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Check and update model list in index.rst automatically (#7527)
* Check and update model list in index.rst automatically * Check and update model list in index.rst automatically * Adapt template
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Sylvain Gugger
GitHub
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b2b7fc7814
@ -54,105 +54,117 @@ The documentation is organized in five parts:
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The library currently contains PyTorch and Tensorflow implementations, pre-trained model weights, usage scripts and
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conversion utilities for the following models:
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1. `ALBERT <https://github.com/google-research/ALBERT>`_ (from Google Research), released together with the paper
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`ALBERT: A Lite BERT for Self-supervised Learning of Language Representations <https://arxiv.org/abs/1909.11942>`_
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by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut.
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2. `BART <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_ (from Facebook) released with the paper
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`BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
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<https://arxiv.org/pdf/1910.13461.pdf>`_ by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman
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Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer.
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3. `BERT <https://github.com/google-research/bert>`_ (from Google) released with the paper `BERT: Pre-training of Deep
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Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`_ by Jacob Devlin, Ming-Wei
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Chang, Kenton Lee, and Kristina Toutanova.
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4. `BERT For Sequence Generation <https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder>`_
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(from Google) released with the paper `Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
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<https://arxiv.org/abs/1907.12461>`_ by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
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5. `CamemBERT <https://huggingface.co/transformers/model_doc/camembert.html>`_ (from FAIR, Inria, Sorbonne Université)
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released together with the paper `CamemBERT: a Tasty French Language Model <https://arxiv.org/abs/1911.03894>`_ by
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Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suarez, Yoann Dupont, Laurent Romary, Eric Villemonte de la
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Clergerie, Djame Seddah, and Benoît Sagot.
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6. `CTRL <https://github.com/pytorch/fairseq/tree/master/examples/ctrl>`_ (from Salesforce), released together with the
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paper `CTRL: A Conditional Transformer Language Model for Controllable Generation
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<https://www.github.com/salesforce/ctrl>`_ by Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong,
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and Richard Socher.
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7. `DeBERTa <https://huggingface.co/transformers/model_doc/deberta.html>`_ (from Microsoft Research) released with the
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paper `DeBERTa: Decoding-enhanced BERT with Disentangled Attention <https://arxiv.org/abs/2006.03654>`_ by Pengcheng
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He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
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8. `DialoGPT <https://github.com/microsoft/DialoGPT>`_ (from Microsoft Research) released with the paper `DialoGPT:
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Large-Scale Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`_ by
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Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu,
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and Bill Dolan.
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9. `DistilBERT <https://huggingface.co/transformers/model_doc/distilbert.html>`_ (from HuggingFace) released together
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..
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This list is updated automatically from the README with `make fix-copies`. Do not update manually!
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1. `ALBERT <https://huggingface.co/transformers/model_doc/albert.html>`__ (from Google Research and the Toyota
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Technological Institute at Chicago) released with the paper `ALBERT: A Lite BERT for Self-supervised Learning of
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Language Representations <https://arxiv.org/abs/1909.11942>`__, by Zhenzhong Lan, Mingda Chen, Sebastian Goodman,
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Kevin Gimpel, Piyush Sharma, Radu Soricut.
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2. `BART <https://huggingface.co/transformers/model_doc/bart.html>`__ (from Facebook) released with the paper `BART:
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Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
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<https://arxiv.org/pdf/1910.13461.pdf>`__ by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman
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Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
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3. `BERT <https://huggingface.co/transformers/model_doc/bert.html>`__ (from Google) released with the paper `BERT:
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Pre-training of Deep Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`__ by
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Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
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4. `BERT For Sequence Generation <https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder>`__ (from
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Google) released with the paper `Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
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<https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
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5. `CamemBERT <https://huggingface.co/transformers/model_doc/camembert.html>`__ (from Inria/Facebook/Sorbonne) released
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with the paper `CamemBERT: a Tasty French Language Model <https://arxiv.org/abs/1911.03894>`__ by Louis Martin*,
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Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé
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Seddah and Benoît Sagot.
