AdapterHub Documentation
Note
This documentation is based on the new Adapters library.
The documentation based on the legacy adapter-transformers library can be found at: https://docs-legacy.adapterhub.ml.
AdapterHub is a framework simplifying the integration, training and usage of adapters and other efficient fine-tuning methods for Transformer-based language models. For a full list of currently implemented methods, see the table in our repository.
The framework consists of two main components:
an add-on to Hugging Face’s Transformers library that adds adapters into transformer models |
a central collection of pre-trained adapter modules |
Currently, we support the PyTorch versions of all models as listed on the Model Overview page.
Adapter Methods
Advanced
Loading and Sharing
Supported Models
Adapter-Related Classes
Citation
If you use _Adapters_ in your work, please consider citing our library paper Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning <https://arxiv.org/abs/2311.11077)>
@inproceedings{poth-etal-2023-adapters,
title = "Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning",
author = {Poth, Clifton and
Sterz, Hannah and
Paul, Indraneil and
Purkayastha, Sukannya and
Engl{\"a}nder, Leon and
Imhof, Timo and
Vuli{\'c}, Ivan and
Ruder, Sebastian and
Gurevych, Iryna and
Pfeiffer, Jonas},
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-demo.13",
pages = "149--160",
}
Alternatively, for the predecessor adapter-transformers, the Hub infrastructure and adapters uploaded by the AdapterHub team, please consider citing our initial paper: AdapterHub: A Framework for Adapting Transformers
@inproceedings{pfeiffer2020AdapterHub,
title={AdapterHub: A Framework for Adapting Transformers},
author={Jonas Pfeiffer and
Andreas R\"uckl\'{e} and
Clifton Poth and
Aishwarya Kamath and
Ivan Vuli\'{c} and
Sebastian Ruder and
Kyunghyun Cho and
Iryna Gurevych},
booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations},
year={2020},
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-demos.7",
pages = "46--54",
}