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* 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>
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31 lines
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Encoder Decoder Models
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The :class:`~transformers.EncoderDecoderModel` can be used to initialize a sequence-to-sequence model with any
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pretrained autoencoding model as the encoder and any pretrained autoregressive model as the decoder.
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The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks
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was shown in `Leveraging Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by
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Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
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After such an :class:`~transformers.EncoderDecoderModel` has been trained/fine-tuned, it can be saved/loaded just like
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any other models (see the examples for more information).
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An application of this architecture could be to leverage two pretrained :class:`~transformers.BertModel` as the encoder
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and decoder for a summarization model as was shown in: `Text Summarization with Pretrained Encoders
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<https://arxiv.org/abs/1908.08345>`__ by Yang Liu and Mirella Lapata.
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EncoderDecoderConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.EncoderDecoderConfig
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:members:
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EncoderDecoderModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.EncoderDecoderModel
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:members: forward, from_encoder_decoder_pretrained
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