تقرير
Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning
| العنوان: | Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning |
|---|---|
| المؤلفون: | Poth, Clifton, Sterz, Hannah, Paul, Indraneil, Purkayastha, Sukannya, Engländer, Leon, Imhof, Timo, Vulić, Ivan, Ruder, Sebastian, Gurevych, Iryna, Pfeiffer, Jonas |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | 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 via https://adapterhub.ml/adapters. Comment: EMNLP 2023: Systems Demonstrations |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2311.11077 |
| رقم الأكسشن: | edsarx.2311.11077 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |