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Summary: # Before submitting - [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements) - [ ] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/master/CONTRIBUTING.md)? - [ ] Did you make sure to update the docs? - [ ] Did you write any new necessary tests? ## What does this PR do? Fixes # (issue). ## PR review Anyone in the community is free to review the PR once the tests have passed. If we didn't discuss your PR in Github issues there's a high chance it will not be merged. ## Did you have fun? Make sure you had fun coding � Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1347 Reviewed By: michaelauli, shruti-bh Differential Revision: D24315287 Pulled By: edunov fbshipit-source-id: d94955866b5424ab9c6a78982140e2bd7d1b279b |
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README.md |
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) |
Pre-trained single models before finetuning
Model | Description | Download |
---|---|---|
transformer.wmt19.en-de |
En->De Single, no finetuning | download (.tar.gz) |
transformer.wmt19.de-en |
De->En Single, no finetuning | download (.tar.gz) |
transformer.wmt19.en-ru |
En->Ru Single, no finetuning | download (.tar.gz) |
transformer.wmt19.ru-en |
Ru->En Single, no finetuning | 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,
}