README.md

Understanding Back-Translation at Scale (Edunov et al., 2018)

This page includes pre-trained models from the paper Understanding Back-Translation at Scale (Edunov et al., 2018).

Pre-trained models

Description	Dataset	Model	Test set(s)
Transformer (Edunov et al., 2018; WMT'18 winner)	WMT'18 English-German	download (.tar.gz)	See NOTE in the archive

Example usage

Interactive generation from the full ensemble via PyTorch Hub:

>>> import torch
>>> en2de_ensemble = torch.hub.load(
...   'pytorch/fairseq',
...   'transformer',
...   model_name_or_path='transformer.wmt18.en-de',
...   checkpoint_file='wmt18.model1.pt:wmt18.model2.pt:wmt18.model3.pt:wmt18.model4.pt:wmt18.model5.pt',
...   data_name_or_path='.',
...   tokenizer='moses',
...   aggressive_dash_splits=True,
...   bpe='subword_nmt',
... )
>>> len(en2de_ensemble.models)
5
>>> print(en2de_ensemble.generate('Hello world!'))
Hallo Welt!

Citation

@inproceedings{edunov2018backtranslation,
  title = {Understanding Back-Translation at Scale},
  author = {Edunov, Sergey and Ott, Myle and Auli, Michael and Grangier, David},
  booktitle = {Conference of the Association for Computational Linguistics (ACL)},
  year = 2018,
}