davanstrien/pytorchmodelmetadata · Datasets at Hugging Face

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huggingtweets/rshowerthoughts-stephenking	5eb9f6c48d0122235fdcbfb05a39ec28f52005b8	2022-06-11T19:50:01.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/rshowerthoughts-stephenking	1	null	transformers	32,800	--- language: en thumbnail: http://www.huggingtweets.com/rshowerthoughts-stephenking/1654976942704/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/378800000836981162/b683f7509ec792c3e481ead332940cdc_400x400.jpeg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/641699738224455680/L_ji6ClT_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Stephen King & Showerthoughts</div> <div style="text-align: center; font-size: 14px;">@rshowerthoughts-stephenking</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Stephen King & Showerthoughts. \| Data \| Stephen King \| Showerthoughts \| \| --- \| --- \| --- \| \| Tweets downloaded \| 3230 \| 3200 \| \| Retweets \| 780 \| 0 \| \| Short tweets \| 202 \| 0 \| \| Tweets kept \| 2248 \| 3200 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2bn3s9yg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @rshowerthoughts-stephenking's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2waq2b3w) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2waq2b3w/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/rshowerthoughts-stephenking') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/conanobrien-mikemancini-wendymolyneux	be13ccafa621d3fad60a0f7e9c02b7a351806386	2022-06-11T19:50:54.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/conanobrien-mikemancini-wendymolyneux	1	null	transformers	32,801	--- language: en thumbnail: http://www.huggingtweets.com/conanobrien-mikemancini-wendymolyneux/1654977049172/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1271404115042676736/PAIbmN-p_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/730612231021322240/Rl0_QYhL_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1044085580651528193/DR7QvrwG_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">mike mancini & Conan O'Brien & Wendy Molyneux</div> <div style="text-align: center; font-size: 14px;">@conanobrien-mikemancini-wendymolyneux</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from mike mancini & Conan O'Brien & Wendy Molyneux. \| Data \| mike mancini \| Conan O'Brien \| Wendy Molyneux \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3150 \| 3250 \| 836 \| \| Retweets \| 286 \| 40 \| 251 \| \| Short tweets \| 290 \| 24 \| 69 \| \| Tweets kept \| 2574 \| 3186 \| 516 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/25wtfzk4/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @conanobrien-mikemancini-wendymolyneux's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1hjizcue) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1hjizcue/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/conanobrien-mikemancini-wendymolyneux') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/elonmusk-rshowerthoughts-stephenking	83949a13c1d88fa22b7fcc87b1a7048115bfd288	2022-06-11T20:15:51.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/elonmusk-rshowerthoughts-stephenking	1	null	transformers	32,802	--- language: en thumbnail: http://www.huggingtweets.com/elonmusk-rshowerthoughts-stephenking/1654978546952/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1529956155937759233/Nyn1HZWF_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/378800000836981162/b683f7509ec792c3e481ead332940cdc_400x400.jpeg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/641699738224455680/L_ji6ClT_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Stephen King & Showerthoughts</div> <div style="text-align: center; font-size: 14px;">@elonmusk-rshowerthoughts-stephenking</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Elon Musk & Stephen King & Showerthoughts. \| Data \| Elon Musk \| Stephen King \| Showerthoughts \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3200 \| 3230 \| 3200 \| \| Retweets \| 147 \| 780 \| 0 \| \| Short tweets \| 954 \| 202 \| 0 \| \| Tweets kept \| 2099 \| 2248 \| 3200 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1fvudd5c/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elonmusk-rshowerthoughts-stephenking's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/39f9xftz) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/39f9xftz/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/elonmusk-rshowerthoughts-stephenking') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1	cfd3f4b6e59d7e7305ed6331c2659091568ab48c	2022-06-11T21:50:40.000Z	[ "pytorch", "tensorboard", "marian", "text2text-generation", "dataset:opus100", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	text2text-generation	false	meghazisofiane	null	meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1	1	null	transformers	32,803	--- license: apache-2.0 tags: - generated_from_trainer datasets: - opus100 metrics: - bleu model-index: - name: opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus100 type: opus100 args: ar-en metrics: - name: Bleu type: bleu value: 26.8232 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 0.1717 - Bleu: 26.8232 - Meteor: 0.172 - Gen Len: 12.1288 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Meteor \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\| \| 0.7364 \| 0.25 \| 100 \| 0.1731 \| 27.2753 \| 0.1729 \| 12.0887 \| \| 0.2175 \| 0.5 \| 200 \| 0.1731 \| 27.2055 \| 0.1722 \| 11.5675 \| \| 0.2193 \| 0.75 \| 300 \| 0.1722 \| 27.3277 \| 0.1798 \| 12.1325 \| \| 0.2321 \| 1.0 \| 400 \| 0.1750 \| 27.5152 \| 0.1762 \| 11.925 \| \| 0.1915 \| 1.25 \| 500 \| 0.1690 \| 27.5043 \| 0.1751 \| 11.9038 \| \| 0.1794 \| 1.5 \| 600 \| 0.1719 \| 26.8607 \| 0.1713 \| 11.8138 \| \| 0.1741 \| 1.75 \| 700 \| 0.1725 \| 26.974 \| 0.1724 \| 11.8462 \| \| 0.1732 \| 2.0 \| 800 \| 0.1717 \| 26.8232 \| 0.172 \| 12.1288 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-4000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1	5a24a5f03b9f9bcc016566271f9656ed7762beb2	2022-06-11T23:49:23.000Z	[ "pytorch", "tensorboard", "marian", "text2text-generation", "dataset:un_multi", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	text2text-generation	false	meghazisofiane	null	meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-4000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1	1	null	transformers	32,804	--- license: apache-2.0 tags: - generated_from_trainer datasets: - un_multi metrics: - bleu model-index: - name: opus-mt-en-ar-evaluated-en-to-ar-4000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: un_multi type: un_multi args: ar-en metrics: - name: Bleu type: bleu value: 51.7715 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-en-ar-evaluated-en-to-ar-4000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the un_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.1850 - Bleu: 51.7715 - Meteor: 0.5164 - Gen Len: 25.5612 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Meteor \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\| \| 0.6999 \| 0.25 \| 100 \| 0.1959 \| 50.1492 \| 0.508 \| 25.2788 \| \| 0.1994 \| 0.5 \| 200 \| 0.1931 \| 51.003 \| 0.513 \| 25.4038 \| \| 0.1863 \| 0.75 \| 300 \| 0.1864 \| 51.3268 \| 0.5145 \| 25.1675 \| \| 0.1826 \| 1.0 \| 400 \| 0.1841 \| 51.2507 \| 0.513 \| 25.2388 \| \| 0.1494 \| 1.25 \| 500 \| 0.1840 \| 51.4291 \| 0.5159 \| 25.4225 \| \| 0.1483 \| 1.5 \| 600 \| 0.1839 \| 51.2645 \| 0.5126 \| 25.395 \| \| 0.1547 \| 1.75 \| 700 \| 0.1837 \| 51.7589 \| 0.5157 \| 25.48 \| \| 0.1487 \| 2.0 \| 800 \| 0.1845 \| 51.896 \| 0.5177 \| 25.3988 \| \| 0.1235 \| 2.25 \| 900 \| 0.1852 \| 52.0583 \| 0.5177 \| 25.5212 \| \| 0.1164 \| 2.5 \| 1000 \| 0.1850 \| 51.7715 \| 0.5164 \| 25.5612 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
AntoDono/DialoGPT-Bopy-Alpha	ace508d9a01179df177c0a1eb62a0e62e872447c	2022-06-11T22:22:35.000Z	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	false	AntoDono	null	AntoDono/DialoGPT-Bopy-Alpha	1	null	transformers	32,805	Entry not found
sactisudesa/robertuito_sp	8a5291a24ad459b2922dc3c2e1f80472ca3164bc	2022-06-11T22:49:32.000Z	[ "pytorch", "roberta", "feature-extraction", "transformers" ]	feature-extraction	false	sactisudesa	null	sactisudesa/robertuito_sp	1	null	transformers	32,806	Entry not found
huggingtweets/laserboat999	173e2d6c04effe0a5009e3ad155e3f45050a6303	2022-06-11T23:53:52.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/laserboat999	1	null	transformers	32,807	--- language: en thumbnail: http://www.huggingtweets.com/laserboat999/1654991516445/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1500274766195793921/bA4siut7_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">donald boat</div> <div style="text-align: center; font-size: 14px;">@laserboat999</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from donald boat. \| Data \| donald boat \| \| --- \| --- \| \| Tweets downloaded \| 3233 \| \| Retweets \| 75 \| \| Short tweets \| 516 \| \| Tweets kept \| 2642 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/38v40fpf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @laserboat999's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/pk1xum9h) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/pk1xum9h/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/laserboat999') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/cancer_blood69	838a404be0bcf0e261091db78c493548896c56da	2022-06-12T00:01:54.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/cancer_blood69	1	null	transformers	32,808	--- language: en thumbnail: http://www.huggingtweets.com/cancer_blood69/1654992058711/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1273429972229804032/_kkJmwqw_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">cancer_blood69 (reanimated decaying corpse)</div> <div style="text-align: center; font-size: 14px;">@cancer_blood69</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from cancer_blood69 (reanimated decaying corpse). \| Data \| cancer_blood69 (reanimated decaying corpse) \| \| --- \| --- \| \| Tweets downloaded \| 3237 \| \| Retweets \| 215 \| \| Short tweets \| 381 \| \| Tweets kept \| 2641 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cav70ew/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @cancer_blood69's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/sp5449e2) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/sp5449e2/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/cancer_blood69') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
lindsayng/t5-base-fullwnc-5epoch-31e6b1e1	7f26b3f69b6aebbb8bedc9b47f8aed64593cea23	2022-06-12T00:08:18.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	lindsayng	null	lindsayng/t5-base-fullwnc-5epoch-31e6b1e1	1	null	transformers	32,809	Entry not found
meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-2000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1	3b0aee150bf11b5aa90a7ed16e00cc844974491b	2022-06-12T00:44:37.000Z	[ "pytorch", "tensorboard", "marian", "text2text-generation", "dataset:un_multi", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	text2text-generation	false	meghazisofiane	null	meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-2000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1	1	null	transformers	32,810	--- license: apache-2.0 tags: - generated_from_trainer datasets: - un_multi metrics: - bleu model-index: - name: opus-mt-en-ar-evaluated-en-to-ar-2000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: un_multi type: un_multi args: ar-en metrics: - name: Bleu type: bleu value: 53.0137 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-en-ar-evaluated-en-to-ar-2000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the un_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.1873 - Bleu: 53.0137 - Meteor: 0.5005 - Gen Len: 25.845 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Meteor \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\| \| 0.6585 \| 0.5 \| 100 \| 0.2085 \| 52.5874 \| 0.4969 \| 25.485 \| \| 0.1802 \| 1.0 \| 200 \| 0.1788 \| 52.9434 \| 0.4982 \| 25.1725 \| \| 0.1501 \| 1.5 \| 300 \| 0.1683 \| 53.6994 \| 0.5033 \| 25.625 \| \| 0.1454 \| 2.0 \| 400 \| 0.1706 \| 53.3946 \| 0.5005 \| 25.6675 \| \| 0.1193 \| 2.5 \| 500 \| 0.1774 \| 53.2011 \| 0.4982 \| 25.58 \| \| 0.1194 \| 3.0 \| 600 \| 0.1741 \| 53.8651 \| 0.5026 \| 25.5775 \| \| 0.1002 \| 3.5 \| 700 \| 0.1878 \| 53.1332 \| 0.5005 \| 25.8975 \| \| 0.0979 \| 4.0 \| 800 \| 0.1881 \| 52.5989 \| 0.4974 \| 25.485 \| \| 0.0807 \| 4.5 \| 900 \| 0.1873 \| 53.0137 \| 0.5005 \| 25.845 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
erickfm/t5-base-finetuned-bias-sweep-46c8cda7	d1a6bff4dc1c61bd32292c3768268a808dca36be	2022-06-12T00:41:28.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	erickfm	null	erickfm/t5-base-finetuned-bias-sweep-46c8cda7	1	null	transformers	32,811	Entry not found
erickfm/t5-base-finetuned-bias-sweep-cca75a85	d3def8abc8127be7cbaa0b2bbf25c2b83130b353	2022-06-12T07:03:32.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	erickfm	null	erickfm/t5-base-finetuned-bias-sweep-cca75a85	1	null	transformers	32,812	Entry not found
Dwayne/opus-mt-en-ro-finetuned-en-to-ro	ad7e33786ba93897bab03fd7553b6ef367b49deb	2022-06-12T19:07:18.000Z	[ "pytorch", "marian", "text2text-generation", "dataset:wmt16", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	text2text-generation	false	Dwayne	null	Dwayne/opus-mt-en-ro-finetuned-en-to-ro	1	null	transformers	32,813	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: opus-mt-en-ro-finetuned-en-to-ro results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt16 type: wmt16 args: ro-en metrics: - name: Bleu type: bleu value: 28.0591 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-en-ro-finetuned-en-to-ro This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.2889 - Bleu: 28.0591 - Gen Len: 34.043 ## 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 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 0.744 \| 1.0 \| 38145 \| 1.2889 \| 28.0591 \| 34.043 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
hangyulmd/t5-squad	2202943b672a4890d619944d93bf40ec93a1309d	2022-06-12T16:00:52.000Z	[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	hangyulmd	null	hangyulmd/t5-squad	1	null	transformers	32,814	Entry not found
nestoralvaro/mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__google_mt5_base	35cbc15c4aad6980d834379c775208afbdbb116c	2022-06-12T18:08:46.000Z	[ "pytorch", "tensorboard", "mt5", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	text2text-generation	false	nestoralvaro	null	nestoralvaro/mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__google_mt5_base	1	null	transformers	32,815	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__google_mt5_base results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__google_mt5_base This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 0.7721 - Rouge2: 0.0701 - Rougel: 0.7721 - Rougelsum: 0.7718 - Gen Len: 6.329 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 0.0 \| 1.0 \| 131773 \| nan \| 0.7721 \| 0.0701 \| 0.7721 \| 0.7718 \| 6.329 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
huggingtweets/warriors	5dc3f3c81fa9d2bc05897123051140c4d59f5bc3	2022-06-12T15:38:14.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/warriors	1	null	transformers	32,816	--- language: en thumbnail: http://www.huggingtweets.com/warriors/1655048290751/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1533845175725719553/yvzbj8iG_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Golden State Warriors</div> <div style="text-align: center; font-size: 14px;">@warriors</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Golden State Warriors. \| Data \| Golden State Warriors \| \| --- \| --- \| \| Tweets downloaded \| 3251 \| \| Retweets \| 261 \| \| Short tweets \| 563 \| \| Tweets kept \| 2427 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/36p28s9n/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @warriors's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/17arirrx) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/17arirrx/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/warriors') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
eunbeee/ainize-kobart-news-eb-finetuned-papers	479667425e72635e2d55368e2b5c33ec3ae85822	2022-06-16T12:07:21.000Z	[ "pytorch", "tensorboard", "bart", "text2text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]	text2text-generation	false	eunbeee	null	eunbeee/ainize-kobart-news-eb-finetuned-papers	1	null	transformers	32,817	--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: ainize-kobart-news-eb-finetuned-papers results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ainize-kobart-news-eb-finetuned-papers This model is a fine-tuned version of [ainize/kobart-news](https://huggingface.co/ainize/kobart-news) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3066 - Rouge1: 14.5433 - Rouge2: 5.2238 - Rougel: 14.4731 - Rougelsum: 14.5183 - Gen Len: 19.9934 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| 0.1918 \| 1.0 \| 7200 \| 0.2403 \| 14.6883 \| 5.2427 \| 14.6306 \| 14.6489 \| 19.9938 \| \| 0.1332 \| 2.0 \| 14400 \| 0.2391 \| 14.5165 \| 5.2443 \| 14.493 \| 14.4908 \| 19.9972 \| \| 0.0966 \| 3.0 \| 21600 \| 0.2539 \| 14.758 \| 5.4976 \| 14.6906 \| 14.7188 \| 19.9941 \| \| 0.0736 \| 4.0 \| 28800 \| 0.2782 \| 14.6267 \| 5.3371 \| 14.5578 \| 14.6014 \| 19.9934 \| \| 0.0547 \| 5.0 \| 36000 \| 0.3066 \| 14.5433 \| 5.2238 \| 14.4731 \| 14.5183 \| 19.9934 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-1000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1	d5e83bb4af959617298a2943aa6455c7dd9476a5	2022-06-12T16:54:18.000Z	[ "pytorch", "tensorboard", "marian", "text2text-generation", "dataset:un_multi", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	text2text-generation	false	meghazisofiane	null	meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-1000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1	1	null	transformers	32,818	--- license: apache-2.0 tags: - generated_from_trainer datasets: - un_multi metrics: - bleu model-index: - name: opus-mt-en-ar-evaluated-en-to-ar-1000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: un_multi type: un_multi args: ar-en metrics: - name: Bleu type: bleu value: 64.0048 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-en-ar-evaluated-en-to-ar-1000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the un_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.1294 - Bleu: 64.0048 - Meteor: 0.4903 - Gen Len: 21.85 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Meteor \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\| \| 0.0489 \| 1.0 \| 100 \| 0.1287 \| 63.7573 \| 0.4877 \| 21.79 \| \| 0.0447 \| 2.0 \| 200 \| 0.1293 \| 63.8776 \| 0.49 \| 21.875 \| \| 0.0442 \| 3.0 \| 300 \| 0.1294 \| 64.0048 \| 0.4903 \| 21.85 \| \| 0.0433 \| 4.0 \| 400 \| 0.1294 \| 64.0048 \| 0.4903 \| 21.85 \| \| 0.0429 \| 5.0 \| 500 \| 0.1294 \| 64.0048 \| 0.4903 \| 21.85 \| \| 0.0435 \| 6.0 \| 600 \| 0.1294 \| 64.0048 \| 0.4903 \| 21.85 \| \| 0.0429 \| 7.0 \| 700 \| 0.1294 \| 64.0048 \| 0.4903 \| 21.85 \| \| 0.0426 \| 8.0 \| 800 \| 0.1294 \| 64.0048 \| 0.4903 \| 21.85 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
nlokam99/ada_sample	9f01f814d7cef9419ab4775a7cfa01a49e4e348a	2022-06-12T17:35:50.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational", "license:mit" ]	conversational	false	nlokam99	null	nlokam99/ada_sample	1	null	transformers	32,819	--- thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png tags: - conversational license: mit ---
huggingtweets/dodecahedra	f6e4e37d0e5b632376a3744b2e1264940e1b03df	2022-06-12T17:42:15.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/dodecahedra	1	null	transformers	32,820	--- language: en thumbnail: http://www.huggingtweets.com/dodecahedra/1655055731499/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/3232494514/760c72bca0af20fac2cd61bcec557e7a_400x400.jpeg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">William Rose</div> <div style="text-align: center; font-size: 14px;">@dodecahedra</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from William Rose. \| Data \| William Rose \| \| --- \| --- \| \| Tweets downloaded \| 3241 \| \| Retweets \| 1115 \| \| Short tweets \| 158 \| \| Tweets kept \| 1968 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1geru0ac/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dodecahedra's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1uy1zk82) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1uy1zk82/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/dodecahedra') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
meghazisofiane/mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instances-un_multi-leaningRate2e-05-batchSize2	1b02b989ac829a8ced504d97bd852eeae35634fd	2022-06-12T20:02:36.000Z	[ "pytorch", "tensorboard", "mbart", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	meghazisofiane	null	meghazisofiane/mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instances-un_multi-leaningRate2e-05-batchSize2	1	null	transformers	32,821	Entry not found
abdoutony207/mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize2	97687f7292bde84c97b21ea31c55be82dcd7740c	2022-06-12T18:25:47.000Z	[ "pytorch", "tensorboard", "mbart", "text2text-generation", "dataset:opus100", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]	text2text-generation	false	abdoutony207	null	abdoutony207/mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize2	1	null	transformers	32,822	--- license: mit tags: - generated_from_trainer datasets: - opus100 metrics: - bleu model-index: - name: mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize2 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus100 type: opus100 args: ar-en metrics: - name: Bleu type: bleu value: 10.5645 --- <!-- 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. --> # mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize2 This model is a fine-tuned version of [akhooli/mbart-large-cc25-en-ar](https://huggingface.co/akhooli/mbart-large-cc25-en-ar) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 0.4673 - Bleu: 10.5645 - Meteor: 0.0783 - Gen Len: 10.23 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Meteor \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\| \| 8.1731 \| 0.25 \| 100 \| 2.8417 \| 0.9599 \| 0.028 \| 230.885 \| \| 0.6743 \| 0.5 \| 200 \| 0.4726 \| 6.4055 \| 0.0692 \| 14.81 \| \| 0.3028 \| 0.75 \| 300 \| 0.4572 \| 6.7544 \| 0.0822 \| 23.92 \| \| 0.2555 \| 1.0 \| 400 \| 0.4172 \| 8.4078 \| 0.0742 \| 13.655 \| \| 0.1644 \| 1.25 \| 500 \| 0.4236 \| 9.284 \| 0.071 \| 13.03 \| \| 0.1916 \| 1.5 \| 600 \| 0.4222 \| 4.8976 \| 0.0779 \| 32.225 \| \| 0.2011 \| 1.75 \| 700 \| 0.4305 \| 7.6909 \| 0.0738 \| 16.675 \| \| 0.1612 \| 2.0 \| 800 \| 0.4416 \| 10.8622 \| 0.0855 \| 10.91 \| \| 0.116 \| 2.25 \| 900 \| 0.4673 \| 10.5645 \| 0.0783 \| 10.23 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
NadiaSan/udesa-model-aah-es-35k	c0b188e56395888d3bdb592c2e030c55703dc53b	2022-06-12T20:28:38.000Z	[ "pytorch", "bert", "feature-extraction", "transformers" ]	feature-extraction	false	NadiaSan	null	NadiaSan/udesa-model-aah-es-35k	1	null	transformers	32,823	Entry not found
rohitsroch/hybrid_utt-clusterrank_bart-base_samsum_sum	2e8eecc91040ccab33b30a110d260fa026595bf8	2022-06-12T23:03:22.000Z	[ "pytorch", "bart", "text2text-generation", "en", "dataset:samsum", "transformers", "dialogue-summarization", "license:apache-2.0", "autotrain_compatible" ]	text2text-generation	false	rohitsroch	null	rohitsroch/hybrid_utt-clusterrank_bart-base_samsum_sum	1	null	transformers	32,824	--- language: - en license: apache-2.0 tags: - dialogue-summarization model_index: - name: hybrid_utt-clusterrank_bart-base_samsum_sum results: - task: name: Summarization type: summarization datasets: - samsum --- ## Paper ## [Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning](https://dl.acm.org/doi/10.1145/3508546.3508640) Authors: Rohit Sroch ## Abstract Recently, the abstractive dialogue summarization task has been gaining a lot of attention from researchers. Also, unlike news articles and documents with well-structured text, dialogue differs in the sense that it often comes from two or more interlocutors, exchanging information with each other and having an inherent hierarchical structure based on the sequence of utterances by different speakers. This paper proposes a simple but effective hybrid approach that consists of two modules and uses transfer learning by leveraging pretrained language models (PLMs) to generate an abstractive summary. The first module highlights important utterances, capturing the utterance level relationship by adapting an auto-encoding model like BERT based on the unsupervised or supervised method. And then, the second module generates a concise abstractive summary by adapting encoder-decoder models like T5, BART, and PEGASUS. Experiment results on benchmark datasets show that our approach achieves a state-of-the-art performance by adapting to dialogue scenarios and can also be helpful in low-resource settings for domain adaptation. Rohit Sroch. 2021. Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning. In 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI'21). Association for Computing Machinery, New York, NY, USA, Article 94, 1–6. https://doi.org/10.1145/3508546.3508640 # hybrid_utt-clusterrank_bart-base_samsum_sum This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on SAMSum dataset for dialogue summarization task. ## Model description More information needed ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-5 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 - label_smoothing_factor: 0.1 ### Results on Test Set - predict_gen_len = 23.9048 - predict_rouge1 = 47.355 - predict_rouge2 = 22.4593 - predict_rougeL = 38.694 - predict_rougeLsum = 42.98 - predict_samples = 819 - predict_samples_per_second = 9.279 - predict_steps_per_second = 2.322 ### Framework versions - Transformers>=4.8.0 - Pytorch>=1.6.0 - Datasets>=1.10.2 - Tokenizers>=0.10.3 If you use this model, please cite the following paper: ``` @inproceedings{10.1145/3508546.3508640, author = {Sroch, Rohit}, title = {Domain Adapted Abstractive Summarization of Dialogue Using Transfer Learning}, year = {2021}, isbn = {9781450385053}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3508546.3508640}, doi = {10.1145/3508546.3508640}, articleno = {94}, numpages = {6}, keywords = {encoder-decoder, T5, abstractive summary, PEGASUS, BART, dialogue summarization, PLMs, BERT}, location = {Sanya, China}, series = {ACAI'21} } ```
