English

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

Computation and Language 2019-04-03 v1
Authors: Myle Ott , Sergey Edunov , Alexei Baevski , Angela Fan , Sam Gross , Nathan Ng , David Grangier , Michael Auli
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Abstract

fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. We also support fast mixed-precision training and inference on modern GPUs. A demo video can be found at https://www.youtube.com/watch?v=OtgDdWtHvto

Keywords

software library compiler optimization image processing

Cite

@article{arxiv.1904.01038,
  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},
  journal= {arXiv preprint arXiv:1904.01038},
  year   = {2019}
}

Comments

NAACL 2019 Demo paper

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