# FSMT

## Overview

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.*

This model was contributed by [stas](https://huggingface.co/stas). The original code can be found

[here](https://github.com/pytorch/fairseq/tree/master/examples/wmt19).

## Implementation Notes

- 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 [`XLMTokenizer`] and the main model is derived from

  [`BartModel`].

## FSMTConfig

[[autodoc]] FSMTConfig

## FSMTTokenizer

[[autodoc]] FSMTTokenizer

    - build_inputs_with_special_tokens

    - get_special_tokens_mask

    - create_token_type_ids_from_sequences

    - save_vocabulary

## FSMTModel

[[autodoc]] FSMTModel

    - forward

## FSMTForConditionalGeneration

[[autodoc]] FSMTForConditionalGeneration

    - forward