Computer Science > Computation and Language
arXiv:2311.11077 (cs)
[Submitted on 18 Nov 2023]
Title:Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning
Authors:Clifton Poth,Hannah Sterz,Indraneil Paul,Sukannya Purkayastha,Leon Engländer,Timo Imhof,Ivan Vulić,Sebastian Ruder,Iryna Gurevych,Jonas Pfeiffer
View a PDF of the paper titled Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning, by Clifton Poth and 9 other authors
View PDFAbstract:We introduce Adapters, an open-source library that unifies parameter-efficient and modular transfer learning in large language models. By integrating 10 diverse adapter methods into a unified interface, Adapters offers ease of use and flexible configuration. Our library allows researchers and practitioners to leverage adapter modularity through composition blocks, enabling the design of complex adapter setups. We demonstrate the library's efficacy by evaluating its performance against full fine-tuning on various NLP tasks. Adapters provides a powerful tool for addressing the challenges of conventional fine-tuning paradigms and promoting more efficient and modular transfer learning. The library is available viathis https URL.
| Comments: | EMNLP 2023: Systems Demonstrations |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2311.11077 [cs.CL] |
| (orarXiv:2311.11077v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2311.11077 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning, by Clifton Poth and 9 other authors
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