metadata
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 for ru-en.
For more details, please see, Facebook FAIR's WMT19 News Translation Task Submission.
The abbreviation FSMT stands for FairSeqMachineTranslation
All four models are available:
Intended uses & limitations
How to use
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
Training data
Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the paper.
Eval results
| pair | fairseq | transformers |
|---|---|---|
| ru-en | 41.3 | 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:
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
BibTeX entry and citation info
@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)