模型:
allenai/wmt19-de-en-6-6-big
这是fairseq基于 wmt19 transformer 进行的de-en的移植版本。
更多详情请参考 Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation 。
提供两个模型:
from transformers import FSMTForConditionalGeneration, FSMTTokenizer mname = "allenai/wmt19-de-en-6-6-big" tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) input = "Maschinelles Lernen ist großartig, nicht wahr?" 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?限制和偏见
预训练权重与allenai发布的原始模型保持一致。更多详情请参考 paper 。
这里是BLEU分数:
model | transformers |
---|---|
wmt19-de-en-6-6-big | 39.9 |
分数是使用以下代码计算的:
git clone https://github.com/huggingface/transformers cd transformers export PAIR=de-en export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=8 export NUM_BEAMS=5 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 allenai/wmt19-de-en-6-6-big $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
@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} }