Small-Text provides implementations of active learning strategies for efficiently training text classifiers with limited labeled data. It offers various query strategies for selecting informative samples, initialization methods, and stopping criteria. The library integrates with PyTorch and Hugging Face Transformers, making it compatible with modern language models and transformer-based architectures for NLP tasks.