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fairseq: A Fast, Extensible Toolkit for Sequence Modeling
April 01, 2019 · Declared Dead · 🏛 North American Chapter of the Association for Computational Linguistics
"No code URL or promise found in abstract"
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Authors
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli
arXiv ID
1904.01038
Category
cs.CL: Computation & Language
Citations
3.3K
Venue
North American Chapter of the Association for Computational Linguistics
Last Checked
2 months ago
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
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