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WMT 19

This page provides pointers to the models of Facebook-FAIR's WMT'19 news translation task submission (Ng et al., 2019).

Pre-trained models

Model Description Download
transformer.wmt19.en-de En->De Ensemble download (.tar.gz)
transformer.wmt19.de-en De->En Ensemble download (.tar.gz)
transformer.wmt19.en-ru En->Ru Ensemble download (.tar.gz)
transformer.wmt19.ru-en Ru->En Ensemble download (.tar.gz)
transformer_lm.wmt19.en En Language Model download (.tar.gz)
transformer_lm.wmt19.de De Language Model download (.tar.gz)
transformer_lm.wmt19.ru Ru Language Model download (.tar.gz)

Example usage (torch.hub)

Requirements

We require a few additional Python dependencies for preprocessing:

pip install fastBPE sacremoses

Translation

import torch

# English to German translation
en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
                       tokenizer='moses', bpe='fastbpe')
en2de.translate("Machine learning is great!")  # 'Maschinelles Lernen ist großartig!'

# German to English translation
de2en = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.de-en', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
                       tokenizer='moses', bpe='fastbpe')
de2en.translate("Maschinelles Lernen ist großartig!")  # 'Machine learning is great!'

# English to Russian translation
en2ru = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-ru', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
                       tokenizer='moses', bpe='fastbpe')
en2ru.translate("Machine learning is great!")  # 'Машинное обучение - это здорово!'

# Russian to English translation
ru2en = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.ru-en', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
                       tokenizer='moses', bpe='fastbpe')
ru2en.translate("Машинное обучение - это здорово!")  # 'Machine learning is great!'

Language Modeling

# Sample from the English LM
en_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt19.en', tokenizer='moses', bpe='fastbpe')
en_lm.sample("Machine learning is")  # 'Machine learning is the future of computing, says Microsoft boss Satya Nadella ...'

# Sample from the German LM
de_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt19.de', tokenizer='moses', bpe='fastbpe')
de_lm.sample("Maschinelles lernen ist")  # 'Maschinelles lernen ist das A und O (neues-deutschland.de) Die Arbeitsbedingungen für Lehrerinnen und Lehrer sind seit Jahren verbesserungswürdig ...'

# Sample from the Russian LM
ru_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt19.ru', tokenizer='moses', bpe='fastbpe')
ru_lm.sample("машинное обучение это")  # 'машинное обучение это то, что мы называем "искусственным интеллектом".'

Citation

@inproceedings{ng2019facebook},
  title = {Facebook FAIR's WMT19 News Translation Task Submission},
  author = {Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey},
  booktitle = {Proc. of WMT},
  year = 2019,
}