact": 81.30, "f1": 88.58, ``` Evaluated on the battery device dataset. ``` "precision": 67.02, "recall": 80.15, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "batterydata/bert-base-cased-squad-v1" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'What is the electrolyte?', 'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": "question answering", "datasets": ["squad", "batterydata/battery-device-data-qa"], "metrics": "squad"}	question-answering	batterydata/bert-base-cased-squad-v1	[ "transformers", "pytorch", "bert", "question-answering", "question answering", "en", "dataset:squad", "dataset:batterydata/battery-device-data-qa", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us	# BERT-base-cased for QA Language model: bert-base-cased Language: English Downstream-task: Extractive QA Training data: SQuAD v1 Eval data: SQuAD v1 Code: See example Infrastructure: 8x DGX A100 ## Hyperparameters ## Performance Evaluated on the SQuAD v1.0 dev set. Evaluated on the battery device dataset. ## Usage ### In Transformers ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BERT-base-cased for QA \r\nLanguage model: bert-base-cased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n", "# BERT-base-cased for QA \r\nLanguage model: bert-base-cased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 67, 66, 5, 23, 3, 6, 50 ]	[ "passage: TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n# BERT-base-cased for QA \r\nLanguage model: bert-base-cased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100## Hyperparameters## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.## Usage### In Transformers## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ -0.08264510333538055, 0.03704140707850456, -0.004181412979960442, 0.05439529940485954, 0.225198432803154, 0.030874891206622124, 0.11174658685922623, 0.04101616516709328, -0.10514313727617264, -0.028222301974892616, 0.04187488183379173, -0.032096099108457565, 0.07252109050750732, 0.08074328303337097, 0.004610520321875811, -0.10788297653198242, 0.04851280897855759, -0.012536558322608471, 0.0020435017067939043, 0.13797545433044434, 0.1188671663403511, -0.10084633529186249, 0.11711350828409195, 0.012419131584465504, -0.0950072854757309, 0.06747550517320633, -0.009153635241091251, -0.08097530901432037, 0.10537535697221756, -0.06524581462144852, 0.1286197006702423, 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null	null	transformers	# BERT-base-uncased for Battery Abstract Classification Language model: bert-base-uncased Language: English Downstream-task: Text Classification Training data: training\_data.csv Eval data: val\_data.csv Code: See [example](https://github.com/ShuHuang/batterybert) Infrastructure: 8x DGX A100 ## Hyperparameters ``` batch_size = 32 n_epochs = 13 base_LM_model = "bert-base-uncased" learning_rate = 2e-5 ``` ## Performance ``` "Validation accuracy": 96.79, "Test accuracy": 96.29, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline model_name = "batterydata/bert-base-uncased-abstract" # a) Get predictions nlp = pipeline('text-classification', model=model_name, tokenizer=model_name) input = {'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'} res = nlp(input) # b) Load model & tokenizer model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": "Text Classification", "datasets": ["batterydata/paper-abstracts"], "metrics": "glue"}	text-classification	batterydata/bert-base-uncased-abstract	[ "transformers", "pytorch", "bert", "text-classification", "Text