adapter-transformers

A friendly fork of HuggingFace's Transformers, adding Adapters to PyTorch language models

adapter-transformers is an extension of HuggingFace's Transformers library, integrating adapters into state-of-the-art language models by incorporating AdapterHub, a central repository for pre-trained adapter modules.

This library can be used as a drop-in replacement for HuggingFace Transformers and regularly synchronizes new upstream changes.

Quick tour

adapter-transformers currently supports Python 3.6+ and PyTorch 1.1.0+. After installing PyTorch, you can install adapter-transformers from PyPI ...

pip install -U adapter-transformers

... or from source by cloning the repository:

git clone https://github.com/adapter-hub/adapter-transformers.git
cd adapter-transformers
pip install .

Getting Started

HuggingFace's great documentation on getting started with Transformers can be found here. adapter-transformers is fully compatible with Transformers.

To get started with adapters, refer to these locations:

• Colab notebook tutorials, a series notebooks providing an introduction to all the main concepts of (adapter-)transformers and AdapterHub
• https://docs.adapterhub.ml, our documentation on training and using adapters with adapter-transformers
• https://adapterhub.ml to explore available pre-trained adapter modules and share your own adapters
• Examples folder of this repository containing HuggingFace's example training scripts, many adapted for training adapters

Citation

If you find this library useful, please cite our paper AdapterHub: A Framework for Adapting Transformers:

@inproceedings{pfeiffer2020AdapterHub,
    title={AdapterHub: A Framework for Adapting Transformers},
    author={Pfeiffer, Jonas and
            R{\"u}ckl{\'e}, Andreas and
            Poth, Clifton and
            Kamath, Aishwarya and
            Vuli{\'c}, Ivan and
            Ruder, Sebastian and
            Cho, Kyunghyun and
            Gurevych, Iryna},
    booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
    pages={46--54},
    year={2020}
}