mw-adapter-transformers 3.2.1
A friendly fork of HuggingFace's Transformers, adding Adapters to PyTorch language models
License: Apache
Requires Python: >=3.8.0
Keywords: NLP deep learning transformer pytorch BERT adapters

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<p align="center">
<img style="vertical-align:middle" src="https://raw.githubusercontent.com/Adapter-Hub/adapter-transformers/master/adapter_docs/logo.png" />
</p>
<h1 align="center">
<span>adapter-transformers</span>
</h1>

<h3 align="center">
A friendly fork of HuggingFace's <i>Transformers</i>, adding Adapters to PyTorch language models
</h3>

![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)](https://pypi.org/project/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.

_💡 Important: This library can be used as a drop-in replacement for HuggingFace Transformers and regularly synchronizes new upstream changes.
Thus, most files in this repository are direct copies from the HuggingFace Transformers source, modified only with changes required for the adapter implementations._

## Installation

`adapter-transformers` currently supports **Python 3.8+** and **PyTorch 1.12.1+**.
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:

- **[Colab notebook tutorials](https://github.com/Adapter-Hub/adapter-transformers/tree/master/notebooks)**, 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](https://github.com/Adapter-Hub/adapter-transformers/tree/master/examples/pytorch)** of this repository containing HuggingFace's example training scripts, many adapted for training adapters

## Implemented Methods

Currently, adapter-transformers integrates all architectures and methods listed below:

| Method | Paper(s) | Quick Links |
| --- | --- | --- |
| Bottleneck adapters | [Houlsby et al. (2019)](https://arxiv.org/pdf/1902.00751.pdf)<br> [Bapna and Firat (2019)](https://arxiv.org/pdf/1909.08478.pdf) | [Quickstart](https://docs.adapterhub.ml/quickstart.html), [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapter-transformers/blob/master/notebooks/01_Adapter_Training.ipynb) |
| AdapterFusion | [Pfeiffer et al. (2021)](https://aclanthology.org/2021.eacl-main.39.pdf) | [Docs: Training](https://docs.adapterhub.ml/training.html#train-adapterfusion), [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapter-transformers/blob/master/notebooks/03_Adapter_Fusion.ipynb) |
| MAD-X,<br> Invertible adapters | [Pfeiffer et al. (2020)](https://aclanthology.org/2020.emnlp-main.617/) | [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapter-transformers/blob/master/notebooks/04_Cross_Lingual_Transfer.ipynb) |
| AdapterDrop | [Rücklé et al. (2021)](https://arxiv.org/pdf/2010.11918.pdf) | [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapter-transformers/blob/master/notebooks/05_Adapter_Drop_Training.ipynb) |
| MAD-X 2.0,<br> Embedding training | [Pfeiffer et al. (2021)](https://arxiv.org/pdf/2012.15562.pdf) | [Docs: Embeddings](https://docs.adapterhub.ml/embeddings.html), [Notebook](https://colab.research.google.com/github/Adapter-Hub/adapter-transformers/blob/master/notebooks/08_NER_Wikiann.ipynb) |
| Prefix Tuning | [Li and Liang (2021)](https://arxiv.org/pdf/2101.00190.pdf) | [Docs](https://docs.adapterhub.ml/overview.html#prefix-tuning) |
| Parallel adapters,<br> Mix-and-Match adapters | [He et al. (2021)](https://arxiv.org/pdf/2110.04366.pdf) | [Docs](https://docs.adapterhub.ml/overview.html#mix-and-match-adapters) |
| Compacter | [Mahabadi et al. (2021)](https://arxiv.org/pdf/2106.04647.pdf) | [Docs](https://docs.adapterhub.ml/overview.html#compacter) |
| LoRA | [Hu et al. (2021)](https://arxiv.org/pdf/2106.09685.pdf) | [Docs](https://docs.adapterhub.ml/overview.html#lora) |
| (IA)^3 | [Liu et al. (2022)](https://arxiv.org/pdf/2205.05638.pdf) | [Docs](https://docs.adapterhub.ml/overview.html#ia-3) |
| UniPELT | [Mao et al. (2022)](https://arxiv.org/pdf/2110.07577.pdf) | [Docs](https://docs.adapterhub.ml/overview.html#unipelt) |

## Supported Models

We currently support the PyTorch versions of all models listed on the **[Model Overview](https://docs.adapterhub.ml/model_overview.html) page** in our documentation.

## Citation

If you use this library for your work, please consider citing our paper [AdapterHub: A Framework for Adapting Transformers](https://arxiv.org/abs/2007.07779):

```
@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}
}
```