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680f1337c3
* add MBartForConditionalGeneration * style * rebase and fixes * add mbart test in TEST_FILES_WITH_NO_COMMON_TESTS * fix docs * don't ignore mbart * doc * fix mbart fairseq link * put mbart before bart * apply doc suggestions
38 lines
1.4 KiB
ReStructuredText
38 lines
1.4 KiB
ReStructuredText
MBart
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**DISCLAIMER:** If you see something strange,
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file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
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@sshleifer
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Overview
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~~~~~~~~~~~~~~~~~~~~~
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The MBart model was presented in `Multilingual Denoising Pre-training for Neural Machine Translation <https://arxiv.org/abs/2001.08210>`_ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov
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Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer. According to the abstract,
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MBART is a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective. mBART is one of the first methods for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text.
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The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/mbart>`__
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MBartConfig
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~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.MBartConfig
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:members:
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MBartTokenizer
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~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.MBartTokenizer
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:members: build_inputs_with_special_tokens, prepare_seq2seq_batch
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MBartForConditionalGeneration
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.MBartForConditionalGeneration
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:members: generate, forward
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