{"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/gradual-fine-tuning-for-low-resource-domain","title":"Gradual Fine-Tuning for Low-Resource Domain Adaptation","arxiv_id":"2103.02205","date":"2021-03-03","proceeding":"EACL (AdaptNLP) 2021 4","authors":["Haoran Xu","Seth Ebner","Mahsa Yarmohammadi","Aaron Steven White","Benjamin Van Durme","Kenton Murray"],"abstract":"Fine-tuning is known to improve NLP models by adapting an initial model trained on more plentiful but less domain-salient examples to data in a target domain. Such domain adaptation is typically done using one stage of fine-tuning. We demonstrate that gradually fine-tuning in a multi-stage process can yield substantial further gains and can be applied without modifying the model or learning objective.","url_abs":"https://arxiv.org/abs/2103.02205v2","url_pdf":"https://arxiv.org/pdf/2103.02205v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"gradual-fine-tuning-for-low-resource-domain","repo_url":"https://github.com/fe1ixxu/Gradual-Finetune","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"gradual-fine-tuning-for-low-resource-domain","repo_url":"https://github.com/isi-boston/ed-pooling","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2103.02205","atlas_url":"https://app.syntology.ai/?focus=2103.02205","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}