erickfm/t5-base-finetuned-bias-sweep-5db9391c	8b4b9724f3771cc1743a7d23fe53b85f2c89aa2b	2022-06-12T22:08:17.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	erickfm	null	erickfm/t5-base-finetuned-bias-sweep-5db9391c	1	null	transformers	32,825	Entry not found
huggingtweets/gronkh	5d7541e18a6c3a6b10b26b3c5771dbdd8407f3b1	2022-06-12T22:07:19.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/gronkh	1	null	transformers	32,826	--- language: en thumbnail: http://www.huggingtweets.com/gronkh/1655071635259/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1291074656049541127/fBvcAq3x_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">𝙂𝙍𝙊𝙉𝙆𝙃</div> <div style="text-align: center; font-size: 14px;">@gronkh</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from 𝙂𝙍𝙊𝙉𝙆𝙃. \| Data \| 𝙂𝙍𝙊𝙉𝙆𝙃 \| \| --- \| --- \| \| Tweets downloaded \| 3250 \| \| Retweets \| 65 \| \| Short tweets \| 97 \| \| Tweets kept \| 3088 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3gqyrznk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gronkh's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2i0lrjh9) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2i0lrjh9/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gronkh') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
mailenpellegrino/transformerv2	742592d00b5928bbb16f7f4980710b9da95cf5d2	2022-06-12T22:28:54.000Z	[ "pytorch", "xlm-roberta", "feature-extraction", "transformers" ]	feature-extraction	false	mailenpellegrino	null	mailenpellegrino/transformerv2	1	null	transformers	32,827	Entry not found
asahi417/lmqg-mt5-small-frquad	3992189a9bba77dbe1af7446d23d7714a97f03da	2022-06-13T00:46:53.000Z	[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	asahi417	null	asahi417/lmqg-mt5-small-frquad	1	null	transformers	32,828	Entry not found
huggingtweets/145gomez	08b1f025fed038ca1b84b789220994a330372981	2022-06-13T04:12:14.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/145gomez	1	null	transformers	32,829	--- language: en thumbnail: http://www.huggingtweets.com/145gomez/1655093529513/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1457362420800835589/0mihXFjt_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Omar Gomez Avalos</div> <div style="text-align: center; font-size: 14px;">@145gomez</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Omar Gomez Avalos. \| Data \| Omar Gomez Avalos \| \| --- \| --- \| \| Tweets downloaded \| 3189 \| \| Retweets \| 472 \| \| Short tweets \| 384 \| \| Tweets kept \| 2333 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3hcxwfgg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @145gomez's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/7qzkruyo) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/7qzkruyo/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/145gomez') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
burner/GPT-J-Step_98500	5a47e95152759184ce13d43372e29f77134c847f	2022-06-13T05:11:42.000Z	[ "pytorch", "gptj", "text-generation", "transformers", "license:apache-2.0" ]	text-generation	false	burner	null	burner/GPT-J-Step_98500	1	null	transformers	32,830	--- license: apache-2.0 ---
zoha/wav2vec2-base-timit-google-colab	659e1d07b09561e637c03022375f6eed869c7cf2	2022-06-13T10:50:12.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	zoha	null	zoha/wav2vec2-base-timit-google-colab	1	null	transformers	32,831	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-google-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4659 - Wer: 0.3080 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 3.5787 \| 0.87 \| 500 \| 1.7648 \| 1.0305 \| \| 0.8692 \| 1.73 \| 1000 \| 0.5136 \| 0.5103 \| \| 0.4346 \| 2.6 \| 1500 \| 0.4364 \| 0.4515 \| \| 0.31 \| 3.46 \| 2000 \| 0.3889 \| 0.4070 \| \| 0.234 \| 4.33 \| 2500 \| 0.4161 \| 0.3863 \| \| 0.2054 \| 5.19 \| 3000 \| 0.3845 \| 0.3722 \| \| 0.165 \| 6.06 \| 3500 \| 0.4035 \| 0.3643 \| \| 0.1436 \| 6.92 \| 4000 \| 0.4090 \| 0.3623 \| \| 0.1381 \| 7.79 \| 4500 \| 0.4007 \| 0.3673 \| \| 0.1175 \| 8.65 \| 5000 \| 0.4588 \| 0.3632 \| \| 0.1052 \| 9.52 \| 5500 \| 0.4441 \| 0.3588 \| \| 0.0988 \| 10.38 \| 6000 \| 0.4133 \| 0.3489 \| \| 0.0877 \| 11.25 \| 6500 \| 0.4758 \| 0.3510 \| \| 0.0856 \| 12.11 \| 7000 \| 0.4454 \| 0.3425 \| \| 0.0731 \| 12.98 \| 7500 \| 0.4252 \| 0.3351 \| \| 0.0712 \| 13.84 \| 8000 \| 0.4163 \| 0.3370 \| \| 0.0711 \| 14.71 \| 8500 \| 0.4166 \| 0.3367 \| \| 0.06 \| 15.57 \| 9000 \| 0.4195 \| 0.3347 \| \| 0.0588 \| 16.44 \| 9500 \| 0.4697 \| 0.3367 \| \| 0.0497 \| 17.3 \| 10000 \| 0.4255 \| 0.3314 \| \| 0.0523 \| 18.17 \| 10500 \| 0.4676 \| 0.3307 \| \| 0.0444 \| 19.03 \| 11000 \| 0.4570 \| 0.3244 \| \| 0.0435 \| 19.9 \| 11500 \| 0.4307 \| 0.3243 \| \| 0.0348 \| 20.76 \| 12000 \| 0.4763 \| 0.3245 \| \| 0.036 \| 21.63 \| 12500 \| 0.4635 \| 0.3238 \| \| 0.0347 \| 22.49 \| 13000 \| 0.4602 \| 0.3212 \| \| 0.0333 \| 23.36 \| 13500 \| 0.4472 \| 0.3195 \| \| 0.0311 \| 24.22 \| 14000 \| 0.4449 \| 0.3183 \| \| 0.0294 \| 25.09 \| 14500 \| 0.4631 \| 0.3175 \| \| 0.025 \| 25.95 \| 15000 \| 0.4466 \| 0.3164 \| \| 0.023 \| 26.82 \| 15500 \| 0.4581 \| 0.3138 \| \| 0.0216 \| 27.68 \| 16000 \| 0.4665 \| 0.3114 \| \| 0.0198 \| 28.55 \| 16500 \| 0.4590 \| 0.3092 \| \| 0.0181 \| 29.41 \| 17000 \| 0.4659 \| 0.3080 \| ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1
shaojie/distilbert-base-uncased-finetuned-squad	5f332a62f125d0aa9b2d57e6ca10494f02c15a21	2022-06-14T07:26:48.000Z	[ "pytorch", "tensorboard", "distilbert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	question-answering	false	shaojie	null	shaojie/distilbert-base-uncased-finetuned-squad	1	null	transformers	32,832	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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: - eval_loss: 1.1585 - eval_runtime: 138.1018 - eval_samples_per_second: 78.087 - eval_steps_per_second: 4.88 - epoch: 1.0 - step: 5533 ## 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 ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
erickfm/t5-base-finetuned-bias-sweep-64f598ca	5449b91f587e66826a8da4554f38e656dbeb19af	2022-06-14T01:40:19.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	erickfm	null	erickfm/t5-base-finetuned-bias-sweep-64f598ca	1	null	transformers	32,833	maybe [sandy-sweep](https://wandb.ai/unbias/t5-base/runs/tkvqsbl3)
yossra/bert-finetuned-squad	1a01bda20e36c699383a30da6d3021bc8e82b25f	2022-06-13T12:03:54.000Z	[ "pytorch", "tensorboard", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	question-answering	false	yossra	null	yossra/bert-finetuned-squad	1	null	transformers	32,834	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bert-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
Nadav/roberta-base-squad-nl	141efed50c80f7e7d44c1793e74821ee8d138f82	2022-06-13T11:55:54.000Z	[ "pytorch", "roberta", "question-answering", "transformers", "license:afl-3.0", "autotrain_compatible" ]	question-answering	false	Nadav	null	Nadav/roberta-base-squad-nl	1	null	transformers	32,835	--- license: afl-3.0 ---
Nadav/xlm-roberta-base-squad-nl	1edf37cbb3bf8708810ef7c1d159753792366910	2022-06-13T11:54:19.000Z	[ "pytorch", "xlm-roberta", "question-answering", "transformers", "autotrain_compatible" ]	question-answering	false	Nadav	null	Nadav/xlm-roberta-base-squad-nl	1	null	transformers	32,836	Entry not found
Shenghao1993/xlm-roberta-base-finetuned-panx-de-fr	9a1cdb7291b41b92387d11c5ea900e4e59a4f1d9	2022-06-13T11:59:43.000Z	[ "pytorch", "xlm-roberta", "token-classification", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]	token-classification	false	Shenghao1993	null	Shenghao1993/xlm-roberta-base-finetuned-panx-de-fr	1	null	transformers	32,837	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1623 - F1: 0.8596 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2865 \| 1.0 \| 715 \| 0.1981 \| 0.8167 \| \| 0.1484 \| 2.0 \| 1430 \| 0.1595 \| 0.8486 \| \| 0.0949 \| 3.0 \| 2145 \| 0.1623 \| 0.8596 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
erickfm/t5-base-finetuned-bias-sweep-1da3c5df	f8628fd94d560d83eba6632e888ade6ad8669915	2022-06-13T12:25:12.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	erickfm	null	erickfm/t5-base-finetuned-bias-sweep-1da3c5df	1	null	transformers	32,838	Entry not found
Shenghao1993/xlm-roberta-base-finetuned-panx-fr	3914ed406dc6abb836d2f7a105405bd6990e9b5d	2022-06-13T12:44:17.000Z	[ "pytorch", "xlm-roberta", "token-classification", "dataset:xtreme", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]	token-classification	false	Shenghao1993	null	Shenghao1993/xlm-roberta-base-finetuned-panx-fr	1	null	transformers	32,839	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.8438448566610455 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2675 - F1: 0.8438 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.5606 \| 1.0 \| 191 \| 0.3157 \| 0.7967 \| \| 0.2755 \| 2.0 \| 382 \| 0.2684 \| 0.8288 \| \| 0.1811 \| 3.0 \| 573 \| 0.2675 \| 0.8438 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
Shenghao1993/xlm-roberta-base-finetuned-panx-it	071f7c6c1b25c6d44d6d0ae4966c7a0c739afe2a	2022-06-13T13:02:39.000Z	[ "pytorch", "xlm-roberta", "token-classification", "dataset:xtreme", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]	token-classification	false	Shenghao1993	null	Shenghao1993/xlm-roberta-base-finetuned-panx-it	1	null	transformers	32,840	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.it metrics: - name: F1 type: f1 value: 0.8358085808580858 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2403 - F1: 0.8358 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.7053 \| 1.0 \| 70 \| 0.3077 \| 0.7587 \| \| 0.2839 \| 2.0 \| 140 \| 0.2692 \| 0.8007 \| \| 0.1894 \| 3.0 \| 210 \| 0.2403 \| 0.8358 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
Shenghao1993/xlm-roberta-base-finetuned-panx-en	3c6281283ab65ce449ba4fd0d0cf8e3da61ea5d2	2022-06-13T13:20:02.000Z	[ "pytorch", "xlm-roberta", "token-classification", "dataset:xtreme", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]	token-classification	false	Shenghao1993	null	Shenghao1993/xlm-roberta-base-finetuned-panx-en	1	null	transformers	32,841	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.en metrics: - name: F1 type: f1 value: 0.7032474804031354 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.3859 - F1: 0.7032 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.0494 \| 1.0 \| 50 \| 0.5464 \| 0.5507 \| \| 0.5329 \| 2.0 \| 100 \| 0.4217 \| 0.6715 \| \| 0.3799 \| 3.0 \| 150 \| 0.3859 \| 0.7032 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
Shenghao1993/xlm-roberta-base-finetuned-panx-all	449d0b762db7caa976af890035d77d0f859c9f1e	2022-06-13T13:45:41.000Z	[ "pytorch", "xlm-roberta", "token-classification", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]	token-classification	false	Shenghao1993	null	Shenghao1993/xlm-roberta-base-finetuned-panx-all	1	null	transformers	32,842	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1719 - F1: 0.8544 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2951 \| 1.0 \| 835 \| 0.1882 \| 0.8171 \| \| 0.1547 \| 2.0 \| 1670 \| 0.1707 \| 0.8454 \| \| 0.1018 \| 3.0 \| 2505 \| 0.1719 \| 0.8544 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
Andrey1989/xlmr-finetuned-ner	568f4227475e804100b72e019378711885178588	2022-06-13T14:03:57.000Z	[ "pytorch", "tensorboard", "xlm-roberta", "token-classification", "dataset:wikiann", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]	token-classification	false	Andrey1989	null	Andrey1989/xlmr-finetuned-ner	1	null	transformers	32,843	--- license: mit tags: - generated_from_trainer datasets: - wikiann metrics: - precision - recall - f1 - accuracy model-index: - name: xlmr-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: wikiann type: wikiann args: et metrics: - name: Precision type: precision value: 0.9044097027481772 - name: Recall type: recall value: 0.9136978539556626 - name: F1 type: f1 value: 0.9090300532008596 - name: Accuracy type: accuracy value: 0.9649304793632428 --- <!-- 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. --> # xlmr-finetuned-ner This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.1395 - Precision: 0.9044 - Recall: 0.9137 - F1: 0.9090 - Accuracy: 0.9649 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.4215 \| 1.0 \| 938 \| 0.1650 \| 0.8822 \| 0.8781 \| 0.8802 \| 0.9529 \| \| 0.1559 \| 2.0 \| 1876 \| 0.1412 \| 0.9018 \| 0.9071 \| 0.9045 \| 0.9631 \| \| 0.1051 \| 3.0 \| 2814 \| 0.1395 \| 0.9044 \| 0.9137 \| 0.9090 \| 0.9649 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
lariskelmer/opus-mt-en-ro-finetuned-en-to-ro	15a2acdb8173eb3af1d94065e5f55d52e6d3cdbd	2022-07-13T21:08:15.000Z	[ "pytorch", "tensorboard", "marian", "text2text-generation", "dataset:wmt16", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	text2text-generation	false	lariskelmer	null	lariskelmer/opus-mt-en-ro-finetuned-en-to-ro	1	null	transformers	32,844	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: opus-mt-en-ro-finetuned-en-to-ro results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt16 type: wmt16 args: ro-en metrics: - name: Bleu type: bleu value: 28.1505 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-en-ro-finetuned-en-to-ro This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.2886 - Bleu: 28.1505 - Gen Len: 34.1036 ## 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 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 0.7437 \| 1.0 \| 38145 \| 1.2886 \| 28.1505 \| 34.1036 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
laboyle1/distilbert-finetuned	e6d4f9f420da11e321745bba753785c19f3c14fc	2022-06-13T21:03:49.000Z	[ "pytorch", "distilbert", "fill-mask", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	fill-mask	false	laboyle1	null	laboyle1/distilbert-finetuned	1	null	transformers	32,845	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-finetuned This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9895 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.2103 \| 1.0 \| 10024 \| 2.0834 \| \| 2.1146 \| 2.0 \| 20048 \| 2.0387 \| \| 2.0721 \| 3.0 \| 30072 \| 2.0095 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu102 - Datasets 2.2.2 - Tokenizers 0.12.1
techiaith/wav2vec2-xls-r-1b-ft-cy	d67b672faff98479c63c9e323a6099094818585c	2022-06-15T13:53:19.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "cy", "dataset:common_voice", "transformers", "audio", "hf-asr-leaderboard", "ken-lm", "robust-speech-event", "speech", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	techiaith	null	techiaith/wav2vec2-xls-r-1b-ft-cy	1	1	transformers	32,846	--- language: cy datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - hf-asr-leaderboard - ken-lm - robust-speech-event - speech license: apache-2.0 model-index: - name: wav2vec2-xls-r-1b-ft-cy with KenLM language model (by Bangor University) results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice cy type: common_voice args: cy metrics: - name: Test WER type: wer value: 12.38% --- # wav2vec2-xls-r-1b-ft-cy Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) with the [Welsh Common Voice version 9 dataset](https://huggingface.co/datasets/common_voice). Source code and scripts for training acoustic and KenLM language models, as well as examples of inference in transcribing or a self-hosted API service, can be found at [https://github.com/techiaith/docker-wav2vec2-xlsr-ft-cy](https://github.com/techiaith/docker-wav2vec2-xlsr-ft-cy). ## Usage The wav2vec2-xls-r-1b-ft-cy (acoustic) model can be used directly (without a language model) as follows: ```python import torch import torchaudio import librosa from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor processor = Wav2Vec2Processor.from_pretrained("techiaith/wav2vec2-xls-r-1b-ft-cy") model = Wav2Vec2ForCTC.from_pretrained("techiaith/wav2vec2-xls-r-1b-ft-cy") audio, rate = librosa.load(audio_file, sr=16000) inputs = processor(audio, sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits # greedy decoding predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) ``` ## Using the Language Model See https://github.com/techiaith/docker-wav2vec2-xlsr-ft-cy/releases/tag/22.06 for more details and examples of a KenLM usage with the Parlance PyTorch CTC decode bindings library: [https://github.com/parlance/ctcdecode](https://github.com/parlance/ctcdecode) ## Evaluation According to the Welsh Common Voice version 9 test set, the WER of techiaith/wav2vec2-xls-r-1b-ft-cy standalone is 19.68% When assisted by the KenLM language model the same test produces a WER of 12.38% See: https://github.com/techiaith/docker-wav2vec2-xlsr-ft-cy/blob/main/train/python/evaluate.py
chiranthans23/xlm-roberta-base-finetuned-panx-de	8c4b638c9807a30392325d10dfaccd7b1b045fd0	2022-06-14T16:13:11.000Z	[ "pytorch", "tensorboard", "xlm-roberta", "token-classification", "dataset:xtreme", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]	token-classification	false	chiranthans23	null	chiranthans23/xlm-roberta-base-finetuned-panx-de	1	null	transformers	32,847	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8620945214069894 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1372 - F1: 0.8621 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2575 \| 1.0 \| 525 \| 0.1621 \| 0.8292 \| \| 0.1287 \| 2.0 \| 1050 \| 0.1378 \| 0.8526 \| \| 0.0831 \| 3.0 \| 1575 \| 0.1372 \| 0.8621 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
income/jpq-document_encoder-base-msmarco-contriever	e3e5eddf8ecb5b2c82ce61fe5d294999a86501f1	2022-06-13T20:57:52.000Z	[ "pytorch", "bert", "transformers", "license:apache-2.0" ]	null	false	income	null	income/jpq-document_encoder-base-msmarco-contriever	1	null	transformers	32,848	--- license: apache-2.0 ---
micamorales/roberta-NLI-abs	e59d6882e7e643211eff91101a432fb4e0ede164	2022-06-13T21:05:31.000Z	[ "pytorch", "roberta", "feature-extraction", "transformers" ]	feature-extraction	false	micamorales	null	micamorales/roberta-NLI-abs	1	null	transformers	32,849	Entry not found
burner/GPT-J-Step_58500	3a2cdc1569b5242e22ecbd48e5434a076e8bf828	2022-06-14T00:42:40.000Z	[ "pytorch", "gptj", "text-generation", "transformers", "license:apache-2.0" ]	text-generation	false	burner	null	burner/GPT-J-Step_58500	1	null	transformers	32,850	--- license: apache-2.0 ---
burner/GPT-J-Step_18500	3994095dd3f37490021d608d6496acbef30ae90e	2022-06-14T03:04:28.000Z	[ "pytorch", "gptj", "text-generation", "transformers", "license:apache-2.0" ]	text-generation	false	burner	null	burner/GPT-J-Step_18500	1	null	transformers	32,851	--- license: apache-2.0 ---
twieland/MIX2_en-ja_helsinki	7dc69e79cfe629a26847cc4e1e585448dcabe5d2	2022-06-16T06:23:56.000Z	[ "pytorch", "marian", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	text2text-generation	false	twieland	null	twieland/MIX2_en-ja_helsinki	1	null	transformers	32,852	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: MIX2_en-ja_helsinki results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MIX2_en-ja_helsinki This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-jap](https://huggingface.co/Helsinki-NLP/opus-mt-en-jap) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6703 ## 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: 96 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 3.5357 \| 0.02 \| 4000 \| 2.9519 \| \| 2.8601 \| 0.04 \| 8000 \| 2.6962 \| \| 2.6183 \| 0.06 \| 12000 \| 2.5156 \| \| 2.4731 \| 0.08 \| 16000 \| 2.4312 \| \| 2.3731 \| 0.1 \| 20000 \| 2.3575 \| \| 2.2964 \| 0.11 \| 24000 \| 2.3319 \| \| 2.238 \| 0.13 \| 28000 \| 2.2802 \| \| 2.1919 \| 0.15 \| 32000 \| 2.2552 \| \| 2.1479 \| 0.17 \| 36000 \| 2.2354 \| \| 2.1104 \| 0.19 \| 40000 \| 2.2210 \| \| 2.0788 \| 0.21 \| 44000 \| 2.1835 \| \| 2.0552 \| 0.23 \| 48000 \| 2.1391 \| \| 2.0228 \| 0.25 \| 52000 \| 2.1338 \| \| 2.0062 \| 0.27 \| 56000 \| 2.1115 \| \| 1.9868 \| 0.29 \| 60000 \| 2.1025 \| \| 1.9628 \| 0.31 \| 64000 \| 2.1334 \| \| 1.9474 \| 0.32 \| 68000 \| 2.0935 \| \| 1.9318 \| 0.34 \| 72000 \| 2.1030 \| \| 1.9187 \| 0.36 \| 76000 \| 2.0605 \| \| 1.9019 \| 0.38 \| 80000 \| 2.0388 \| \| 1.8916 \| 0.4 \| 84000 \| 2.0360 \| \| 1.8775 \| 0.42 \| 88000 \| 2.0356 \| \| 1.8689 \| 0.44 \| 92000 \| 2.0315 \| \| 1.8558 \| 0.46 \| 96000 \| 2.0169 \| \| 1.8431 \| 0.48 \| 100000 \| 2.0213 \| \| 1.8373 \| 0.5 \| 104000 \| 2.0071 \| \| 1.8224 \| 0.52 \| 108000 \| 2.0093 \| \| 1.8181 \| 0.53 \| 112000 \| 1.9952 \| \| 1.8087 \| 0.55 \| 116000 \| 1.9927 \| \| 1.7998 \| 0.57 \| 120000 \| 1.9726 \| \| 1.7947 \| 0.59 \| 124000 \| 1.9817 \| \| 1.7874 \| 0.61 \| 128000 \| 1.9650 \| \| 1.7781 \| 0.63 \| 132000 \| 1.9688 \| \| 1.7712 \| 0.65 \| 136000 \| 1.9655 \| \| 1.7631 \| 0.67 \| 140000 \| 1.9561 \| \| 1.7577 \| 0.69 \| 144000 \| 1.9529 \| \| 1.7528 \| 0.71 \| 148000 \| 1.9447 \| \| 1.746 \| 0.73 \| 152000 \| 1.9700 \| \| 1.7386 \| 0.74 \| 156000 \| 1.9413 \| \| 1.7329 \| 0.76 \| 160000 \| 1.9329 \| \| 1.7285 \| 0.78 \| 164000 \| 1.9289 \| \| 1.7227 \| 0.8 \| 168000 \| 1.9337 \| \| 1.7186 \| 0.82 \| 172000 \| 1.9263 \| \| 1.7116 \| 0.84 \| 176000 \| 1.9407 \| \| 1.7072 \| 0.86 \| 180000 \| 1.9059 \| \| 1.7032 \| 0.88 \| 184000 \| 1.9380 \| \| 1.6932 \| 0.9 \| 188000 \| 1.9183 \| \| 1.6921 \| 0.92 \| 192000 \| 1.9131 \| \| 1.6875 \| 0.94 \| 196000 \| 1.9180 \| \| 1.6846 \| 0.96 \| 200000 \| 1.9040 \| \| 1.6797 \| 0.97 \| 204000 \| 1.9089 \| \| 1.6725 \| 0.99 \| 208000 \| 1.9024 \| \| 1.6589 \| 1.01 \| 212000 \| 1.8909 \| \| 1.6507 \| 1.03 \| 216000 \| 1.8837 \| \| 1.6441 \| 1.05 \| 220000 \| 1.8906 \| \| 1.6445 \| 1.07 \| 224000 \| 1.8914 \| \| 1.6394 \| 1.09 \| 228000 \| 1.8833 \| \| 1.6382 \| 1.11 \| 232000 \| 1.8837 \| \| 1.6376 \| 1.13 \| 236000 \| 1.8869 \| \| 1.6329 \| 1.15 \| 240000 \| 1.8829 \| \| 1.6294 \| 1.17 \| 244000 \| 1.8845 \| \| 1.6273 \| 1.18 \| 248000 \| 1.8888 \| \| 1.6243 \| 1.2 \| 252000 \| 1.8709 \| \| 1.6226 \| 1.22 \| 256000 \| 1.8418 \| \| 1.6177 \| 1.24 \| 260000 \| 1.8587 \| \| 1.6151 \| 1.26 \| 264000 \| 1.8526 \| \| 1.6111 \| 1.28 \| 268000 \| 1.8494 \| \| 1.6084 \| 1.3 \| 272000 \| 1.8781 \| \| 1.6043 \| 1.32 \| 276000 \| 1.8390 \| \| 1.6011 \| 1.34 \| 280000 \| 1.8603 \| \| 1.5999 \| 1.36 \| 284000 \| 1.8515 \| \| 1.5954 \| 1.38 \| 288000 \| 1.8356 \| \| 1.5936 \| 1.39 \| 292000 \| 1.8530 \| \| 1.5916 \| 1.41 \| 296000 \| 1.8475 \| \| 1.5886 \| 1.43 \| 300000 \| 1.8410 \| \| 1.5883 \| 1.45 \| 304000 \| 1.8153 \| \| 1.5828 \| 1.47 \| 308000 \| 1.8254 \| \| 1.582 \| 1.49 \| 312000 \| 1.8139 \| \| 1.578 \| 1.51 \| 316000 \| 1.8366 \| \| 1.5723 \| 1.53 \| 320000 \| 1.8353 \| \| 1.5705 \| 1.55 \| 324000 \| 1.8230 \| \| 1.5691 \| 1.57 \| 328000 \| 1.8194 \| \| 1.5656 \| 1.59 \| 332000 \| 1.8069 \| \| 1.566 \| 1.6 \| 336000 \| 1.8204 \| \| 1.5604 \| 1.62 \| 340000 \| 1.8307 \| \| 1.5573 \| 1.64 \| 344000 \| 1.8209 \| \| 1.5547 \| 1.66 \| 348000 \| 1.8320 \| \| 1.5545 \| 1.68 \| 352000 \| 1.8179 \| \| 1.5519 \| 1.7 \| 356000 \| 1.8323 \| \| 1.545 \| 1.72 \| 360000 \| 1.8005 \| \| 1.5483 \| 1.74 \| 364000 \| 1.8034 \| \| 1.5454 \| 1.76 \| 368000 \| 1.7997 \| \| 1.5393 \| 1.78 \| 372000 \| 1.8078 \| \| 1.5381 \| 1.8 \| 376000 \| 1.8204 \| \| 1.5347 \| 1.81 \| 380000 \| 1.8071 \| \| 1.5327 \| 1.83 \| 384000 \| 1.7997 \| \| 1.529 \| 1.85 \| 388000 \| 1.8012 \| \| 1.5287 \| 1.87 \| 392000 \| 1.8028 \| \| 1.5273 \| 1.89 \| 396000 \| 1.8103 \| \| 1.5194 \| 1.91 \| 400000 \| 1.8008 \| \| 1.5197 \| 1.93 \| 404000 \| 1.8004 \| \| 1.5218 \| 1.95 \| 408000 \| 1.8024 \| \| 1.514 \| 1.97 \| 412000 \| 1.7852 \| \| 1.5146 \| 1.99 \| 416000 \| 1.7908 \| \| 1.5045 \| 2.01 \| 420000 \| 1.7864 \| \| 1.4876 \| 2.02 \| 424000 \| 1.7813 \| \| 1.4846 \| 2.04 \| 428000 \| 1.7822 \| \| 1.4865 \| 2.06 \| 432000 \| 1.7737 \| \| 1.4857 \| 2.08 \| 436000 \| 1.7668 \| \| 1.4825 \| 2.1 \| 440000 \| 1.7681 \| \| 1.4828 \| 2.12 \| 444000 \| 1.7685 \| \| 1.4821 \| 2.14 \| 448000 \| 1.7636 \| \| 1.4778 \| 2.16 \| 452000 \| 1.7778 \| \| 1.4803 \| 2.18 \| 456000 \| 1.7834 \| \| 1.4766 \| 2.2 \| 460000 \| 1.7801 \| \| 1.4741 \| 2.22 \| 464000 \| 1.7601 \| \| 1.4705 \| 2.23 \| 468000 \| 1.7665 \| \| 1.4739 \| 2.25 \| 472000 \| 1.7604 \| \| 1.4694 \| 2.27 \| 476000 \| 1.7803 \| \| 1.4665 \| 2.29 \| 480000 \| 1.7835 \| \| 1.4668 \| 2.31 \| 484000 \| 1.7670 \| \| 1.4605 \| 2.33 \| 488000 \| 1.7629 \| \| 1.4626 \| 2.35 \| 492000 \| 1.7612 \| \| 1.4627 \| 2.37 \| 496000 \| 1.7612 \| \| 1.4569 \| 2.39 \| 500000 \| 1.7557 \| \| 1.455 \| 2.41 \| 504000 \| 1.7599 \| \| 1.4547 \| 2.43 \| 508000 \| 1.7569 \| \| 1.453 \| 2.44 \| 512000 \| 1.7589 \| \| 1.4515 \| 2.46 \| 516000 \| 1.7679 \| \| 1.4501 \| 2.48 \| 520000 \| 1.7574 \| \| 1.4446 \| 2.5 \| 524000 \| 1.7526 \| \| 1.4456 \| 2.52 \| 528000 \| 1.7506 \| \| 1.4445 \| 2.54 \| 532000 \| 1.7484 \| \| 1.4428 \| 2.56 \| 536000 \| 1.7447 \| \| 1.439 \| 2.58 \| 540000 \| 1.7468 \| \| 1.441 \| 2.6 \| 544000 \| 1.7609 \| \| 1.4358 \| 2.62 \| 548000 \| 1.7498 \| \| 1.4318 \| 2.64 \| 552000 \| 1.7592 \| \| 1.4276 \| 2.65 \| 556000 \| 1.7452 \| \| 1.4317 \| 2.67 \| 560000 \| 1.7500 \| \| 1.4277 \| 2.69 \| 564000 \| 1.7392 \| \| 1.4259 \| 2.71 \| 568000 \| 1.7351 \| \| 1.4239 \| 2.73 \| 572000 \| 1.7385 \| \| 1.4191 \| 2.75 \| 576000 \| 1.7487 \| \| 1.4204 \| 2.77 \| 580000 \| 1.7392 \| \| 1.4176 \| 2.79 \| 584000 \| 1.7372 \| \| 1.4147 \| 2.81 \| 588000 \| 1.7347 \| \| 1.4154 \| 2.83 \| 592000 \| 1.7085 \| \| 1.4134 \| 2.85 \| 596000 \| 1.7103 \| \| 1.4091 \| 2.87 \| 600000 \| 1.7124 \| \| 1.4091 \| 2.88 \| 604000 \| 1.7369 \| \| 1.406 \| 2.9 \| 608000 \| 1.7142 \| \| 1.4028 \| 2.92 \| 612000 \| 1.7376 \| \| 1.4019 \| 2.94 \| 616000 \| 1.7201 \| \| 1.4018 \| 2.96 \| 620000 \| 1.7230 \| \| 1.3959 \| 2.98 \| 624000 \| 1.7206 \| \| 1.3985 \| 3.0 \| 628000 \| 1.7183 \| \| 1.3681 \| 3.02 \| 632000 \| 1.7283 \| \| 1.3668 \| 3.04 \| 636000 \| 1.7330 \| \| 1.3687 \| 3.06 \| 640000 \| 1.7187 \| \| 1.3681 \| 3.08 \| 644000 \| 1.7163 \| \| 1.3687 \| 3.09 \| 648000 \| 1.7249 \| \| 1.364 \| 3.11 \| 652000 \| 1.7283 \| \| 1.364 \| 3.13 \| 656000 \| 1.7091 \| \| 1.3652 \| 3.15 \| 660000 \| 1.7030 \| \| 1.3623 \| 3.17 \| 664000 \| 1.7058 \| \| 1.3604 \| 3.19 \| 668000 \| 1.7101 \| \| 1.3598 \| 3.21 \| 672000 \| 1.7104 \| \| 1.3577 \| 3.23 \| 676000 \| 1.7028 \| \| 1.3574 \| 3.25 \| 680000 \| 1.7023 \| \| 1.3546 \| 3.27 \| 684000 \| 1.7197 \| \| 1.3549 \| 3.29 \| 688000 \| 1.7045 \| \| 1.3534 \| 3.3 \| 692000 \| 1.6990 \| \| 1.3511 \| 3.32 \| 696000 \| 1.6971 \| \| 1.3504 \| 3.34 \| 700000 \| 1.6894 \| \| 1.346 \| 3.36 \| 704000 \| 1.6820 \| \| 1.3467 \| 3.38 \| 708000 \| 1.6920 \| \| 1.3461 \| 3.4 \| 712000 \| 1.6897 \| \| 1.3425 \| 3.42 \| 716000 \| 1.6962 \| \| 1.34 \| 3.44 \| 720000 \| 1.6864 \| \| 1.3408 \| 3.46 \| 724000 \| 1.6860 \| \| 1.3387 \| 3.48 \| 728000 \| 1.6924 \| \| 1.3377 \| 3.5 \| 732000 \| 1.6919 \| \| 1.3378 \| 3.51 \| 736000 \| 1.6858 \| \| 1.334 \| 3.53 \| 740000 \| 1.6816 \| \| 1.3347 \| 3.55 \| 744000 \| 1.6867 \| \| 1.3307 \| 3.57 \| 748000 \| 1.6859 \| \| 1.3316 \| 3.59 \| 752000 \| 1.6896 \| \| 1.3257 \| 3.61 \| 756000 \| 1.6824 \| \| 1.3222 \| 3.63 \| 760000 \| 1.6819 \| \| 1.3247 \| 3.65 \| 764000 \| 1.6809 \| \| 1.3207 \| 3.67 \| 768000 \| 1.6775 \| \| 1.3227 \| 3.69 \| 772000 \| 1.6807 \| \| 1.3203 \| 3.71 \| 776000 \| 1.6750 \| \| 1.3203 \| 3.72 \| 780000 \| 1.6758 \| \| 1.316 \| 3.74 \| 784000 \| 1.6787 \| \| 1.3147 \| 3.76 \| 788000 \| 1.6747 \| \| 1.3146 \| 3.78 \| 792000 \| 1.6718 \| \| 1.3137 \| 3.8 \| 796000 \| 1.6744 \| \| 1.3143 \| 3.82 \| 800000 \| 1.6733 \| \| 1.3123 \| 3.84 \| 804000 \| 1.6754 \| \| 1.3069 \| 3.86 \| 808000 \| 1.6734 \| \| 1.3122 \| 3.88 \| 812000 \| 1.6742 \| \| 1.3074 \| 3.9 \| 816000 \| 1.6742 \| \| 1.3006 \| 3.92 \| 820000 \| 1.6709 \| \| 1.308 \| 3.93 \| 824000 \| 1.6714 \| \| 1.3063 \| 3.95 \| 828000 \| 1.6727 \| \| 1.3036 \| 3.97 \| 832000 \| 1.6711 \| \| 1.3048 \| 3.99 \| 836000 \| 1.6703 \| ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
