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--- |
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language: |
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- en |
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- de |
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thumbnail: |
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tags: |
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- translation |
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- wmt16 |
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- allenai |
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license: apache-2.0 |
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datasets: |
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- wmt16 |
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metrics: |
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- bleu |
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--- |
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# FSMT |
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## Model description |
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This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for en-de. |
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For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369). |
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All 3 models are available: |
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* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1) |
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* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1) |
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* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1) |
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## Intended uses & limitations |
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#### How to use |
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```python |
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from transformers import FSMTForConditionalGeneration, FSMTTokenizer |
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mname = "allenai/wmt16-en-de-dist-12-1" |
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tokenizer = FSMTTokenizer.from_pretrained(mname) |
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model = FSMTForConditionalGeneration.from_pretrained(mname) |
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input = "Machine learning is great, isn't it?" |
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input_ids = tokenizer.encode(input, return_tensors="pt") |
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outputs = model.generate(input_ids) |
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decoded = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(decoded) # Maschinelles Lernen ist großartig, nicht wahr? |
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``` |
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#### Limitations and bias |
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## Training data |
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Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369). |
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## Eval results |
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Here are the BLEU scores: |
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model | fairseq | transformers |
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-------|---------|---------- |
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wmt16-en-de-dist-12-1 | 28.3 | 27.52 |
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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. |
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The score was calculated using this code: |
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```bash |
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git clone https://github.com/huggingface/transformers |
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cd transformers |
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export PAIR=en-de |
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export DATA_DIR=data/$PAIR |
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export SAVE_DIR=data/$PAIR |
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export BS=8 |
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export NUM_BEAMS=5 |
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mkdir -p $DATA_DIR |
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sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source |
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sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target |
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echo $PAIR |
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PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/wmt16-en-de-dist-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 |
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``` |
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## Data Sources |
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- [training, etc.](http://www.statmt.org/wmt16/) |
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- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372) |
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### BibTeX entry and citation info |
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``` |
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@misc{kasai2020deep, |
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title={Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}, |
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author={Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}, |
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year={2020}, |
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eprint={2006.10369}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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