jiant: A software toolkit for research on general-purpose text understanding models

Yada Pruksachatkun, Phil Yeres, Haokun Liu, Jason Phang, Phu Mon Htut, Alex Wang, Ian Tenney, Samuel R. Bowman

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    We introduce jiant, an open source toolkit for conducting multitask and transfer learning experiments on English NLU tasks. jiant enables modular and configuration-driven experimentation with state-of-the-art models and implements a broad set of tasks for probing, transfer learning, and multitask training experiments. jiant implements over 50 NLU tasks, including all GLUE and SuperGLUE benchmark tasks. We demonstrate that jiant reproduces published performance on a variety of tasks and models, including BERT and RoBERTa. jiant is available at https://jiant.info.

    Original languageEnglish (US)
    Title of host publicationACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the System Demonstrations
    PublisherAssociation for Computational Linguistics (ACL)
    Pages109-117
    Number of pages9
    ISBN (Electronic)9781952148040
    StatePublished - 2020
    Event58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States
    Duration: Jul 5 2020Jul 10 2020

    Publication series

    NameProceedings of the Annual Meeting of the Association for Computational Linguistics
    ISSN (Print)0736-587X

    Conference

    Conference58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
    Country/TerritoryUnited States
    CityVirtual, Online
    Period7/5/207/10/20

    ASJC Scopus subject areas

    • Computer Science Applications
    • Linguistics and Language
    • Language and Linguistics

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