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83 lines
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MBart
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**DISCLAIMER:** If you see something strange, file a `Github Issue
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<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
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<https://arxiv.org/abs/2001.08210>`_ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov Marjan
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Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
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According to the abstract, MBART is a sequence-to-sequence denoising auto-encoder pretrained on large-scale monolingual
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corpora in many languages using the BART objective. mBART is one of the first methods for pre-training a complete
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sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only
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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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Training
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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MBart is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation task. As the model is
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multilingual it expects the sequences in a different format. A special language id token is added in both the source
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and target text. The source text format is :obj:`X [eos, src_lang_code]` where :obj:`X` is the source text. The target
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text format is :obj:`[tgt_lang_code] X [eos]`. :obj:`bos` is never used.
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The :meth:`~transformers.MBartTokenizer.prepare_seq2seq_batch` handles this automatically and should be used to encode
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the sequences for sequence-to-sequence fine-tuning.
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- Supervised training
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.. code-block::
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example_english_phrase = "UN Chief Says There Is No Military Solution in Syria"
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expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
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batch = tokenizer.prepare_seq2seq_batch(example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian)
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input_ids = batch["input_ids"]
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target_ids = batch["decoder_input_ids"]
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decoder_input_ids = target_ids[:, :-1].contiguous()
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labels = target_ids[:, 1:].clone()
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model(input_ids=input_ids, decoder_input_ids=decoder_input_ids, labels=labels) #forward
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- Generation
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While generating the target text set the :obj:`decoder_start_token_id` to the target language id. The following
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example shows how to translate English to Romanian using the `facebook/mbart-large-en-ro` model.
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.. code-block::
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from transformers import MBartForConditionalGeneration, MBartTokenizer
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model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
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tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro")
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article = "UN Chief Says There Is No Military Solution in Syria"
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batch = tokenizer.prepare_seq2seq_batch(src_texts=[article], src_lang="en_XX")
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translated_tokens = model.generate(**batch, decoder_start_token_id=tokenizer.lang_code_to_id["ro_RO"])
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translation = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
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assert translation == "Şeful ONU declară că nu există o soluţie militară în Siria"
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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: forward
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