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Sergey Edunov 3544f5f24e Releasing single pre-finetuning models (#1347 ) 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	2020-10-14 14:19:54 -07:00
..
README.md	Releasing single pre-finetuning models (#1347 )	2020-10-14 14:19:54 -07:00

Sergey Edunov 3544f5f24e Releasing single pre-finetuning models (#1347 )

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

2020-10-14 14:19:54 -07:00

README.md

Releasing single pre-finetuning models (#1347 )

2020-10-14 14:19:54 -07:00

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,
}