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This commit is contained in:
Sylvain Gugger
2020-10-26 18:26:02 -04:00
committed by GitHub
parent 04a17f8550
commit 08f534d2da
271 changed files with 9726 additions and 8991 deletions
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docs/source/model_doc/mbart.rst
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@@ -7,9 +7,10 @@ MBart
Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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
Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
<https://arxiv.org/abs/2001.08210>`_ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov Marjan
Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
According to the abstract, MBART is a sequence-to-sequence denoising auto-encoder pretrained on large-scale monolingual
corpora in many languages using the BART objective. mBART is one of the first methods for pre-training a complete
@@ -21,12 +22,13 @@ The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/ma
Training
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
MBart is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation task.
As the model is multilingual it expects the sequences in a different format. A special language id token
is added in both the source and target text. The source text format is :obj:`X [eos, src_lang_code]`
where :obj:`X` is the source text. The target text format is :obj:`[tgt_lang_code] X [eos]`. :obj:`bos` is never used.
The :meth:`~transformers.MBartTokenizer.prepare_seq2seq_batch` handles this automatically and should be used to encode
MBart is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation task. As the model is
multilingual it expects the sequences in a different format. A special language id token is added in both the source
and target text. The source text format is :obj:`X [eos, src_lang_code]` where :obj:`X` is the source text. The target
text format is :obj:`[tgt_lang_code] X [eos]`. :obj:`bos` is never used.
The :meth:`~transformers.MBartTokenizer.prepare_seq2seq_batch` handles this automatically and should be used to encode
the sequences for sequence-to-sequence fine-tuning.
- Supervised training
@@ -44,8 +46,8 @@ the sequences for sequence-to-sequence fine-tuning.
- Generation
While generating the target text set the :obj:`decoder_start_token_id` to the target language id.
The following example shows how to translate English to Romanian using the `facebook/mbart-large-en-ro` model.
While generating the target text set the :obj:`decoder_start_token_id` to the target language id. The following
example shows how to translate English to Romanian using the `facebook/mbart-large-en-ro` model.
.. code-block::