ProphetNet (#7157)
* add new model prophetnet prophetnet modified modify codes as suggested v1 add prophetnet test files * still bugs, because of changed output formats of encoder and decoder * move prophetnet into the latest version * clean integration tests * clean tokenizers * add xlm config to init * correct typo in init * further refactoring * continue refactor * save parallel * add decoder_attention_mask * fix use_cache vs. past_key_values * fix common tests * change decoder output logits * fix xlm tests * make common tests pass * change model architecture * add tokenizer tests * finalize model structure * no weight mapping * correct n-gram stream attention mask as discussed with qweizhen * remove unused import * fix index.rst * fix tests * delete unnecessary code * add fast integration test * rename weights * final weight remapping * save intermediate * Descriptions for Prophetnet Config File * finish all models * finish new model outputs * delete unnecessary files * refactor encoder layer * add dummy docs * code quality * fix tests * add model pages to doctree * further refactor * more refactor, more tests * finish code refactor and tests * remove unnecessary files * further clean up * add docstring template * finish tokenizer doc * finish prophetnet * fix copies * fix typos * fix tf tests * fix fp16 * fix tf test 2nd try * fix code quality * add test for each model * merge new tests to branch * Update model_cards/microsoft/prophetnet-large-uncased-cnndm/README.md Co-authored-by: Sam Shleifer <sshleifer@gmail.com> * Update model_cards/microsoft/prophetnet-large-uncased-cnndm/README.md Co-authored-by: Sam Shleifer <sshleifer@gmail.com> * Update src/transformers/modeling_prophetnet.py Co-authored-by: Sam Shleifer <sshleifer@gmail.com> * Update utils/check_repo.py Co-authored-by: Sam Shleifer <sshleifer@gmail.com> * apply sams and sylvains comments * make style * remove unnecessary code * Update README.md Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update README.md Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/configuration_prophetnet.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * implement lysandres comments * correct docs * fix isort * fix tokenizers * fix copies Co-authored-by: weizhen <weizhen@mail.ustc.edu.cn> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Sam Shleifer <sshleifer@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
This commit is contained in:
Weizhen
@@ -129,32 +129,40 @@ conversion utilities for the following models:
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22. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
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Gap-sentences for Abstractive Summarization <https://arxiv.org/abs/1912.08777>`__> by Jingqing Zhang, Yao Zhao,
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Mohammad Saleh and Peter J. Liu.
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23. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
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23. `ProphetNet <https://huggingface.co/transformers/master/model_doc/prophetnet.html>`__ (from Microsoft Research)
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released with the paper `ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
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<https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen,
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Ruofei Zhang and Ming Zhou.
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24. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
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Transformer <https://arxiv.org/abs/2001.04451>`__ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
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24. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
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25. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
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Pretraining Approach <https://arxiv.org/abs/1907.11692>`__ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar
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Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. 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/master/model_doc/squeezebert.html>`__ released with the paper
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26. `SqueezeBert <https://huggingface.co/transformers/master/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. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
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27. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
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Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`__ by Colin Raffel and Noam Shazeer and Adam
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Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
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27. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
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28. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
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Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*,
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Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
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28. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
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29. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
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Pretraining <https://arxiv.org/abs/1901.07291>`__ by Guillaume Lample and Alexis Conneau.
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29. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
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30. `XLM-ProphetNet <https://huggingface.co/transformers/master/model_doc/xlmprophetnet.html>`__ (from Microsoft
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Research) released with the paper `ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
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<https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen,
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Ruofei Zhang and Ming Zhou.
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31. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
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Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay
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Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke
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Zettlemoyer and Veselin Stoyanov.
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30. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `XLNet: Generalized Autoregressive
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32. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `XLNet: Generalized Autoregressive
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Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`__ by Zhilin Yang*, Zihang Dai*, Yiming
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Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
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31. `Other community models <https://huggingface.co/models>`__, contributed by the `community
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33. `Other community models <https://huggingface.co/models>`__, contributed by the `community
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<https://huggingface.co/users>`__.