dz-xt/stt_en_conformer_ctc_small	33045d3effb0dce21b18eaeac9c2b3b59e3bc909	2022-06-14T10:13:47.000Z	[ "nemo", "en", "dataset:librispeech_asr", "dataset:mozilla-foundation/common_voice_7_0", "dataset:vctk", "dataset:fisher_corpus", "dataset:Switchboard-1", "dataset:WSJ-0", "dataset:WSJ-1", "dataset:National Singapore Corpus Part 1", "dataset:National Singapore Corpus Part 6", "dataset:VoxPopuli (EN)", "dataset:Europarl-ASR (EN)", "dataset:Multilingual LibriSpeech (2000 hours)", "automatic-speech-recognition", "speech", "audio", "CTC", "Conformer", "Transformer", "NeMo", "pytorch", "license:cc-by-4.0", "model-index" ]	automatic-speech-recognition	false	dz-xt	null	dz-xt/stt_en_conformer_ctc_small	1	0	nemo	32,853	--- language: - en license: cc-by-4.0 library_name: nemo datasets: - librispeech_asr - mozilla-foundation/common_voice_7_0 - vctk - fisher_corpus - Switchboard-1 - WSJ-0 - WSJ-1 - National Singapore Corpus Part 1 - National Singapore Corpus Part 6 - VoxPopuli (EN) - Europarl-ASR (EN) - Multilingual LibriSpeech (2000 hours) thumbnail: null tags: - automatic-speech-recognition - speech - audio - CTC - Conformer - Transformer - NeMo - pytorch model-index: - name: stt_en_conformer_ctc_small results: - task: type: automatic-speech-recognition dataset: type: librispeech_asr name: Librispeech (clean) config: other split: test args: language: en metrics: - type: wer value: 8.1 name: WER --- ## Model Overview <DESCRIBE IN ONE LINE THE MODEL AND ITS USE> ## NVIDIA NeMo: Training To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version. ``` pip install nemo_toolkit['all'] ``` ## How to Use this Model The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. ### Automatically instantiate the model ```python import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("dz-xt/stt_en_conformer_ctc_small") ``` ### Transcribing using Python First, let's get a sample ``` wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav ``` Then simply do: ``` asr_model.transcribe(['2086-149220-0033.wav']) ``` ### Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="dz-xt/stt_en_conformer_ctc_small" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` ### Input This model accepts 16000 KHz Mono-channel Audio (wav files) as input. ### Output This model provides transcribed speech as a string for a given audio sample. ## Model Architecture <ADD SOME INFORMATION ABOUT THE ARCHITECTURE> ## Training <ADD INFORMATION ABOUT HOW THE MODEL WAS TRAINED - HOW MANY EPOCHS, AMOUNT OF COMPUTE ETC> ### Datasets <LIST THE NAME AND SPLITS OF DATASETS USED TO TRAIN THIS MODEL (ALONG WITH LANGUAGE AND ANY ADDITIONAL INFORMATION)> ## Performance <LIST THE SCORES OF THE MODEL - OR USE THE Hugging Face Evaluate LiBRARY TO UPLOAD METRICS> ## Limitations <DECLARE ANY POTENTIAL LIMITATIONS OF THE MODEL> Eg: Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech. ## References <ADD ANY REFERENCES HERE AS NEEDED> [1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
lmqg/t5-small-squadshifts-nyt	16cda39345c8a8c557ebf4d3a7d059d5ae023cea	2022-06-14T10:36:00.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	lmqg	null	lmqg/t5-small-squadshifts-nyt	1	null	transformers	32,854	Entry not found
lmqg/t5-small-squadshifts-reddit	dae0414bcbd805c23fdc8957dce8e50727e310ac	2022-06-14T10:37:16.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	lmqg	null	lmqg/t5-small-squadshifts-reddit	1	null	transformers	32,855	Entry not found
janeel/distilbert-base-uncased-finetuned-squad	a933f5e7ddd6f1927246d7a4bbea3935d9de1b41	2022-06-16T13:42:47.000Z	[ "pytorch", "tensorboard", "roberta", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]	question-answering	false	janeel	null	janeel/distilbert-base-uncased-finetuned-squad	1	null	transformers	32,856	--- tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [klue/roberta-small](https://huggingface.co/klue/roberta-small) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 2.1272 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.398 \| 1.0 \| 2767 \| 2.2567 \| \| 2.0729 \| 2.0 \| 5534 \| 2.1272 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
Salvatore/mt5-finetuned-amazon-en-es	8e4979d36e6ce0e7c54eda9fd3db63cf833770f0	2022-06-14T14:25:12.000Z	[ "pytorch", "tensorboard", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	Salvatore	null	Salvatore/mt5-finetuned-amazon-en-es	1	null	transformers	32,857	Entry not found
AntoDono/DialoGPT-Bopy-Alpha-1.01	7b78c239a217ee6543bbfca33397615aba4a930d	2022-06-24T17:05:47.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	AntoDono	null	AntoDono/DialoGPT-Bopy-Alpha-1.01	1	null	transformers	32,858	--- tags: - conversational ---
Waleed-bin-Qamar/ConvNext-For-Covid-Classification-30EP	87de07e7431f3adccb4812a68e98edc1d552507b	2022-06-14T15:04:53.000Z	[ "pytorch", "convnext", "image-classification", "transformers" ]	image-classification	false	Waleed-bin-Qamar	null	Waleed-bin-Qamar/ConvNext-For-Covid-Classification-30EP	1	null	transformers	32,859	Entry not found
mgtoxd/tstw2	00e8e43ccfa3109170b95dc351d2e6ba920bab13	2022-06-23T01:59:33.000Z	[ "pytorch", "tf", "wav2vec2", "automatic-speech-recognition", "transformers" ]	automatic-speech-recognition	false	mgtoxd	null	mgtoxd/tstw2	1	null	transformers	32,860	Entry not found
AntoDono/DialoGPT-Bopy-Alpha-1.03	af3fca9beb2a826f58f9eec40b02fc0b5cf0e23e	2022-06-14T16:32:43.000Z	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	false	AntoDono	null	AntoDono/DialoGPT-Bopy-Alpha-1.03	1	null	transformers	32,861	Entry not found
mahfooz/bert-base-cased-dv-v2	6d1304b254227107ceeb6ad5e305e49b23c90d86	2022-06-15T13:11:46.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	false	mahfooz	null	mahfooz/bert-base-cased-dv-v2	1	null	transformers	32,862	Entry not found
Waleed-bin-Qamar/ConvNext-For-Covid-Classification-30EP-BS64	12cfd445b034e2dc26e90aabbe5b2e7ff8b9ce81	2022-07-03T13:17:29.000Z	[ "pytorch", "convnext", "image-classification", "transformers" ]	image-classification	false	Waleed-bin-Qamar	null	Waleed-bin-Qamar/ConvNext-For-Covid-Classification-30EP-BS64	1	null	transformers	32,863	Entry not found
Browbon/DialoGPT-small-LucaChangretta	f3e4d20c8330c1b202e7c1de741aff8f53213d31	2022-06-14T20:25:27.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	Browbon	null	Browbon/DialoGPT-small-LucaChangretta	1	null	transformers	32,864	--- tags: - conversational --- # Luca Changretta GPT Model
Renukswamy/distilbert-base-uncased-finetuned-squad	e4eb8e751657468fa9c92ee7ed37b19bb311dfb2	2022-06-15T21:27:19.000Z	[ "pytorch", "tensorboard", "distilbert", "question-answering", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	question-answering	false	Renukswamy	null	Renukswamy/distilbert-base-uncased-finetuned-squad	1	null	transformers	32,865	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7108 ## 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.6869 \| 1.0 \| 6941 \| 0.7765 \| \| 0.5083 \| 2.0 \| 13882 \| 0.6917 \| \| 0.372 \| 3.0 \| 20823 \| 0.7108 \| ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.3.1 - Tokenizers 0.12.1
steven123/teeth_test	7622a568dc990833829535825be03f32965735f1	2022-06-14T23:57:13.000Z	[ "pytorch", "tensorboard", "vit", "image-classification", "transformers", "huggingpics", "model-index" ]	image-classification	false	steven123	null	steven123/teeth_test	1	null	transformers	32,866	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: teeth_test results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.5555555820465088 --- # teeth_test 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 #### Good Teeth ![Good Teeth](images/Good_Teeth.jpg) #### Missing Teeth ![Missing Teeth](images/Missing_Teeth.jpg) #### Rotten Teeth ![Rotten Teeth](images/Rotten_Teeth.jpg)
lmqg/t5-base-squadshifts-amazon	b47caf4b07825adc86b0c3301276518ad5cdabd5	2022-06-15T00:08:17.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	lmqg	null	lmqg/t5-base-squadshifts-amazon	1	null	transformers	32,867	Entry not found
erickfm/frosty-sweep-1	70793b04abe6c3e0f87326ac9d64a19fe594c643	2022-06-15T01:16:32.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	erickfm	null	erickfm/frosty-sweep-1	1	null	transformers	32,868	Entry not found
steven123/Teeth_A	06ac242cddf9812c3e6078ec252f50d7a17c3415	2022-06-15T02:42:35.000Z	[ "pytorch", "tensorboard", "vit", "image-classification", "transformers", "huggingpics", "model-index" ]	image-classification	false	steven123	null	steven123/Teeth_A	1	null	transformers	32,869	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: Teeth_A results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.4545454680919647 --- # Teeth_A 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 #### Good Teeth ![Good Teeth](images/Good_Teeth.jpg) #### Missing Teeth ![Missing Teeth](images/Missing_Teeth.jpg) #### Rotten Teeth ![Rotten Teeth](images/Rotten_Teeth.jpg)
steven123/Teeth_C	48308ba60c00f48eb2abddac6a634bb03f5c1888	2022-06-15T02:53:44.000Z	[ "pytorch", "tensorboard", "vit", "image-classification", "transformers", "huggingpics", "model-index" ]	image-classification	false	steven123	null	steven123/Teeth_C	1	null	transformers	32,870	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: Teeth_C results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.5 --- # Teeth_C 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 #### Good Teeth ![Good Teeth](images/Good_Teeth.jpg) #### Missing Teeth ![Missing Teeth](images/Missing_Teeth.jpg) #### Rotten Teeth ![Rotten Teeth](images/Rotten_Teeth.jpg)
x574chen/wav2vec2-librispeech-clean-100h-demo-dist	fe4ea29671955245412c37dde6d6799fbd5ac3fd	2022-06-15T16:43:25.000Z	[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]	automatic-speech-recognition	false	x574chen	null	x574chen/wav2vec2-librispeech-clean-100h-demo-dist	1	null	transformers	32,871	Entry not found
huggingtweets/mysteriousgam54	41d9e51ecb294a2b003365456f001d85f233e9f4	2022-06-15T04:06:06.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/mysteriousgam54	1	null	transformers	32,872	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1429866660299689984/CGXAQuWf_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">themysteriousgamer</div> <div style="text-align: center; font-size: 14px;">@mysteriousgam54</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from themysteriousgamer. \| Data \| themysteriousgamer \| \| --- \| --- \| \| Tweets downloaded \| 1315 \| \| Retweets \| 210 \| \| Short tweets \| 168 \| \| Tweets kept \| 937 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/m4i8lg1e/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mysteriousgam54's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3rz0m12t) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3rz0m12t/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/mysteriousgam54') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
RuiqianLi/Malaya-speech_fine-tune_MrBrown_15_Jun	75705930f26da94c57a24b79510c355e9e35eee9	2022-06-15T08:23:28.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:uob_singlish", "transformers", "generated_from_trainer", "model-index" ]	automatic-speech-recognition	false	RuiqianLi	null	RuiqianLi/Malaya-speech_fine-tune_MrBrown_15_Jun	1	null	transformers	32,873	--- tags: - generated_from_trainer datasets: - uob_singlish model-index: - name: Malaya-speech_fine-tune_MrBrown_15_Jun results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Malaya-speech_fine-tune_MrBrown_15_Jun This model is a fine-tuned version of [malay-huggingface/wav2vec2-xls-r-300m-mixed](https://huggingface.co/malay-huggingface/wav2vec2-xls-r-300m-mixed) on the uob_singlish dataset. It achieves the following results on the evaluation set: - Loss: 0.4822 - Wer: 0.2449 ## 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.1607 \| 5.26 \| 200 \| 0.3983 \| 0.2381 \| \| 0.5184 \| 10.52 \| 400 \| 0.3256 \| 0.2245 \| \| 0.2993 \| 15.78 \| 600 \| 0.3437 \| 0.2426 \| \| 0.2485 \| 21.05 \| 800 \| 0.4547 \| 0.2585 \| \| 0.1917 \| 26.31 \| 1000 \| 0.4598 \| 0.2517 \| \| 0.1586 \| 31.57 \| 1200 \| 0.4050 \| 0.2290 \| \| 0.1486 \| 36.83 \| 1400 \| 0.4186 \| 0.2653 \| \| 0.1307 \| 42.1 \| 1600 \| 0.4284 \| 0.2857 \| \| 0.0895 \| 47.36 \| 1800 \| 0.5158 \| 0.2562 \| \| 0.0526 \| 52.62 \| 2000 \| 0.4525 \| 0.2449 \| \| 0.0553 \| 57.88 \| 2200 \| 0.4364 \| 0.2336 \| \| 0.037 \| 63.16 \| 2400 \| 0.3873 \| 0.2449 \| \| 0.0439 \| 68.42 \| 2600 \| 0.3914 \| 0.2404 \| \| 0.0411 \| 73.68 \| 2800 \| 0.4673 \| 0.2494 \| \| 0.0242 \| 78.94 \| 3000 \| 0.4801 \| 0.2426 \| \| 0.0833 \| 84.21 \| 3200 \| 0.4641 \| 0.2630 \| \| 0.034 \| 89.47 \| 3400 \| 0.4607 \| 0.2449 \| \| 0.02 \| 94.73 \| 3600 \| 0.4825 \| 0.2449 \| \| 0.0211 \| 99.99 \| 3800 \| 0.4822 \| 0.2449 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
huggingtweets/danny_macaskill-martynashton	62b5c2c93d0f1d99d654e95aa0ce7d3b969fea36	2022-06-15T04:59:30.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/danny_macaskill-martynashton	1	null	transformers	32,874	--- language: en thumbnail: http://www.huggingtweets.com/danny_macaskill-martynashton/1655269165002/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/770573812991754240/gyUr23bS_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/616596420230021120/w-kK8IT6_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Danny MacAskill & Martyn Ashton</div> <div style="text-align: center; font-size: 14px;">@danny_macaskill-martynashton</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Danny MacAskill & Martyn Ashton. \| Data \| Danny MacAskill \| Martyn Ashton \| \| --- \| --- \| --- \| \| Tweets downloaded \| 2971 \| 3179 \| \| Retweets \| 505 \| 810 \| \| Short tweets \| 79 \| 136 \| \| Tweets kept \| 2387 \| 2233 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/31ege8zb/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @danny_macaskill-martynashton's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/g4d86tk2) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/g4d86tk2/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/danny_macaskill-martynashton') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/wikisignpost	4e141e788fed31b4bd75a446e001bfa36f166c81	2022-06-15T06:24:26.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/wikisignpost	1	null	transformers	32,875	--- language: en thumbnail: http://www.huggingtweets.com/wikisignpost/1655274233816/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/795028567398576128/GG1GUpJ7_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">The Signpost</div> <div style="text-align: center; font-size: 14px;">@wikisignpost</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from The Signpost. \| Data \| The Signpost \| \| --- \| --- \| \| Tweets downloaded \| 3216 \| \| Retweets \| 522 \| \| Short tweets \| 47 \| \| Tweets kept \| 2647 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/7z6btxad/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wikisignpost's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/27ceco72) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/27ceco72/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/wikisignpost') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
camillelacan1/vit-demo	bd89be887d3a213bfd1cbf11986e409d1ceedf88	2022-06-15T06:32:15.000Z	[ "pytorch", "vit", "image-classification", "transformers" ]	image-classification	false	camillelacan1	null	camillelacan1/vit-demo	1	null	transformers	32,876	Entry not found
sdugar/cross-en-de-fr-xlmr-200d-sentence-transformer	5d3318b787af76da38ec6d4b73ddc0a90fd167f7	2022-06-15T08:21:33.000Z	[ "pytorch", "xlm-roberta", "feature-extraction", "sentence-transformers", "sentence-similarity" ]	sentence-similarity	false	sdugar	null	sdugar/cross-en-de-fr-xlmr-200d-sentence-transformer	1	null	sentence-transformers	32,877	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 200 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 124278 with parameters: ``` {'batch_size': 25, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MSELoss.MSELoss` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (dense): Dense({'in_features': 768, 'out_features': 200, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
lewtun/dog-vs-chicken	ab3046fda5d5f04d8f66b8f392522f5969bbc716	2022-06-15T07:09:02.000Z	[ "pytorch", "tensorboard", "vit", "image-classification", "transformers", "huggingpics", "model-index" ]	image-classification	false	lewtun	null	lewtun/dog-vs-chicken	1	null	transformers	32,878	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: dog-vs-chicken results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 1.0 --- # dog-vs-chicken 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 #### crispy fried chicken ![crispy fried chicken](images/crispy_fried_chicken.jpg) #### poodle ![poodle](images/poodle.jpg)
aware-ai/wav2vec2-1b-marian-german	373e0f3cb274b05678731477dbd496614df6e3a9	2022-06-16T04:47:55.000Z	[ "pytorch", "tensorboard", "speech-encoder-decoder", "automatic-speech-recognition", "transformers" ]	automatic-speech-recognition	false	aware-ai	null	aware-ai/wav2vec2-1b-marian-german	1	null	transformers	32,879	Entry not found
huggingtweets/ravenel_jeremy	92cd293ca227e90ad5fbe581bd80ed249f879a35	2022-06-15T07:43:01.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/ravenel_jeremy	1	null	transformers	32,880	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1440653599420268547/-h0yYTlI_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Jeremy Ravenel</div> <div style="text-align: center; font-size: 14px;">@ravenel_jeremy</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Jeremy Ravenel. \| Data \| Jeremy Ravenel \| \| --- \| --- \| \| Tweets downloaded \| 899 \| \| Retweets \| 180 \| \| Short tweets \| 65 \| \| Tweets kept \| 654 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cp3aewt/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ravenel_jeremy's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2arfza2u) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2arfza2u/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/ravenel_jeremy') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
Salvatore/t5-finetuned-xsum	96b2ffc1678aea0fcbbd47557e169dbfcec3831c	2022-06-15T12:19:55.000Z	[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	Salvatore	null	Salvatore/t5-finetuned-xsum	1	null	transformers	32,881	Entry not found
gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53	6465818021332b142c2481b76ac0d69adde38671	2022-06-17T07:30:08.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "gary109/AI_Light_Dance", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	gary109	null	gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53	1	1	transformers	32,882	--- license: apache-2.0 tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer model-index: - name: ai-light-dance_singing_ft_wav2vec2-large-xlsr-53 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ai-light-dance_singing_ft_wav2vec2-large-xlsr-53 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING dataset. It achieves the following results on the evaluation set: - Loss: 0.4327 - Wer: 0.2043 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.4089 \| 1.0 \| 552 \| 1.4750 \| 0.9054 \| \| 0.7995 \| 2.0 \| 1104 \| 0.9044 \| 0.6163 \| \| 0.6232 \| 3.0 \| 1656 \| 0.6645 \| 0.3980 \| \| 0.5351 \| 4.0 \| 2208 \| 0.5674 \| 0.3120 \| \| 0.472 \| 5.0 \| 2760 \| 0.5167 \| 0.2579 \| \| 0.3913 \| 6.0 \| 3312 \| 0.4553 \| 0.2335 \| \| 0.3306 \| 7.0 \| 3864 \| 0.4476 \| 0.2114 \| \| 0.3028 \| 8.0 \| 4416 \| 0.4327 \| 0.2043 \| \| 0.317 \| 9.0 \| 4968 \| 0.4355 \| 0.2033 \| \| 0.2494 \| 10.0 \| 5520 \| 0.4405 \| 0.2022 \| ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.1.dev0 - Tokenizers 0.12.1
huggingtweets/contrapoints-iamcardib	aca4a76a2304d8c02e559e55abc9735062e4cf83	2022-06-15T09:54:48.000Z	[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]	text-generation	false	huggingtweets	null	huggingtweets/contrapoints-iamcardib	1	null	transformers	32,883	--- language: en thumbnail: http://www.huggingtweets.com/contrapoints-iamcardib/1655286883789/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1425907860773515264/a30IKa1f_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1536222087299350528/rMyNxbwV_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Natalie Wynn & Cardi B</div> <div style="text-align: center; font-size: 14px;">@contrapoints-iamcardib</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Natalie Wynn & Cardi B. \| Data \| Natalie Wynn \| Cardi B \| \| --- \| --- \| --- \| \| Tweets downloaded \| 3191 \| 3058 \| \| Retweets \| 299 \| 1521 \| \| Short tweets \| 527 \| 371 \| \| Tweets kept \| 2365 \| 1166 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3q3i0l7z/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @contrapoints-iamcardib's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2vgppsp0) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2vgppsp0/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/contrapoints-iamcardib') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
winson/distilbert-base-uncased-finetuned-imdb	dcf77d1aecad87243668a76f976b28fae061142a	2022-06-15T12:51:06.000Z	[ "pytorch", "tensorboard", "distilbert", "fill-mask", "dataset:imdb", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]	fill-mask	false	winson	null	winson/distilbert-base-uncased-finetuned-imdb	1	null	transformers	32,884	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - eval_loss: 3.1139 - eval_runtime: 1.8873 - eval_samples_per_second: 529.866 - eval_steps_per_second: 8.478 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.3.1 - Tokenizers 0.12.1
meghazisofiane/mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instances-OPUS-leaningRate2e-05-batchSize2	8b02c90b7f63d755131b072fbcf2d4e4f81d1fcf	2022-06-15T11:11:28.000Z	[ "pytorch", "tensorboard", "mbart", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	meghazisofiane	null	meghazisofiane/mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instances-OPUS-leaningRate2e-05-batchSize2	1	null	transformers	32,885	Entry not found