Classification", "en", "dataset:batterydata/paper-abstracts", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# BERT-base-uncased for Battery Abstract Classification Language model: bert-base-uncased Language: English Downstream-task: Text Classification Training data: training\_data.csv Eval data: val\_data.csv Code: See example Infrastructure: 8x DGX A100 ## Hyperparameters ## Performance ## Usage ### In Transformers ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BERT-base-uncased for Battery Abstract Classification \r\nLanguage model: bert-base-uncased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# BERT-base-uncased for Battery Abstract Classification \r\nLanguage model: bert-base-uncased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 64, 73, 5, 2, 3, 6, 50 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #Text Classification #en #dataset-batterydata/paper-abstracts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# BERT-base-uncased for Battery Abstract Classification \r\nLanguage model: bert-base-uncased\r\nLanguage: English \r\nDownstream-task: Text Classification\r\nTraining data: training\\_data.csv\r\nEval data: val\\_data.csv\r\nCode: See example \r\nInfrastructure: 8x DGX A100## Hyperparameters## Performance## Usage### In Transformers## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ -0.06228888779878616, 0.11250824481248856, -0.0041213142685592175, 0.055747926235198975, 0.23391050100326538, 0.023527516052126884, 0.1881936639547348, 0.052966222167015076, -0.08502889424562454, -0.03743405267596245, 0.0547482855618, 0.04717966541647911, 0.048833444714546204, 0.05789508298039436, -0.06720832735300064, -0.17673532664775848, 0.03977479785680771, 0.005881349090486765, 0.013472475111484528, 0.10886987298727036, 0.12927909195423126, -0.08997895568609238, 0.11126676201820374, -0.004677884746342897, -0.09576999396085739, 0.02236267551779747, 0.009153943508863449, -0.13596896827220917, 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null	null	transformers	# BERT-base-cased for QA Language model: bert-base-uncased Language: English Downstream-task: Extractive QA Training data: SQuAD v1 Eval data: SQuAD v1 Code: See [example](https://github.com/ShuHuang/batterybert) Infrastructure: 8x DGX A100 ## Hyperparameters ``` batch_size = 32 n_epochs = 3 base_LM_model = "bert-base-uncased" max_seq_len = 386 learning_rate = 3e-5 doc_stride=128 max_query_length=64 ``` ## Performance Evaluated on the SQuAD v1.0 dev set. ``` "exact": 80.93, "f1": 88.20, ``` Evaluated on the battery device dataset. ``` "precision": 62.19, "recall": 75.00, ``` ## Usage ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "batterydata/bert-base-uncased-squad-v1" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'What is the electrolyte?', 'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ## Authors Shu Huang: `sh2009 [at] cam.ac.uk` Jacqueline Cole: `jmc61 [at] cam.ac.uk` ## Citation BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	{"language": "en", "license": "apache-2.0", "tags": "question answering", "datasets": ["squad", "batterydata/battery-device-data-qa"], "metrics": "squad"}	question-answering	batterydata/bert-base-uncased-squad-v1	[ "transformers", "pytorch", "bert", "question-answering", "question answering", "en", "dataset:squad", "dataset:batterydata/battery-device-data-qa", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us	# BERT-base-cased for QA Language model: bert-base-uncased Language: English Downstream-task: Extractive QA Training data: SQuAD v1 Eval data: SQuAD v1 Code: See example Infrastructure: 8x DGX A100 ## Hyperparameters ## Performance Evaluated on the SQuAD v1.0 dev set. Evaluated on the battery device dataset. ## Usage ### In Transformers ## Authors Shu Huang: 'sh2009 [at] URL' Jacqueline Cole: 'jmc61 [at] URL' BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement	[ "# BERT-base-cased for QA \r\nLanguage model: bert-base-uncased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ "TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n", "# BERT-base-cased for QA \r\nLanguage model: bert-base-uncased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100", "## Hyperparameters", "## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.", "## Usage", "### In Transformers", "## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ 67, 67, 5, 23, 3, 6, 50 ]	[ "passage: TAGS\n#transformers #pytorch #bert #question-answering #question answering #en #dataset-squad #dataset-batterydata/battery-device-data-qa #license-apache-2.0 #endpoints_compatible #region-us \n# BERT-base-cased for QA \r\nLanguage model: bert-base-uncased\r\nLanguage: English \r\nDownstream-task: Extractive QA \r\nTraining data: SQuAD v1\r\nEval data: SQuAD v1\r\nCode: See example \r\nInfrastructure: 8x DGX A100## Hyperparameters## Performance\r\nEvaluated on the SQuAD v1.0 dev set.\r\n\r\nEvaluated on the battery device dataset.## Usage### In Transformers## Authors\r\nShu Huang: 'sh2009 [at] URL'\r\n\r\nJacqueline Cole: 'jmc61 [at] URL'\r\n\r\nBatteryBERT: A Pre-trained Language Model for Battery Database Enhancement" ]	[ -0.08233732730150223, 0.04036601632833481, -0.004108112771064043, 0.05932421609759331, 0.22697557508945465, 0.033763810992240906, 0.1229172870516777, 0.04320504516363144, -0.10582517832517624, -0.031084313988685608, 0.04113712161779404, -0.02831280417740345, 0.07124070823192596, 0.07246532291173935, 0.0069641415029764175, -0.10598722845315933, 0.05146188661456108, -0.017674662172794342, -0.002047397196292877, 0.13380834460258484, 0.11374549567699432, -0.09296327084302902, 0.11312771588563919, 0.009924502111971378, -0.09377788007259369, 0.06567202508449554, -0.014292634092271328, -0.08079706132411957, 0.09910152107477188, -0.06517492979764938, 0.1299445629119873, 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null	null	transformers	# ALBERT-Mongolian [pretraining repo link](https://github.com/bayartsogt-ya/albert-mongolian) ## Model description Here we provide pretrained ALBERT model and trained SentencePiece model for Mongolia text. Training data is the Mongolian wikipedia corpus from Wikipedia Downloads and Mongolian News corpus. ## Evaluation Result: ``` loss = 1.7478163 masked_lm_accuracy = 0.6838185 masked_lm_loss = 1.6687671 sentence_order_accuracy = 0.998125 sentence_order_loss = 0.007942731 ``` ## Fine-tuning Result on Eduge Dataset: ``` precision recall f1-score support байгал орчин 0.85 0.83 0.84 999 боловсрол 0.80 0.80 0.80 873 спорт 0.98 0.98 0.98 2736 технологи 0.88 0.93 0.91 1102 улс төр 0.92 0.85 0.89 2647 урлаг соёл 0.93 0.94 0.94 1457 хууль 0.89 0.87 0.88 1651 эдийн засаг 0.83 0.88 0.86 2509 эрүүл мэнд 0.89 0.92 0.90 1159 accuracy 0.90 15133 macro avg 0.89 0.89 0.89 15133 weighted avg 0.90 0.90 0.90 15133 ``` ## Reference 1. [ALBERT - official repo](https://github.com/google-research/albert) 2. [WikiExtrator](https://github.com/attardi/wikiextractor) 3. [Mongolian BERT](https://github.com/tugstugi/mongolian-bert) 4. [ALBERT - Japanese](https://github.com/alinear-corp/albert-japanese) 5. [Mongolian Text Classification](https://github.com/sharavsambuu/mongolian-text-classification) 6. [You's paper](https://arxiv.org/abs/1904.00962) ## Citation ``` @misc{albert-mongolian, author = {Bayartsogt Yadamsuren}, title = {ALBERT Pretrained Model on Mongolian Datasets}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/bayartsogt-ya/albert-mongolian/}} } ``` ## For More Information Please contact by [email protected]	{"language": "mn"}	fill-mask	bayartsogt/albert-mongolian	[ "transformers", "pytorch", "tf", "safetensors", "albert", "fill-mask", "mn", "arxiv:1904.00962", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[ "1904.00962" ]	[ "mn" ]	TAGS #transformers #pytorch #tf #safetensors #albert #fill-mask #mn #arxiv-1904.00962 #autotrain_compatible #endpoints_compatible #region-us	# ALBERT-Mongolian pretraining repo link ## Model description Here we provide pretrained ALBERT model and trained SentencePiece model for Mongolia text. Training data is the Mongolian wikipedia corpus from Wikipedia Downloads and Mongolian News corpus. ## Evaluation Result: ## Fine-tuning Result on Eduge Dataset: ## Reference 1. ALBERT - official repo 2. WikiExtrator 3. Mongolian BERT 4. ALBERT - Japanese 5. Mongolian Text Classification 6. You's paper ## For More Information Please contact by bayartsogtyadamsuren@URL	[ "# ALBERT-Mongolian\npretraining repo link", "## Model description\nHere we provide pretrained ALBERT model and trained SentencePiece model for Mongolia text. Training data is the Mongolian wikipedia corpus from Wikipedia Downloads and Mongolian News corpus.", "## Evaluation Result:", "## Fine-tuning Result on Eduge Dataset:", "## Reference\n1. ALBERT - official repo\n2. WikiExtrator\n3. Mongolian BERT\n4. ALBERT - Japanese\n5. Mongolian Text Classification\n6. You's paper", "## For More Information\nPlease contact by bayartsogtyadamsuren@URL" ]	[ "TAGS\n#transformers #pytorch #tf #safetensors #albert #fill-mask #mn #arxiv-1904.00962 #autotrain_compatible #endpoints_compatible #region-us \n", "# ALBERT-Mongolian\npretraining repo link", "## Model description\nHere we provide pretrained ALBERT model and trained SentencePiece model for Mongolia text. Training data is the Mongolian wikipedia corpus from Wikipedia Downloads and Mongolian News corpus.", "## Evaluation Result:", "## Fine-tuning Result on Eduge Dataset:", "## Reference\n1. ALBERT - official repo\n2. WikiExtrator\n3. Mongolian BERT\n4. ALBERT - Japanese\n5. Mongolian Text Classification\n6. You's paper", "## For More Information\nPlease contact by bayartsogtyadamsuren@URL" ]	[ 55, 12, 44, 5, 12, 36, 15 ]	[ "passage: TAGS\n#transformers #pytorch #tf #safetensors #albert #fill-mask #mn #arxiv-1904.00962 #autotrain_compatible #endpoints_compatible #region-us \n# ALBERT-Mongolian\npretraining repo link## Model description\nHere we provide pretrained ALBERT model and trained SentencePiece model for Mongolia text. Training data is the Mongolian wikipedia corpus from Wikipedia Downloads and Mongolian News corpus.## Evaluation Result:## Fine-tuning Result on Eduge Dataset:## Reference\n1. ALBERT - official repo\n2. WikiExtrator\n3. Mongolian BERT\n4. ALBERT - Japanese\n5. Mongolian Text Classification\n6. 