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MBart and MBart-50
-----------------------------------------------------------------------------------------------------------------------

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<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
@patrickvonplaten

Overview of MBart
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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.

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 pretraining 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.

The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/mbart>`__

Training of MBart
_______________________________________________________________________________________________________________________

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

.. code-block::

    example_english_phrase = "UN Chief Says There Is No Military Solution in Syria"
    expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
    batch = tokenizer.prepare_seq2seq_batch(example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian, return_tensors="pt")
    model(input_ids=batch['input_ids'], labels=batch['labels']) # forward pass

- 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.

.. code-block::

    from transformers import MBartForConditionalGeneration, MBartTokenizer
    model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
    tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro")
    article = "UN Chief Says There Is No Military Solution in Syria"
    batch = tokenizer.prepare_seq2seq_batch(src_texts=[article], src_lang="en_XX", return_tensors="pt")
    translated_tokens = model.generate(**batch, decoder_start_token_id=tokenizer.lang_code_to_id["ro_RO"])
    translation = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
    assert translation == "Şeful ONU declară că nu există o soluţie militară în Siria"


Overview of MBart-50
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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
Chaudhary, Jiatao Gu, Angela Fan. MBart-50 is created using the original `mbart-large-cc25` checkpoint by extendeding
its embedding layers with randomly initialized vectors for an extra set of 25 language tokens and then pretrained on 50
languages.

According to the abstract

*Multilingual translation models can be created through multilingual finetuning. Instead of finetuning on one
direction, a pretrained model is finetuned on many directions at the same time. It demonstrates that pretrained models
can be extended to incorporate additional languages without loss of performance. Multilingual finetuning improves on
average 1 BLEU over the strongest baselines (being either multilingual from scratch or bilingual finetuning) while
improving 9.3 BLEU on average over bilingual baselines from scratch.*


Training of MBart-50
_______________________________________________________________________________________________________________________

The text format for MBart-50 is slightly different from mBART. For MBart-50 the language id token is used as a prefix
for both source and target text i.e the text format is :obj:`[lang_code] X [eos]`, where :obj:`lang_code` is source
language id for source text and target language id for target text, with :obj:`X` being the source or target text
respectively.


MBart-50 has its own tokenizer :class:`~transformers.MBart50Tokenizer`.

-  Supervised training

.. code-block::

    from transformers import MBartForConditionalGeneration, MBart50TokenizerFast

    model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50")
    tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50", src_lang="en_XX", tgt_lang="ro_RO")

    src_text = " UN Chief Says There Is No Military Solution in Syria"
    tgt_text =  "Şeful ONU declară că nu există o soluţie militară în Siria"

    model_inputs = tokenizer(src_text, return_tensors="pt")
    with tokenizer.as_target_tokenizer():
        labels = tokenizer(tgt_text, return_tensors="pt").input_ids

    model(**model_inputs, labels=labels) # forward pass


- Generation

    To generate using the mBART-50 multilingual translation models, :obj:`eos_token_id` is used as the
    :obj:`decoder_start_token_id` and the target language id is forced as the first generated token. To force the
    target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method.
    The following example shows how to translate between Hindi to French and Arabic to English using the
    `facebook/mbart-50-large-many-to-many` checkpoint.

.. code-block::

    from transformers import MBartForConditionalGeneration, MBart50TokenizerFast

    article_hi = "संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है"
    article_ar = "الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا."

    model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
    tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")

    # translate Hindi to French
    tokenizer.src_lang = "hi_IN"
    encoded_hi = tokenizer(article_hi, return_tensors="pt")
    generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.lang_code_to_id["fr_XX"])
    tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
    # => "Le chef de l 'ONU affirme qu 'il n 'y a pas de solution militaire en Syria."

    # translate Arabic to English
    tokenizer.src_lang = "ar_AR"
    encoded_ar = tokenizer(article_ar, return_tensors="pt")
    generated_tokens = model.generate(**encoded_ar, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
    tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
    # => "The Secretary-General of the United Nations says there is no military solution in Syria."


MBartConfig
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.MBartConfig
    :members:


MBartTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.MBartTokenizer
    :members: build_inputs_with_special_tokens, prepare_seq2seq_batch


MBartTokenizerFast
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.MBartTokenizerFast
    :members:


MBart50Tokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.MBart50Tokenizer
    :members:


MBart50TokenizerFast
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.MBart50TokenizerFast
    :members:


MBartModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.MBartModel
    :members:


MBartForConditionalGeneration
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.MBartForConditionalGeneration
    :members:


MBartForQuestionAnswering
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.MBartForQuestionAnswering
    :members:


MBartForSequenceClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.MBartForSequenceClassification


MBartForCausalLM
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.MBartForCausalLM
    :members: forward


TFMBartModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.TFMBartModel
    :members: call


TFMBartForConditionalGeneration
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.TFMBartForConditionalGeneration
    :members: call