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6. `CTRL <https://huggingface.co/transformers/model_doc/ctrl.html>`__ (from Salesforce) released with the paper `CTRL:
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A Conditional Transformer Language Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`__ by Nitish
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Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
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7. `DeBERTa <https://huggingface.co/transformers/model_doc/deberta.html>`__ (from Microsoft Research) released with the
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paper `DeBERTa: Decoding-enhanced BERT with Disentangled Attention <https://arxiv.org/abs/2006.03654>`__ by
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Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
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8. `DialoGPT <https://huggingface.co/transformers/model_doc/dialogpt.html>`__ (from Microsoft Research) released with
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the paper `DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
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<https://arxiv.org/abs/1911.00536>`__ by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang
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Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
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9. `DistilBERT <https://huggingface.co/transformers/model_doc/distilbert.html>`__ (from HuggingFace), released together
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with the paper `DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
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<https://arxiv.org/abs/1910.01108>`_ by Victor Sanh, Lysandre Debut, and Thomas Wolf. The same method has been
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applied to compress GPT2 into
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`DistilGPT2 <https://github.com/huggingface/transformers/tree/master/examples/distillation>`_.
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10. `DPR <https://github.com/facebookresearch/DPR>`_ (from Facebook) released with the paper `Dense Passage Retrieval
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for Open-Domain Question Answering <https://arxiv.org/abs/2004.04906>`_ by Vladimir Karpukhin, Barlas Oğuz, Sewon
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<https://arxiv.org/abs/1910.01108>`__ by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been
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applied to compress GPT2 into `DistilGPT2
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<https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, RoBERTa into `DistilRoBERTa
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<https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, Multilingual BERT into
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`DistilmBERT <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German version
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of DistilBERT.
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10. `DPR <https://github.com/facebookresearch/DPR>`__ (from Facebook) released with the paper `Dense Passage Retrieval
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for Open-Domain Question Answering <https://arxiv.org/abs/2004.04906>`__ by Vladimir Karpukhin, Barlas Oğuz, Sewon
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Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
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11. `ELECTRA <https://github.com/google-research/electra>`_ (from Google Research/Stanford University) released with
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the paper `ELECTRA: Pre-training text encoders as discriminators rather than generators
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<https://arxiv.org/abs/2003.10555>`_ by Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning.
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12. `FlauBERT <https://github.com/getalp/Flaubert>`_ (from CNRS) released with the paper `FlauBERT: Unsupervised
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Language Model Pre-training for French <https://arxiv.org/abs/1912.05372>`_ by Hang Le, Loïc Vial, Jibril Frej,
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Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, and
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Didier Schwab.
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13. `Funnel Transformer <https://github.com/laiguokun/Funnel-Transformer>`_ (from CMU/Google Brain) released with the paper
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`Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
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<https://arxiv.org/abs/2006.03236>`_ by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
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14. `GPT <https://github.com/openai/finetune-transformer-lm>`_ (from OpenAI) released with the paper `Improving Language
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Understanding by Generative Pre-Training <https://blog.openai.com/language-unsupervised>`_ by Alec Radford, Karthik
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Narasimhan, Tim Salimans, and Ilya Sutskever.
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15. `GPT-2 <https://blog.openai.com/better-language-models>`_ (from OpenAI) released with the paper `Language Models are
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Unsupervised Multitask Learners <https://blog.openai.com/better-language-models>`_ by Alec Radford, Jeffrey Wu,
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Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever.
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16. `LayoutLM <https://github.com/microsoft/unilm/tree/master/layoutlm>`_ (from Microsoft Research Asia) released with
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11. `ELECTRA <https://huggingface.co/transformers/model_doc/electra.html>`__ (from Google Research/Stanford University)
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released with the paper `ELECTRA: Pre-training text encoders as discriminators rather than generators
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<https://arxiv.org/abs/2003.10555>`__ by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
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12. `FlauBERT <https://huggingface.co/transformers/model_doc/flaubert.html>`__ (from CNRS) released with the paper
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`FlauBERT: Unsupervised Language Model Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le,
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Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé,
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Laurent Besacier, Didier Schwab.
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13. `Funnel Transformer <https://github.com/laiguokun/Funnel-Transformer>`__ (from CMU/Google Brain) released with the
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paper `Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
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<https://arxiv.org/abs/2006.03236>`__ by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
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14. `GPT <https://huggingface.co/transformers/model_doc/gpt.html>`__ (from OpenAI) released with the paper `Improving
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Language Understanding by Generative Pre-Training <https://blog.openai.com/language-unsupervised/>`__ by Alec
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Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
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15. `GPT-2 <https://huggingface.co/transformers/model_doc/gpt2.html>`__ (from OpenAI) released with the paper `Language
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Models are Unsupervised Multitask Learners <https://blog.openai.com/better-language-models/>`__ by Alec Radford*,
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Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
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16. `LayoutLM <https://github.com/microsoft/unilm/tree/master/layoutlm>`__ (from Microsoft Research Asia) released with
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the paper `LayoutLM: Pre-training of Text and Layout for Document Image Understanding
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<https://arxiv.org/abs/1912.13318>`_ by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
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17. `Longformer <https://github.com/allenai/longformer>`_ (from AllenAI) released with the paper `Longformer: The
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Long-Document Transformer <https://arxiv.org/abs/2004.05150>`_ by Iz Beltagy, Matthew E. Peters, and Arman Cohan.