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null	null	null	\|fold\|accuracy\| \|-\|-\| \| fold 0 \| 0.974197247706422 \| \| fold 1 \| 0.9627293577981652 \| \| fold 2 \| 0.9724770642201835 \| \| fold 3 \| 0.9696100917431193 \| \| fold 4 \| 0.9684633027522935 \| \| OOF Acc \| 0.9694954128440367 \|	{}	null	bayartsogt/mlub-bert-base-uncased-tr5meaning	[ "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #region-us		[]	[ "TAGS\n#region-us \n" ]	[ 6 ]	[ "passage: TAGS\n#region-us \n" ]	[ 0.024608636274933815, -0.026205500587821007, -0.009666500613093376, -0.10395516455173492, 0.08638657629489899, 0.059816278517246246, 0.01882290467619896, 0.020661840215325356, 0.23975107073783875, -0.005599027033895254, 0.1219947561621666, 0.0015615287702530622, -0.037353623658418655, 0.03733762726187706, -0.0035912662278860807, -0.17583473026752472, 0.03876631706953049, -0.018274923786520958, 0.01843859627842903, 0.026470553129911423, -0.07776834815740585, -0.07564429938793182, 0.015296397730708122, -0.10247814655303955, -0.083692267537117, 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null	null	null	\|fold\|accuracy\| \|-\|-\| \| fold 0 \| 0.9730504587155964 \| \| fold 1 \| 0.9690366972477065 \| \| fold 2 \| 0.970756880733945 \| \| fold 3 \| 0.9684633027522935 \| \| fold 4 \| 0.9719036697247706 \| \| OOF Acc \| 0.9706422018348624 \|	{}	null	bayartsogt/mlub-bert-large-cased-tr5do30ep25s42	[ "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #region-us		[]	[ "TAGS\n#region-us \n" ]	[ 6 ]	[ "passage: TAGS\n#region-us \n" ]	[ 0.024608636274933815, -0.026205500587821007, -0.009666500613093376, -0.10395516455173492, 0.08638657629489899, 0.059816278517246246, 0.01882290467619896, 0.020661840215325356, 0.23975107073783875, -0.005599027033895254, 0.1219947561621666, 0.0015615287702530622, -0.037353623658418655, 0.03733762726187706, -0.0035912662278860807, -0.17583473026752472, 0.03876631706953049, -0.018274923786520958, 0.01843859627842903, 0.026470553129911423, -0.07776834815740585, -0.07564429938793182, 0.015296397730708122, -0.10247814655303955, -0.083692267537117, 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null	null	null	\|fold\|accuracy\| \|-\|-\| \| fold 0 \| 0.9753440366972477 \| \| fold 1 \| 0.9678899082568807 \| \| fold 2 \| 0.9747706422018348 \| \| fold 3 \| 0.9690366972477065 \| \| fold 4 \| 0.9759174311926605 \| \| OOF Acc \| 0.9725917431192661 \|	{}	null	bayartsogt/mlub-bert-large-uncased-tr5do20ep25s42	[ "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #region-us		[]	[ "TAGS\n#region-us \n" ]	[ 6 ]	[ "passage: TAGS\n#region-us \n" ]	[ 0.024608636274933815, -0.026205500587821007, -0.009666500613093376, -0.10395516455173492, 0.08638657629489899, 0.059816278517246246, 0.01882290467619896, 0.020661840215325356, 0.23975107073783875, -0.005599027033895254, 0.1219947561621666, 0.0015615287702530622, -0.037353623658418655, 0.03733762726187706, -0.0035912662278860807, -0.17583473026752472, 0.03876631706953049, -0.018274923786520958, 0.01843859627842903, 0.026470553129911423, -0.07776834815740585, -0.07564429938793182, 0.015296397730708122, -0.10247814655303955, -0.083692267537117, 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null	null	null	\|fold\|accuracy\| \|-\|-\| \| fold 0 \| 0.974197247706422 \| \| fold 1 \| 0.9678899082568807 \| \| fold 2 \| 0.9724770642201835 \| \| fold 3 \| 0.9701834862385321 \| \| fold 4 \| 0.9736238532110092 \| \| OOF Acc \| 0.9716743119266055 \| ``` synset_word ав 1.000000 ам 0.931507 баг 0.980000 байр 0.943548 бараа 0.964789 гар 0.950210 гол 0.938731 гүн 0.912088 зах 0.946667 зуу 0.995798 зүрх 0.918367 мөнгө 0.973333 нуруу 0.968750 нүд 1.000000 нүүр 0.987805 салбар 0.963636 сар 0.996627 сум 0.816667 тэрэг 0.822581 түүх 