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[Docs] Add DialoGPT (#3755)
* add dialoGPT * update README.md * fix conflict * update readme * add code links to docs * Update README.md * Update dialo_gpt2.rst * Update pretrained_models.rst * Update docs/source/model_doc/dialo_gpt2.rst Co-Authored-By: Julien Chaumond <chaumond@gmail.com> * change filename of dialogpt Co-authored-by: Julien Chaumond <chaumond@gmail.com>
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README.md
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README.md
@ -148,23 +148,24 @@ At some point in the future, you'll be able to seamlessly move from pre-training
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🤗 Transformers currently provides the following NLU/NLG architectures:
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1. **[BERT](https://github.com/google-research/bert)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
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2. **[GPT](https://github.com/openai/finetune-transformer-lm)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
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3. **[GPT-2](https://blog.openai.com/better-language-models/)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
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4. **[Transformer-XL](https://github.com/kimiyoung/transformer-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
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5. **[XLNet](https://github.com/zihangdai/xlnet/)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
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6. **[XLM](https://github.com/facebookresearch/XLM/)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
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7. **[RoBERTa](https://github.com/pytorch/fairseq/tree/master/examples/roberta)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
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8. **[DistilBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
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9. **[CTRL](https://github.com/salesforce/ctrl/)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
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10. **[CamemBERT](https://camembert-model.fr)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
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11. **[ALBERT](https://github.com/google-research/ALBERT)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
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12. **[T5](https://github.com/google-research/text-to-text-transfer-transformer)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
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13. **[XLM-RoBERTa](https://github.com/pytorch/fairseq/tree/master/examples/xlmr)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
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1. **[BERT](https://huggingface.co/transformers/model_doc/bert.html)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
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2. **[GPT](https://huggingface.co/transformers/model_doc/gpt.html)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
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3. **[GPT-2](https://huggingface.co/transformers/model_doc/gpt2.html)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
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4. **[Transformer-XL](https://huggingface.co/transformers/model_doc/transformerxl.html)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
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5. **[XLNet](https://huggingface.co/transformers/model_doc/xlnet.html)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
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6. **[XLM](https://huggingface.co/transformers/model_doc/xlm.html)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
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7. **[RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
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8. **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
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9. **[CTRL](https://huggingface.co/transformers/model_doc/ctrl.html)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
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10. **[CamemBERT](https://huggingface.co/transformers/model_doc/camembert.html)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
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11. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
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12. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
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13. **[XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
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14. **[MMBT](https://github.com/facebookresearch/mmbt/)** (from Facebook), released together with the paper a [Supervised Multimodal Bitransformers for Classifying Images and Text](https://arxiv.org/pdf/1909.02950.pdf) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.
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15. **[FlauBERT](https://github.com/getalp/Flaubert)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
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16. **[BART](https://github.com/pytorch/fairseq/tree/master/examples/bart)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
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17. **[ELECTRA](https://github.com/google-research/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
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15. **[FlauBERT](https://huggingface.co/transformers/model_doc/flaubert.html)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
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16. **[BART](https://huggingface.co/transformers/model_doc/bart.html)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
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17. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
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18. **[DialoGPT](https://huggingface.co/transformers/model_doc/dialogpt.html)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
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18. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
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19. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR.
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@ -104,4 +104,5 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
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model_doc/flaubert
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model_doc/bart
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model_doc/t5
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model_doc/electra
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model_doc/electra
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model_doc/dialogpt
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@ -30,6 +30,8 @@ Tips:
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similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same
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number of (repeating) layers.
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The original code can be found `here <https://github.com/google-research/ALBERT>`_.
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AlbertConfig
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~~~~~~~~~~~~~~~~~~~~~
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prediction rather than a token prediction. However, averaging over the sequence may yield better results than using
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the [CLS] token.
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The original code can be found `here <https://github.com/google-research/bert>`_.
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BertConfig
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~~~~~~~~~~~~~~~~~~~~~
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@ -22,6 +22,8 @@ Tips:
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- This implementation is the same as RoBERTa. Refer to the `documentation of RoBERTa <./roberta.html>`__ for usage
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examples as well as the information relative to the inputs and outputs.
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The original code can be found `here <https://camembert-model.fr/>`_.
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CamembertConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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@ -31,6 +31,8 @@ Tips:
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See `reusing the past in generative models <../quickstart.html#using-the-past>`_ for more information on the usage
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of this argument.
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The original code can be found `here <https://github.com/salesforce/ctrl>`_.
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CTRLConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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29
docs/source/model_doc/dialogpt.rst
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29
docs/source/model_doc/dialogpt.rst
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DialoGPT
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----------------------------------------------------
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Overview
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~~~~~~~~~~~~~~~~~~~~~
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DialoGPT was proposed in
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`DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`_
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by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
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It's a GPT2 Model trained on 147M conversation-like exchanges extracted from Reddit.
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The abstract from the paper is the following:
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*We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer).
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Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human both in terms of automatic and human evaluation in single-turn dialogue settings.
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We show that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems.
