Small-Text
Small-Text provides active learning for text classification. It is designed to offer a robust and modular set of components for both experimental and applied active learning.
Why Small-Text?
Interchangeable components: All components are based around the ActiveLearner class. You can mix and match different many initialization strategies, query strategies, and classifiers.
Integrations: Optional Integrations allow you to use GPU-based models from the pytorch and transformers libraries.
Common patterns: We provide solutions to common challenges when building experiments and/or applications, such as Data Management and Serialization.
Multiple scientifically evaluated components are pre-implemented and ready to use (query strategies, initialization strategies, and stopping criteria).
Getting Started
Start: Installation | Active Learning Overview
Examples: Notebooks | Code Examples
Citation
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"
}