0.980237 төр 0.998428 хий 0.993077 хураа 0.858268 хэлбэр 0.727273 хөндий 1.000000 шат 1.000000 эм 1.000000 эрүүл 1.000000 dtype: float64 ```	{}	null	bayartsogt/mlub-bert-large-uncased-tr5do30ep25	[ "region:us" ]	2022-03-02T23:29:05+00:00	[]	[]	TAGS #region-us		[]	[ "TAGS\n#region-us \n" ]	[ 6 ]	[ "passage: TAGS\n#region-us \n" ]	[ 0.024608636274933815, -0.026205500587821007, -0.009666500613093376, -0.10395516455173492, 0.08638657629489899, 0.059816278517246246, 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null	null	transformers	# StructBERT: Un-Official Copy Official Repository Link: https://github.com/alibaba/AliceMind/tree/main/StructBERT Claimer * This model card is not produced by [AliceMind Team](https://github.com/alibaba/AliceMind/) ## Reproduce HFHub models: Download model/tokenizer vocab ```bash wget https://raw.githubusercontent.com/alibaba/AliceMind/main/StructBERT/config/large_bert_config.json && mv large_bert_config.json config.json wget https://raw.githubusercontent.com/alibaba/AliceMind/main/StructBERT/config/vocab.txt wget https://alice-open.oss-cn-zhangjiakou.aliyuncs.com/StructBERT/en_model && mv en_model pytorch_model.bin ``` ```python from transformers import AutoConfig, AutoModelForMaskedLM, AutoTokenizer config = AutoConfig.from_pretrained("./config.json") model = AutoModelForMaskedLM.from_pretrained(".", config=config) tokenizer = AutoTokenizer.from_pretrained(".", config=config) model.push_to_hub("structbert-large") tokenizer.push_to_hub("structbert-large") ``` [https://arxiv.org/abs/1908.04577](https://arxiv.org/abs/1908.04577) # StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding ## Introduction We extend BERT to a new model, StructBERT, by incorporating language structures into pre-training. Specifically, we pre-train StructBERT with two auxiliary tasks to make the most of the sequential order of words and sentences, which leverage language structures at the word and sentence levels, respectively. ## Pre-trained models \|Model \| Description \| #params \| Download \| \|------------------------\|-------------------------------------------\|------\|------\| \|structbert.en.large \| StructBERT using the BERT-large architecture \| 340M \| [structbert.en.large](https://alice-open.oss-cn-zhangjiakou.aliyuncs.com/StructBERT/en_model) \| \|structroberta.en.large \| StructRoBERTa continue training from RoBERTa \| 355M \| Coming soon \| \|structbert.ch.large \| Chinese StructBERT; BERT-large architecture \| 330M \| [structbert.ch.large](https://alice-open.oss-cn-zhangjiakou.aliyuncs.com/StructBERT/ch_model) \| ## Results The results of GLUE & CLUE tasks can be reproduced using the hyperparameters listed in the following "Example usage" section. #### structbert.en.large [GLUE benchmark](https://gluebenchmark.com/leaderboard) \|Model\| MNLI \| QNLIv2 \| QQP \| SST-2 \| MRPC \| \|--------------------\|-------\|-------\|-------\|-------\|-------\| \|structbert.en.large \|86.86% \|93.04% \|91.67% \|93.23% \|86.51% \| #### structbert.ch.large [CLUE benchmark](https://www.cluebenchmarks.com/) \|Model \| CMNLI \| OCNLI \| TNEWS \| AFQMC \| \|--------------------\|-------\|-------\|-------\|-------\| \|structbert.ch.large \|84.47% \|81.28% \|68.67% \|76.11% \| ## Example usage #### Requirements and Installation * [PyTorch](https://pytorch.org/) version >= 1.0.1 * Install other libraries via ``` pip install -r requirements.txt ``` * For faster training install NVIDIA's [apex](https://github.com/NVIDIA/apex) library #### Finetune