In constrained real-world scenarios, where it may be challenging or costly to
generate data, disciplined methods for acquiring informative new data points
are of fundamental importance for the efficient training of machine learning
(ML) models. Active learning (AL) is a sub-field of ML focused on the
development of methods to iteratively and economically acquire data through
strategically querying new data points that are the most useful for a
particular task. Here, we introduce PyRelationAL, an open source library for AL
research. We describe a modular toolkit that is compatible with diverse ML
frameworks (e.g. PyTorch, scikit-learn, TensorFlow, JAX). Furthermore, the
library implements a wide range of published methods and provides API access to
wide-ranging benchmark datasets and AL task configurations based on existing
literature. The library is supplemented by an expansive set of tutorials,
demos, and documentation to help users get started. PyRelationAL is maintained
using modern software engineering practices -- with an inclusive contributor
code of conduct -- to promote long term library quality and utilisation.
PyRelationAL is available under a permissive Apache licence on PyPi and at
this https URL.

介绍了一个名为 PyRelationAL 的机器学习活动学习的开源库，它可以帮助通过经济的数据获取方法迭代式地训练机器学习模型。该库实现了各种已发布的方法，并根据现有文献提供了广泛的基准数据集和 AL 任务配置。