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afc4ece462
* fix generate for GPT2 Double Head * fix gpt2 double head model * fix bart / t5 * also add for no beam search * fix no beam search * fix encoder decoder * simplify t5 * simplify t5 * fix t5 tests * fix BART * fix transfo-xl * fix conflict * integrating sylvains and sams comments * fix tf past_decoder_key_values * fix enc dec test
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1.2 KiB
ReStructuredText
25 lines
1.2 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 pre-trained autoencoding model as the encoder and any pre-trained autoregressive model as the decoder.
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The effectiveness of initializing sequence-to-sequence models with pre-trained checkpoints for sequence generation tasks was shown in `Leveraging Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by 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 any other models (see Examples for more information).
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An application of this architecture could be to leverage two pre-trained :obj:`transformers.BertModel` models as the encoder and decoder for a summarization model as was shown in: `Text Summarization with Pretrained Encoders <https://arxiv.org/abs/1910.13461>`_ 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:
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