Metadata-Version: 2.1
Name: small-text
Version: 1.4.0
Summary: Active Learning for Text Classification in Python.
Home-page: https://github.com/webis-de/small-text
Author: Christopher Schröder
Author-email: small-text@protonmail.com
License: MIT License
Keywords: active learning,text classification
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Text Processing :: Linguistic
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: dill
Requires-Dist: scipy
Requires-Dist: numpy >=1.20.0
Requires-Dist: scikit-learn >=0.24.1
Requires-Dist: tqdm
Requires-Dist: packaging
Provides-Extra: pytorch
Requires-Dist: torch >=1.6.0 ; extra == 'pytorch'
Requires-Dist: torchtext >=0.7.0 ; extra == 'pytorch'
Provides-Extra: transformers
Requires-Dist: torch >=1.6.0 ; extra == 'transformers'
Requires-Dist: torchtext >=0.7.0 ; extra == 'transformers'
Requires-Dist: transformers >=4.0.0 ; extra == 'transformers'

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</p>

> Active Learning for Text Classification in Python.
<hr>

[Installation](#installation) | [Quick Start](#quick-start) | [Contribution](CONTRIBUTING.md) | [Changelog][changelog] | [**Docs**][documentation_main]

Small-Text provides state-of-the-art **Active Learning** for Text Classification. 
Several pre-implemented Query Strategies, Initialization Strategies, and Stopping Critera are provided, 
which can be easily mixed and matched to build active learning experiments or applications.

## Features

- Provides unified interfaces for Active Learning so that you can 
  easily mix and match query strategies with classifiers provided by [sklearn](https://scikit-learn.org/), [Pytorch](https://pytorch.org/), or [transformers](https://github.com/huggingface/transformers).
- Supports GPU-based [Pytorch](https://pytorch.org/) models and integrates [transformers](https://github.com/huggingface/transformers) 
  so that you can use state-of-the-art Text Classification models for Active Learning.
- GPU is supported but not required. In case of a CPU-only use case, 
  a lightweight installation only requires a minimal set of dependencies.
- Multiple scientifically evaluated components are pre-implemented and ready to use (Query Strategies, Initialization Strategies, and Stopping Criteria).

## What is Active Learning?
[Active Learning](https://small-text.readthedocs.io/en/latest/active_learning.html) allows you to efficiently label training data for supervised learning in a scenario where you have little to no labeled data.

<p align="center">

<img src="https://raw.githubusercontent.com/webis-de/small-text/dev/docs/_static/learning-curve-example.gif?raw=true" alt="Learning curve example for the TREC-6 dataset." width="60%">

</p>

---

## News

- **Version 1.4.0** ([v1.4.0][changelog_1.4.0]) - June 9th, 2024
  - New query strategy: [AnchorSubsampling](https://small-text.readthedocs.io/en/v1.3.3/components/query_strategies.html#small_text.query_strategies.subsampling.AnchorSubsampling) (aka [AnchorAL](https://arxiv.org/abs/2404.05623)).  
    Special thanks to [Pietro Lesci](https://github.com/pietrolesci) for the correspondence and code review. 

- **June 2024 Update**
  - [Version 2.0.0 is in progress](https://github.com/webis-de/small-text/tree/dev) and should be ready soon. 

- **Version 1.3.3** ([v1.3.3][changelog_1.3.3]) - December 29th, 2023
  - Bugfix release.

- **Version 1.3.2** ([v1.3.2][changelog_1.3.2]) - August 19th, 2023
  - Bugfix release.

- **Paper accepted at EACL 2023 🎉**
  - The [paper][paper_arxiv] introducing small-text has been accepted at [EACL 2023](https://2023.eacl.org/). Meet us at the conference in May!
  - Update: the paper was awarded [EACL Best System Demonstration](https://aclanthology.org/2023.eacl-demo.11/). Thank you, for your support!

[For a complete list of changes, see the change log.][changelog]

---

## Installation

Small-Text can be easily installed via pip (or conda):

```bash
pip install small-text
```

The command results in a [slim installation][documentation_install] with only the necessary dependencies. 
For a full installation via pip, you just need to include the `transformers` extra requirement:

```bash
pip install small-text[transformers]
```

For conda, which lacks the extra requirements feature, a full installation can be achieved as follows:

```bash
conda install "torch>=1.6.0" "torchtext>=0.7.0" transformers small-text
```

The library requires Python 3.7 or newer. For using the GPU, CUDA 10.1 or newer is required. 
More information regarding the installation can be found in the 
[documentation][documentation_install].


## Quick Start

For a quick start, see the provided examples for [binary classification](examples/examplecode/binary_classification.py),
[pytorch multi-class classification](examples/examplecode/pytorch_multiclass_classification.py), and 
[transformer-based multi-class classification](examples/examplecode/transformers_multiclass_classification.py),
or check out the notebooks.

