@inproceedings{ott-etal-2019-fairseq,
title = "fairseq: A Fast, Extensible Toolkit for Sequence Modeling",
author = "Ott, Myle and
Edunov, Sergey and
Baevski, Alexei and
Fan, Angela and
Gross, Sam and
Ng, Nathan and
Grangier, David and
Auli, Michael",
editor = "Ammar, Waleed and
Louis, Annie and
Mostafazadeh, Nasrin",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-4009",
doi = "10.18653/v1/N19-4009",
pages = "48--53",
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 \url{https://www.youtube.com/watch?v=OtgDdWtHvto}",
}
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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</abstract>
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%0 Conference Proceedings
%T fairseq: A Fast, Extensible Toolkit for Sequence Modeling
%A Ott, Myle
%A Edunov, Sergey
%A Baevski, Alexei
%A Fan, Angela
%A Gross, Sam
%A Ng, Nathan
%A Grangier, David
%A Auli, Michael
%Y Ammar, Waleed
%Y Louis, Annie
%Y Mostafazadeh, Nasrin
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F ott-etal-2019-fairseq
%X 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
%R 10.18653/v1/N19-4009
%U https://aclanthology.org/N19-4009
%U https://doi.org/10.18653/v1/N19-4009
%P 48-53
Markdown (Informal)
[fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://aclanthology.org/N19-4009) (Ott et al., NAACL 2019)
ACL
- Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019. fairseq: A Fast, Extensible Toolkit for Sequence Modeling. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations), pages 48–53, Minneapolis, Minnesota. Association for Computational Linguistics.