Metadata-Version: 2.1
Name: adapters
Version: 0.0.0.dev20230804
Summary: Efficient and scalable fine-tuning of Transformers with adapters
Home-page: https://github.com/adapter-hub/adapters
Author: The AdapterHub team and community contributors
Author-email: pfeiffer@ukp.tu-darmstadt.de
License: Apache
Keywords: NLP deep learning transformer pytorch BERT adapters
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8.0
Description-Content-Type: text/markdown
Provides-Extra: sklearn
Provides-Extra: torch
Provides-Extra: onnxruntime
Provides-Extra: sentencepiece
Provides-Extra: testing
Provides-Extra: quality
Provides-Extra: docs
Provides-Extra: dev
License-File: LICENSE

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## Self-Contained `adapters` Library

This branch disentangles `adapter-transformers` from HF Transformers and adds Transformers as an external dependency.

### Breaking changes

- All adapter-related classes now have to imported via `adapters` namespace, e.g.:
    ```python
    from adapters import BertAdapterModel
    # ...
    ```
- Built-in HF model classes can be adapted for usage with adapters via a wrapper method, e.g.:
    ```python
    import adapters
    from transformers import BertModel

    model = BertModel.from_pretrained("bert-base-uncased")
    adapters.init(model)
    ```

### Model support

- [x] Albert
- [x] Bart
- [x] BEiT
- [x] Bert
- [x] Bert Generation
- [x] CLIP
- [x] Deberta
- [x] Deberta V2
- [x] DistilBert
- [ ] Encoder-Decoder
- [x] GPT-2
- [x] GPT-J
- [x] MBart
- [x] Roberta
- [x] T5
- [x] ViT
- [x] XLM-R

### TODO

Features not (yet) working:

- Loading model + adapter checkpoints using HF classes
- ~~Text generation with adapters~~ (hacked working version)
- ~~Parallel generation with adapters~~
- Using Transformers pipelines with adapters
- Using HF language modeling classes with invertible adapters

Tasks to do for first usable version:

- ~~Remove utils folder and use utils of HF~~
- Make all tests passing
- Update example scripts w. breaking changes
- Update docs w. breaking changes
- Update contributing guides for new code structure

---

<p align="center">
<img style="vertical-align:middle" src="https://raw.githubusercontent.com/Adapter-Hub/adapters/main/docs/logo.png" />
</p>
<h1 align="center">
<span>adapters</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/adapters/workflows/Tests/badge.svg)
[![GitHub](https://img.shields.io/github/license/adapter-hub/adapters.svg?color=blue)](https://github.com/adapter-hub/adapters/blob/main/LICENSE)
[![PyPI](https://img.shields.io/pypi/v/adapters)](https://pypi.org/project/adapters/)

`adapters` 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

`adapters` currently supports **Python 3.7+** and **PyTorch 1.3.1+**.
After [installing PyTorch](https://pytorch.org/get-started/locally/), you can install `adapters` from PyPI ...

```
pip install -U adapters
```

... or from source by cloning the repository:

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

## Getting Started

HuggingFace's great documentation on getting started with _Transformers_ can be found [here](https://huggingface.co/transformers/index.html). `adapters` is fully compatible with _Transformers_.

To get started with adapters, refer to these locations:

- **[Colab notebook tutorials](https://github.com/Adapter-Hub/adapters/tree/main/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 _adapters_
- **https://adapterhub.ml** to explore available pre-trained adapter modules and share your own adapters
- **[Examples folder](https://github.com/Adapter-Hub/adapters/tree/main/examples/pytorch)** of this repository containing HuggingFace's example training scripts, many adapted for training adapters

## Implemented Methods

Currently, adapters 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/adapters/blob/main/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/adapters/blob/main/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/adapters/blob/main/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/adapters/blob/main/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/adapters/blob/main/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}
}
```
