| Description | Dataset | Model |
|---|---|---|
| MASK-PREDICT | [WMT14 English-German] | download (.tar.bz2) |
| MASK-PREDICT | [WMT14 German-English] | download (.tar.bz2) |
| MASK-PREDICT | [WMT16 English-Romanian] | download (.tar.bz2) |
| MASK-PREDICT | [WMT16 Romanian-English] | download (.tar.bz2) |
| MASK-PREDICT | [WMT17 English-Chinese] | download (.tar.bz2) |
| MASK-PREDICT | [WMT17 Chinese-English] | download (.tar.bz2) |
text=PATH_YOUR_DATA
output_dir=PATH_YOUR_OUTPUT
src=source_language
tgt=target_language
model_path=PATH_TO_MASKPREDICT_MODEL_DIR
python preprocess.py --source-lang ${src} --target-lang ${tgt} --trainpref $text/train --validpref $text/valid --testpref
model_dir=PLACE_TO_SAVE_YOUR_MODEL
python train.py ${output_dir}/data-bin --arch bert_transformer_seq2seq --share-all-embeddings --criterion label_smoothed_length_cross_entropy --label-smoothing 0.1 --lr 5e-4 --warmup-init-lr 1e-7 --min-lr 1e-9 --lr-scheduler inverse_sqrt --warmup-updates 10000 --optimizer adam --adam-betas '(0.9, 0.999)' --adam-eps 1e-6 --task translation_self --max-tokens 8192 --weight-decay 0.01 --dropout 0.3 --encoder-layers 6 --encoder-embed-dim 512 --decoder-layers 6 --decoder-embed-dim 512 --fp16 --max-source-positions 10000 --max-target-positions 10000 --max-update 300000 --seed 0 --save-dir ${model_dir}
python generate_cmlm.py ${output_dir}/data-bin --path ${model_dir}/checkpoint_best_average.pt --task translation_self --remove-bpe --max-sentences 20 --decoding-iterations 10 --decoding-strategy mask_predict
MASK-PREDICT is CC-BY-NC 4.0. The license applies to the pre-trained models as well.
Please cite as:
@inproceedings{ghazvininejad2019MaskPredict,
title = {Mask-Predict: Parallel Decoding of Conditional Masked Language Models},
author = {Marjan Ghazvininejad, Omer Levy, Yinhan Liu, Luke Zettlemoyer},
booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing},
year = {2019},
}