mikeluck/wav2vec2-base-timit-demo-google-colab	b30e92ae9531d985612796c9dfbc1f829fd399f9	2022-06-15T12:43:38.000Z	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	false	mikeluck	null	mikeluck/wav2vec2-base-timit-demo-google-colab	1	null	transformers	32,886	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-google-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5351 - Wer: 0.3384 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 3.6311 \| 1.0 \| 500 \| 2.6700 \| 1.0 \| \| 1.0104 \| 2.01 \| 1000 \| 0.5289 \| 0.5277 \| \| 0.4483 \| 3.01 \| 1500 \| 0.4576 \| 0.4623 \| \| 0.3089 \| 4.02 \| 2000 \| 0.4483 \| 0.4255 \| \| 0.2278 \| 5.02 \| 2500 \| 0.4463 \| 0.4022 \| \| 0.1886 \| 6.02 \| 3000 \| 0.4653 \| 0.3938 \| \| 0.1578 \| 7.03 \| 3500 \| 0.4624 \| 0.3855 \| \| 0.1429 \| 8.03 \| 4000 \| 0.4420 \| 0.3854 \| \| 0.1244 \| 9.04 \| 4500 \| 0.4980 \| 0.3787 \| \| 0.1126 \| 10.04 \| 5000 \| 0.4311 \| 0.3785 \| \| 0.1082 \| 11.04 \| 5500 \| 0.5114 \| 0.3782 \| \| 0.0888 \| 12.05 \| 6000 \| 0.5392 \| 0.3725 \| \| 0.0835 \| 13.05 \| 6500 \| 0.6011 \| 0.3941 \| \| 0.074 \| 14.06 \| 7000 \| 0.5030 \| 0.3652 \| \| 0.0667 \| 15.06 \| 7500 \| 0.5041 \| 0.3583 \| \| 0.0595 \| 16.06 \| 8000 \| 0.5125 \| 0.3605 \| \| 0.0578 \| 17.07 \| 8500 \| 0.5206 \| 0.3592 \| \| 0.0573 \| 18.07 \| 9000 \| 0.5208 \| 0.3643 \| \| 0.0469 \| 19.08 \| 9500 \| 0.4670 \| 0.3537 \| \| 0.0442 \| 20.08 \| 10000 \| 0.5388 \| 0.3497 \| \| 0.0417 \| 21.08 \| 10500 \| 0.5213 \| 0.3581 \| \| 0.0361 \| 22.09 \| 11000 \| 0.5096 \| 0.3465 \| \| 0.0338 \| 23.09 \| 11500 \| 0.5178 \| 0.3459 \| \| 0.0333 \| 24.1 \| 12000 \| 0.5240 \| 0.3490 \| \| 0.0256 \| 25.1 \| 12500 \| 0.5438 \| 0.3464 \| \| 0.0248 \| 26.1 \| 13000 \| 0.5182 \| 0.3412 \| \| 0.0231 \| 27.11 \| 13500 \| 0.5628 \| 0.3423 \| \| 0.0228 \| 28.11 \| 14000 \| 0.5416 \| 0.3419 \| \| 0.0223 \| 29.12 \| 14500 \| 0.5351 \| 0.3384 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1
Fluffypillow/DialoGPT-small-Rem	c50613085ddc35f6f026169932981ea7b7ae139c	2022-06-15T11:17:40.000Z	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	false	Fluffypillow	null	Fluffypillow/DialoGPT-small-Rem	1	null	transformers	32,887	--- tags: - conversational --- # Rem DialoGPT Model
erickfm/stellar-sweep-3	ab6ea46b52f5380517123b7b1baede7ab4af3454	2022-06-15T12:06:33.000Z	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	false	erickfm	null	erickfm/stellar-sweep-3	1	null	transformers	32,888	Entry not found
Addedk/mbert-swedish-distilled-cased	0ebe8585a87a5e1f414517e645b626dd3d07c613	2022-06-15T16:32:43.000Z	[ "pytorch", "tf", "bert", "fill-mask", "multilingual", "arxiv:2103.06418", "transformers", "license:apache-2.0", "autotrain_compatible" ]	fill-mask	false	Addedk	null	Addedk/mbert-swedish-distilled-cased	1	1	transformers	32,889	--- language: multilingual license: apache-2.0 --- # mBERT swedish distilled base model (cased) This model is a distilled version of [mBERT](https://huggingface.co/bert-base-multilingual-cased). It was distilled using Swedish data, the 2010-2015 portion of the [Swedish Culturomics Gigaword Corpus](https://spraakbanken.gu.se/en/resources/gigaword). The code for the distillation process can be found [here](https://github.com/AddedK/swedish-mbert-distillation/blob/main/azureML/pretrain_distillation.py). This was done as part of my Master's Thesis: Task-agnostic knowledge distillation of mBERT to Swedish. ## Model description This is a 6-layer version of mBERT, having been distilled using the [LightMBERT](https://arxiv.org/abs/2103.06418) distillation method, but without freezing the embedding layer. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. ## Training data The data used for distillation was the 2010-2015 portion of the [Swedish Culturomics Gigaword Corpus](https://spraakbanken.gu.se/en/resources/gigaword). The tokenized data had a file size of approximately 9 GB. ## Evaluation results When evaluated on the [SUCX 3.0 ](https://huggingface.co/datasets/KBLab/sucx3_ner) dataset, it achieved an average F1 score of 0.859 which is competitive with the score mBERT obtained, 0.866. When evaluated on the [English WikiANN](https://huggingface.co/datasets/wikiann) dataset, it achieved an average F1 score of 0.826 which is competitive with the score mBERT obtained, 0.849. Additional results and comparisons are presented in my Master's Thesis
KM4STfulltext/CSSCI_ABS_roberta	800dcf0d97b97114cbf49c3333822ff142b25572	2022-06-20T07:07:00.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "license:apache-2.0", "autotrain_compatible" ]	fill-mask	false	KM4STfulltext	null	KM4STfulltext/CSSCI_ABS_roberta	1	null	transformers	32,890	--- license: apache-2.0 --- # Pre-trained Language Model for the Humanities and Social Sciences in Chinese ## Introduction The research for social science texts in Chinese needs the support natural language processing tools. The pre-trained language model has greatly improved the accuracy of text mining in general texts. At present, there is an urgent need for a pre-trained language model specifically for the automatic processing of scientific texts in Chinese social science. We used the abstract of social science research as the training set. Based on the deep language model framework of BERT, we constructed CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm pre-training language models by [transformers/run_mlm.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py) and [transformers/mlm_wwm](https://github.com/huggingface/transformers/tree/main/examples/research_projects/mlm_wwm). We designed four downstream tasks of Text Classification on different Chinese social scientific article corpus to verify the performance of the model. - CSSCI_ABS_BERT , CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm are trained on the abstract of articles published in CSSCI journals. The training set involved in the experiment included a total of `510,956,094 words`. - Based on the idea of Domain-Adaptive Pretraining, `CSSCI_ABS_BERT` and `CSSCI_ABS_roberta` combine a large amount of abstracts of scientific articles in Chinese based on the BERT structure, and continue to train the BERT and Chinese-RoBERTa models respectively to obtain pre-training models for the automatic processing of Chinese Social science research texts. ## News - 2022-06-15 : CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm has been put forward for the first time. ## How to use ### Huggingface Transformers The `from_pretrained` method based on [Huggingface Transformers](https://github.com/huggingface/transformers) can directly obtain CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm models online. - CSSCI_ABS_BERT ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_BERT") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_BERT") ``` - CSSCI_ABS_roberta ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta") ``` - CSSCI_ABS_roberta-wwm ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta_wwm") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta_wwm") ``` ### Download Models - The version of the model we provide is `PyTorch`. ### From Huggingface - Download directly through Huggingface's official website. - [KM4STfulltext/CSSCI_ABS_BERT](https://huggingface.co/KM4STfulltext/CSSCI_ABS_BERT) - [KM4STfulltext/CSSCI_ABS_roberta](https://huggingface.co/KM4STfulltext/CSSCI_ABS_roberta) - [KM4STfulltext/CSSCI_ABS_roberta_wwm](https://huggingface.co/KM4STfulltext/CSSCI_ABS_roberta_wwm) ## Evaluation & Results - We useCSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm to perform Text Classificationon different social science research corpus. The experimental results are as follows. #### Discipline classification experiments of articles published in CSSCI journals https://github.com/S-T-Full-Text-Knowledge-Mining/CSSCI-BERT #### Movement recognition experiments for data analysis and knowledge discovery abstract \| Tag \| bert-base-Chinese \| chinese-roberta-wwm,ext \| CSSCI_ABS_BERT \| CSSCI_ABS_roberta \| CSSCI_ABS_roberta_wwm \| support \| \| ------------ \| ----------------- \| ----------------------- \| -------------- \| ----------------- \| --------------------- \| ------- \| \| Abstract \| 55.23 \| 62.44 \| 56.8 \| 57.96 \| 58.26 \| 223 \| \| Location \| 61.61 \| 54.38 \| 61.83 \| 61.4 \| 61.94 \| 2866 \| \| Metric \| 45.08 \| 41 \| 45.27 \| 46.74 \| 47.13 \| 622 \| \| Organization \| 46.85 \| 35.29 \| 45.72 \| 45.44 \| 44.65 \| 327 \| \| Person \| 88.66 \| 82.79 \| 88.21 \| 88.29 \| 88.51 \| 4850 \| \| Thing \| 71.68 \| 65.34 \| 71.88 \| 71.68 \| 71.81 \| 5993 \| \| Time \| 65.35 \| 60.38 \| 64.15 \| 65.26 \| 66.03 \| 1272 \| \| avg \| 72.69 \| 66.62 \| 72.59 \| 72.61 \| 72.89 \| 16153 \| #### Chinese literary entity recognition \| Tag \| bert-base-Chinese \| chinese-roberta-wwm,ext \| CSSCI_ABS_BERT \| CSSCI_ABS_roberta \| CSSCI_ABS_roberta_wwm \| support \| \| ------------ \| ----------------- \| ----------------------- \| -------------- \| ----------------- \| --------------------- \| ------- \| \| Abstract \| 55.23 \| 62.44 \| 56.8 \| 57.96 \| 58.26 \| 223 \| \| Location \| 61.61 \| 54.38 \| 61.83 \| 61.4 \| 61.94 \| 2866 \| \| Metric \| 45.08 \| 41 \| 45.27 \| 46.74 \| 47.13 \| 622 \| \| Organization \| 46.85 \| 35.29 \| 45.72 \| 45.44 \| 44.65 \| 327 \| \| Person \| 88.66 \| 82.79 \| 88.21 \| 88.29 \| 88.51 \| 4850 \| \| Thing \| 71.68 \| 65.34 \| 71.88 \| 71.68 \| 71.81 \| 5993 \| \| Time \| 65.35 \| 60.38 \| 64.15 \| 65.26 \| 66.03 \| 1272 \| \| avg \| 72.69 \| 66.62 \| 72.59 \| 72.61 \| 72.89 \| 16153 \| ## Cited - If our content is helpful for your research work, please quote our research in your article. - If you want to quote our research, you can use this url [S-T-Full-Text-Knowledge-Mining/CSSCI-BERT (github.com)](https://github.com/S-T-Full-Text-Knowledge-Mining/CSSCI-BERT) as an alternative before our paper is published. ## Disclaimer - The experimental results presented in the report only show the performance under a specific data set and hyperparameter combination, and cannot represent the essence of each model. The experimental results may change due to random number seeds and computing equipment. - Users can use the model arbitrarily within the scope of the license, but we are not responsible for the direct or indirect losses caused by using the content of the project. ## Acknowledgment - CSSCI_ABS_BERT was trained based on [BERT-Base-Chinese]([google-research/bert: TensorFlow code and pre-trained models for BERT (github.com)](https://github.com/google-research/bert)). - CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm was trained based on [RoBERTa-wwm-ext, Chinese]([ymcui/Chinese-BERT-wwm: Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型） (github.com)](https://github.com/ymcui/Chinese-BERT-wwm)).
KM4STfulltext/CSSCI_ABS_BERT	15b1539ae22a00134ca20cf261bb2b81b7aaeb73	2022-06-20T07:07:53.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "license:apache-2.0", "autotrain_compatible" ]	fill-mask	false	KM4STfulltext	null	KM4STfulltext/CSSCI_ABS_BERT	1	null	transformers	32,891	--- license: apache-2.0 --- # Pre-trained Language Model for the Humanities and Social Sciences in Chinese ## Introduction The research for social science texts in Chinese needs the support natural language processing tools. The pre-trained language model has greatly improved the accuracy of text mining in general texts. At present, there is an urgent need for a pre-trained language model specifically for the automatic processing of scientific texts in Chinese social science. We used the abstract of social science research as the training set. Based on the deep language model framework of BERT, we constructed CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm pre-training language models by [transformers/run_mlm.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py) and [transformers/mlm_wwm](https://github.com/huggingface/transformers/tree/main/examples/research_projects/mlm_wwm). We designed four downstream tasks of Text Classification on different Chinese social scientific article corpus to verify the performance of the model. - CSSCI_ABS_BERT , CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm are trained on the abstract of articles published in CSSCI journals. The training set involved in the experiment included a total of `510,956,094 words`. - Based on the idea of Domain-Adaptive Pretraining, `CSSCI_ABS_BERT` and `CSSCI_ABS_roberta` combine a large amount of abstracts of scientific articles in Chinese based on the BERT structure, and continue to train the BERT and Chinese-RoBERTa models respectively to obtain pre-training models for the automatic processing of Chinese Social science research texts. ## News - 2022-06-15 : CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm has been put forward for the first time. ## How to use ### Huggingface Transformers The `from_pretrained` method based on [Huggingface Transformers](https://github.com/huggingface/transformers) can directly obtain CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm models online. - CSSCI_ABS_BERT ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_BERT") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_BERT") ``` - CSSCI_ABS_roberta ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta") ``` - CSSCI_ABS_roberta-wwm ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta_wwm") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta_wwm") ``` ### Download Models - The version of the model we provide is `PyTorch`. ### From Huggingface - Download directly through Huggingface's official website. - [KM4STfulltext/CSSCI_ABS_BERT](https://huggingface.co/KM4STfulltext/CSSCI_ABS_BERT) - [KM4STfulltext/CSSCI_ABS_roberta](https://huggingface.co/KM4STfulltext/CSSCI_ABS_roberta) - [KM4STfulltext/CSSCI_ABS_roberta_wwm](https://huggingface.co/KM4STfulltext/CSSCI_ABS_roberta_wwm) ## Evaluation & Results - We useCSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm to perform Text Classificationon different social science research corpus. The experimental results are as follows. #### Discipline classification experiments of articles published in CSSCI journals https://github.com/S-T-Full-Text-Knowledge-Mining/CSSCI-BERT #### Movement recognition experiments for data analysis and knowledge discovery abstract \| Tag \| bert-base-Chinese \| chinese-roberta-wwm,ext \| CSSCI_ABS_BERT \| CSSCI_ABS_roberta \| CSSCI_ABS_roberta_wwm \| support \| \| ------------ \| ----------------- \| ----------------------- \| -------------- \| ----------------- \| --------------------- \| ------- \| \| Abstract \| 55.23 \| 62.44 \| 56.8 \| 57.96 \| 58.26 \| 223 \| \| Location \| 61.61 \| 54.38 \| 61.83 \| 61.4 \| 61.94 \| 2866 \| \| Metric \| 45.08 \| 41 \| 45.27 \| 46.74 \| 47.13 \| 622 \| \| Organization \| 46.85 \| 35.29 \| 45.72 \| 45.44 \| 44.65 \| 327 \| \| Person \| 88.66 \| 82.79 \| 88.21 \| 88.29 \| 88.51 \| 4850 \| \| Thing \| 71.68 \| 65.34 \| 71.88 \| 71.68 \| 71.81 \| 5993 \| \| Time \| 65.35 \| 60.38 \| 64.15 \| 65.26 \| 66.03 \| 1272 \| \| avg \| 72.69 \| 66.62 \| 72.59 \| 72.61 \| 72.89 \| 16153 \| #### Chinese literary entity recognition \| Tag \| bert-base-Chinese \| chinese-roberta-wwm,ext \| CSSCI_ABS_BERT \| CSSCI_ABS_roberta \| CSSCI_ABS_roberta_wwm \| support \| \| ------------ \| ----------------- \| ----------------------- \| -------------- \| ----------------- \| --------------------- \| ------- \| \| Abstract \| 55.23 \| 62.44 \| 56.8 \| 57.96 \| 58.26 \| 223 \| \| Location \| 61.61 \| 54.38 \| 61.83 \| 61.4 \| 61.94 \| 2866 \| \| Metric \| 45.08 \| 41 \| 45.27 \| 46.74 \| 47.13 \| 622 \| \| Organization \| 46.85 \| 35.29 \| 45.72 \| 45.44 \| 44.65 \| 327 \| \| Person \| 88.66 \| 82.79 \| 88.21 \| 88.29 \| 88.51 \| 4850 \| \| Thing \| 71.68 \| 65.34 \| 71.88 \| 71.68 \| 71.81 \| 5993 \| \| Time \| 65.35 \| 60.38 \| 64.15 \| 65.26 \| 66.03 \| 1272 \| \| avg \| 72.69 \| 66.62 \| 72.59 \| 72.61 \| 72.89 \| 16153 \| ## Cited - If our content is helpful for your research work, please quote our research in your article. - If you want to quote our research, you can use this url [S-T-Full-Text-Knowledge-Mining/CSSCI-BERT (github.com)](https://github.com/S-T-Full-Text-Knowledge-Mining/CSSCI-BERT) as an alternative before our paper is published. ## Disclaimer - The experimental results presented in the report only show the performance under a specific data set and hyperparameter combination, and cannot represent the essence of each model. The experimental results may change due to random number seeds and computing equipment. - Users can use the model arbitrarily within the scope of the license, but we are not responsible for the direct or indirect losses caused by using the content of the project. ## Acknowledgment - CSSCI_ABS_BERT was trained based on [BERT-Base-Chinese]([google-research/bert: TensorFlow code and pre-trained models for BERT (github.com)](https://github.com/google-research/bert)). - CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm was trained based on [RoBERTa-wwm-ext, Chinese]([ymcui/Chinese-BERT-wwm: Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型） (github.com)](https://github.com/ymcui/Chinese-BERT-wwm)).
KM4STfulltext/CSSCI_ABS_roberta_wwm	55833d811d62c162f0664f0ca629a7a07bca9bb2	2022-06-20T07:06:48.000Z	[ "pytorch", "bert", "fill-mask", "transformers", "license:apache-2.0", "autotrain_compatible" ]	fill-mask	false	KM4STfulltext	null	KM4STfulltext/CSSCI_ABS_roberta_wwm	1	null	transformers	32,892	--- license: apache-2.0 --- # Pre-trained Language Model for the Humanities and Social Sciences in Chinese ## Introduction The research for social science texts in Chinese needs the support natural language processing tools. The pre-trained language model has greatly improved the accuracy of text mining in general texts. At present, there is an urgent need for a pre-trained language model specifically for the automatic processing of scientific texts in Chinese social science. We used the abstract of social science research as the training set. Based on the deep language model framework of BERT, we constructed CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm pre-training language models by [transformers/run_mlm.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py) and [transformers/mlm_wwm](https://github.com/huggingface/transformers/tree/main/examples/research_projects/mlm_wwm). We designed four downstream tasks of Text Classification on different Chinese social scientific article corpus to verify the performance of the model. - CSSCI_ABS_BERT , CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm are trained on the abstract of articles published in CSSCI journals. The training set involved in the experiment included a total of `510,956,094 words`. - Based on the idea of Domain-Adaptive Pretraining, `CSSCI_ABS_BERT` and `CSSCI_ABS_roberta` combine a large amount of abstracts of scientific articles in Chinese based on the BERT structure, and continue to train the BERT and Chinese-RoBERTa models respectively to obtain pre-training models for the automatic processing of Chinese Social science research texts. ## News - 2022-06-15 : CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm has been put forward for the first time. ## How to use ### Huggingface Transformers The `from_pretrained` method based on [Huggingface Transformers](https://github.com/huggingface/transformers) can directly obtain CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm models online. - CSSCI_ABS_BERT ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_BERT") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_BERT") ``` - CSSCI_ABS_roberta ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta") ``` - CSSCI_ABS_roberta-wwm ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta_wwm") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta_wwm") ``` ### Download Models - The version of the model we provide is `PyTorch`. ### From Huggingface - Download directly through Huggingface's official website. - [KM4STfulltext/CSSCI_ABS_BERT](https://huggingface.co/KM4STfulltext/CSSCI_ABS_BERT) - [KM4STfulltext/CSSCI_ABS_roberta](https://huggingface.co/KM4STfulltext/CSSCI_ABS_roberta) - [KM4STfulltext/CSSCI_ABS_roberta_wwm](https://huggingface.co/KM4STfulltext/CSSCI_ABS_roberta_wwm) ## Evaluation & Results - We useCSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm to perform Text Classificationon different social science research corpus. The experimental results are as follows. #### Discipline classification experiments of articles published in CSSCI journals https://github.com/S-T-Full-Text-Knowledge-Mining/CSSCI-BERT #### Movement recognition experiments for data analysis and knowledge discovery abstract \| Tag \| bert-base-Chinese \| chinese-roberta-wwm,ext \| CSSCI_ABS_BERT \| CSSCI_ABS_roberta \| CSSCI_ABS_roberta_wwm \| support \| \| ------------ \| ----------------- \| ----------------------- \| -------------- \| ----------------- \| --------------------- \| ------- \| \| Abstract \| 55.23 \| 62.44 \| 56.8 \| 57.96 \| 58.26 \| 223 \| \| Location \| 61.61 \| 54.38 \| 61.83 \| 61.4 \| 61.94 \| 2866 \| \| Metric \| 45.08 \| 41 \| 45.27 \| 46.74 \| 47.13 \| 622 \| \| Organization \| 46.85 \| 35.29 \| 45.72 \| 45.44 \| 44.65 \| 327 \| \| Person \| 88.66 \| 82.79 \| 88.21 \| 88.29 \| 88.51 \| 4850 \| \| Thing \| 71.68 \| 65.34 \| 71.88 \| 71.68 \| 71.81 \| 5993 \| \| Time \| 65.35 \| 60.38 \| 64.15 \| 65.26 \| 66.03 \| 1272 \| \| avg \| 72.69 \| 66.62 \| 72.59 \| 72.61 \| 72.89 \| 16153 \| #### Chinese literary entity recognition \| Tag \| bert-base-Chinese \| chinese-roberta-wwm,ext \| CSSCI_ABS_BERT \| CSSCI_ABS_roberta \| CSSCI_ABS_roberta_wwm \| support \| \| ------------ \| ----------------- \| ----------------------- \| -------------- \| ----------------- \| --------------------- \| ------- \| \| Abstract \| 55.23 \| 62.44 \| 56.8 \| 57.96 \| 58.26 \| 223 \| \| Location \| 61.61 \| 54.38 \| 61.83 \| 61.4 \| 61.94 \| 2866 \| \| Metric \| 45.08 \| 41 \| 45.27 \| 46.74 \| 47.13 \| 622 \| \| Organization \| 46.85 \| 35.29 \| 45.72 \| 45.44 \| 44.65 \| 327 \| \| Person \| 88.66 \| 82.79 \| 88.21 \| 88.29 \| 88.51 \| 4850 \| \| Thing \| 71.68 \| 65.34 \| 71.88 \| 71.68 \| 71.81 \| 5993 \| \| Time \| 65.35 \| 60.38 \| 64.15 \| 65.26 \| 66.03 \| 1272 \| \| avg \| 72.69 \| 66.62 \| 72.59 \| 72.61 \| 72.89 \| 16153 \| ## Cited - If our content is helpful for your research work, please quote our research in your article. - If you want to quote our research, you can use this url [S-T-Full-Text-Knowledge-Mining/CSSCI-BERT (github.com)](https://github.com/S-T-Full-Text-Knowledge-Mining/CSSCI-BERT) as an alternative before our paper is published. ## Disclaimer - The experimental results presented in the report only show the performance under a specific data set and hyperparameter combination, and cannot represent the essence of each model. The experimental results may change due to random number seeds and computing equipment. - Users can use the model arbitrarily within the scope of the license, but we are not responsible for the direct or indirect losses caused by using the content of the project. ## Acknowledgment - CSSCI_ABS_BERT was trained based on [BERT-Base-Chinese]([google-research/bert: TensorFlow code and pre-trained models for BERT (github.com)](https://github.com/google-research/bert)). - CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm was trained based on [RoBERTa-wwm-ext, Chinese]([ymcui/Chinese-BERT-wwm: Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型） (github.com)](https://github.com/ymcui/Chinese-BERT-wwm)).
SimulSt/xlm-roberta-base-finetuned-panx-de	972906f307f9f8047662ca749425bd82fd3c8bb1	2022-06-15T16:59:24.000Z	[ "pytorch", "tensorboard", "xlm-roberta", "token-classification", "dataset:xtreme", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]	token-classification	false	SimulSt	null	SimulSt/xlm-roberta-base-finetuned-panx-de	1	null	transformers	32,893	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8620945214069894 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1372 - F1: 0.8621 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2575 \| 1.0 \| 525 \| 0.1621 \| 0.8292 \| \| 0.1287 \| 2.0 \| 1050 \| 0.1378 \| 0.8526 \| \| 0.0831 \| 3.0 \| 1575 \| 0.1372 \| 0.8621 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
income/jpq-gpl-arguana-question_encoder-base-msmarco-distilbert-tas-b	c9c200c33e0342f4b6a51fac01a1170924aa1d5a	2022-06-15T17:01:15.000Z	[ "pytorch", "distilbert", "transformers", "license:apache-2.0" ]	null	false	income	null	income/jpq-gpl-arguana-question_encoder-base-msmarco-distilbert-tas-b	1	null	transformers	32,894	--- license: apache-2.0 ---
income/jpq-gpl-arguana-document_encoder-base-msmarco-distilbert-tas-b	5d3913e16feea20c4d500c5f0dadf4556bcb2e60	2022-06-15T17:02:21.000Z	[ "pytorch", "distilbert", "transformers", "license:apache-2.0" ]	null	false	income	null	income/jpq-gpl-arguana-document_encoder-base-msmarco-distilbert-tas-b	1	null	transformers	32,895	--- license: apache-2.0 ---
income/jpq-gpl-climate-fever-question_encoder-base-msmarco-distilbert-tas-b	ba5e4006f5afe15f8698ccfc858c0c47c8d36435	2022-06-15T17:03:39.000Z	[ "pytorch", "distilbert", "transformers", "license:apache-2.0" ]	null	false	income	null	income/jpq-gpl-climate-fever-question_encoder-base-msmarco-distilbert-tas-b	1	null	transformers	32,896	--- license: apache-2.0 ---
income/jpq-gpl-climate-fever-document_encoder-base-msmarco-distilbert-tas-b	669c2ce1a3f3c3191bf540fe39ea0942947075f6	2022-06-15T17:04:14.000Z	[ "pytorch", "distilbert", "transformers", "license:apache-2.0" ]	null	false	income	null	income/jpq-gpl-climate-fever-document_encoder-base-msmarco-distilbert-tas-b	1	null	transformers	32,897	--- license: apache-2.0 ---
income/jpq-gpl-dbpedia-entity-question_encoder-base-msmarco-distilbert-tas-b	cc31d6970db49ec67f7e4d84f525631e656497a6	2022-06-15T17:05:45.000Z	[ "pytorch", "distilbert", "transformers", "license:apache-2.0" ]	null	false	income	null	income/jpq-gpl-dbpedia-entity-question_encoder-base-msmarco-distilbert-tas-b	1	null	transformers	32,898	--- license: apache-2.0 ---
income/jpq-gpl-fiqa-question_encoder-base-msmarco-distilbert-tas-b	e61e9aa7faa99d39137049b59c64a9a0f7f5d80a	2022-06-15T17:15:29.000Z	[ "pytorch", "distilbert", "transformers", "license:apache-2.0" ]	null	false	income	null	income/jpq-gpl-fiqa-question_encoder-base-msmarco-distilbert-tas-b	1	null	transformers	32,899	--- license: apache-2.0 ---

