fairseq: A Fast, Extensible Toolkit for Sequence Modeling
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Cited by 22 in Mixture-of-experts, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
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
Key facts. Cited by 1,486 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Apr 2019.
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Citing papers per year: 2020: 2; 2021: 415; 2022: 384; 2023: 358; 2024: 202; 2025: 86; 2026: 39.
1,486 papers.
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- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
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38 more
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50 most cited of 1,486 All 1,486 (JSON)
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50 newest of 1,486 All 1,486 (JSON)
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