Active Learning for Text Classification in Python.
Installation | Quick Start | Contribution | Changelog | Docs
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.
Active Learning allows you to efficiently label training data for supervised learning in a scenario where you have little to no labeled data.
- Provides unified interfaces for Active Learning so that you can easily mix and match query strategies with classifiers provided by sklearn, Pytorch, or transformers.
- Supports GPU-based Pytorch models and integrates 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).
Version 2.0.0 dev1 (v2.0.0.dev1) - November 24th, 2024
- This a development release with the most changes so far. You can consider this an alpha release, which does not guarantee you stable interfaces yet, but is otherwise ready to use.
- Version 2.0.0 offers cleaned up interfaces, new query strategies, improved classifiers, and new functionality such as vector indices. See the changelog for a full list of changes.
Version 1.4.1 (v1.4.1) - August 18th, 2024
- Bugfix release.
Version 1.4.0 (v1.4.0) - June 9th, 2024
- New query strategy: AnchorSubsampling (aka AnchorAL).
Special thanks to Pietro Lesci for the correspondence and code review.
Paper published at EACL 2023 🎉
- The paper introducing small-text has been accepted at EACL 2023. Meet us at the conference in May!
- Update: the paper was awarded EACL Best System Demonstration. Thank you, for your support!
For a complete list of changes, see the change log.
Small-Text can be easily installed via pip:
pip install small-textThe command results in a slim installation with only the necessary dependencies.
For a full installation via pip, you just need to include the transformers extra requirement:
pip install small-text[transformers]The library requires Python 3.8 or newer. For using the GPU, CUDA 10.1 or newer is required. More information regarding the installation can be found in the documentation.
For a quick start, see the provided examples for binary classification, pytorch multi-class classification, and transformer-based multi-class classification, or check out the notebooks.
- Tutorial: 👂 Active learning for text classification with small-text (Use small-text conveniently from the argilla UI.)
A full list of showcases can be found in the docs.
🎀 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 or even here.
Read the latest documentation here. Noteworthy pages include:
| Name | Active Learning | |
|---|---|---|
| Query Strategies | Stopping Criteria | |
| small-text v1.3.0 | 14 | 5 |
| small-text v2.0.0 | 19 | 5 |
We use the numbers only to show to tremendous progress that small-text has made over time. There many features and improvements that are not reflected in these numbers.
modAL, ALiPy, libact, ALToolbox
Contributions are welcome. Details can be found in CONTRIBUTING.md.
This software was created by Christopher Schröder (@chschroeder) at Leipzig University's NLP group which is a part of the Webis research network. The encompassing project was funded by the Development Bank of Saxony (SAB) under project number 100335729.
Small-Text has been introduced in detail in the EACL23 System Demonstration Paper "Small-Text: Active Learning for Text Classification in Python" 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"
}