huggingtweets/rshowerthoughts-stephenking

5eb9f6c48d0122235fdcbfb05a39ec28f52005b8

2022-06-11T19:50:01.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/rshowerthoughts-stephenking

1

null

transformers

32,800

--- language: en thumbnail: http://www.huggingtweets.com/rshowerthoughts-stephenking/1654976942704/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/378800000836981162/b683f7509ec792c3e481ead332940cdc_400x400.jpeg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/641699738224455680/L_ji6ClT_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Stephen King & Showerthoughts</div> <div style="text-align: center; font-size: 14px;">@rshowerthoughts-stephenking</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Stephen King & Showerthoughts. | Data | Stephen King | Showerthoughts | | --- | --- | --- | | Tweets downloaded | 3230 | 3200 | | Retweets | 780 | 0 | | Short tweets | 202 | 0 | | Tweets kept | 2248 | 3200 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2bn3s9yg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @rshowerthoughts-stephenking's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2waq2b3w) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2waq2b3w/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/rshowerthoughts-stephenking') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

huggingtweets/conanobrien-mikemancini-wendymolyneux

be13ccafa621d3fad60a0f7e9c02b7a351806386

2022-06-11T19:50:54.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/conanobrien-mikemancini-wendymolyneux

1

null

transformers

32,801

--- language: en thumbnail: http://www.huggingtweets.com/conanobrien-mikemancini-wendymolyneux/1654977049172/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1271404115042676736/PAIbmN-p_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/730612231021322240/Rl0_QYhL_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1044085580651528193/DR7QvrwG_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">mike mancini & Conan O'Brien & Wendy Molyneux</div> <div style="text-align: center; font-size: 14px;">@conanobrien-mikemancini-wendymolyneux</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from mike mancini & Conan O'Brien & Wendy Molyneux. | Data | mike mancini | Conan O'Brien | Wendy Molyneux | | --- | --- | --- | --- | | Tweets downloaded | 3150 | 3250 | 836 | | Retweets | 286 | 40 | 251 | | Short tweets | 290 | 24 | 69 | | Tweets kept | 2574 | 3186 | 516 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/25wtfzk4/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @conanobrien-mikemancini-wendymolyneux's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1hjizcue) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1hjizcue/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/conanobrien-mikemancini-wendymolyneux') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

huggingtweets/elonmusk-rshowerthoughts-stephenking

83949a13c1d88fa22b7fcc87b1a7048115bfd288

2022-06-11T20:15:51.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/elonmusk-rshowerthoughts-stephenking

1

null

transformers

32,802

--- language: en thumbnail: http://www.huggingtweets.com/elonmusk-rshowerthoughts-stephenking/1654978546952/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1529956155937759233/Nyn1HZWF_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/378800000836981162/b683f7509ec792c3e481ead332940cdc_400x400.jpeg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/641699738224455680/L_ji6ClT_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Stephen King & Showerthoughts</div> <div style="text-align: center; font-size: 14px;">@elonmusk-rshowerthoughts-stephenking</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Elon Musk & Stephen King & Showerthoughts. | Data | Elon Musk | Stephen King | Showerthoughts | | --- | --- | --- | --- | | Tweets downloaded | 3200 | 3230 | 3200 | | Retweets | 147 | 780 | 0 | | Short tweets | 954 | 202 | 0 | | Tweets kept | 2099 | 2248 | 3200 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1fvudd5c/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elonmusk-rshowerthoughts-stephenking's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/39f9xftz) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/39f9xftz/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/elonmusk-rshowerthoughts-stephenking') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1

cfd3f4b6e59d7e7305ed6331c2659091568ab48c

2022-06-11T21:50:40.000Z

[ "pytorch", "tensorboard", "marian", "text2text-generation", "dataset:opus100", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

text2text-generation

false

meghazisofiane

null

meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1

1

null

transformers

32,803

--- license: apache-2.0 tags: - generated_from_trainer datasets: - opus100 metrics: - bleu model-index: - name: opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus100 type: opus100 args: ar-en metrics: - name: Bleu type: bleu value: 26.8232 ---  # opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 0.1717 - Bleu: 26.8232 - Meteor: 0.172 - Gen Len: 12.1288 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:| | 0.7364 | 0.25 | 100 | 0.1731 | 27.2753 | 0.1729 | 12.0887 | | 0.2175 | 0.5 | 200 | 0.1731 | 27.2055 | 0.1722 | 11.5675 | | 0.2193 | 0.75 | 300 | 0.1722 | 27.3277 | 0.1798 | 12.1325 | | 0.2321 | 1.0 | 400 | 0.1750 | 27.5152 | 0.1762 | 11.925 | | 0.1915 | 1.25 | 500 | 0.1690 | 27.5043 | 0.1751 | 11.9038 | | 0.1794 | 1.5 | 600 | 0.1719 | 26.8607 | 0.1713 | 11.8138 | | 0.1741 | 1.75 | 700 | 0.1725 | 26.974 | 0.1724 | 11.8462 | | 0.1732 | 2.0 | 800 | 0.1717 | 26.8232 | 0.172 | 12.1288 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-4000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1

5a24a5f03b9f9bcc016566271f9656ed7762beb2

2022-06-11T23:49:23.000Z

[ "pytorch", "tensorboard", "marian", "text2text-generation", "dataset:un_multi", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

text2text-generation

false

meghazisofiane

null

meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-4000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1

1

null

transformers

32,804

--- license: apache-2.0 tags: - generated_from_trainer datasets: - un_multi metrics: - bleu model-index: - name: opus-mt-en-ar-evaluated-en-to-ar-4000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: un_multi type: un_multi args: ar-en metrics: - name: Bleu type: bleu value: 51.7715 ---  # opus-mt-en-ar-evaluated-en-to-ar-4000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the un_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.1850 - Bleu: 51.7715 - Meteor: 0.5164 - Gen Len: 25.5612 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:| | 0.6999 | 0.25 | 100 | 0.1959 | 50.1492 | 0.508 | 25.2788 | | 0.1994 | 0.5 | 200 | 0.1931 | 51.003 | 0.513 | 25.4038 | | 0.1863 | 0.75 | 300 | 0.1864 | 51.3268 | 0.5145 | 25.1675 | | 0.1826 | 1.0 | 400 | 0.1841 | 51.2507 | 0.513 | 25.2388 | | 0.1494 | 1.25 | 500 | 0.1840 | 51.4291 | 0.5159 | 25.4225 | | 0.1483 | 1.5 | 600 | 0.1839 | 51.2645 | 0.5126 | 25.395 | | 0.1547 | 1.75 | 700 | 0.1837 | 51.7589 | 0.5157 | 25.48 | | 0.1487 | 2.0 | 800 | 0.1845 | 51.896 | 0.5177 | 25.3988 | | 0.1235 | 2.25 | 900 | 0.1852 | 52.0583 | 0.5177 | 25.5212 | | 0.1164 | 2.5 | 1000 | 0.1850 | 51.7715 | 0.5164 | 25.5612 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

AntoDono/DialoGPT-Bopy-Alpha

ace508d9a01179df177c0a1eb62a0e62e872447c

2022-06-11T22:22:35.000Z

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

false

AntoDono

null

AntoDono/DialoGPT-Bopy-Alpha

1

null

transformers

32,805

Entry not found

sactisudesa/robertuito_sp

8a5291a24ad459b2922dc3c2e1f80472ca3164bc

2022-06-11T22:49:32.000Z

[ "pytorch", "roberta", "feature-extraction", "transformers" ]

feature-extraction

false

sactisudesa

null

sactisudesa/robertuito_sp

1

null

transformers

32,806

Entry not found

huggingtweets/laserboat999

173e2d6c04effe0a5009e3ad155e3f45050a6303

2022-06-11T23:53:52.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/laserboat999

1

null

transformers

32,807

--- language: en thumbnail: http://www.huggingtweets.com/laserboat999/1654991516445/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1500274766195793921/bA4siut7_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">donald boat</div> <div style="text-align: center; font-size: 14px;">@laserboat999</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from donald boat. | Data | donald boat | | --- | --- | | Tweets downloaded | 3233 | | Retweets | 75 | | Short tweets | 516 | | Tweets kept | 2642 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/38v40fpf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @laserboat999's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/pk1xum9h) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/pk1xum9h/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/laserboat999') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

huggingtweets/cancer_blood69

838a404be0bcf0e261091db78c493548896c56da

2022-06-12T00:01:54.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/cancer_blood69

1

null

transformers

32,808

--- language: en thumbnail: http://www.huggingtweets.com/cancer_blood69/1654992058711/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1273429972229804032/_kkJmwqw_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">cancer_blood69 (reanimated decaying corpse)</div> <div style="text-align: center; font-size: 14px;">@cancer_blood69</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from cancer_blood69 (reanimated decaying corpse). | Data | cancer_blood69 (reanimated decaying corpse) | | --- | --- | | Tweets downloaded | 3237 | | Retweets | 215 | | Short tweets | 381 | | Tweets kept | 2641 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cav70ew/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @cancer_blood69's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/sp5449e2) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/sp5449e2/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/cancer_blood69') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

lindsayng/t5-base-fullwnc-5epoch-31e6b1e1

7f26b3f69b6aebbb8bedc9b47f8aed64593cea23

2022-06-12T00:08:18.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

lindsayng

null

lindsayng/t5-base-fullwnc-5epoch-31e6b1e1

1

null

transformers

32,809

Entry not found

meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-2000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1

3b0aee150bf11b5aa90a7ed16e00cc844974491b

2022-06-12T00:44:37.000Z

[ "pytorch", "tensorboard", "marian", "text2text-generation", "dataset:un_multi", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

text2text-generation

false

meghazisofiane

null

meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-2000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1

1

null

transformers

32,810

--- license: apache-2.0 tags: - generated_from_trainer datasets: - un_multi metrics: - bleu model-index: - name: opus-mt-en-ar-evaluated-en-to-ar-2000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: un_multi type: un_multi args: ar-en metrics: - name: Bleu type: bleu value: 53.0137 ---  # opus-mt-en-ar-evaluated-en-to-ar-2000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the un_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.1873 - Bleu: 53.0137 - Meteor: 0.5005 - Gen Len: 25.845 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:| | 0.6585 | 0.5 | 100 | 0.2085 | 52.5874 | 0.4969 | 25.485 | | 0.1802 | 1.0 | 200 | 0.1788 | 52.9434 | 0.4982 | 25.1725 | | 0.1501 | 1.5 | 300 | 0.1683 | 53.6994 | 0.5033 | 25.625 | | 0.1454 | 2.0 | 400 | 0.1706 | 53.3946 | 0.5005 | 25.6675 | | 0.1193 | 2.5 | 500 | 0.1774 | 53.2011 | 0.4982 | 25.58 | | 0.1194 | 3.0 | 600 | 0.1741 | 53.8651 | 0.5026 | 25.5775 | | 0.1002 | 3.5 | 700 | 0.1878 | 53.1332 | 0.5005 | 25.8975 | | 0.0979 | 4.0 | 800 | 0.1881 | 52.5989 | 0.4974 | 25.485 | | 0.0807 | 4.5 | 900 | 0.1873 | 53.0137 | 0.5005 | 25.845 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

erickfm/t5-base-finetuned-bias-sweep-46c8cda7

d1a6bff4dc1c61bd32292c3768268a808dca36be

2022-06-12T00:41:28.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

erickfm

null

erickfm/t5-base-finetuned-bias-sweep-46c8cda7

1

null

transformers

32,811

Entry not found

erickfm/t5-base-finetuned-bias-sweep-cca75a85

d3def8abc8127be7cbaa0b2bbf25c2b83130b353

2022-06-12T07:03:32.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

erickfm

null

erickfm/t5-base-finetuned-bias-sweep-cca75a85

1

null

transformers

32,812

Entry not found

Dwayne/opus-mt-en-ro-finetuned-en-to-ro

ad7e33786ba93897bab03fd7553b6ef367b49deb

2022-06-12T19:07:18.000Z

[ "pytorch", "marian", "text2text-generation", "dataset:wmt16", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

text2text-generation

false

Dwayne

null

Dwayne/opus-mt-en-ro-finetuned-en-to-ro

1

null

transformers

32,813

--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: opus-mt-en-ro-finetuned-en-to-ro results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt16 type: wmt16 args: ro-en metrics: - name: Bleu type: bleu value: 28.0591 ---  # opus-mt-en-ro-finetuned-en-to-ro This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.2889 - Bleu: 28.0591 - Gen Len: 34.043 ## 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.744 | 1.0 | 38145 | 1.2889 | 28.0591 | 34.043 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

hangyulmd/t5-squad

2202943b672a4890d619944d93bf40ec93a1309d

2022-06-12T16:00:52.000Z

[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

hangyulmd

null

hangyulmd/t5-squad

1

null

transformers

32,814

Entry not found

nestoralvaro/mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__google_mt5_base

35cbc15c4aad6980d834379c775208afbdbb116c

2022-06-12T18:08:46.000Z

[ "pytorch", "tensorboard", "mt5", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

text2text-generation

false

nestoralvaro

null

nestoralvaro/mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__google_mt5_base

1

null

transformers

32,815

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__google_mt5_base results: [] ---  # mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__google_mt5_base This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 0.7721 - Rouge2: 0.0701 - Rougel: 0.7721 - Rougelsum: 0.7718 - Gen Len: 6.329 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 0.0 | 1.0 | 131773 | nan | 0.7721 | 0.0701 | 0.7721 | 0.7718 | 6.329 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

huggingtweets/warriors

5dc3f3c81fa9d2bc05897123051140c4d59f5bc3

2022-06-12T15:38:14.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/warriors

1

null

transformers

32,816

--- language: en thumbnail: http://www.huggingtweets.com/warriors/1655048290751/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1533845175725719553/yvzbj8iG_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Golden State Warriors</div> <div style="text-align: center; font-size: 14px;">@warriors</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Golden State Warriors. | Data | Golden State Warriors | | --- | --- | | Tweets downloaded | 3251 | | Retweets | 261 | | Short tweets | 563 | | Tweets kept | 2427 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/36p28s9n/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @warriors's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/17arirrx) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/17arirrx/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/warriors') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

eunbeee/ainize-kobart-news-eb-finetuned-papers

479667425e72635e2d55368e2b5c33ec3ae85822

2022-06-16T12:07:21.000Z

[ "pytorch", "tensorboard", "bart", "text2text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]

text2text-generation

false

eunbeee

null

eunbeee/ainize-kobart-news-eb-finetuned-papers

1

null

transformers

32,817

--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: ainize-kobart-news-eb-finetuned-papers results: [] ---  # ainize-kobart-news-eb-finetuned-papers This model is a fine-tuned version of [ainize/kobart-news](https://huggingface.co/ainize/kobart-news) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3066 - Rouge1: 14.5433 - Rouge2: 5.2238 - Rougel: 14.4731 - Rougelsum: 14.5183 - Gen Len: 19.9934 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 0.1918 | 1.0 | 7200 | 0.2403 | 14.6883 | 5.2427 | 14.6306 | 14.6489 | 19.9938 | | 0.1332 | 2.0 | 14400 | 0.2391 | 14.5165 | 5.2443 | 14.493 | 14.4908 | 19.9972 | | 0.0966 | 3.0 | 21600 | 0.2539 | 14.758 | 5.4976 | 14.6906 | 14.7188 | 19.9941 | | 0.0736 | 4.0 | 28800 | 0.2782 | 14.6267 | 5.3371 | 14.5578 | 14.6014 | 19.9934 | | 0.0547 | 5.0 | 36000 | 0.3066 | 14.5433 | 5.2238 | 14.4731 | 14.5183 | 19.9934 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-1000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1

d5e83bb4af959617298a2943aa6455c7dd9476a5

2022-06-12T16:54:18.000Z

[ "pytorch", "tensorboard", "marian", "text2text-generation", "dataset:un_multi", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

text2text-generation

false

meghazisofiane

null

meghazisofiane/opus-mt-en-ar-evaluated-en-to-ar-1000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1

1

null

transformers

32,818

--- license: apache-2.0 tags: - generated_from_trainer datasets: - un_multi metrics: - bleu model-index: - name: opus-mt-en-ar-evaluated-en-to-ar-1000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: un_multi type: un_multi args: ar-en metrics: - name: Bleu type: bleu value: 64.0048 ---  # opus-mt-en-ar-evaluated-en-to-ar-1000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the un_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.1294 - Bleu: 64.0048 - Meteor: 0.4903 - Gen Len: 21.85 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:| | 0.0489 | 1.0 | 100 | 0.1287 | 63.7573 | 0.4877 | 21.79 | | 0.0447 | 2.0 | 200 | 0.1293 | 63.8776 | 0.49 | 21.875 | | 0.0442 | 3.0 | 300 | 0.1294 | 64.0048 | 0.4903 | 21.85 | | 0.0433 | 4.0 | 400 | 0.1294 | 64.0048 | 0.4903 | 21.85 | | 0.0429 | 5.0 | 500 | 0.1294 | 64.0048 | 0.4903 | 21.85 | | 0.0435 | 6.0 | 600 | 0.1294 | 64.0048 | 0.4903 | 21.85 | | 0.0429 | 7.0 | 700 | 0.1294 | 64.0048 | 0.4903 | 21.85 | | 0.0426 | 8.0 | 800 | 0.1294 | 64.0048 | 0.4903 | 21.85 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

nlokam99/ada_sample

9f01f814d7cef9419ab4775a7cfa01a49e4e348a

2022-06-12T17:35:50.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational", "license:mit" ]

conversational

false

nlokam99

null

nlokam99/ada_sample

1

null

transformers

32,819

--- thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png tags: - conversational license: mit ---

huggingtweets/dodecahedra

f6e4e37d0e5b632376a3744b2e1264940e1b03df

2022-06-12T17:42:15.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/dodecahedra

1

null

transformers

32,820

--- language: en thumbnail: http://www.huggingtweets.com/dodecahedra/1655055731499/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/3232494514/760c72bca0af20fac2cd61bcec557e7a_400x400.jpeg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">William Rose</div> <div style="text-align: center; font-size: 14px;">@dodecahedra</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from William Rose. | Data | William Rose | | --- | --- | | Tweets downloaded | 3241 | | Retweets | 1115 | | Short tweets | 158 | | Tweets kept | 1968 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1geru0ac/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @dodecahedra's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1uy1zk82) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1uy1zk82/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/dodecahedra') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

meghazisofiane/mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instances-un_multi-leaningRate2e-05-batchSize2

1b02b989ac829a8ced504d97bd852eeae35634fd

2022-06-12T20:02:36.000Z

[ "pytorch", "tensorboard", "mbart", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

meghazisofiane

null

meghazisofiane/mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instances-un_multi-leaningRate2e-05-batchSize2

1

null

transformers

32,821

Entry not found

abdoutony207/mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize2

97687f7292bde84c97b21ea31c55be82dcd7740c

2022-06-12T18:25:47.000Z

[ "pytorch", "tensorboard", "mbart", "text2text-generation", "dataset:opus100", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]

text2text-generation

false

abdoutony207

null

abdoutony207/mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize2

1

null

transformers

32,822

--- license: mit tags: - generated_from_trainer datasets: - opus100 metrics: - bleu model-index: - name: mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize2 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus100 type: opus100 args: ar-en metrics: - name: Bleu type: bleu value: 10.5645 ---  # mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize2 This model is a fine-tuned version of [akhooli/mbart-large-cc25-en-ar](https://huggingface.co/akhooli/mbart-large-cc25-en-ar) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 0.4673 - Bleu: 10.5645 - Meteor: 0.0783 - Gen Len: 10.23 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:| | 8.1731 | 0.25 | 100 | 2.8417 | 0.9599 | 0.028 | 230.885 | | 0.6743 | 0.5 | 200 | 0.4726 | 6.4055 | 0.0692 | 14.81 | | 0.3028 | 0.75 | 300 | 0.4572 | 6.7544 | 0.0822 | 23.92 | | 0.2555 | 1.0 | 400 | 0.4172 | 8.4078 | 0.0742 | 13.655 | | 0.1644 | 1.25 | 500 | 0.4236 | 9.284 | 0.071 | 13.03 | | 0.1916 | 1.5 | 600 | 0.4222 | 4.8976 | 0.0779 | 32.225 | | 0.2011 | 1.75 | 700 | 0.4305 | 7.6909 | 0.0738 | 16.675 | | 0.1612 | 2.0 | 800 | 0.4416 | 10.8622 | 0.0855 | 10.91 | | 0.116 | 2.25 | 900 | 0.4673 | 10.5645 | 0.0783 | 10.23 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