### Notebooks

| # | Notebook                                                                                                                                                                                                       |                                                                                                                                                                                                                                                  |
| --- |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1 | [Intro: Active Learning for Text Classification with Small-Text](https://github.com/webis-de/small-text/blob/v1.4.0/examples/notebooks/01-active-learning-for-text-classification-with-small-text-intro.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/webis-de/small-text/blob/v1.4.0/examples/notebooks/01-active-learning-for-text-classification-with-small-text-intro.ipynb) |
| 2 | [Using Stopping Criteria for Active Learning](https://github.com/webis-de/small-text/blob/v1.4.0/examples/notebooks/02-active-learning-with-stopping-criteria.ipynb)                                           | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/webis-de/small-text/blob/v1.4.0/examples/notebooks/02-active-learning-with-stopping-criteria.ipynb)                        |
| 3 | [Active Learning using SetFit](https://github.com/webis-de/small-text/blob/v1.4.0/examples/notebooks/03-active-learning-with-setfit.ipynb)                                                                     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/webis-de/small-text/blob/v1.4.0/examples/notebooks/03-active-learning-with-setfit.ipynb)                                   |
| 4 | [Using SetFit's Zero Shot Capabilities for Cold Start Initialization](https://github.com/webis-de/small-text/blob/v1.4.0/examples/notebooks/04-zero-shot-cold-start.ipynb)                                     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/webis-de/small-text/blob/v1.4.0/examples/notebooks/04-zero-shot-cold-start.ipynb)                                          |

### Showcase

- [Tutorial: 👂 Active learning for text classification with small-text][argilla_al_tutorial] (Use small-text conveniently from the [argilla][argilla] UI.)

A full list of showcases can be found [in the docs][documentation_showcase].

🎀 **Would you like to share your use case?** Regardless if it is a paper, an experiment, a practical application, a thesis, a dataset, or other, let us know and we will add you to the [showcase section][documentation_showcase] or even here.

## Documentation

Read the latest documentation [here][documentation_main]. Noteworthy pages include:

- [Overview of Query Strategies][documentation_query_strategies]
- [Reproducibility Notes][documentation_reproducibility_notes]

---

## Alternatives

[modAL](https://github.com/modAL-python/modAL), [ALiPy](https://github.com/NUAA-AL/ALiPy), [libact](https://github.com/ntucllab/libact), [ALToolbox](https://github.com/AIRI-Institute/al_toolbox)

## Contribution

Contributions are welcome. Details can be found in [CONTRIBUTING.md](CONTRIBUTING.md).

## Acknowledgments

This software was created by Christopher Schröder ([@chschroeder](https://github.com/chschroeder)) at Leipzig University's [NLP group](http://asv.informatik.uni-leipzig.de/) 
which is a part of the [Webis](https://webis.de/) research network. 
The encompassing project was funded by the Development Bank of Saxony (SAB) under project number 100335729.

## Citation

Small-Text has been introduced in detail in the EACL23 System Demonstration Paper ["Small-Text: Active Learning for Text Classification in Python"](https://aclanthology.org/2023.eacl-demo.11/) which can be cited as follows:
```
@inproceedings{schroeder2023small-text,
    title = "Small-Text: Active Learning for Text Classification in Python",
    author = {Schr{\"o}der, Christopher  and  M{\"u}ller, Lydia  and  Niekler, Andreas  and  Potthast, Martin},
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.eacl-demo.11",
    pages = "84--95"
}
```

## License

[MIT License](LICENSE)


[documentation_main]: https://small-text.readthedocs.io/en/v1.4.0/
[documentation_install]: https://small-text.readthedocs.io/en/v1.4.0/install.html
[documentation_query_strategies]: https://small-text.readthedocs.io/en/v1.4.0/components/query_strategies.html
[documentation_showcase]: https://small-text.readthedocs.io/en/v1.4.0/showcase.html
[documentation_reproducibility_notes]: https://small-text.readthedocs.io/en/v1.4.0/reproducibility_notes.html
[changelog]: https://small-text.readthedocs.io/en/latest/changelog.html
[changelog_1.3.2]: https://small-text.readthedocs.io/en/latest/changelog.html#version-1-3-2-2023-08-19
[changelog_1.3.3]: https://small-text.readthedocs.io/en/latest/changelog.html#version-1-3-3-2023-12-29
[changelog_1.4.0]: https://small-text.readthedocs.io/en/latest/changelog.html#version-1-4-0-2024-06-09
[argilla]: https://github.com/argilla-io/argilla
[argilla_al_tutorial]: https://docs.argilla.io/en/latest/tutorials/notebooks/training-textclassification-smalltext-activelearning.html
[paper_arxiv]: https://arxiv.org/abs/2107.10314
