  This paper presents Crowd-Kit, a general-purpose computational quality
control toolkit for crowdsourcing. Crowd-Kit provides efficient and convenient
implementations of popular quality control algorithms in Python, including
methods for truth inference, deep learning from crowds, and data quality
estimation. Our toolkit supports multiple modalities of answers and provides
dataset loaders and example notebooks for faster prototyping. We extensively
evaluated our toolkit on several datasets of different natures, enabling
benchmarking computational quality control methods in a uniform, systematic,
and reproducible way using the same codebase. We release our code and data
under the Apache License 2.0 at https://github.com/Toloka/crowd-kit.