NadiaSan/udesa-model-aah-es-35k

c0b188e56395888d3bdb592c2e030c55703dc53b

2022-06-12T20:28:38.000Z

[ "pytorch", "bert", "feature-extraction", "transformers" ]

feature-extraction

false

NadiaSan

null

NadiaSan/udesa-model-aah-es-35k

1

null

transformers

32,823

Entry not found

rohitsroch/hybrid_utt-clusterrank_bart-base_samsum_sum

2e8eecc91040ccab33b30a110d260fa026595bf8

2022-06-12T23:03:22.000Z

[ "pytorch", "bart", "text2text-generation", "en", "dataset:samsum", "transformers", "dialogue-summarization", "license:apache-2.0", "autotrain_compatible" ]

text2text-generation

false

rohitsroch

null

rohitsroch/hybrid_utt-clusterrank_bart-base_samsum_sum

1

null

transformers

32,824

--- language: - en license: apache-2.0 tags: - dialogue-summarization model_index: - name: hybrid_utt-clusterrank_bart-base_samsum_sum results: - task: name: Summarization type: summarization datasets: - samsum --- ## Paper ## [Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning](https://dl.acm.org/doi/10.1145/3508546.3508640) Authors: *Rohit Sroch* ## Abstract Recently, the abstractive dialogue summarization task has been gaining a lot of attention from researchers. Also, unlike news articles and documents with well-structured text, dialogue differs in the sense that it often comes from two or more interlocutors, exchanging information with each other and having an inherent hierarchical structure based on the sequence of utterances by different speakers. This paper proposes a simple but effective hybrid approach that consists of two modules and uses transfer learning by leveraging pretrained language models (PLMs) to generate an abstractive summary. The first module highlights important utterances, capturing the utterance level relationship by adapting an auto-encoding model like BERT based on the unsupervised or supervised method. And then, the second module generates a concise abstractive summary by adapting encoder-decoder models like T5, BART, and PEGASUS. Experiment results on benchmark datasets show that our approach achieves a state-of-the-art performance by adapting to dialogue scenarios and can also be helpful in low-resource settings for domain adaptation. *Rohit Sroch. 2021. Domain Adapted Abstractive Summarization of Dialogue using Transfer Learning. In 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI'21). Association for Computing Machinery, New York, NY, USA, Article 94, 1–6. https://doi.org/10.1145/3508546.3508640* # hybrid_utt-clusterrank_bart-base_samsum_sum This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on SAMSum dataset for dialogue summarization task. ## Model description More information needed ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-5 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 - label_smoothing_factor: 0.1 ### Results on Test Set - predict_gen_len = 23.9048 - predict_rouge1 = **47.355** - predict_rouge2 = **22.4593** - predict_rougeL = **38.694** - predict_rougeLsum = **42.98** - predict_samples = 819 - predict_samples_per_second = 9.279 - predict_steps_per_second = 2.322 ### Framework versions - Transformers>=4.8.0 - Pytorch>=1.6.0 - Datasets>=1.10.2 - Tokenizers>=0.10.3 If you use this model, please cite the following paper: ``` @inproceedings{10.1145/3508546.3508640, author = {Sroch, Rohit}, title = {Domain Adapted Abstractive Summarization of Dialogue Using Transfer Learning}, year = {2021}, isbn = {9781450385053}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3508546.3508640}, doi = {10.1145/3508546.3508640}, articleno = {94}, numpages = {6}, keywords = {encoder-decoder, T5, abstractive summary, PEGASUS, BART, dialogue summarization, PLMs, BERT}, location = {Sanya, China}, series = {ACAI'21} } ```

erickfm/t5-base-finetuned-bias-sweep-5db9391c

8b4b9724f3771cc1743a7d23fe53b85f2c89aa2b

2022-06-12T22:08:17.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

erickfm

null

erickfm/t5-base-finetuned-bias-sweep-5db9391c

1

null

transformers

32,825

Entry not found

huggingtweets/gronkh

5d7541e18a6c3a6b10b26b3c5771dbdd8407f3b1

2022-06-12T22:07:19.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/gronkh

1

null

transformers

32,826

--- language: en thumbnail: http://www.huggingtweets.com/gronkh/1655071635259/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1291074656049541127/fBvcAq3x_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">𝙂𝙍𝙊𝙉𝙆𝙃</div> <div style="text-align: center; font-size: 14px;">@gronkh</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from 𝙂𝙍𝙊𝙉𝙆𝙃. | Data | 𝙂𝙍𝙊𝙉𝙆𝙃 | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 65 | | Short tweets | 97 | | Tweets kept | 3088 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3gqyrznk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gronkh's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2i0lrjh9) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2i0lrjh9/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gronkh') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

mailenpellegrino/transformerv2

742592d00b5928bbb16f7f4980710b9da95cf5d2

2022-06-12T22:28:54.000Z

[ "pytorch", "xlm-roberta", "feature-extraction", "transformers" ]

feature-extraction

false

mailenpellegrino

null

mailenpellegrino/transformerv2

1

null

transformers

32,827

Entry not found

asahi417/lmqg-mt5-small-frquad

3992189a9bba77dbe1af7446d23d7714a97f03da

2022-06-13T00:46:53.000Z

[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

asahi417

null

asahi417/lmqg-mt5-small-frquad

1

null

transformers

32,828

Entry not found

huggingtweets/145gomez

08b1f025fed038ca1b84b789220994a330372981

2022-06-13T04:12:14.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/145gomez

1

null

transformers

32,829

--- language: en thumbnail: http://www.huggingtweets.com/145gomez/1655093529513/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1457362420800835589/0mihXFjt_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Omar Gomez Avalos</div> <div style="text-align: center; font-size: 14px;">@145gomez</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Omar Gomez Avalos. | Data | Omar Gomez Avalos | | --- | --- | | Tweets downloaded | 3189 | | Retweets | 472 | | Short tweets | 384 | | Tweets kept | 2333 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3hcxwfgg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @145gomez's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/7qzkruyo) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/7qzkruyo/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/145gomez') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

burner/GPT-J-Step_98500

5a47e95152759184ce13d43372e29f77134c847f

2022-06-13T05:11:42.000Z

[ "pytorch", "gptj", "text-generation", "transformers", "license:apache-2.0" ]

text-generation

false

burner

null

burner/GPT-J-Step_98500

1

null

transformers

32,830

--- license: apache-2.0 ---

zoha/wav2vec2-base-timit-google-colab

659e1d07b09561e637c03022375f6eed869c7cf2

2022-06-13T10:50:12.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

zoha

null

zoha/wav2vec2-base-timit-google-colab

1

null

transformers

32,831

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-google-colab results: [] ---  # wav2vec2-base-timit-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4659 - Wer: 0.3080 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5787 | 0.87 | 500 | 1.7648 | 1.0305 | | 0.8692 | 1.73 | 1000 | 0.5136 | 0.5103 | | 0.4346 | 2.6 | 1500 | 0.4364 | 0.4515 | | 0.31 | 3.46 | 2000 | 0.3889 | 0.4070 | | 0.234 | 4.33 | 2500 | 0.4161 | 0.3863 | | 0.2054 | 5.19 | 3000 | 0.3845 | 0.3722 | | 0.165 | 6.06 | 3500 | 0.4035 | 0.3643 | | 0.1436 | 6.92 | 4000 | 0.4090 | 0.3623 | | 0.1381 | 7.79 | 4500 | 0.4007 | 0.3673 | | 0.1175 | 8.65 | 5000 | 0.4588 | 0.3632 | | 0.1052 | 9.52 | 5500 | 0.4441 | 0.3588 | | 0.0988 | 10.38 | 6000 | 0.4133 | 0.3489 | | 0.0877 | 11.25 | 6500 | 0.4758 | 0.3510 | | 0.0856 | 12.11 | 7000 | 0.4454 | 0.3425 | | 0.0731 | 12.98 | 7500 | 0.4252 | 0.3351 | | 0.0712 | 13.84 | 8000 | 0.4163 | 0.3370 | | 0.0711 | 14.71 | 8500 | 0.4166 | 0.3367 | | 0.06 | 15.57 | 9000 | 0.4195 | 0.3347 | | 0.0588 | 16.44 | 9500 | 0.4697 | 0.3367 | | 0.0497 | 17.3 | 10000 | 0.4255 | 0.3314 | | 0.0523 | 18.17 | 10500 | 0.4676 | 0.3307 | | 0.0444 | 19.03 | 11000 | 0.4570 | 0.3244 | | 0.0435 | 19.9 | 11500 | 0.4307 | 0.3243 | | 0.0348 | 20.76 | 12000 | 0.4763 | 0.3245 | | 0.036 | 21.63 | 12500 | 0.4635 | 0.3238 | | 0.0347 | 22.49 | 13000 | 0.4602 | 0.3212 | | 0.0333 | 23.36 | 13500 | 0.4472 | 0.3195 | | 0.0311 | 24.22 | 14000 | 0.4449 | 0.3183 | | 0.0294 | 25.09 | 14500 | 0.4631 | 0.3175 | | 0.025 | 25.95 | 15000 | 0.4466 | 0.3164 | | 0.023 | 26.82 | 15500 | 0.4581 | 0.3138 | | 0.0216 | 27.68 | 16000 | 0.4665 | 0.3114 | | 0.0198 | 28.55 | 16500 | 0.4590 | 0.3092 | | 0.0181 | 29.41 | 17000 | 0.4659 | 0.3080 | ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

shaojie/distilbert-base-uncased-finetuned-squad

5f332a62f125d0aa9b2d57e6ca10494f02c15a21

2022-06-14T07:26:48.000Z

[ "pytorch", "tensorboard", "distilbert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

question-answering

false

shaojie

null

shaojie/distilbert-base-uncased-finetuned-squad

1

null

transformers

32,832

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] ---  # 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: - eval_loss: 1.1585 - eval_runtime: 138.1018 - eval_samples_per_second: 78.087 - eval_steps_per_second: 4.88 - epoch: 1.0 - step: 5533 ## 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 ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

erickfm/t5-base-finetuned-bias-sweep-64f598ca

5449b91f587e66826a8da4554f38e656dbeb19af

2022-06-14T01:40:19.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

erickfm

null

erickfm/t5-base-finetuned-bias-sweep-64f598ca

1

null

transformers

32,833

maybe [sandy-sweep](https://wandb.ai/unbias/t5-base/runs/tkvqsbl3)

yossra/bert-finetuned-squad

1a01bda20e36c699383a30da6d3021bc8e82b25f

2022-06-13T12:03:54.000Z

[ "pytorch", "tensorboard", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

question-answering

false

yossra

null

yossra/bert-finetuned-squad

1

null

transformers

32,834

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bert-finetuned-squad results: [] ---  # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

Nadav/roberta-base-squad-nl

141efed50c80f7e7d44c1793e74821ee8d138f82

2022-06-13T11:55:54.000Z

[ "pytorch", "roberta", "question-answering", "transformers", "license:afl-3.0", "autotrain_compatible" ]

question-answering

false

Nadav

null

Nadav/roberta-base-squad-nl

1

null

transformers

32,835

--- license: afl-3.0 ---

Nadav/xlm-roberta-base-squad-nl

1edf37cbb3bf8708810ef7c1d159753792366910

2022-06-13T11:54:19.000Z

[ "pytorch", "xlm-roberta", "question-answering", "transformers", "autotrain_compatible" ]

question-answering

false

Nadav

null

Nadav/xlm-roberta-base-squad-nl

1

null

transformers

32,836

Entry not found

Shenghao1993/xlm-roberta-base-finetuned-panx-de-fr

9a1cdb7291b41b92387d11c5ea900e4e59a4f1d9

2022-06-13T11:59:43.000Z

[ "pytorch", "xlm-roberta", "token-classification", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]

token-classification

false

Shenghao1993

null

Shenghao1993/xlm-roberta-base-finetuned-panx-de-fr

1

null

transformers

32,837

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] ---  # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1623 - F1: 0.8596 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2865 | 1.0 | 715 | 0.1981 | 0.8167 | | 0.1484 | 2.0 | 1430 | 0.1595 | 0.8486 | | 0.0949 | 3.0 | 2145 | 0.1623 | 0.8596 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

erickfm/t5-base-finetuned-bias-sweep-1da3c5df

f8628fd94d560d83eba6632e888ade6ad8669915

2022-06-13T12:25:12.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

erickfm

null

erickfm/t5-base-finetuned-bias-sweep-1da3c5df

1

null

transformers

32,838

Entry not found

Shenghao1993/xlm-roberta-base-finetuned-panx-fr

3914ed406dc6abb836d2f7a105405bd6990e9b5d

2022-06-13T12:44:17.000Z

[ "pytorch", "xlm-roberta", "token-classification", "dataset:xtreme", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]

token-classification

false

Shenghao1993

null

Shenghao1993/xlm-roberta-base-finetuned-panx-fr

1

null

transformers

32,839

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.8438448566610455 ---  # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2675 - F1: 0.8438 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5606 | 1.0 | 191 | 0.3157 | 0.7967 | | 0.2755 | 2.0 | 382 | 0.2684 | 0.8288 | | 0.1811 | 3.0 | 573 | 0.2675 | 0.8438 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

Shenghao1993/xlm-roberta-base-finetuned-panx-it

071f7c6c1b25c6d44d6d0ae4966c7a0c739afe2a

2022-06-13T13:02:39.000Z

[ "pytorch", "xlm-roberta", "token-classification", "dataset:xtreme", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]

token-classification

false

Shenghao1993

null

Shenghao1993/xlm-roberta-base-finetuned-panx-it

1

null

transformers

32,840

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.it metrics: - name: F1 type: f1 value: 0.8358085808580858 ---  # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2403 - F1: 0.8358 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7053 | 1.0 | 70 | 0.3077 | 0.7587 | | 0.2839 | 2.0 | 140 | 0.2692 | 0.8007 | | 0.1894 | 3.0 | 210 | 0.2403 | 0.8358 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

Shenghao1993/xlm-roberta-base-finetuned-panx-en

3c6281283ab65ce449ba4fd0d0cf8e3da61ea5d2

2022-06-13T13:20:02.000Z

[ "pytorch", "xlm-roberta", "token-classification", "dataset:xtreme", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]

token-classification

false

Shenghao1993

null

Shenghao1993/xlm-roberta-base-finetuned-panx-en

1

null

transformers

32,841

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.en metrics: - name: F1 type: f1 value: 0.7032474804031354 ---  # xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.3859 - F1: 0.7032 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.0494 | 1.0 | 50 | 0.5464 | 0.5507 | | 0.5329 | 2.0 | 100 | 0.4217 | 0.6715 | | 0.3799 | 3.0 | 150 | 0.3859 | 0.7032 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

Shenghao1993/xlm-roberta-base-finetuned-panx-all

449d0b762db7caa976af890035d77d0f859c9f1e

2022-06-13T13:45:41.000Z

[ "pytorch", "xlm-roberta", "token-classification", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]

token-classification

false

Shenghao1993

null

Shenghao1993/xlm-roberta-base-finetuned-panx-all

1

null

transformers

32,842

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all results: [] ---  # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1719 - F1: 0.8544 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2951 | 1.0 | 835 | 0.1882 | 0.8171 | | 0.1547 | 2.0 | 1670 | 0.1707 | 0.8454 | | 0.1018 | 3.0 | 2505 | 0.1719 | 0.8544 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

Andrey1989/xlmr-finetuned-ner

568f4227475e804100b72e019378711885178588

2022-06-13T14:03:57.000Z

[ "pytorch", "tensorboard", "xlm-roberta", "token-classification", "dataset:wikiann", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]

token-classification

false

Andrey1989

null

Andrey1989/xlmr-finetuned-ner

1

null

transformers

32,843

--- license: mit tags: - generated_from_trainer datasets: - wikiann metrics: - precision - recall - f1 - accuracy model-index: - name: xlmr-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: wikiann type: wikiann args: et metrics: - name: Precision type: precision value: 0.9044097027481772 - name: Recall type: recall value: 0.9136978539556626 - name: F1 type: f1 value: 0.9090300532008596 - name: Accuracy type: accuracy value: 0.9649304793632428 ---  # xlmr-finetuned-ner This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.1395 - Precision: 0.9044 - Recall: 0.9137 - F1: 0.9090 - Accuracy: 0.9649 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.4215 | 1.0 | 938 | 0.1650 | 0.8822 | 0.8781 | 0.8802 | 0.9529 | | 0.1559 | 2.0 | 1876 | 0.1412 | 0.9018 | 0.9071 | 0.9045 | 0.9631 | | 0.1051 | 3.0 | 2814 | 0.1395 | 0.9044 | 0.9137 | 0.9090 | 0.9649 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

lariskelmer/opus-mt-en-ro-finetuned-en-to-ro

15a2acdb8173eb3af1d94065e5f55d52e6d3cdbd

2022-07-13T21:08:15.000Z

[ "pytorch", "tensorboard", "marian", "text2text-generation", "dataset:wmt16", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

text2text-generation

false

lariskelmer

null

lariskelmer/opus-mt-en-ro-finetuned-en-to-ro

1

null

transformers

32,844

--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: opus-mt-en-ro-finetuned-en-to-ro results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt16 type: wmt16 args: ro-en metrics: - name: Bleu type: bleu value: 28.1505 ---  # opus-mt-en-ro-finetuned-en-to-ro This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.2886 - Bleu: 28.1505 - Gen Len: 34.1036 ## 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.7437 | 1.0 | 38145 | 1.2886 | 28.1505 | 34.1036 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

laboyle1/distilbert-finetuned

e6d4f9f420da11e321745bba753785c19f3c14fc

2022-06-13T21:03:49.000Z

[ "pytorch", "distilbert", "fill-mask", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

fill-mask

false

laboyle1

null

laboyle1/distilbert-finetuned

1

null

transformers

32,845

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-finetuned results: [] ---  # distilbert-finetuned This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9895 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.2103 | 1.0 | 10024 | 2.0834 | | 2.1146 | 2.0 | 20048 | 2.0387 | | 2.0721 | 3.0 | 30072 | 2.0095 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu102 - Datasets 2.2.2 - Tokenizers 0.12.1

techiaith/wav2vec2-xls-r-1b-ft-cy

d67b672faff98479c63c9e323a6099094818585c

2022-06-15T13:53:19.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "cy", "dataset:common_voice", "transformers", "audio", "hf-asr-leaderboard", "ken-lm", "robust-speech-event", "speech", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

techiaith

null

techiaith/wav2vec2-xls-r-1b-ft-cy

1

transformers

32,846

--- language: cy datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - hf-asr-leaderboard - ken-lm - robust-speech-event - speech license: apache-2.0 model-index: - name: wav2vec2-xls-r-1b-ft-cy with KenLM language model (by Bangor University) results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice cy type: common_voice args: cy metrics: - name: Test WER type: wer value: 12.38% --- # wav2vec2-xls-r-1b-ft-cy Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) with the [Welsh Common Voice version 9 dataset](https://huggingface.co/datasets/common_voice). Source code and scripts for training acoustic and KenLM language models, as well as examples of inference in transcribing or a self-hosted API service, can be found at [https://github.com/techiaith/docker-wav2vec2-xlsr-ft-cy](https://github.com/techiaith/docker-wav2vec2-xlsr-ft-cy). ## Usage The wav2vec2-xls-r-1b-ft-cy (acoustic) model can be used directly (without a language model) as follows: ```python import torch import torchaudio import librosa from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor processor = Wav2Vec2Processor.from_pretrained("techiaith/wav2vec2-xls-r-1b-ft-cy") model = Wav2Vec2ForCTC.from_pretrained("techiaith/wav2vec2-xls-r-1b-ft-cy") audio, rate = librosa.load(audio_file, sr=16000) inputs = processor(audio, sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits # greedy decoding predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) ``` ## Using the Language Model See https://github.com/techiaith/docker-wav2vec2-xlsr-ft-cy/releases/tag/22.06 for more details and examples of a KenLM usage with the Parlance PyTorch CTC decode bindings library: [https://github.com/parlance/ctcdecode](https://github.com/parlance/ctcdecode) ## Evaluation According to the Welsh Common Voice version 9 test set, the WER of techiaith/wav2vec2-xls-r-1b-ft-cy standalone is **19.68%** When assisted by the KenLM language model the same test produces a WER of **12.38%** See: https://github.com/techiaith/docker-wav2vec2-xlsr-ft-cy/blob/main/train/python/evaluate.py

chiranthans23/xlm-roberta-base-finetuned-panx-de

8c4b638c9807a30392325d10dfaccd7b1b045fd0

2022-06-14T16:13:11.000Z

[ "pytorch", "tensorboard", "xlm-roberta", "token-classification", "dataset:xtreme", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]

token-classification

false

chiranthans23

null

chiranthans23/xlm-roberta-base-finetuned-panx-de

1

null

transformers

32,847

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8620945214069894 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1372 - F1: 0.8621 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 | | 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 | | 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

income/jpq-document_encoder-base-msmarco-contriever

e3e5eddf8ecb5b2c82ce61fe5d294999a86501f1

2022-06-13T20:57:52.000Z

[ "pytorch", "bert", "transformers", "license:apache-2.0" ]

null

false

income

null

income/jpq-document_encoder-base-msmarco-contriever

1

null

transformers

32,848

--- license: apache-2.0 ---

micamorales/roberta-NLI-abs

e59d6882e7e643211eff91101a432fb4e0ede164

2022-06-13T21:05:31.000Z

[ "pytorch", "roberta", "feature-extraction", "transformers" ]

feature-extraction

false

micamorales

null

micamorales/roberta-NLI-abs

1

null

transformers

32,849

Entry not found

burner/GPT-J-Step_58500

3a2cdc1569b5242e22ecbd48e5434a076e8bf828

2022-06-14T00:42:40.000Z

[ "pytorch", "gptj", "text-generation", "transformers", "license:apache-2.0" ]

text-generation

false

burner

null

burner/GPT-J-Step_58500

1

null

transformers

32,850

--- license: apache-2.0 ---

burner/GPT-J-Step_18500

3994095dd3f37490021d608d6496acbef30ae90e

2022-06-14T03:04:28.000Z

[ "pytorch", "gptj", "text-generation", "transformers", "license:apache-2.0" ]