MNLI ``` python run_classifier_multi_task.py \ --task_name MNLI \ --do_train \ --do_eval \ --do_test \ --amp_type O1 \ --lr_decay_factor 1 \ --dropout 0.1 \ --do_lower_case \ --detach_index -1 \ --core_encoder bert \ --data_dir path_to_glue_data \ --vocab_file config/vocab.txt \ --bert_config_file config/large_bert_config.json \ --init_checkpoint path_to_pretrained_model \ --max_seq_length 128 \ --train_batch_size 32 \ --learning_rate 2e-5 \ --num_train_epochs 3 \ --fast_train \ --gradient_accumulation_steps 1 \ --output_dir path_to_output_dir ``` ## Citation If you use our work, please cite: ``` @article{wang2019structbert, title={Structbert: Incorporating language structures into pre-training for deep language understanding}, author={Wang, Wei and Bi, Bin and Yan, Ming and Wu, Chen and Bao, Zuyi and Xia, Jiangnan and Peng, Liwei and Si, Luo}, journal={arXiv preprint arXiv:1908.04577}, year={2019} } ```	{}	fill-mask	bayartsogt/structbert-large	[ "transformers", "pytorch", "bert", "fill-mask", "arxiv:1908.04577", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2022-03-02T23:29:05+00:00	[ "1908.04577" ]	[]	TAGS #transformers #pytorch #bert #fill-mask #arxiv-1908.04577 #autotrain_compatible #endpoints_compatible #region-us	StructBERT: Un-Official Copy ============================ Official Repository Link: URL Claimer * This model card is not produced by AliceMind Team Reproduce HFHub models: ----------------------- Download model/tokenizer vocab URL StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding =============================================================================================== Introduction ------------ We extend BERT to a new model, StructBERT, by incorporating language structures into pre-training. Specifically, we pre-train StructBERT with two auxiliary tasks to make the most of the sequential order of words and sentences, which leverage language structures at the word and sentence levels, respectively. Pre-trained models ------------------ Results ------- The results of GLUE & CLUE tasks can be reproduced using the hyperparameters listed in the following "Example usage" section. #### URL GLUE benchmark #### URL CLUE benchmark Example usage ------------- #### Requirements and Installation * PyTorch version >= 1.0.1 * Install other libraries via * For faster training install NVIDIA's apex library #### Finetune MNLI If you use our work, please cite:	[ "#### URL\n\n\nGLUE benchmark", "#### URL\n\n\nCLUE benchmark\n\n\n\nExample usage\n-------------", "#### Requirements and Installation\n\n\n* PyTorch version >= 1.0.1\n* Install other libraries via\n* For faster training install NVIDIA's apex library", "#### Finetune MNLI\n\n\nIf you use our work, please cite:" ]	[ "TAGS\n#transformers #pytorch #bert #fill-mask #arxiv-1908.04577 #autotrain_compatible #endpoints_compatible #region-us \n", "#### URL\n\n\nGLUE benchmark", "#### URL\n\n\nCLUE benchmark\n\n\n\nExample usage\n-------------", "#### Requirements and Installation\n\n\n* PyTorch version >= 1.0.1\n* Install other libraries via\n* For faster training install NVIDIA's apex library", "#### Finetune MNLI\n\n\nIf you use our work, please cite:" ]	[ 44, 6, 11, 36, 16 ]	[ "passage: TAGS\n#transformers #pytorch #bert #fill-mask #arxiv-1908.04577 #autotrain_compatible #endpoints_compatible #region-us \n#### URL\n\n\nGLUE benchmark#### URL\n\n\nCLUE benchmark\n\n\n\nExample usage\n-------------#### Requirements and Installation\n\n\n* PyTorch version >= 1.0.1\n* Install