semvec/pypi-packages huggingface dataset:
adapter-transformers 4.0.0
Deprecated adapter-transformers package. Use adapters package instead.
License: Apache
Requires Python: >=3.8.0
Keywords: NLP deep learning transformer pytorch BERT adapters

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## ⚠️ IMPORTANT NOTE ⚠️

This is the legacy `adapter-transformers` library, which has been replaced by the new **Adapters library, found here: https://github.com/adapter-hub/adapters**.

- **⚠️ Beginning with version 4.0.0, the `adapter-transformers` package will automatically install the latest `adapters` package version instead. ⚠️**  
  From this version on, the `adapter-transformers` package does not contain any own functionality.
- Older versions of `adapter-transformers` are kept for archival purposes, and should not be used for active projects.

Install the new library directly via: `pip install adapters`.

The documentation of adapter-transformers can be found at https://docs-legacy.adapterhub.ml.
The documentation of the new _Adapters_ library can be found at https://docs.adapterhub.ml.
For transitioning, please read: https://docs.adapterhub.ml/transitioning.html.

---


<p align="center">
<img style="vertical-align:middle" src="https://raw.githubusercontent.com/Adapter-Hub/adapters/main/docs/img/adapter-bert.png" width="80" />
</p>
<h1 align="center">
<span><i>Adapters</i></span>
</h1>

<h3 align="center">
A Unified Library for Parameter-Efficient and Modular Transfer Learning
</h3>
<h3 align="center">
    <a href="https://adapterhub.ml">🌍 Website</a>
    &nbsp; • &nbsp;
    <a href="https://github.com/Adapter-Hub/adapters/tree/main/notebooks">💻 GitHub</a>
    &nbsp; • &nbsp;
    <a href="https://docs.adapterhub.ml">📚 Docs</a>
    &nbsp; • &nbsp;
    <a href="https://arxiv.org/abs/2311.11077">📜 Paper</a>
    &nbsp; • &nbsp;
    <a href="https://github.com/Adapter-Hub/adapters/tree/main/notebooks">🧪 Tutorials</a>
</h3>

_Adapters_ is an add-on library to [HuggingFace's Transformers](https://github.com/huggingface/transformers), integrating [various adapter methods](https://docs.adapterhub.ml/overview.html) into [state-of-the-art pre-trained language models](https://docs.adapterhub.ml/model_overview.html) with minimal coding overhead for training and inference.

```
pip install adapters
```