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


A preprint which introduces small-text is available here:
    title={Small-Text: Active Learning for Text Classification in Python},
    author={Christopher Schröder and Lydia Müller and Andreas Niekler and Martin Potthast},


MIT License

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