Fairseq: A Fast, Extensible Toolkit For Sequence Modeling

Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli . Proceedings of the 2019 Conference of the North 2019 – 723 citations

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Content Enrichment RAG Time Series Training Techniques Variational Autoencoders

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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