{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adapterhub-a-framework-for-adapting","title":"AdapterHub: A Framework for Adapting Transformers","arxiv_id":"2007.07779","date":"2020-07-15","proceeding":"EMNLP 2020 11","authors":["Jonas Pfeiffer","Andreas Rücklé","Clifton Poth","Aishwarya Kamath","Ivan Vulić","Sebastian Ruder","Kyunghyun Cho","Iryna Gurevych"],"abstract":"The current modus operandi in NLP involves downloading and fine-tuning pre-trained models consisting of millions or billions of parameters. 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