https://github.com/adapter-hub/cpgadapters

https://github.com/adapter-hub/cpgadapters

Last synced: 18 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/adapter-hub/cpgadapters
• Owner: adapter-hub
• License: apache-2.0
• Created: 2020-10-30T10:49:40.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2021-07-21T15:17:31.000Z (almost 4 years ago)
• Last Synced: 2025-04-14T23:11:39.854Z (18 days ago)
• Language: Python
• Size: 9.79 MB
• Stars: 0
• Watchers: 4
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        








adapter-transformers





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



![Tests](https://github.com/Adapter-Hub/adapter-transformers/workflows/Tests/badge.svg)

[![GitHub](https://img.shields.io/github/license/adapter-hub/adapter-transformers.svg?color=blue)](https://github.com/adapter-hub/adapter-transformers/blob/master/LICENSE)

![PyPI](https://img.shields.io/pypi/v/adapter-transformers)

`adapter-transformers` is an extension of [HuggingFace's Transformers](https://github.com/huggingface/transformers) library, integrating adapters into state-of-the-art language models by incorporating **[AdapterHub](https://adapterhub.ml)**, 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.

## Installation

_adapter-transformers_ currently supports **Python 3.6+** and **PyTorch 1.1.0+**.

After [installing PyTorch](https://pytorch.org/get-started/locally/), 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](https://huggingface.co/transformers/index.html). _adapter-transformers_ is fully compatible with _Transformers_.

To get started with adapters, refer to these locations:

- **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](https://github.com/Adapter-Hub/adapter-transformers/tree/master/examples)** 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](https://arxiv.org/abs/2007.07779):

```

@article{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},

    journal={arXiv preprint},

    year={2020},

    url={https://arxiv.org/abs/2007.07779}

}

```