We present small-text, a simple modular active learning library, which offers pool-based active learning for text classification in Python. It comes with various pre-implemented state-of-the-art query strategies, including some which can leverage the GPU. Clearly defined interfaces allow to combine a multitude of such query strategies with different classifiers, thereby facilitating a quick mix and match, and enabling a rapid development of both active learning experiments and applications. To make various classifiers accessible in a consistent way, it integrates several well-known machine learning libraries, namely, scikit-learn, PyTorch, and huggingface transformers -- for which the latter integrations are available as optionally installable extensions. The library is available under the MIT License at https://github.com/webis-de/small-text.

介绍一个易于使用的主动学习库，名为small-text，它提供了用于单标签和多标签文本分类的基于池的主动学习。该库集成了许多先进的查询策略，包括一些利用GPU的策略，并且可以与多种分类器、查询策略和停止标准结合使用。我们使用该库研究了最近发布的SetFit训练范式的性能，发现它在分类准确性方面与常规transformer微调相同，但在曲线下面积方面表现更好。

Python中的文本分类主动学习