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Papers

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

2019-04-01 · NAACL 2019 6 · Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli

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

📄 PDF Abstract BibTeX arXiv:1904.01038

Code (6)

pytorch/fairseq 공식 구현 pytorch
facebookresearch/fairseq pytorch
guxm2021/MM_ALT pytorch
lucylow/En_francais_si_vous_plait- pytorch
shamanez/BERT-like-is-All-You-Need pytorch
shamanez/Self-Supervised-Embedding-Fusion-Transformer pytorch

Tasks

Language ModelingLanguage ModellingText GenerationTranslation

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