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.. toctree::
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@@ -245,6 +253,7 @@ conversion utilities for the following models:
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model_doc/gpt
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model_doc/gpt2
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model_doc/pegasus
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model_doc/prophetnet
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model_doc/rag
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model_doc/reformer
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model_doc/retribert
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@@ -253,6 +262,7 @@ conversion utilities for the following models:
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model_doc/t5
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model_doc/transformerxl
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model_doc/xlm
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model_doc/xlmprophetnet
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model_doc/xlmroberta
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model_doc/xlnet
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@@ -27,4 +27,4 @@ EncoderDecoderModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.EncoderDecoderModel
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:members: forward
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:members: forward, from_encoder_decoder_pretrained
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83
docs/source/model_doc/prophetnet.rst
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83
docs/source/model_doc/prophetnet.rst
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ProphetNet
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-----------------------------------------------------------------------------------------------------------------------
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**DISCLAIMER:** If you see something strange, file a `Github Issue
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<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
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@patrickvonplaten
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The ProphetNet model was proposed in `ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training, <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou on 13 Jan, 2020.
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ProphetNet is an encoder-decoder model and can predict n-future tokens for "ngram" language modeling instead of just the next token.
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The abstract from the paper is the following:
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*In this paper, we present a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new state-of-the-art results on all these datasets compared to the models using the same scale pre-training corpus.*
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The Authors' code can be found `here <https://github.com/microsoft/ProphetNet>`__.
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ProphetNetConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.ProphetNetConfig
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:members:
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ProphetNetTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.ProphetNetTokenizer
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:members:
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ProphetNet specific outputs
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_prophetnet.ProphetNetSeq2SeqLMOutput
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:members:
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.. autoclass:: transformers.modeling_prophetnet.ProphetNetSeq2SeqModelOutput
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:members:
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.. autoclass:: transformers.modeling_prophetnet.ProphetNetDecoderModelOutput
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:members:
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.. autoclass:: transformers.modeling_prophetnet.ProphetNetDecoderLMOutput
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:members:
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ProphetNetModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.ProphetNetModel
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:members: forward
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ProphetNetEncoder
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.ProphetNetEncoder
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:members: forward
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ProphetNetDecoder
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.ProphetNetDecoder
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:members: forward
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ProphetNetForConditionalGeneration
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.ProphetNetForConditionalGeneration
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:members: forward
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ProphetNetForCausalLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.ProphetNetForCausalLM
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:members: forward
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63
docs/source/model_doc/xlmprophetnet.rst
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63
docs/source/model_doc/xlmprophetnet.rst
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XLM-ProphetNet
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-----------------------------------------------------------------------------------------------------------------------
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**DISCLAIMER:** If you see something strange, file a `Github Issue
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<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
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@patrickvonplaten
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The XLM-ProphetNet model was proposed in `ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training, <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou on 13 Jan, 2020.
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XLM-ProphetNet is an encoder-decoder model and can predict n-future tokens for "ngram" language modeling instead of just the next token. Its architecture is identical to ProhpetNet, but the model was trained on the multi-lingual "wiki100" Wikipedia dump.
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The abstract from the paper is the following:
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*In this paper, we present a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new state-of-the-art results on all these datasets compared to the models using the same scale pre-training corpus.*
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The Authors' code can be found `here <https://github.com/microsoft/ProphetNet>`__.
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XLMProphetNetConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.XLMProphetNetConfig
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:members:
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XLMProphetNetTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.XLMProphetNetTokenizer
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:members:
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XLMProphetNetModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.XLMProphetNetModel
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XLMProphetNetEncoder
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.XLMProphetNetEncoder
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XLMProphetNetDecoder
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.XLMProphetNetDecoder
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XLMProphetNetForConditionalGeneration
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.XLMProphetNetForConditionalGeneration
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XLMProphetNetForCausalLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.XLMProphetNetForCausalLM
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