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18. `LXMERT <https://github.com/airsplay/lxmert>`_ (from UNC Chapel Hill) released with the paper `LXMERT: Learning
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Cross-Modality Encoder Representations from Transformers for Open-Domain Question
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Answering <https://arxiv.org/abs/1908.07490>`_ by Hao Tan and Mohit Bansal.
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19. `MarianMT <https://marian-nmt.github.io/>`_ (developed by the Microsoft Translator Team) machine translation models
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trained using `OPUS <http://opus.nlpl.eu/>`_ pretrained_models data by Jörg Tiedemann.
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20. `MBart <https://github.com/pytorch/fairseq/tree/master/examples/mbart>`_ (from Facebook) released with the paper
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`Multilingual Denoising Pre-training for Neural Machine Translation <https://arxiv.org/abs/2001.08210>`_ by Yinhan
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<https://arxiv.org/abs/1912.13318>`__ by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
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17. `Longformer <https://huggingface.co/transformers/model_doc/longformer.html>`__ (from AllenAI) released with the
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paper `Longformer: The Long-Document Transformer <https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E.
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Peters, Arman Cohan.
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18. `LXMERT <https://github.com/airsplay/lxmert>`__ (from UNC Chapel Hill) released with the paper `LXMERT: Learning
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Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering
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<https://arxiv.org/abs/1908.07490>`__ by Hao Tan and Mohit Bansal.
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19. `MarianMT <https://huggingface.co/transformers/model_doc/marian.html>`__ Machine translation models trained using
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`OPUS <http://opus.nlpl.eu/>`__ data by Jörg Tiedemann. The `Marian Framework <https://marian-nmt.github.io/>`__ is
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being developed by the Microsoft Translator Team.
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20. `MBart <https://github.com/pytorch/fairseq/tree/master/examples/mbart>`__ (from Facebook) released with the paper
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`Multilingual Denoising Pre-training for Neural Machine Translation <https://arxiv.org/abs/2001.08210>`__ by Yinhan
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Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
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21. `MMBT <https://github.com/facebookresearch/mmbt/>`_ (from Facebook), released together with the paper a `Supervised
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Multimodal Bitransformers for Classifying Images and Text <https://arxiv.org/pdf/1909.02950.pdf>`_ by Douwe Kiela,
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Suvrat Bhooshan, Hamed Firooz, and Davide Testuggine.
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22. `Pegasus <https://github.com/google-research/pegasus>`_ (from Google) released with the paper `PEGASUS:
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Pre-training with Extracted Gap-sentences for Abstractive Summarization <https://arxiv.org/abs/1912.08777>`_ by
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21. `MMBT <https://github.com/facebookresearch/mmbt/>`__ (from Facebook), released together with the paper a
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`Supervised Multimodal Bitransformers for Classifying Images and Text <https://arxiv.org/pdf/1909.02950.pdf>`__ by
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Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.
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22. `Pegasus <https://github.com/google-research/pegasus>`__ (from Google) released with the paper `PEGASUS:
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Pre-training with Extracted Gap-sentences for Abstractive Summarization <https://arxiv.org/abs/1912.08777>`__> by
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Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
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23. `Reformer <https://github.com/google/trax/tree/master/trax/models/reformer>`_ (from Google Research) released with
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the paper `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`_ by Nikita Kitaev, Łukasz
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Kaiser, and Anselm Levskaya.
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24. `RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`_ (from Facebook), released together with
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the paper a `Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`_ by Yinhan Liu, Myle
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Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin
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Stoyanov.
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25. `T5 <https://github.com/google-research/text-to-text-transfer-transformer>`_ (from Google) released with the paper
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`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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<https://arxiv.org/abs/1910.10683>`_ by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang,
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Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu.
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26. `Transformer-XL <https://github.com/kimiyoung/transformer-xl>`_ (from Google/CMU) released with the paper
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`Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`_ by
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Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, and Ruslan Salakhutdinov.
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27. `XLM <https://github.com/facebookresearch/XLM>`_ (from Facebook) released together with the paper `Cross-lingual
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Language Model Pretraining <https://arxiv.org/abs/1901.07291>`_ by Guillaume Lample and Alexis Conneau.
