fsmt.rst

FSMT
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Overview
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FSMT (FairSeq MachineTranslation) models were introduced in `Facebook FAIR's WMT19 News Translation Task Submission
<https://arxiv.org/abs/1907.06616>`__ by Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov.

The abstract of the paper is the following:

*This paper describes Facebook FAIR's submission to the WMT19 shared news translation task. We participate in two
language pairs and four language directions, English <-> German and English <-> Russian. Following our submission from
last year, our baseline systems are large BPE-based transformer models trained with the Fairseq sequence modeling
toolkit which rely on sampled back-translations. This year we experiment with different bitext data filtering schemes,
as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific
data, then decode using noisy channel model reranking. Our submissions are ranked first in all four directions of the
human evaluation campaign. On En->De, our system significantly outperforms other systems as well as human translations.
This system improves upon our WMT'18 submission by 4.5 BLEU points.*

The original code can be found here <https://github.com/pytorch/fairseq/tree/master/examples/wmt19>__.

Implementation Notes
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- FSMT uses source and target vocabulary pairs that aren't combined into one. It doesn't share embeddings tokens
  either. Its tokenizer is very similar to :class:`~transformers.XLMTokenizer` and the main model is derived from
  :class:`~transformers.BartModel`.


FSMTConfig
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.. autoclass:: transformers.FSMTConfig
    :members:


FSMTTokenizer
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.. autoclass:: transformers.FSMTTokenizer
    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
        create_token_type_ids_from_sequences, prepare_seq2seq_batch, save_vocabulary


FSMTModel
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.. autoclass:: transformers.FSMTModel
    :members: forward


FSMTForConditionalGeneration
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.. autoclass:: transformers.FSMTForConditionalGeneration
    :members: forward