Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks.
en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de.single_model')
en2de.translate('Hello world', beam=5)
# 'Hallo Welt'
The full documentation contains instructions for getting started, training new models and extending fairseq with new model types and tasks.
We provide pre-trained models and pre-processed, binarized test sets for several tasks listed below, as well as example training and evaluation commands.
fairseq(-py) is MIT-licensed. The license applies to the pre-trained models as well.
@inproceedings{ott2019fairseq,
title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling},
author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli},
booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations},
year = {2019},
}
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