text-generation

false

burner

null

burner/GPT-J-Step_18500

1

null

transformers

32,851

--- license: apache-2.0 ---

twieland/MIX2_en-ja_helsinki

7dc69e79cfe629a26847cc4e1e585448dcabe5d2

2022-06-16T06:23:56.000Z

[ "pytorch", "marian", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

text2text-generation

false

twieland

null

twieland/MIX2_en-ja_helsinki

1

null

transformers

32,852

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: MIX2_en-ja_helsinki results: [] ---  # MIX2_en-ja_helsinki This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-jap](https://huggingface.co/Helsinki-NLP/opus-mt-en-jap) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6703 ## 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: 96 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 3.5357 | 0.02 | 4000 | 2.9519 | | 2.8601 | 0.04 | 8000 | 2.6962 | | 2.6183 | 0.06 | 12000 | 2.5156 | | 2.4731 | 0.08 | 16000 | 2.4312 | | 2.3731 | 0.1 | 20000 | 2.3575 | | 2.2964 | 0.11 | 24000 | 2.3319 | | 2.238 | 0.13 | 28000 | 2.2802 | | 2.1919 | 0.15 | 32000 | 2.2552 | | 2.1479 | 0.17 | 36000 | 2.2354 | | 2.1104 | 0.19 | 40000 | 2.2210 | | 2.0788 | 0.21 | 44000 | 2.1835 | | 2.0552 | 0.23 | 48000 | 2.1391 | | 2.0228 | 0.25 | 52000 | 2.1338 | | 2.0062 | 0.27 | 56000 | 2.1115 | | 1.9868 | 0.29 | 60000 | 2.1025 | | 1.9628 | 0.31 | 64000 | 2.1334 | | 1.9474 | 0.32 | 68000 | 2.0935 | | 1.9318 | 0.34 | 72000 | 2.1030 | | 1.9187 | 0.36 | 76000 | 2.0605 | | 1.9019 | 0.38 | 80000 | 2.0388 | | 1.8916 | 0.4 | 84000 | 2.0360 | | 1.8775 | 0.42 | 88000 | 2.0356 | | 1.8689 | 0.44 | 92000 | 2.0315 | | 1.8558 | 0.46 | 96000 | 2.0169 | | 1.8431 | 0.48 | 100000 | 2.0213 | | 1.8373 | 0.5 | 104000 | 2.0071 | | 1.8224 | 0.52 | 108000 | 2.0093 | | 1.8181 | 0.53 | 112000 | 1.9952 | | 1.8087 | 0.55 | 116000 | 1.9927 | | 1.7998 | 0.57 | 120000 | 1.9726 | | 1.7947 | 0.59 | 124000 | 1.9817 | | 1.7874 | 0.61 | 128000 | 1.9650 | | 1.7781 | 0.63 | 132000 | 1.9688 | | 1.7712 | 0.65 | 136000 | 1.9655 | | 1.7631 | 0.67 | 140000 | 1.9561 | | 1.7577 | 0.69 | 144000 | 1.9529 | | 1.7528 | 0.71 | 148000 | 1.9447 | | 1.746 | 0.73 | 152000 | 1.9700 | | 1.7386 | 0.74 | 156000 | 1.9413 | | 1.7329 | 0.76 | 160000 | 1.9329 | | 1.7285 | 0.78 | 164000 | 1.9289 | | 1.7227 | 0.8 | 168000 | 1.9337 | | 1.7186 | 0.82 | 172000 | 1.9263 | | 1.7116 | 0.84 | 176000 | 1.9407 | | 1.7072 | 0.86 | 180000 | 1.9059 | | 1.7032 | 0.88 | 184000 | 1.9380 | | 1.6932 | 0.9 | 188000 | 1.9183 | | 1.6921 | 0.92 | 192000 | 1.9131 | | 1.6875 | 0.94 | 196000 | 1.9180 | | 1.6846 | 0.96 | 200000 | 1.9040 | | 1.6797 | 0.97 | 204000 | 1.9089 | | 1.6725 | 0.99 | 208000 | 1.9024 | | 1.6589 | 1.01 | 212000 | 1.8909 | | 1.6507 | 1.03 | 216000 | 1.8837 | | 1.6441 | 1.05 | 220000 | 1.8906 | | 1.6445 | 1.07 | 224000 | 1.8914 | | 1.6394 | 1.09 | 228000 | 1.8833 | | 1.6382 | 1.11 | 232000 | 1.8837 | | 1.6376 | 1.13 | 236000 | 1.8869 | | 1.6329 | 1.15 | 240000 | 1.8829 | | 1.6294 | 1.17 | 244000 | 1.8845 | | 1.6273 | 1.18 | 248000 | 1.8888 | | 1.6243 | 1.2 | 252000 | 1.8709 | | 1.6226 | 1.22 | 256000 | 1.8418 | | 1.6177 | 1.24 | 260000 | 1.8587 | | 1.6151 | 1.26 | 264000 | 1.8526 | | 1.6111 | 1.28 | 268000 | 1.8494 | | 1.6084 | 1.3 | 272000 | 1.8781 | | 1.6043 | 1.32 | 276000 | 1.8390 | | 1.6011 | 1.34 | 280000 | 1.8603 | | 1.5999 | 1.36 | 284000 | 1.8515 | | 1.5954 | 1.38 | 288000 | 1.8356 | | 1.5936 | 1.39 | 292000 | 1.8530 | | 1.5916 | 1.41 | 296000 | 1.8475 | | 1.5886 | 1.43 | 300000 | 1.8410 | | 1.5883 | 1.45 | 304000 | 1.8153 | | 1.5828 | 1.47 | 308000 | 1.8254 | | 1.582 | 1.49 | 312000 | 1.8139 | | 1.578 | 1.51 | 316000 | 1.8366 | | 1.5723 | 1.53 | 320000 | 1.8353 | | 1.5705 | 1.55 | 324000 | 1.8230 | | 1.5691 | 1.57 | 328000 | 1.8194 | | 1.5656 | 1.59 | 332000 | 1.8069 | | 1.566 | 1.6 | 336000 | 1.8204 | | 1.5604 | 1.62 | 340000 | 1.8307 | | 1.5573 | 1.64 | 344000 | 1.8209 | | 1.5547 | 1.66 | 348000 | 1.8320 | | 1.5545 | 1.68 | 352000 | 1.8179 | | 1.5519 | 1.7 | 356000 | 1.8323 | | 1.545 | 1.72 | 360000 | 1.8005 | | 1.5483 | 1.74 | 364000 | 1.8034 | | 1.5454 | 1.76 | 368000 | 1.7997 | | 1.5393 | 1.78 | 372000 | 1.8078 | | 1.5381 | 1.8 | 376000 | 1.8204 | | 1.5347 | 1.81 | 380000 | 1.8071 | | 1.5327 | 1.83 | 384000 | 1.7997 | | 1.529 | 1.85 | 388000 | 1.8012 | | 1.5287 | 1.87 | 392000 | 1.8028 | | 1.5273 | 1.89 | 396000 | 1.8103 | | 1.5194 | 1.91 | 400000 | 1.8008 | | 1.5197 | 1.93 | 404000 | 1.8004 | | 1.5218 | 1.95 | 408000 | 1.8024 | | 1.514 | 1.97 | 412000 | 1.7852 | | 1.5146 | 1.99 | 416000 | 1.7908 | | 1.5045 | 2.01 | 420000 | 1.7864 | | 1.4876 | 2.02 | 424000 | 1.7813 | | 1.4846 | 2.04 | 428000 | 1.7822 | | 1.4865 | 2.06 | 432000 | 1.7737 | | 1.4857 | 2.08 | 436000 | 1.7668 | | 1.4825 | 2.1 | 440000 | 1.7681 | | 1.4828 | 2.12 | 444000 | 1.7685 | | 1.4821 | 2.14 | 448000 | 1.7636 | | 1.4778 | 2.16 | 452000 | 1.7778 | | 1.4803 | 2.18 | 456000 | 1.7834 | | 1.4766 | 2.2 | 460000 | 1.7801 | | 1.4741 | 2.22 | 464000 | 1.7601 | | 1.4705 | 2.23 | 468000 | 1.7665 | | 1.4739 | 2.25 | 472000 | 1.7604 | | 1.4694 | 2.27 | 476000 | 1.7803 | | 1.4665 | 2.29 | 480000 | 1.7835 | | 1.4668 | 2.31 | 484000 | 1.7670 | | 1.4605 | 2.33 | 488000 | 1.7629 | | 1.4626 | 2.35 | 492000 | 1.7612 | | 1.4627 | 2.37 | 496000 | 1.7612 | | 1.4569 | 2.39 | 500000 | 1.7557 | | 1.455 | 2.41 | 504000 | 1.7599 | | 1.4547 | 2.43 | 508000 | 1.7569 | | 1.453 | 2.44 | 512000 | 1.7589 | | 1.4515 | 2.46 | 516000 | 1.7679 | | 1.4501 | 2.48 | 520000 | 1.7574 | | 1.4446 | 2.5 | 524000 | 1.7526 | | 1.4456 | 2.52 | 528000 | 1.7506 | | 1.4445 | 2.54 | 532000 | 1.7484 | | 1.4428 | 2.56 | 536000 | 1.7447 | | 1.439 | 2.58 | 540000 | 1.7468 | | 1.441 | 2.6 | 544000 | 1.7609 | | 1.4358 | 2.62 | 548000 | 1.7498 | | 1.4318 | 2.64 | 552000 | 1.7592 | | 1.4276 | 2.65 | 556000 | 1.7452 | | 1.4317 | 2.67 | 560000 | 1.7500 | | 1.4277 | 2.69 | 564000 | 1.7392 | | 1.4259 | 2.71 | 568000 | 1.7351 | | 1.4239 | 2.73 | 572000 | 1.7385 | | 1.4191 | 2.75 | 576000 | 1.7487 | | 1.4204 | 2.77 | 580000 | 1.7392 | | 1.4176 | 2.79 | 584000 | 1.7372 | | 1.4147 | 2.81 | 588000 | 1.7347 | | 1.4154 | 2.83 | 592000 | 1.7085 | | 1.4134 | 2.85 | 596000 | 1.7103 | | 1.4091 | 2.87 | 600000 | 1.7124 | | 1.4091 | 2.88 | 604000 | 1.7369 | | 1.406 | 2.9 | 608000 | 1.7142 | | 1.4028 | 2.92 | 612000 | 1.7376 | | 1.4019 | 2.94 | 616000 | 1.7201 | | 1.4018 | 2.96 | 620000 | 1.7230 | | 1.3959 | 2.98 | 624000 | 1.7206 | | 1.3985 | 3.0 | 628000 | 1.7183 | | 1.3681 | 3.02 | 632000 | 1.7283 | | 1.3668 | 3.04 | 636000 | 1.7330 | | 1.3687 | 3.06 | 640000 | 1.7187 | | 1.3681 | 3.08 | 644000 | 1.7163 | | 1.3687 | 3.09 | 648000 | 1.7249 | | 1.364 | 3.11 | 652000 | 1.7283 | | 1.364 | 3.13 | 656000 | 1.7091 | | 1.3652 | 3.15 | 660000 | 1.7030 | | 1.3623 | 3.17 | 664000 | 1.7058 | | 1.3604 | 3.19 | 668000 | 1.7101 | | 1.3598 | 3.21 | 672000 | 1.7104 | | 1.3577 | 3.23 | 676000 | 1.7028 | | 1.3574 | 3.25 | 680000 | 1.7023 | | 1.3546 | 3.27 | 684000 | 1.7197 | | 1.3549 | 3.29 | 688000 | 1.7045 | | 1.3534 | 3.3 | 692000 | 1.6990 | | 1.3511 | 3.32 | 696000 | 1.6971 | | 1.3504 | 3.34 | 700000 | 1.6894 | | 1.346 | 3.36 | 704000 | 1.6820 | | 1.3467 | 3.38 | 708000 | 1.6920 | | 1.3461 | 3.4 | 712000 | 1.6897 | | 1.3425 | 3.42 | 716000 | 1.6962 | | 1.34 | 3.44 | 720000 | 1.6864 | | 1.3408 | 3.46 | 724000 | 1.6860 | | 1.3387 | 3.48 | 728000 | 1.6924 | | 1.3377 | 3.5 | 732000 | 1.6919 | | 1.3378 | 3.51 | 736000 | 1.6858 | | 1.334 | 3.53 | 740000 | 1.6816 | | 1.3347 | 3.55 | 744000 | 1.6867 | | 1.3307 | 3.57 | 748000 | 1.6859 | | 1.3316 | 3.59 | 752000 | 1.6896 | | 1.3257 | 3.61 | 756000 | 1.6824 | | 1.3222 | 3.63 | 760000 | 1.6819 | | 1.3247 | 3.65 | 764000 | 1.6809 | | 1.3207 | 3.67 | 768000 | 1.6775 | | 1.3227 | 3.69 | 772000 | 1.6807 | | 1.3203 | 3.71 | 776000 | 1.6750 | | 1.3203 | 3.72 | 780000 | 1.6758 | | 1.316 | 3.74 | 784000 | 1.6787 | | 1.3147 | 3.76 | 788000 | 1.6747 | | 1.3146 | 3.78 | 792000 | 1.6718 | | 1.3137 | 3.8 | 796000 | 1.6744 | | 1.3143 | 3.82 | 800000 | 1.6733 | | 1.3123 | 3.84 | 804000 | 1.6754 | | 1.3069 | 3.86 | 808000 | 1.6734 | | 1.3122 | 3.88 | 812000 | 1.6742 | | 1.3074 | 3.9 | 816000 | 1.6742 | | 1.3006 | 3.92 | 820000 | 1.6709 | | 1.308 | 3.93 | 824000 | 1.6714 | | 1.3063 | 3.95 | 828000 | 1.6727 | | 1.3036 | 3.97 | 832000 | 1.6711 | | 1.3048 | 3.99 | 836000 | 1.6703 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

dz-xt/stt_en_conformer_ctc_small

33045d3effb0dce21b18eaeac9c2b3b59e3bc909

2022-06-14T10:13:47.000Z

[ "nemo", "en", "dataset:librispeech_asr", "dataset:mozilla-foundation/common_voice_7_0", "dataset:vctk", "dataset:fisher_corpus", "dataset:Switchboard-1", "dataset:WSJ-0", "dataset:WSJ-1", "dataset:National Singapore Corpus Part 1", "dataset:National Singapore Corpus Part 6", "dataset:VoxPopuli (EN)", "dataset:Europarl-ASR (EN)", "dataset:Multilingual LibriSpeech (2000 hours)", "automatic-speech-recognition", "speech", "audio", "CTC", "Conformer", "Transformer", "NeMo", "pytorch", "license:cc-by-4.0", "model-index" ]

automatic-speech-recognition

false

dz-xt

null

dz-xt/stt_en_conformer_ctc_small

1

0

nemo

32,853

--- language: - en license: cc-by-4.0 library_name: nemo datasets: - librispeech_asr - mozilla-foundation/common_voice_7_0 - vctk - fisher_corpus - Switchboard-1 - WSJ-0 - WSJ-1 - National Singapore Corpus Part 1 - National Singapore Corpus Part 6 - VoxPopuli (EN) - Europarl-ASR (EN) - Multilingual LibriSpeech (2000 hours) thumbnail: null tags: - automatic-speech-recognition - speech - audio - CTC - Conformer - Transformer - NeMo - pytorch model-index: - name: stt_en_conformer_ctc_small results: - task: type: automatic-speech-recognition dataset: type: librispeech_asr name: Librispeech (clean) config: other split: test args: language: en metrics: - type: wer value: 8.1 name: WER --- ## Model Overview <DESCRIBE IN ONE LINE THE MODEL AND ITS USE> ## NVIDIA NeMo: Training To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version. ``` pip install nemo_toolkit['all'] ``` ## How to Use this Model The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. ### Automatically instantiate the model ```python import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("dz-xt/stt_en_conformer_ctc_small") ``` ### Transcribing using Python First, let's get a sample ``` wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav ``` Then simply do: ``` asr_model.transcribe(['2086-149220-0033.wav']) ``` ### Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="dz-xt/stt_en_conformer_ctc_small" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` ### Input This model accepts 16000 KHz Mono-channel Audio (wav files) as input. ### Output This model provides transcribed speech as a string for a given audio sample. ## Model Architecture <ADD SOME INFORMATION ABOUT THE ARCHITECTURE> ## Training <ADD INFORMATION ABOUT HOW THE MODEL WAS TRAINED - HOW MANY EPOCHS, AMOUNT OF COMPUTE ETC> ### Datasets <LIST THE NAME AND SPLITS OF DATASETS USED TO TRAIN THIS MODEL (ALONG WITH LANGUAGE AND ANY ADDITIONAL INFORMATION)> ## Performance <LIST THE SCORES OF THE MODEL - OR USE THE Hugging Face Evaluate LiBRARY TO UPLOAD METRICS> ## Limitations <DECLARE ANY POTENTIAL LIMITATIONS OF THE MODEL> Eg: Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech. ## References <ADD ANY REFERENCES HERE AS NEEDED> [1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)

lmqg/t5-small-squadshifts-nyt

16cda39345c8a8c557ebf4d3a7d059d5ae023cea

2022-06-14T10:36:00.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

lmqg

null

lmqg/t5-small-squadshifts-nyt

1

null

transformers

32,854

Entry not found

lmqg/t5-small-squadshifts-reddit

dae0414bcbd805c23fdc8957dce8e50727e310ac

2022-06-14T10:37:16.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

lmqg

null

lmqg/t5-small-squadshifts-reddit

1

null

transformers

32,855

Entry not found

janeel/distilbert-base-uncased-finetuned-squad

a933f5e7ddd6f1927246d7a4bbea3935d9de1b41

2022-06-16T13:42:47.000Z

[ "pytorch", "tensorboard", "roberta", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]

question-answering

false

janeel

null

janeel/distilbert-base-uncased-finetuned-squad

1

null

transformers

32,856

--- tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] ---  # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [klue/roberta-small](https://huggingface.co/klue/roberta-small) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 2.1272 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.398 | 1.0 | 2767 | 2.2567 | | 2.0729 | 2.0 | 5534 | 2.1272 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

Salvatore/mt5-finetuned-amazon-en-es

8e4979d36e6ce0e7c54eda9fd3db63cf833770f0

2022-06-14T14:25:12.000Z

[ "pytorch", "tensorboard", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

Salvatore

null

Salvatore/mt5-finetuned-amazon-en-es

1

null

transformers

32,857

Entry not found

AntoDono/DialoGPT-Bopy-Alpha-1.01

7b78c239a217ee6543bbfca33397615aba4a930d

2022-06-24T17:05:47.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

false

AntoDono

null

AntoDono/DialoGPT-Bopy-Alpha-1.01

1

null

transformers

32,858

--- tags: - conversational ---

Waleed-bin-Qamar/ConvNext-For-Covid-Classification-30EP

87de07e7431f3adccb4812a68e98edc1d552507b

2022-06-14T15:04:53.000Z

[ "pytorch", "convnext", "image-classification", "transformers" ]

image-classification

false

Waleed-bin-Qamar

null

Waleed-bin-Qamar/ConvNext-For-Covid-Classification-30EP

1

null

transformers

32,859

Entry not found

mgtoxd/tstw2

00e8e43ccfa3109170b95dc351d2e6ba920bab13

2022-06-23T01:59:33.000Z

[ "pytorch", "tf", "wav2vec2", "automatic-speech-recognition", "transformers" ]

automatic-speech-recognition

false

mgtoxd

null

mgtoxd/tstw2

1

null

transformers

32,860

Entry not found

AntoDono/DialoGPT-Bopy-Alpha-1.03

af3fca9beb2a826f58f9eec40b02fc0b5cf0e23e

2022-06-14T16:32:43.000Z

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

false

AntoDono

null

AntoDono/DialoGPT-Bopy-Alpha-1.03

1

null

transformers

32,861

Entry not found

mahfooz/bert-base-cased-dv-v2

6d1304b254227107ceeb6ad5e305e49b23c90d86

2022-06-15T13:11:46.000Z

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

false

mahfooz

null

mahfooz/bert-base-cased-dv-v2

1

null

transformers

32,862

Entry not found

Waleed-bin-Qamar/ConvNext-For-Covid-Classification-30EP-BS64

12cfd445b034e2dc26e90aabbe5b2e7ff8b9ce81

2022-07-03T13:17:29.000Z

[ "pytorch", "convnext", "image-classification", "transformers" ]

image-classification

false

Waleed-bin-Qamar

null

Waleed-bin-Qamar/ConvNext-For-Covid-Classification-30EP-BS64

1

null

transformers

32,863

Entry not found

Browbon/DialoGPT-small-LucaChangretta

f3e4d20c8330c1b202e7c1de741aff8f53213d31

2022-06-14T20:25:27.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

false

Browbon

null

Browbon/DialoGPT-small-LucaChangretta

1

null

transformers

32,864

--- tags: - conversational --- # Luca Changretta GPT Model

Renukswamy/distilbert-base-uncased-finetuned-squad

e4eb8e751657468fa9c92ee7ed37b19bb311dfb2

2022-06-15T21:27:19.000Z

[ "pytorch", "tensorboard", "distilbert", "question-answering", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

question-answering

false

Renukswamy

null

Renukswamy/distilbert-base-uncased-finetuned-squad

1

null

transformers

32,865

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-squad results: [] ---  # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7108 ## 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 | |:-------------:|:-----:|:-----:|:---------------:| | 0.6869 | 1.0 | 6941 | 0.7765 | | 0.5083 | 2.0 | 13882 | 0.6917 | | 0.372 | 3.0 | 20823 | 0.7108 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.3.1 - Tokenizers 0.12.1

steven123/teeth_test

7622a568dc990833829535825be03f32965735f1

2022-06-14T23:57:13.000Z

[ "pytorch", "tensorboard", "vit", "image-classification", "transformers", "huggingpics", "model-index" ]

image-classification

false

steven123

null

steven123/teeth_test

1

null

transformers

32,866

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: teeth_test results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.5555555820465088 --- # teeth_test 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 #### Good Teeth ![Good Teeth](images/Good_Teeth.jpg) #### Missing Teeth ![Missing Teeth](images/Missing_Teeth.jpg) #### Rotten Teeth ![Rotten Teeth](images/Rotten_Teeth.jpg)

lmqg/t5-base-squadshifts-amazon

b47caf4b07825adc86b0c3301276518ad5cdabd5

2022-06-15T00:08:17.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

lmqg

null

lmqg/t5-base-squadshifts-amazon

1

null

transformers

32,867

Entry not found

erickfm/frosty-sweep-1

70793b04abe6c3e0f87326ac9d64a19fe594c643

2022-06-15T01:16:32.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

erickfm

null

erickfm/frosty-sweep-1

1

null

transformers

32,868

Entry not found

steven123/Teeth_A

06ac242cddf9812c3e6078ec252f50d7a17c3415

2022-06-15T02:42:35.000Z

[ "pytorch", "tensorboard", "vit", "image-classification", "transformers", "huggingpics", "model-index" ]

image-classification

false

steven123

null

steven123/Teeth_A

1

null

transformers

32,869

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: Teeth_A results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.4545454680919647 --- # Teeth_A 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 #### Good Teeth ![Good Teeth](images/Good_Teeth.jpg) #### Missing Teeth ![Missing Teeth](images/Missing_Teeth.jpg) #### Rotten Teeth ![Rotten Teeth](images/Rotten_Teeth.jpg)

steven123/Teeth_C

48308ba60c00f48eb2abddac6a634bb03f5c1888

2022-06-15T02:53:44.000Z

[ "pytorch", "tensorboard", "vit", "image-classification", "transformers", "huggingpics", "model-index" ]

image-classification

false

steven123

null

steven123/Teeth_C

1

null

transformers

32,870

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: Teeth_C results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.5 --- # Teeth_C 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 #### Good Teeth ![Good Teeth](images/Good_Teeth.jpg) #### Missing Teeth ![Missing Teeth](images/Missing_Teeth.jpg) #### Rotten Teeth ![Rotten Teeth](images/Rotten_Teeth.jpg)

x574chen/wav2vec2-librispeech-clean-100h-demo-dist

fe4ea29671955245412c37dde6d6799fbd5ac3fd

2022-06-15T16:43:25.000Z

[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]

automatic-speech-recognition

false

x574chen

null

x574chen/wav2vec2-librispeech-clean-100h-demo-dist

1

null

transformers

32,871

Entry not found

huggingtweets/mysteriousgam54

41d9e51ecb294a2b003365456f001d85f233e9f4

2022-06-15T04:06:06.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/mysteriousgam54

1

null

transformers

32,872

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1429866660299689984/CGXAQuWf_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">themysteriousgamer</div> <div style="text-align: center; font-size: 14px;">@mysteriousgam54</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from themysteriousgamer. | Data | themysteriousgamer | | --- | --- | | Tweets downloaded | 1315 | | Retweets | 210 | | Short tweets | 168 | | Tweets kept | 937 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/m4i8lg1e/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mysteriousgam54's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3rz0m12t) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3rz0m12t/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/mysteriousgam54') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

RuiqianLi/Malaya-speech_fine-tune_MrBrown_15_Jun

75705930f26da94c57a24b79510c355e9e35eee9

2022-06-15T08:23:28.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "dataset:uob_singlish", "transformers", "generated_from_trainer", "model-index" ]

automatic-speech-recognition

false

RuiqianLi

null

RuiqianLi/Malaya-speech_fine-tune_MrBrown_15_Jun

1

null

transformers

32,873

--- tags: - generated_from_trainer datasets: - uob_singlish model-index: - name: Malaya-speech_fine-tune_MrBrown_15_Jun results: [] ---  # Malaya-speech_fine-tune_MrBrown_15_Jun This model is a fine-tuned version of [malay-huggingface/wav2vec2-xls-r-300m-mixed](https://huggingface.co/malay-huggingface/wav2vec2-xls-r-300m-mixed) on the uob_singlish dataset. It achieves the following results on the evaluation set: - Loss: 0.4822 - Wer: 0.2449 ## 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1607 | 5.26 | 200 | 0.3983 | 0.2381 | | 0.5184 | 10.52 | 400 | 0.3256 | 0.2245 | | 0.2993 | 15.78 | 600 | 0.3437 | 0.2426 | | 0.2485 | 21.05 | 800 | 0.4547 | 0.2585 | | 0.1917 | 26.31 | 1000 | 0.4598 | 0.2517 | | 0.1586 | 31.57 | 1200 | 0.4050 | 0.2290 | | 0.1486 | 36.83 | 1400 | 0.4186 | 0.2653 | | 0.1307 | 42.1 | 1600 | 0.4284 | 0.2857 | | 0.0895 | 47.36 | 1800 | 0.5158 | 0.2562 | | 0.0526 | 52.62 | 2000 | 0.4525 | 0.2449 | | 0.0553 | 57.88 | 2200 | 0.4364 | 0.2336 | | 0.037 | 63.16 | 2400 | 0.3873 | 0.2449 | | 0.0439 | 68.42 | 2600 | 0.3914 | 0.2404 | | 0.0411 | 73.68 | 2800 | 0.4673 | 0.2494 | | 0.0242 | 78.94 | 3000 | 0.4801 | 0.2426 | | 0.0833 | 84.21 | 3200 | 0.4641 | 0.2630 | | 0.034 | 89.47 | 3400 | 0.4607 | 0.2449 | | 0.02 | 94.73 | 3600 | 0.4825 | 0.2449 | | 0.0211 | 99.99 | 3800 | 0.4822 | 0.2449 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

huggingtweets/danny_macaskill-martynashton

62b5c2c93d0f1d99d654e95aa0ce7d3b969fea36

2022-06-15T04:59:30.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/danny_macaskill-martynashton

1

null

transformers

32,874

--- language: en thumbnail: http://www.huggingtweets.com/danny_macaskill-martynashton/1655269165002/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/770573812991754240/gyUr23bS_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/616596420230021120/w-kK8IT6_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Danny MacAskill & Martyn Ashton</div> <div style="text-align: center; font-size: 14px;">@danny_macaskill-martynashton</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Danny MacAskill & Martyn Ashton. | Data | Danny MacAskill | Martyn Ashton | | --- | --- | --- | | Tweets downloaded | 2971 | 3179 | | Retweets | 505 | 810 | | Short tweets | 79 | 136 | | Tweets kept | 2387 | 2233 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/31ege8zb/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @danny_macaskill-martynashton's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/g4d86tk2) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/g4d86tk2/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/danny_macaskill-martynashton') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

huggingtweets/wikisignpost

4e141e788fed31b4bd75a446e001bfa36f166c81

2022-06-15T06:24:26.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/wikisignpost

1

null

transformers

32,875

--- language: en thumbnail: http://www.huggingtweets.com/wikisignpost/1655274233816/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/795028567398576128/GG1GUpJ7_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">The Signpost</div> <div style="text-align: center; font-size: 14px;">@wikisignpost</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from The Signpost. | Data | The Signpost | | --- | --- | | Tweets downloaded | 3216 | | Retweets | 522 | | Short tweets | 47 | | Tweets kept | 2647 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/7z6btxad/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wikisignpost's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/27ceco72) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/27ceco72/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/wikisignpost') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

camillelacan1/vit-demo

bd89be887d3a213bfd1cbf11986e409d1ceedf88

2022-06-15T06:32:15.000Z

[ "pytorch", "vit", "image-classification", "transformers" ]

image-classification

false

camillelacan1

null

camillelacan1/vit-demo

1

null

transformers

32,876

Entry not found

sdugar/cross-en-de-fr-xlmr-200d-sentence-transformer

5d3318b787af76da38ec6d4b73ddc0a90fd167f7

2022-06-15T08:21:33.000Z

[ "pytorch", "xlm-roberta", "feature-extraction", "sentence-transformers", "sentence-similarity" ]

sentence-similarity

false

sdugar

null

sdugar/cross-en-de-fr-xlmr-200d-sentence-transformer

1

null

sentence-transformers

32,877

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 200 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 124278 with parameters: ``` {'batch_size': 25, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MSELoss.MSELoss` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (dense): Dense({'in_features': 768, 'out_features': 200, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors

lewtun/dog-vs-chicken

ab3046fda5d5f04d8f66b8f392522f5969bbc716

2022-06-15T07:09:02.000Z

[ "pytorch", "tensorboard", "vit", "image-classification", "transformers", "huggingpics", "model-index" ]

image-classification

false

lewtun

null

lewtun/dog-vs-chicken

1

null

transformers

32,878

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: dog-vs-chicken results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 1.0 --- # dog-vs-chicken 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 #### crispy fried chicken ![crispy fried chicken](images/crispy_fried_chicken.jpg) #### poodle ![poodle](images/poodle.jpg)

aware-ai/wav2vec2-1b-marian-german

373e0f3cb274b05678731477dbd496614df6e3a9

2022-06-16T04:47:55.000Z

[ "pytorch", "tensorboard", "speech-encoder-decoder", "automatic-speech-recognition", "transformers" ]

automatic-speech-recognition

false

aware-ai

null

aware-ai/wav2vec2-1b-marian-german

1

null

transformers

32,879

Entry not found

huggingtweets/ravenel_jeremy

92cd293ca227e90ad5fbe581bd80ed249f879a35

2022-06-15T07:43:01.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/ravenel_jeremy

1

null

transformers

32,880

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1440653599420268547/-h0yYTlI_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Jeremy Ravenel</div> <div style="text-align: center; font-size: 14px;">@ravenel_jeremy</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Jeremy Ravenel. | Data | Jeremy Ravenel | | --- | --- | | Tweets downloaded | 899 | | Retweets | 180 | | Short tweets | 65 | | Tweets kept | 654 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cp3aewt/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ravenel_jeremy's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2arfza2u) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2arfza2u/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/ravenel_jeremy') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

Salvatore/t5-finetuned-xsum

96b2ffc1678aea0fcbbd47557e169dbfcec3831c

2022-06-15T12:19:55.000Z

[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

Salvatore

null

Salvatore/t5-finetuned-xsum

1

null

transformers

32,881

Entry not found

gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53

6465818021332b142c2481b76ac0d69adde38671

2022-06-17T07:30:08.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "gary109/AI_Light_Dance", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

gary109

null

gary109/ai-light-dance_singing_ft_wav2vec2-large-xlsr-53

1

transformers

32,882

--- license: apache-2.0 tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer model-index: - name: ai-light-dance_singing_ft_wav2vec2-large-xlsr-53 results: [] ---  # ai-light-dance_singing_ft_wav2vec2-large-xlsr-53 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING dataset. It achieves the following results on the evaluation set: - Loss: 0.4327 - Wer: 0.2043 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.4089 | 1.0 | 552 | 1.4750 | 0.9054 | | 0.7995 | 2.0 | 1104 | 0.9044 | 0.6163 | | 0.6232 | 3.0 | 1656 | 0.6645 | 0.3980 | | 0.5351 | 4.0 | 2208 | 0.5674 | 0.3120 | | 0.472 | 5.0 | 2760 | 0.5167 | 0.2579 | | 0.3913 | 6.0 | 3312 | 0.4553 | 0.2335 | | 0.3306 | 7.0 | 3864 | 0.4476 | 0.2114 | | 0.3028 | 8.0 | 4416 | 0.4327 | 0.2043 | | 0.317 | 9.0 | 4968 | 0.4355 | 0.2033 | | 0.2494 | 10.0 | 5520 | 0.4405 | 0.2022 | ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.1.dev0 - Tokenizers 0.12.1

huggingtweets/contrapoints-iamcardib

aca4a76a2304d8c02e559e55abc9735062e4cf83

2022-06-15T09:54:48.000Z

[ "pytorch", "gpt2", "text-generation", "en", "transformers", "huggingtweets" ]

text-generation

false

huggingtweets

null

huggingtweets/contrapoints-iamcardib

1

null

transformers

32,883

--- language: en thumbnail: http://www.huggingtweets.com/contrapoints-iamcardib/1655286883789/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1425907860773515264/a30IKa1f_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1536222087299350528/rMyNxbwV_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Natalie Wynn & Cardi B</div> <div style="text-align: center; font-size: 14px;">@contrapoints-iamcardib</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Natalie Wynn & Cardi B. | Data | Natalie Wynn | Cardi B | | --- | --- | --- | | Tweets downloaded | 3191 | 3058 | | Retweets | 299 | 1521 | | Short tweets | 527 | 371 | | Tweets kept | 2365 | 1166 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3q3i0l7z/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @contrapoints-iamcardib's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2vgppsp0) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2vgppsp0/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/contrapoints-iamcardib') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

winson/distilbert-base-uncased-finetuned-imdb

dcf77d1aecad87243668a76f976b28fae061142a

2022-06-15T12:51:06.000Z

[ "pytorch", "tensorboard", "distilbert", "fill-mask", "dataset:imdb", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]

fill-mask

false

winson

null

winson/distilbert-base-uncased-finetuned-imdb

1

null

transformers

32,884

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] ---  # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - eval_loss: 3.1139 - eval_runtime: 1.8873 - eval_samples_per_second: 529.866 - eval_steps_per_second: 8.478 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.3.1 - Tokenizers 0.12.1

meghazisofiane/mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instances-OPUS-leaningRate2e-05-batchSize2

