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* Clean up model documentation * Formatting * Preparation work * Long lines * Main work on rst files * Cleanup all config files * Syntax fix * Clean all tokenizers * Work on first models * Models beginning * FaluBERT * All PyTorch models * All models * Long lines again * Fixes * More fixes * Update docs/source/model_doc/bert.rst Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update docs/source/model_doc/electra.rst Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Last fixes Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
31 lines
1.5 KiB
ReStructuredText
31 lines
1.5 KiB
ReStructuredText
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
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