FSMT
Model description
This is a ported version of fairseq-based wmt16 transformer for en-de.
For more details, please, see Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation.
All 3 models are available:
Intended uses & limitations
How to use
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "allenai/wmt16-en-de-12-1"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = "Machine learning is great, isn't it?"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # Maschinelles Lernen ist großartig, nicht wahr?
Limitations and bias
Training data
Pretrained weights were left identical to the original model released by allenai. For more details, please, see the paper.
Eval results
Here are the BLEU scores:
| model | fairseq | transformers |
|---|---|---|
| wmt16-en-de-12-1 | 26.9 | 25.75 |
The score is slightly below the score reported in the paper, as the researchers don't use sacrebleu and measure the score on tokenized outputs. transformers score was measured using sacrebleu on detokenized outputs.
The score was calculated using this code:
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR=en-de
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/wmt16-en-de-12-1 $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
Data Sources
BibTeX entry and citation info
@misc{kasai2020deep,
title={Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation},
author={Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith},
year={2020},
eprint={2006.10369},
archivePrefix={arXiv},
primaryClass={cs.CL}
}