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The pre-trained model and training pipeline are publicly released to facilitate research into neural response generation and the development of more intelligent open-domain dialogue systems.*
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Tips:
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- DialoGPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
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the right rather than the left.
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- DialoGPT was trained with a causal language modeling (CLM) objective on conversational data and is therefore powerful at response generation in open-domain dialogue systems.
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- DialoGPT enables the user to create a chat bot in just 10 lines of code as shown on `DialoGPT's model card <https://huggingface.co/microsoft/DialoGPT-medium>`_.
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DialoGPT's architecture is based on the GPT2 model, so one can refer to GPT2's `docstring <https://huggingface.co/transformers/model_doc/gpt2.html>`_.
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The original code can be found `here <https://github.com/microsoft/DialoGPT>`_.
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@ -27,6 +27,8 @@ Tips:
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- DistilBert doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or `[SEP]`)
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- DistilBert doesn't have options to select the input positions (`position_ids` input). This could be added if necessary though, just let's us know if you need this option.
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The original code can be found `here <https://github.com/huggingface/transformers/tree/master/examples/distillation>`_.
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DistilBertConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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and the generator may be loaded in the `ElectraForPreTraining` model (the classification head will be randomly
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initialized as it doesn't exist in the generator).
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The original code can be found `here <https://github.com/google-research/electra>`_.
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ElectraConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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@ -20,6 +20,8 @@ of the time they outperform other pre-training approaches. Different versions of
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evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared
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to the research community for further reproducible experiments in French NLP.*
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The original code can be found `here <https://github.com/getalp/Flaubert>`_.
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FlaubertConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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@ -36,6 +36,9 @@ Tips:
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`Write With Transformer <https://transformer.huggingface.co/doc/gpt>`__ is a webapp created and hosted by
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Hugging Face showcasing the generative capabilities of several models. GPT is one of them.
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The original code can be found `here <https://github.com/openai/finetune-transformer-lm>`_.
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OpenAIGPTConfig
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~~~~~~~~~~~~~~~~~~~~~
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Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five
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different sizes: small, medium, large, xl and a distilled version of the small checkpoint: distilgpt-2.
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The original code can be found `here <https://openai.com/blog/better-language-models/>`_.
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GPT2Config
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~~~~~~~~~~~~~~~~~~~~~
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- RoBERTa doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or `</s>`)
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- `Camembert <./camembert.html>`__ is a wrapper around RoBERTa. Refer to this page for usage examples.
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The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`_.
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RobertaConfig
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~~~~~~~~~~~~~~~~~~~~~
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@ -57,6 +57,8 @@ Tips
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- For sequence to sequence generation, it is recommended to use ``T5ForConditionalGeneration.generate()``. The method takes care of feeding the encoded input via cross-attention layers to the decoder and auto-regressively generates the decoder output.
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- T5 uses relative scalar embeddings. Encoder input padding can be done on the left and on the right.
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The original code can be found `here <https://github.com/google-research/text-to-text-transfer-transformer>`_.
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T5Config
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~~~~~~~~~~~~~~~~~~~~~
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The original implementation trains on SQuAD with padding on the left, therefore the padding defaults are set to left.
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- Transformer-XL is one of the few models that has no sequence length limit.
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The original code can be found `here <https://github.com/kimiyoung/transformer-xl>`_.
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TransfoXLConfig
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~~~~~~~~~~~~~~~~~~~~~
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- XLM has multilingual checkpoints which leverage a specific `lang` parameter. Check out the
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`multi-lingual <../multilingual.html>`__ page for more information.
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The original code can be found `here <https://github.com/facebookresearch/XLM/>`_.
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XLMConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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@ -28,6 +28,9 @@ Tips:
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- This implementation is the same as RoBERTa. Refer to the `documentation of RoBERTa <./roberta.html>`__ for usage
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examples as well as the information relative to the inputs and outputs.
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The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/xlmr>`_.
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XLMRobertaConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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`target_mapping` inputs to control the attention span and outputs (see examples in `examples/run_generation.py`)
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- XLNet is one of the few models that has no sequence length limit.
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The original code can be found `here <https://github.com/zihangdai/xlnet/>`_.
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XLNetConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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@ -287,3 +287,12 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
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| | ``mbart-large-en-ro`` | | 12-layer, 1024-hidden, 16-heads, 880M parameters |
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| | | | bart-large architecture pretrained on cc25 multilingual data , finetuned on WMT english romanian translation. |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| DialoGPT | ``DialoGPT-small`` | | 12-layer, 768-hidden, 12-heads, 124M parameters |
|
||||
| | | | Trained on English text: 147M conversation-like exchanges extracted from Reddit. |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``DialoGPT-medium`` | | 24-layer, 1024-hidden, 16-heads, 355M parameters |
|
||||
| | | | Trained on English text: 147M conversation-like exchanges extracted from Reddit. |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``DialoGPT-large`` | | 36-layer, 1280-hidden, 20-heads, 774M parameters |
|
||||
| | | | Trained on English text: 147M conversation-like exchanges extracted from Reddit. |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
|
||||
Loading…
Reference in New Issue
Block a user