This is a brief summary of paper for me to study and organize it, fairseq: A Fast, Extensible Toolkit for Sequence Modeling (Ott et al., NAACL 2019) that I read and studied.
They introduce the toolkit to make researches and developer to train custom models for translation, summarization, language modeling, and other text generataion task.
The toolkit was written in PyTorch, and demo video can be found below
fairseq Youtube
For detailed introduction, you can found in their paper, fairseq: A Fast, Extensible Toolkit for Sequence Modeling (Ott et al., NAACL 2019) and github
Note(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. They also support fast mixed-precision training and inference on modern GPUs.
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The paper: fairseq: A Fast, Extensible Toolkit for Sequence Modeling (Ott et al., NAACL 2019)
The paper: fairseq: A Fast, Extensible Toolkit for Sequence Modeling (Ott et al., NAACL 2019)
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