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28. `XLM-RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/xlmr>`_ (from Facebook AI), released together
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with the paper `Unsupervised Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`_ by
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Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard
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Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov.
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29. `XLNet <https://github.com/zihangdai/xlnet>`_ (from Google/CMU) released with the paper `XLNet: Generalized
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Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`_ by Zhilin Yang, Zihang
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Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V. Le.
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30. SqueezeBERT (from UC Berkeley) released with the paper
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`SqueezeBERT: What can computer vision teach NLP about efficient neural networks? <https://arxiv.org/abs/2006.11316>`_
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by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
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31. `Other community models <https://huggingface.co/models>`_, contributed by the `community
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<https://huggingface.co/users>`_.
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23. `Reformer <https://huggingface.co/transformers/model_doc/reformer.html>`__ (from Google Research) released with the
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paper `Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`__ by Nikita Kitaev, Łukasz Kaiser,
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Anselm Levskaya.
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24. `RoBERTa <https://huggingface.co/transformers/model_doc/roberta.html>`__ (from Facebook), released together with
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the paper a `Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`__ by Yinhan Liu, Myle
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Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
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ultilingual BERT into `DistilmBERT
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<https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German version of
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DistilBERT.
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25. `SqueezeBert <https://huggingface.co/transformers/model_doc/squeezebert.html>`__ released with the paper
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`SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
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<https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
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26. `T5 <https://huggingface.co/transformers/model_doc/t5.html>`__ (from Google AI) released with the paper `Exploring
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the Limits of Transfer Learning with a Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`__ by
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Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi
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Zhou and Wei Li and Peter J. Liu.
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27. `Transformer-XL <https://huggingface.co/transformers/model_doc/transformerxl.html>`__ (from Google/CMU) released
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with the paper `Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
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<https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le,
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Ruslan Salakhutdinov.
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28. `XLM <https://huggingface.co/transformers/model_doc/xlm.html>`__ (from Facebook) released together with the paper
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`Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`__ by Guillaume Lample and Alexis
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Conneau.
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29. `XLM-RoBERTa <https://huggingface.co/transformers/model_doc/xlmroberta.html>`__ (from Facebook AI), released
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together with the paper `Unsupervised Cross-lingual Representation Learning at Scale
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<https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary,
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Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
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30. `XLNet <https://huggingface.co/transformers/model_doc/xlnet.html>`__ (from Google/CMU) released with the paper
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`XLNet: Generalized Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`__ by
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Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
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31. `Other community models <https://huggingface.co/models>`__, contributed by the `community
|
||||
<https://huggingface.co/users>`__.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
@ -83,7 +83,7 @@ You can then finish the addition step by adding imports for your classes in the
|
||||
- [ ] Edit the PyTorch to TF 2.0 conversion script to add your model in the `convert_pytorch_checkpoint_to_tf2.py`
|
||||
file.
|
||||
- [ ] Add a mention of your model in the doc: `README.md` and the documentation itself
|
||||
in `docs/source/index.rst` and `docs/source/pretrained_models.rst`.
|
||||
in `docs/source/pretrained_models.rst`. Rune `make fix-copies` to update `docs/source/index.rst` with your changes.
|
||||
- [ ] Upload the pretrained weights, configurations and vocabulary files.
|
||||
- [ ] Create model card(s) for your models on huggingface.co. For those last two steps, check the
|
||||
[model sharing documentation](https://huggingface.co/transformers/model_sharing.html).
|
||||
|
||||
@ -23,6 +23,7 @@ import tempfile
|
||||
# All paths are set with the intent you should run this script from the root of the repo with the command
|
||||
# python utils/check_copies.py
|
||||
TRANSFORMERS_PATH = "src/transformers"
|
||||
PATH_TO_DOCS = "docs/source"
|
||||
|
||||
|
||||
def find_code_in_transformers(object_name):
|
||||
@ -166,6 +167,113 @@ def check_copies(overwrite: bool = False):
|
||||
+ diff
|
||||
+ "\nRun `make fix-copies` or `python utils/check_copies --fix_and_overwrite` to fix them."
|
||||
)
|
||||
check_model_list_copy(overwrite=overwrite)
|
||||
|
||||
|
||||
def get_model_list():
|
||||
""" Extracts the model list from the README. """
|
||||
# If the introduction or the conclusion of the list change, the prompts may need to be updated.
|
||||
_start_prompt = "🤗 Transformers currently provides the following architectures"
|
||||
_end_prompt = "1. Want to contribute a new model?"