8b02c90b7f63d755131b072fbcf2d4e4f81d1fcf

2022-06-15T11:11:28.000Z

[ "pytorch", "tensorboard", "mbart", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

meghazisofiane

null

meghazisofiane/mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instances-OPUS-leaningRate2e-05-batchSize2

1

null

transformers

32,885

Entry not found

mikeluck/wav2vec2-base-timit-demo-google-colab

b30e92ae9531d985612796c9dfbc1f829fd399f9

2022-06-15T12:43:38.000Z

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

false

mikeluck

null

mikeluck/wav2vec2-base-timit-demo-google-colab

1

null

transformers

32,886

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-google-colab results: [] ---  # wav2vec2-base-timit-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5351 - Wer: 0.3384 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.6311 | 1.0 | 500 | 2.6700 | 1.0 | | 1.0104 | 2.01 | 1000 | 0.5289 | 0.5277 | | 0.4483 | 3.01 | 1500 | 0.4576 | 0.4623 | | 0.3089 | 4.02 | 2000 | 0.4483 | 0.4255 | | 0.2278 | 5.02 | 2500 | 0.4463 | 0.4022 | | 0.1886 | 6.02 | 3000 | 0.4653 | 0.3938 | | 0.1578 | 7.03 | 3500 | 0.4624 | 0.3855 | | 0.1429 | 8.03 | 4000 | 0.4420 | 0.3854 | | 0.1244 | 9.04 | 4500 | 0.4980 | 0.3787 | | 0.1126 | 10.04 | 5000 | 0.4311 | 0.3785 | | 0.1082 | 11.04 | 5500 | 0.5114 | 0.3782 | | 0.0888 | 12.05 | 6000 | 0.5392 | 0.3725 | | 0.0835 | 13.05 | 6500 | 0.6011 | 0.3941 | | 0.074 | 14.06 | 7000 | 0.5030 | 0.3652 | | 0.0667 | 15.06 | 7500 | 0.5041 | 0.3583 | | 0.0595 | 16.06 | 8000 | 0.5125 | 0.3605 | | 0.0578 | 17.07 | 8500 | 0.5206 | 0.3592 | | 0.0573 | 18.07 | 9000 | 0.5208 | 0.3643 | | 0.0469 | 19.08 | 9500 | 0.4670 | 0.3537 | | 0.0442 | 20.08 | 10000 | 0.5388 | 0.3497 | | 0.0417 | 21.08 | 10500 | 0.5213 | 0.3581 | | 0.0361 | 22.09 | 11000 | 0.5096 | 0.3465 | | 0.0338 | 23.09 | 11500 | 0.5178 | 0.3459 | | 0.0333 | 24.1 | 12000 | 0.5240 | 0.3490 | | 0.0256 | 25.1 | 12500 | 0.5438 | 0.3464 | | 0.0248 | 26.1 | 13000 | 0.5182 | 0.3412 | | 0.0231 | 27.11 | 13500 | 0.5628 | 0.3423 | | 0.0228 | 28.11 | 14000 | 0.5416 | 0.3419 | | 0.0223 | 29.12 | 14500 | 0.5351 | 0.3384 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 1.18.3 - Tokenizers 0.12.1

Fluffypillow/DialoGPT-small-Rem

c50613085ddc35f6f026169932981ea7b7ae139c

2022-06-15T11:17:40.000Z

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

false

Fluffypillow

null

Fluffypillow/DialoGPT-small-Rem

1

null

transformers

32,887

--- tags: - conversational --- # Rem DialoGPT Model

erickfm/stellar-sweep-3

ab6ea46b52f5380517123b7b1baede7ab4af3454

2022-06-15T12:06:33.000Z

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

false

erickfm

null

erickfm/stellar-sweep-3

1

null

transformers

32,888

Entry not found

Addedk/mbert-swedish-distilled-cased

0ebe8585a87a5e1f414517e645b626dd3d07c613

2022-06-15T16:32:43.000Z

[ "pytorch", "tf", "bert", "fill-mask", "multilingual", "arxiv:2103.06418", "transformers", "license:apache-2.0", "autotrain_compatible" ]

fill-mask

false

Addedk

null

Addedk/mbert-swedish-distilled-cased

1

transformers

32,889

--- language: multilingual license: apache-2.0 --- # mBERT swedish distilled base model (cased) This model is a distilled version of [mBERT](https://huggingface.co/bert-base-multilingual-cased). It was distilled using Swedish data, the 2010-2015 portion of the [Swedish Culturomics Gigaword Corpus](https://spraakbanken.gu.se/en/resources/gigaword). The code for the distillation process can be found [here](https://github.com/AddedK/swedish-mbert-distillation/blob/main/azureML/pretrain_distillation.py). This was done as part of my Master's Thesis: *Task-agnostic knowledge distillation of mBERT to Swedish*. ## Model description This is a 6-layer version of mBERT, having been distilled using the [LightMBERT](https://arxiv.org/abs/2103.06418) distillation method, but without freezing the embedding layer. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. ## Training data The data used for distillation was the 2010-2015 portion of the [Swedish Culturomics Gigaword Corpus](https://spraakbanken.gu.se/en/resources/gigaword). The tokenized data had a file size of approximately 9 GB. ## Evaluation results When evaluated on the [SUCX 3.0 ](https://huggingface.co/datasets/KBLab/sucx3_ner) dataset, it achieved an average F1 score of 0.859 which is competitive with the score mBERT obtained, 0.866. When evaluated on the [English WikiANN](https://huggingface.co/datasets/wikiann) dataset, it achieved an average F1 score of 0.826 which is competitive with the score mBERT obtained, 0.849. Additional results and comparisons are presented in my Master's Thesis

KM4STfulltext/CSSCI_ABS_roberta

800dcf0d97b97114cbf49c3333822ff142b25572

2022-06-20T07:07:00.000Z

[ "pytorch", "bert", "fill-mask", "transformers", "license:apache-2.0", "autotrain_compatible" ]

fill-mask

false

KM4STfulltext

null

KM4STfulltext/CSSCI_ABS_roberta

1

null

transformers

32,890

--- license: apache-2.0 --- # Pre-trained Language Model for the Humanities and Social Sciences in Chinese ## Introduction The research for social science texts in Chinese needs the support natural language processing tools. The pre-trained language model has greatly improved the accuracy of text mining in general texts. At present, there is an urgent need for a pre-trained language model specifically for the automatic processing of scientific texts in Chinese social science. We used the abstract of social science research as the training set. Based on the deep language model framework of BERT, we constructed CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm pre-training language models by [transformers/run_mlm.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py) and [transformers/mlm_wwm](https://github.com/huggingface/transformers/tree/main/examples/research_projects/mlm_wwm). We designed four downstream tasks of Text Classification on different Chinese social scientific article corpus to verify the performance of the model. - CSSCI_ABS_BERT , CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm are trained on the abstract of articles published in CSSCI journals. The training set involved in the experiment included a total of `510,956,094 words`. - Based on the idea of Domain-Adaptive Pretraining, `CSSCI_ABS_BERT` and `CSSCI_ABS_roberta` combine a large amount of abstracts of scientific articles in Chinese based on the BERT structure, and continue to train the BERT and Chinese-RoBERTa models respectively to obtain pre-training models for the automatic processing of Chinese Social science research texts. ## News - 2022-06-15 : CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm has been put forward for the first time. ## How to use ### Huggingface Transformers The `from_pretrained` method based on [Huggingface Transformers](https://github.com/huggingface/transformers) can directly obtain CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm models online. - CSSCI_ABS_BERT ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_BERT") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_BERT") ``` - CSSCI_ABS_roberta ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta") ``` - CSSCI_ABS_roberta-wwm ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta_wwm") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta_wwm") ``` ### Download Models - The version of the model we provide is `PyTorch`. ### From Huggingface - Download directly through Huggingface's official website. - [KM4STfulltext/CSSCI_ABS_BERT](https://huggingface.co/KM4STfulltext/CSSCI_ABS_BERT) - [KM4STfulltext/CSSCI_ABS_roberta](https://huggingface.co/KM4STfulltext/CSSCI_ABS_roberta) - [KM4STfulltext/CSSCI_ABS_roberta_wwm](https://huggingface.co/KM4STfulltext/CSSCI_ABS_roberta_wwm) ## Evaluation & Results - We useCSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm to perform Text Classificationon different social science research corpus. The experimental results are as follows. #### Discipline classification experiments of articles published in CSSCI journals https://github.com/S-T-Full-Text-Knowledge-Mining/CSSCI-BERT #### Movement recognition experiments for data analysis and knowledge discovery abstract | Tag | bert-base-Chinese | chinese-roberta-wwm,ext | CSSCI_ABS_BERT | CSSCI_ABS_roberta | CSSCI_ABS_roberta_wwm | support | | ------------ | ----------------- | ----------------------- | -------------- | ----------------- | --------------------- | ------- | | Abstract | 55.23 | 62.44 | 56.8 | 57.96 | 58.26 | 223 | | Location | 61.61 | 54.38 | 61.83 | 61.4 | 61.94 | 2866 | | Metric | 45.08 | 41 | 45.27 | 46.74 | 47.13 | 622 | | Organization | 46.85 | 35.29 | 45.72 | 45.44 | 44.65 | 327 | | Person | 88.66 | 82.79 | 88.21 | 88.29 | 88.51 | 4850 | | Thing | 71.68 | 65.34 | 71.88 | 71.68 | 71.81 | 5993 | | Time | 65.35 | 60.38 | 64.15 | 65.26 | 66.03 | 1272 | | avg | 72.69 | 66.62 | 72.59 | 72.61 | 72.89 | 16153 | #### Chinese literary entity recognition | Tag | bert-base-Chinese | chinese-roberta-wwm,ext | CSSCI_ABS_BERT | CSSCI_ABS_roberta | CSSCI_ABS_roberta_wwm | support | | ------------ | ----------------- | ----------------------- | -------------- | ----------------- | --------------------- | ------- | | Abstract | 55.23 | 62.44 | 56.8 | 57.96 | 58.26 | 223 | | Location | 61.61 | 54.38 | 61.83 | 61.4 | 61.94 | 2866 | | Metric | 45.08 | 41 | 45.27 | 46.74 | 47.13 | 622 | | Organization | 46.85 | 35.29 | 45.72 | 45.44 | 44.65 | 327 | | Person | 88.66 | 82.79 | 88.21 | 88.29 | 88.51 | 4850 | | Thing | 71.68 | 65.34 | 71.88 | 71.68 | 71.81 | 5993 | | Time | 65.35 | 60.38 | 64.15 | 65.26 | 66.03 | 1272 | | avg | 72.69 | 66.62 | 72.59 | 72.61 | 72.89 | 16153 | ## Cited - If our content is helpful for your research work, please quote our research in your article. - If you want to quote our research, you can use this url [S-T-Full-Text-Knowledge-Mining/CSSCI-BERT (github.com)](https://github.com/S-T-Full-Text-Knowledge-Mining/CSSCI-BERT) as an alternative before our paper is published. ## Disclaimer - The experimental results presented in the report only show the performance under a specific data set and hyperparameter combination, and cannot represent the essence of each model. The experimental results may change due to random number seeds and computing equipment. - **Users can use the model arbitrarily within the scope of the license, but we are not responsible for the direct or indirect losses caused by using the content of the project.** ## Acknowledgment - CSSCI_ABS_BERT was trained based on [BERT-Base-Chinese]([google-research/bert: TensorFlow code and pre-trained models for BERT (github.com)](https://github.com/google-research/bert)). - CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm was trained based on [RoBERTa-wwm-ext, Chinese]([ymcui/Chinese-BERT-wwm: Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型） (github.com)](https://github.com/ymcui/Chinese-BERT-wwm)).

KM4STfulltext/CSSCI_ABS_BERT

15b1539ae22a00134ca20cf261bb2b81b7aaeb73

2022-06-20T07:07:53.000Z

[ "pytorch", "bert", "fill-mask", "transformers", "license:apache-2.0", "autotrain_compatible" ]

fill-mask

false

KM4STfulltext

null

KM4STfulltext/CSSCI_ABS_BERT

1

null

transformers

32,891

--- license: apache-2.0 --- # Pre-trained Language Model for the Humanities and Social Sciences in Chinese ## Introduction The research for social science texts in Chinese needs the support natural language processing tools. The pre-trained language model has greatly improved the accuracy of text mining in general texts. At present, there is an urgent need for a pre-trained language model specifically for the automatic processing of scientific texts in Chinese social science. We used the abstract of social science research as the training set. Based on the deep language model framework of BERT, we constructed CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm pre-training language models by [transformers/run_mlm.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py) and [transformers/mlm_wwm](https://github.com/huggingface/transformers/tree/main/examples/research_projects/mlm_wwm). We designed four downstream tasks of Text Classification on different Chinese social scientific article corpus to verify the performance of the model. - CSSCI_ABS_BERT , CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm are trained on the abstract of articles published in CSSCI journals. The training set involved in the experiment included a total of `510,956,094 words`. - Based on the idea of Domain-Adaptive Pretraining, `CSSCI_ABS_BERT` and `CSSCI_ABS_roberta` combine a large amount of abstracts of scientific articles in Chinese based on the BERT structure, and continue to train the BERT and Chinese-RoBERTa models respectively to obtain pre-training models for the automatic processing of Chinese Social science research texts. ## News - 2022-06-15 : CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm has been put forward for the first time. ## How to use ### Huggingface Transformers The `from_pretrained` method based on [Huggingface Transformers](https://github.com/huggingface/transformers) can directly obtain CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm models online. - CSSCI_ABS_BERT ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_BERT") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_BERT") ``` - CSSCI_ABS_roberta ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta") ``` - CSSCI_ABS_roberta-wwm ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta_wwm") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta_wwm") ``` ### Download Models - The version of the model we provide is `PyTorch`. ### From Huggingface - Download directly through Huggingface's official website. - [KM4STfulltext/CSSCI_ABS_BERT](https://huggingface.co/KM4STfulltext/CSSCI_ABS_BERT) - [KM4STfulltext/CSSCI_ABS_roberta](https://huggingface.co/KM4STfulltext/CSSCI_ABS_roberta) - [KM4STfulltext/CSSCI_ABS_roberta_wwm](https://huggingface.co/KM4STfulltext/CSSCI_ABS_roberta_wwm) ## Evaluation & Results - We useCSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm to perform Text Classificationon different social science research corpus. The experimental results are as follows. #### Discipline classification experiments of articles published in CSSCI journals https://github.com/S-T-Full-Text-Knowledge-Mining/CSSCI-BERT #### Movement recognition experiments for data analysis and knowledge discovery abstract | Tag | bert-base-Chinese | chinese-roberta-wwm,ext | CSSCI_ABS_BERT | CSSCI_ABS_roberta | CSSCI_ABS_roberta_wwm | support | | ------------ | ----------------- | ----------------------- | -------------- | ----------------- | --------------------- | ------- | | Abstract | 55.23 | 62.44 | 56.8 | 57.96 | 58.26 | 223 | | Location | 61.61 | 54.38 | 61.83 | 61.4 | 61.94 | 2866 | | Metric | 45.08 | 41 | 45.27 | 46.74 | 47.13 | 622 | | Organization | 46.85 | 35.29 | 45.72 | 45.44 | 44.65 | 327 | | Person | 88.66 | 82.79 | 88.21 | 88.29 | 88.51 | 4850 | | Thing | 71.68 | 65.34 | 71.88 | 71.68 | 71.81 | 5993 | | Time | 65.35 | 60.38 | 64.15 | 65.26 | 66.03 | 1272 | | avg | 72.69 | 66.62 | 72.59 | 72.61 | 72.89 | 16153 | #### Chinese literary entity recognition | Tag | bert-base-Chinese | chinese-roberta-wwm,ext | CSSCI_ABS_BERT | CSSCI_ABS_roberta | CSSCI_ABS_roberta_wwm | support | | ------------ | ----------------- | ----------------------- | -------------- | ----------------- | --------------------- | ------- | | Abstract | 55.23 | 62.44 | 56.8 | 57.96 | 58.26 | 223 | | Location | 61.61 | 54.38 | 61.83 | 61.4 | 61.94 | 2866 | | Metric | 45.08 | 41 | 45.27 | 46.74 | 47.13 | 622 | | Organization | 46.85 | 35.29 | 45.72 | 45.44 | 44.65 | 327 | | Person | 88.66 | 82.79 | 88.21 | 88.29 | 88.51 | 4850 | | Thing | 71.68 | 65.34 | 71.88 | 71.68 | 71.81 | 5993 | | Time | 65.35 | 60.38 | 64.15 | 65.26 | 66.03 | 1272 | | avg | 72.69 | 66.62 | 72.59 | 72.61 | 72.89 | 16153 | ## Cited - If our content is helpful for your research work, please quote our research in your article. - If you want to quote our research, you can use this url [S-T-Full-Text-Knowledge-Mining/CSSCI-BERT (github.com)](https://github.com/S-T-Full-Text-Knowledge-Mining/CSSCI-BERT) as an alternative before our paper is published. ## Disclaimer - The experimental results presented in the report only show the performance under a specific data set and hyperparameter combination, and cannot represent the essence of each model. The experimental results may change due to random number seeds and computing equipment. - **Users can use the model arbitrarily within the scope of the license, but we are not responsible for the direct or indirect losses caused by using the content of the project.** ## Acknowledgment - CSSCI_ABS_BERT was trained based on [BERT-Base-Chinese]([google-research/bert: TensorFlow code and pre-trained models for BERT (github.com)](https://github.com/google-research/bert)). - CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm was trained based on [RoBERTa-wwm-ext, Chinese]([ymcui/Chinese-BERT-wwm: Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型） (github.com)](https://github.com/ymcui/Chinese-BERT-wwm)).

KM4STfulltext/CSSCI_ABS_roberta_wwm

55833d811d62c162f0664f0ca629a7a07bca9bb2

2022-06-20T07:06:48.000Z

[ "pytorch", "bert", "fill-mask", "transformers", "license:apache-2.0", "autotrain_compatible" ]

fill-mask

false

KM4STfulltext

null

KM4STfulltext/CSSCI_ABS_roberta_wwm

1

null

transformers

32,892

--- license: apache-2.0 --- # Pre-trained Language Model for the Humanities and Social Sciences in Chinese ## Introduction The research for social science texts in Chinese needs the support natural language processing tools. The pre-trained language model has greatly improved the accuracy of text mining in general texts. At present, there is an urgent need for a pre-trained language model specifically for the automatic processing of scientific texts in Chinese social science. We used the abstract of social science research as the training set. Based on the deep language model framework of BERT, we constructed CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm pre-training language models by [transformers/run_mlm.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py) and [transformers/mlm_wwm](https://github.com/huggingface/transformers/tree/main/examples/research_projects/mlm_wwm). We designed four downstream tasks of Text Classification on different Chinese social scientific article corpus to verify the performance of the model. - CSSCI_ABS_BERT , CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm are trained on the abstract of articles published in CSSCI journals. The training set involved in the experiment included a total of `510,956,094 words`. - Based on the idea of Domain-Adaptive Pretraining, `CSSCI_ABS_BERT` and `CSSCI_ABS_roberta` combine a large amount of abstracts of scientific articles in Chinese based on the BERT structure, and continue to train the BERT and Chinese-RoBERTa models respectively to obtain pre-training models for the automatic processing of Chinese Social science research texts. ## News - 2022-06-15 : CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm has been put forward for the first time. ## How to use ### Huggingface Transformers The `from_pretrained` method based on [Huggingface Transformers](https://github.com/huggingface/transformers) can directly obtain CSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm models online. - CSSCI_ABS_BERT ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_BERT") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_BERT") ``` - CSSCI_ABS_roberta ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta") ``` - CSSCI_ABS_roberta-wwm ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta_wwm") model = AutoModel.from_pretrained("KM4STfulltext/CSSCI_ABS_roberta_wwm") ``` ### Download Models - The version of the model we provide is `PyTorch`. ### From Huggingface - Download directly through Huggingface's official website. - [KM4STfulltext/CSSCI_ABS_BERT](https://huggingface.co/KM4STfulltext/CSSCI_ABS_BERT) - [KM4STfulltext/CSSCI_ABS_roberta](https://huggingface.co/KM4STfulltext/CSSCI_ABS_roberta) - [KM4STfulltext/CSSCI_ABS_roberta_wwm](https://huggingface.co/KM4STfulltext/CSSCI_ABS_roberta_wwm) ## Evaluation & Results - We useCSSCI_ABS_BERT, CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm to perform Text Classificationon different social science research corpus. The experimental results are as follows. #### Discipline classification experiments of articles published in CSSCI journals https://github.com/S-T-Full-Text-Knowledge-Mining/CSSCI-BERT #### Movement recognition experiments for data analysis and knowledge discovery abstract | Tag | bert-base-Chinese | chinese-roberta-wwm,ext | CSSCI_ABS_BERT | CSSCI_ABS_roberta | CSSCI_ABS_roberta_wwm | support | | ------------ | ----------------- | ----------------------- | -------------- | ----------------- | --------------------- | ------- | | Abstract | 55.23 | 62.44 | 56.8 | 57.96 | 58.26 | 223 | | Location | 61.61 | 54.38 | 61.83 | 61.4 | 61.94 | 2866 | | Metric | 45.08 | 41 | 45.27 | 46.74 | 47.13 | 622 | | Organization | 46.85 | 35.29 | 45.72 | 45.44 | 44.65 | 327 | | Person | 88.66 | 82.79 | 88.21 | 88.29 | 88.51 | 4850 | | Thing | 71.68 | 65.34 | 71.88 | 71.68 | 71.81 | 5993 | | Time | 65.35 | 60.38 | 64.15 | 65.26 | 66.03 | 1272 | | avg | 72.69 | 66.62 | 72.59 | 72.61 | 72.89 | 16153 | #### Chinese literary entity recognition | Tag | bert-base-Chinese | chinese-roberta-wwm,ext | CSSCI_ABS_BERT | CSSCI_ABS_roberta | CSSCI_ABS_roberta_wwm | support | | ------------ | ----------------- | ----------------------- | -------------- | ----------------- | --------------------- | ------- | | Abstract | 55.23 | 62.44 | 56.8 | 57.96 | 58.26 | 223 | | Location | 61.61 | 54.38 | 61.83 | 61.4 | 61.94 | 2866 | | Metric | 45.08 | 41 | 45.27 | 46.74 | 47.13 | 622 | | Organization | 46.85 | 35.29 | 45.72 | 45.44 | 44.65 | 327 | | Person | 88.66 | 82.79 | 88.21 | 88.29 | 88.51 | 4850 | | Thing | 71.68 | 65.34 | 71.88 | 71.68 | 71.81 | 5993 | | Time | 65.35 | 60.38 | 64.15 | 65.26 | 66.03 | 1272 | | avg | 72.69 | 66.62 | 72.59 | 72.61 | 72.89 | 16153 | ## Cited - If our content is helpful for your research work, please quote our research in your article. - If you want to quote our research, you can use this url [S-T-Full-Text-Knowledge-Mining/CSSCI-BERT (github.com)](https://github.com/S-T-Full-Text-Knowledge-Mining/CSSCI-BERT) as an alternative before our paper is published. ## Disclaimer - The experimental results presented in the report only show the performance under a specific data set and hyperparameter combination, and cannot represent the essence of each model. The experimental results may change due to random number seeds and computing equipment. - **Users can use the model arbitrarily within the scope of the license, but we are not responsible for the direct or indirect losses caused by using the content of the project.** ## Acknowledgment - CSSCI_ABS_BERT was trained based on [BERT-Base-Chinese]([google-research/bert: TensorFlow code and pre-trained models for BERT (github.com)](https://github.com/google-research/bert)). - CSSCI_ABS_roberta and CSSCI_ABS_roberta-wwm was trained based on [RoBERTa-wwm-ext, Chinese]([ymcui/Chinese-BERT-wwm: Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型） (github.com)](https://github.com/ymcui/Chinese-BERT-wwm)).

SimulSt/xlm-roberta-base-finetuned-panx-de

972906f307f9f8047662ca749425bd82fd3c8bb1

2022-06-15T16:59:24.000Z

[ "pytorch", "tensorboard", "xlm-roberta", "token-classification", "dataset:xtreme", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]

token-classification

false

SimulSt

null

SimulSt/xlm-roberta-base-finetuned-panx-de

1

null

transformers

32,893

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8620945214069894 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1372 - F1: 0.8621 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2575 | 1.0 | 525 | 0.1621 | 0.8292 | | 0.1287 | 2.0 | 1050 | 0.1378 | 0.8526 | | 0.0831 | 3.0 | 1575 | 0.1372 | 0.8621 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

income/jpq-gpl-arguana-question_encoder-base-msmarco-distilbert-tas-b

c9c200c33e0342f4b6a51fac01a1170924aa1d5a

2022-06-15T17:01:15.000Z

[ "pytorch", "distilbert", "transformers", "license:apache-2.0" ]

null

false

income

null

income/jpq-gpl-arguana-question_encoder-base-msmarco-distilbert-tas-b

1

null

transformers

32,894

--- license: apache-2.0 ---

income/jpq-gpl-arguana-document_encoder-base-msmarco-distilbert-tas-b

5d3913e16feea20c4d500c5f0dadf4556bcb2e60

2022-06-15T17:02:21.000Z

[ "pytorch", "distilbert", "transformers", "license:apache-2.0" ]

null

false

income

null

income/jpq-gpl-arguana-document_encoder-base-msmarco-distilbert-tas-b

1

null

transformers

32,895

--- license: apache-2.0 ---

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