  AI automation tools need machine-readable hyperparameter schemas to define
their search spaces. At the same time, AI libraries often come with good
human-readable documentation. While such documentation contains most of the
necessary information, it is unfortunately not ready to consume by tools. This
paper describes how to automatically mine Python docstrings in AI libraries to
extract JSON Schemas for their hyperparameters. We evaluate our approach on 119
transformers and estimators from three different libraries and find that it is
effective at extracting machine-readable schemas. Our vision is to reduce the
burden to manually create and maintain such schemas for AI automation tools and
broaden the reach of automation to larger libraries and richer schemas.
