257 lines
9.5 KiB
Markdown
257 lines
9.5 KiB
Markdown
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# MBart and MBart-50
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<div class="flex flex-wrap space-x-1">
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<a href="https://huggingface.co/models?filter=mbart">
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<img alt="Models" src="https://img.shields.io/badge/All_model_pages-mbart-blueviolet">
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</a>
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<a href="https://huggingface.co/spaces/docs-demos/mbart-large-50-one-to-many-mmt">
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<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue">
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</a>
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</div>
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## Overview of MBart
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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 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 pretraining 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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This model was contributed by [valhalla](https://huggingface.co/valhalla). The Authors' code can be found [here](https://github.com/pytorch/fairseq/tree/master/examples/mbart)
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### Training of MBart
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MBart is a multilingual encoder-decoder (sequence-to-sequence) model primarily intended for translation task. As the
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model is multilingual it expects the sequences in a different format. A special language id token is added in both the
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source and target text. The source text format is `X [eos, src_lang_code]` where `X` is the source text. The
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target text format is `[tgt_lang_code] X [eos]`. `bos` is never used.
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The regular [`~MBartTokenizer.__call__`] will encode source text format passed as first argument or with the `text`
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keyword, and target text format passed with the `text_label` keyword argument.
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- Supervised training
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```python
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>>> from transformers import MBartForConditionalGeneration, MBartTokenizer
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>>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro", src_lang="en_XX", tgt_lang="ro_RO")
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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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>>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_romanian, return_tensors="pt")
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>>> model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
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>>> # forward pass
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>>> model(**inputs)
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```
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- Generation
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While generating the target text set the `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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```python
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>>> from transformers import MBartForConditionalGeneration, MBartTokenizer
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>>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro", src_lang="en_XX")
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>>> article = "UN Chief Says There Is No Military Solution in Syria"
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>>> inputs = tokenizer(article, return_tensors="pt")
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>>> translated_tokens = model.generate(**inputs, decoder_start_token_id=tokenizer.lang_code_to_id["ro_RO"])
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>>> tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
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"Şeful ONU declară că nu există o soluţie militară în Siria"
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```
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## Overview of MBart-50
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MBart-50 was introduced in the [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) paper by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav
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Chaudhary, Jiatao Gu, Angela Fan. MBart-50 is created using the original *mbart-large-cc25* checkpoint by extendeding
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its embedding layers with randomly initialized vectors for an extra set of 25 language tokens and then pretrained on 50
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languages.
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According to the abstract
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*Multilingual translation models can be created through multilingual finetuning. Instead of finetuning on one
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direction, a pretrained model is finetuned on many directions at the same time. It demonstrates that pretrained models
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can be extended to incorporate additional languages without loss of performance. Multilingual finetuning improves on
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average 1 BLEU over the strongest baselines (being either multilingual from scratch or bilingual finetuning) while
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improving 9.3 BLEU on average over bilingual baselines from scratch.*
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### Training of MBart-50
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The text format for MBart-50 is slightly different from mBART. For MBart-50 the language id token is used as a prefix
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for both source and target text i.e the text format is `[lang_code] X [eos]`, where `lang_code` is source
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language id for source text and target language id for target text, with `X` being the source or target text
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respectively.
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MBart-50 has its own tokenizer [`MBart50Tokenizer`].
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- Supervised training
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```python
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from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
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model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50")
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tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50", src_lang="en_XX", tgt_lang="ro_RO")
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src_text = " UN Chief Says There Is No Military Solution in Syria"
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tgt_text = "Şeful ONU declară că nu există o soluţie militară în Siria"
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model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt")
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model(**model_inputs) # forward pass
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```
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- Generation
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To generate using the mBART-50 multilingual translation models, `eos_token_id` is used as the
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`decoder_start_token_id` and the target language id is forced as the first generated token. To force the
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target language id as the first generated token, pass the *forced_bos_token_id* parameter to the *generate* method.
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The following example shows how to translate between Hindi to French and Arabic to English using the
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*facebook/mbart-50-large-many-to-many* checkpoint.
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```python
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from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
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article_hi = "संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है"
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article_ar = "الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا."
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model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
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tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
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# translate Hindi to French
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tokenizer.src_lang = "hi_IN"
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encoded_hi = tokenizer(article_hi, return_tensors="pt")
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generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.lang_code_to_id["fr_XX"])
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tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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# => "Le chef de l 'ONU affirme qu 'il n 'y a pas de solution militaire en Syria."
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# translate Arabic to English
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tokenizer.src_lang = "ar_AR"
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encoded_ar = tokenizer(article_ar, return_tensors="pt")
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generated_tokens = model.generate(**encoded_ar, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
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tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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# => "The Secretary-General of the United Nations says there is no military solution in Syria."
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```
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## Documentation resources
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- [Text classification task guide](../tasks/sequence_classification)
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- [Question answering task guide](../tasks/question_answering)
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- [Causal language modeling task guide](../tasks/language_modeling)
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- [Masked language modeling task guide](../tasks/masked_language_modeling)
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- [Translation task guide](../tasks/translation)
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- [Summarization task guide](../tasks/summarization)
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## MBartConfig
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[[autodoc]] MBartConfig
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## MBartTokenizer
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[[autodoc]] MBartTokenizer
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- build_inputs_with_special_tokens
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## MBartTokenizerFast
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[[autodoc]] MBartTokenizerFast
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## MBart50Tokenizer
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[[autodoc]] MBart50Tokenizer
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## MBart50TokenizerFast
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[[autodoc]] MBart50TokenizerFast
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<frameworkcontent>
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<pt>
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## MBartModel
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[[autodoc]] MBartModel
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## MBartForConditionalGeneration
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[[autodoc]] MBartForConditionalGeneration
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## MBartForQuestionAnswering
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[[autodoc]] MBartForQuestionAnswering
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## MBartForSequenceClassification
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[[autodoc]] MBartForSequenceClassification
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## MBartForCausalLM
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[[autodoc]] MBartForCausalLM
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- forward
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</pt>
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<tf>
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## TFMBartModel
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[[autodoc]] TFMBartModel
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- call
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## TFMBartForConditionalGeneration
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[[autodoc]] TFMBartForConditionalGeneration
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- call
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</tf>
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<jax>
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## FlaxMBartModel
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[[autodoc]] FlaxMBartModel
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- __call__
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- encode
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- decode
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## FlaxMBartForConditionalGeneration
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[[autodoc]] FlaxMBartForConditionalGeneration
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- __call__
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- encode
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- decode
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## FlaxMBartForSequenceClassification
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[[autodoc]] FlaxMBartForSequenceClassification
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- __call__
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- encode
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- decode
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## FlaxMBartForQuestionAnswering
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[[autodoc]] FlaxMBartForQuestionAnswering
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- __call__
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- encode
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- decode
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</jax>
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</frameworkcontent>
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