fairseq: A Fast, Extensible Toolkit for Sequence Modeling
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
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DOI：
10.18653/v1/n19-4009
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发表时间：
2019-04
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通讯作者：
Myle Ott;Sergey Edunov;Alexei Baevski;Angela Fan;Sam Gross;Nathan Ng;David Grangier;Michael Auli-Michael-Aul
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文献类型：
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作者：
Myle Ott;Sergey Edunov;Alexei Baevski;Angela Fan;Sam Gross;Nathan Ng;David Grangier;Michael Auli-Michael-Aul
Fairseq是一个开源序列建模工具包，允许研究人员和开发人员训练自定义模型，用于翻译、摘要、语言建模和其他文本生成任务。该工具包基于PyTorch，支持跨多个gpu和机器的分布式训练。我们还支持现代gpu上的快速混合精度训练和推理。可以在https://www.youtube.com/watch?v=OtgDdWtHvto上找到演示视频
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