readme: ---
  language: 
  - ru
  - en
  tags:
  - translation
  - wmt19
  - facebook
  license: apache-2.0
  datasets:
  - wmt19
  metrics:
  - bleu
  thumbnail: https://huggingface.co/front/thumbnails/facebook.png
  ---
  
  # FSMT
  
  ## Model description
  
  This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for ru-en.
  
  For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).
  
  The abbreviation FSMT stands for FairSeqMachineTranslation
  
  All four models are available:
  
  * [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
  * [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
  * [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
  * [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)
  
  ## Intended uses & limitations
  
  #### How to use
  
  ```python
  from transformers import FSMTForConditionalGeneration, FSMTTokenizer
  mname = "facebook/wmt19-ru-en"
  tokenizer = FSMTTokenizer.from_pretrained(mname)
  model = FSMTForConditionalGeneration.from_pretrained(mname)
  
  input = "Машинное обучение - это здорово, не так ли?"
  input_ids = tokenizer.encode(input, return_tensors="pt")
  outputs = model.generate(input_ids)
  decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
  print(decoded) # Machine learning is great, isn't it?
  
  ```
  
  #### Limitations and bias
  
  - The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)
  
  ## Training data
  
  Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).
  
  ## Eval results
  
  pair   | fairseq | transformers
  -------|---------|----------
  ru-en  | [41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937) | 39.20
  
  The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:
  - model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
  - re-ranking
  
  The score was calculated using this code:
  
  ```bash
  git clone https://github.com/huggingface/transformers
  cd transformers
  export PAIR=ru-en
  export DATA_DIR=data/$PAIR
  export SAVE_DIR=data/$PAIR
  export BS=8
  export NUM_BEAMS=15
  mkdir -p $DATA_DIR
  sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
  sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
  echo $PAIR
  PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
  ```
  note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.
  
  ## Data Sources
  
  - [training, etc.](http://www.statmt.org/wmt19/)
  - [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)
  
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @inproceedings{...,
    year={2020},
    title={Facebook FAIR's WMT19 News Translation Task Submission},
    author={Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey},
    booktitle={Proc. of WMT},
  }
  ```
  
  
  ## TODO
  
  - port model ensemble (fairseq uses 4 model checkpoints)
  
  