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. The original code can be found
here.
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
