fairseq: A Fast, Extensible Toolkit for Sequence Modeling

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

cs.CL Computer Science

Paper

References (51)

01Mixture Models for Diverse Machine Translation: Tricks of the TradeT. Shen, Myle Ott, Michael Auli et al.2019 · International Conference on Machine Learning · 168 citations In Library

02Strategies for Structuring Story GenerationAngela Fan, M. Lewis, Yann Dauphin2019 · Annual Meeting of the Association for Computational Linguistics · 228 citations In Library

03Pay Less Attention with Lightweight and Dynamic ConvolutionsFelix Wu, Angela Fan, Alexei Baevski et al.2019 · International Conference on Learning Representations · 667 citations In Library

04Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and BeyondMikel Artetxe, Holger Schwenk2018 · Transactions of the Association for Computational Linguistics · 1.2k citations In Library

05Wizard of Wikipedia: Knowledge-Powered Conversational agentsEmily Dinan, Stephen Roller, Kurt Shuster et al.2018 · International Conference on Learning Representations · 1.0k citations In Library

06Adaptive Input Representations for Neural Language ModelingAlexei Baevski, Michael Auli2018 · International Conference on Learning Representations · 440 citations In Library

07Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme SummarizationShashi Narayan, Shay B. Cohen, Mirella Lapata2018 · Conference on Empirical Methods in Natural Language Processing · 2.1k citations In Library

08Bottom-Up Abstractive SummarizationSebastian Gehrmann, Yuntian Deng, Alexander M. Rush2018 · Conference on Empirical Methods in Natural Language Processing · 723 citations In Library

09Double Path Networks for Sequence to Sequence LearningKaitao Song, Xu Tan, Di He et al.2018 · International Conference on Computational Linguistics · 15 citations In Library

10Scaling Neural Machine TranslationMyle Ott, Sergey Edunov, David Grangier et al.2018 · Conference on Machine Translation · 647 citations In Library

11OpenSeq2Seq: Extensible Toolkit for Distributed and Mixed Precision Training of Sequence-to-Sequence ModelsOleksii Kuchaiev, Boris Ginsburg, Igor Gitman et al.2018 · arXiv.org · 33 citations

12Hierarchical Neural Story GenerationAngela Fan, M. Lewis, Yann Dauphin2018 · Annual Meeting of the Association for Computational Linguistics · 2.1k citations In Library

Scroll for more · 38 remaining

Similar papers

fairseq S2T: Fast Speech-to-Text Modeling with fairseqChanghan Wang, Yun Tang, Xutai Ma et al.2020 · arXiv (Cornell University) In Library

fairseq S^2: A Scalable and Integrable Speech Synthesis ToolkitChanghan Wang, Wei-Ning Hsu, Yossi Adi et al.2021 · arXiv (Cornell University) · 35 citations In Library

fairseq S^2: A Scalable and Integrable Speech Synthesis ToolkitChanghan Wang, Wei-Ning Hsu, Yossi Adi et al.2021 · Empirical Methods in Natural Language Processing In Library

Fairseq S2T: Fast Speech-to-Text Modeling with FairseqChanghan Wang, Yun Tang, Xutai Ma et al.2020 In Library

FastSeq: Make Sequence Generation FasterYu Yan, Fei Hu, Jiusheng Chen et al.2021 · arXiv (Cornell University) · 18 citations In Library

FPSeq: Simplifying and Accelerating Task-Oriented Dialogue Systems via Fully Parallel Sequence-to-Sequence FrameworkMeina Song, Zhongfu Chen, Peiqing Niu et al.2019 · IEEE International Conference on Tools with Artificial Intelligence · 1 citation In Library

Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2SeqOleksii Kuchaiev, Boris Ginsburg, Igor Gitman et al.2018 · arXiv: Computation and Language · 52 citations In Library

fairseq Sˆ2: A Scalable and Integrable Speech Synthesis ToolkitChanghan Wang, Wei-Ning Hsu, Yossi Adi et al.2021 In Library

AutoNMT: A Framework to Streamline the Research of Seq2Seq ModelsSalvador Carri'on, F. Casacuberta2023 · arXiv.org · 3 citations In Library

deepFEPS: Deep Learning-Oriented Feature Extraction for Biological SequencesHamid D. Ismail, Marwan Bikdash2025 · 0 citations In Library

Source of record

arXiv

44B

Welcome.

This panel sticks with you. Pick anything below and it opens right beside it, so you can dig through 60,000-plus records without ever losing your spot here.

Travel 44B

LibraryPapers, policy, standards, statute LabsEvery organization building AI ModelsIntelligence, price, and speed BenchmarksThe evaluation catalog IncidentsAI failures and harms SearchOne field across everything DashboardThe state of AI accountability in NY 44B RegistryThe Art. 44-B compliance portal