other libraries via\n* For faster training install NVIDIA's apex library#### Finetune MNLI\n\n\nIf you use our work, please cite:" ]	[ -0.042748548090457916, 0.15704379975795746, -0.0016536741750314832, 0.12826275825500488, 0.12493099272251129, 0.07056711614131927, 0.0520964153110981, 0.1499430537223816, 0.08936437964439392, -0.0028121881186962128, 0.19695255160331726, 0.22007960081100464, 0.04100190848112106, 0.14242789149284363, 0.038566019386053085, 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null	null	fairseq	# tts_transformer-mn-mbspeech [Transformer](https://arxiv.org/abs/1809.08895) text-to-speech model from fairseq S^2 ([paper](https://arxiv.org/abs/2109.06912)/[code](https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis)): - Mongolian - Single-speaker male voice - Trained on [MBSpeech](https://github.com/tugstugi/mongolian-nlp/blob/master/datasets/MBSpeech-1.0-csv.zip)	{"language": "mn", "library_name": "fairseq", "tags": ["fairseq", "audio", "text-to-speech"], "datasets": ["mbspeech"], "task": "text-to-speech", "widget": [{"text": "\u043c\u0438\u043d\u0438\u0439 \u043d\u044d\u0440\u0438\u0439\u0433 \u0431\u0430\u044f\u0440\u0446\u043e\u0433\u0442 \u0433\u044d\u0434\u044d\u0433", "example_title": "Say my name!"}, {"text": "\u0431\u0438 \u043c\u043e\u043d\u0433\u043e\u043b \u0443\u043b\u0441\u044b\u043d \u043d\u0438\u0439\u0441\u043b\u044d\u043b, \u0443\u043b\u0430\u0430\u043d\u0431\u0430\u0430\u0442\u0430\u0440 \u0445\u043e\u0442\u043e\u0434 \u0430\u043c\u044c\u0434\u0430\u0440\u0434\u0430\u0433", "example_title": "Where I am from?"}, {"text": "\u044d\u043d\u044d\u0445\u04af\u04af \u04e9\u0433\u04e9\u0433\u0434\u043b\u0438\u0439\u0433 \u043d\u044d\u044d\u043b\u0442\u0442\u044d\u0439 \u0431\u043e\u043b\u0433\u043e\u0441\u043e\u043d, \u0431\u043e\u043b\u043e\u0440 \u0441\u043e\u043e\u0444\u0442\u044b\u043d\u0445\u043e\u043d\u0434 \u0431\u0430\u044f\u0440\u043b\u0430\u043b\u0430\u0430", "example_title": "Thank you!"}, {"text": "\u044d\u043d\u044d\u0445\u04af\u04af \u0430\u0436\u043b\u044b\u043d \u0438\u0445\u044d\u043d\u0445 \u0445\u044d\u0441\u0433\u0438\u0439\u0433, \u0442\u04e9\u0433\u04e9\u043b\u0434\u04e9\u0440 \u0430\u0445 \u0445\u0438\u0439\u0441\u044d\u043d \u0431\u043e\u043b\u043d\u043e", "example_title": "Shout out to original creater"}]}	text-to-speech	bayartsogt/tts_transformer-mn-mbspeech	[ "fairseq", "audio", "text-to-speech", "mn", "dataset:mbspeech", "arxiv:1809.08895", "arxiv:2109.06912", "region:us" ]	2022-03-02T23:29:05+00:00	[ "1809.08895", "2109.06912" ]	[ "mn" ]	TAGS #fairseq #audio #text-to-speech #mn #dataset-mbspeech #arxiv-1809.08895 #arxiv-2109.06912 #region-us	# tts_transformer-mn-mbspeech Transformer text-to-speech model from fairseq S^2 (paper/code): - Mongolian - Single-speaker male voice - Trained on MBSpeech	[ "# tts_transformer-mn-mbspeech\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- Mongolian\n- Single-speaker male voice\n- Trained on MBSpeech" ]	[ "TAGS\n#fairseq #audio #text-to-speech #mn #dataset-mbspeech #arxiv-1809.08895 #arxiv-2109.06912 #region-us \n", "# tts_transformer-mn-mbspeech\nTransformer text-to-speech model from fairseq S^2 (paper/code):\n- Mongolian\n- Single-speaker male voice\n- Trained on MBSpeech" ]	[ 46, 52 ]	[ "passage: TAGS\n#fairseq #audio #text-to-speech #mn #dataset-mbspeech #arxiv-1809.08895 #arxiv-2109.06912 #region-us \n# tts_transformer-mn-mbspeech\nTransformer text-to-speech model from fairseq S^2 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