#Active Learning

The ASReview project enhances text data screening through AI and Active Learning by prioritizing effectively. It boosts review efficiency, quality, and transparency with advanced machine learning algorithms that assist researchers interactively. Supporting Oracle, Exploration, and Simulation modes, it serves varied research needs from data labeling to model evaluation. Ideal for systematic reviews across disciplines, it minimizes false negatives and is compatible with Python 3.8 or later. Installation is straightforward with guides for Windows, macOS, and Docker. This open-source framework emphasizes transparency and efficiency, supported by documentation and community resources.

Small-Text offers efficient active learning techniques for text classification, featuring pre-implemented query and initialization strategies, along with stopping criteria. Compatible with sklearn, Pytorch, and transformers classifiers, it supports GPU models and lightweight installation suitable for CPU environments. Access detailed documentation and community support for streamlined integration.