|
||||
with open(os.path.join("README.md"), "r", encoding="utf-8") as f:
|
||||
lines = f.readlines()
|
||||
# Find the start of the list.
|
||||
start_index = 0
|
||||
while not lines[start_index].startswith(_start_prompt):
|
||||
start_index += 1
|
||||
start_index += 1
|
||||
|
||||
result = []
|
||||
current_line = ""
|
||||
end_index = start_index
|
||||
|
||||
while not lines[end_index].startswith(_end_prompt):
|
||||
if lines[end_index].startswith("1."):
|
||||
if len(current_line) > 1:
|
||||
result.append(current_line)
|
||||
current_line = lines[end_index]
|
||||
elif len(lines[end_index]) > 1:
|
||||
current_line = f"{current_line[:-1]} {lines[end_index].lstrip()}"
|
||||
end_index += 1
|
||||
if len(current_line) > 1:
|
||||
result.append(current_line)
|
||||
|
||||
return "".join(result)
|
||||
|
||||
|
||||
def split_long_line_with_indent(line, max_per_line, indent):
|
||||
""" Split the `line` so that it doesn't go over `max_per_line` and adds `indent` to new lines. """
|
||||
words = line.split(" ")
|
||||
lines = []
|
||||
current_line = words[0]
|
||||
for word in words[1:]:
|
||||
if len(f"{current_line} {word}") > max_per_line:
|
||||
lines.append(current_line)
|
||||
current_line = " " * indent + word
|
||||
else:
|
||||
current_line = f"{current_line} {word}"
|
||||
lines.append(current_line)
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def convert_to_rst(model_list, max_per_line=None):
|
||||
""" Convert `model_list` to rst format. """
|
||||
# Convert **[description](link)** to `description <link>`__
|
||||
model_list = re.sub(r"\*\*\[([^\]]*)\]\(([^\)]*)\)\*\*", r"`\1 <\2>`__", model_list)
|
||||
|
||||
# Convert [description](link) to `description <link>`__
|
||||
model_list = re.sub(r"\[([^\]]*)\]\(([^\)]*)\)", r"`\1 <\2>`__", model_list)
|
||||
|
||||
# Enumerate the lines properly
|
||||
lines = model_list.split("\n")
|
||||
result = []
|
||||
for i, line in enumerate(lines):
|
||||
line = re.sub(r"^\s*(\d+)\.", f"{i+1}.", line)
|
||||
# Split the lines that are too long
|
||||
if max_per_line is not None and len(line) > max_per_line:
|
||||
prompt = re.search(r"^(\s*\d+\.\s+)\S", line)
|
||||
indent = len(prompt.groups()[0]) if prompt is not None else 0
|
||||
line = split_long_line_with_indent(line, max_per_line, indent)
|
||||
|
||||
result.append(line)
|
||||
return "\n".join(result)
|
||||
|
||||
|
||||
def check_model_list_copy(overwrite=False, max_per_line=119):
|
||||
""" Check the model lists in the README and index.rst are consistent and maybe `overwrite`. """
|
||||
_start_prompt = " This list is updated automatically from the README"
|
||||
_end_prompt = ".. toctree::"
|
||||
with open(os.path.join(PATH_TO_DOCS, "index.rst"), "r", encoding="utf-8") as f:
|
||||
lines = f.readlines()
|
||||
# Find the start of the list.
|
||||
start_index = 0
|
||||
while not lines[start_index].startswith(_start_prompt):
|
||||
start_index += 1
|
||||
start_index += 1
|
||||
|
||||
end_index = start_index
|
||||
while not lines[end_index].startswith(_end_prompt):
|
||||
end_index += 1
|
||||
end_index -= 1
|
||||
|
||||
while len(lines[start_index]) <= 1:
|
||||
start_index += 1
|
||||
while len(lines[end_index]) <= 1:
|
||||
end_index -= 1
|
||||
end_index += 1
|
||||
|
||||
rst_list = "".join(lines[start_index:end_index])
|
||||
md_list = get_model_list()
|
||||
converted_list = convert_to_rst(md_list, max_per_line=max_per_line)
|
||||
|
||||
if converted_list != rst_list:
|
||||
if overwrite:
|
||||
with open(os.path.join(PATH_TO_DOCS, "index.rst"), "w", encoding="utf-8") as f:
|
||||
f.writelines(lines[:start_index] + [converted_list] + lines[end_index:])
|
||||
else:
|
||||
raise ValueError(
|
||||
"The model list in the README changed and the list in `index.rst` has not been updated. Run `make fix-copies` to fix this."
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
Loading…
Reference in New Issue
Block a user