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Crowd-Kit is a powerful Python library that implements commonly-used aggregation methods for crowdsourced annotation and offers the relevant metrics and datasets. We strive to implement functionality that simplifies working with crowdsourced data.

Crowd-Kit

Currently, Crowd-Kit contains:

  • implementations of commonly-used aggregation methods for categorical, pairwise, textual, and segmentation responses;
  • implementations of deep learning from crowds methods and advanced aggregation algorithms in PyTorch;
  • metrics of uncertainty, consistency, and agreement with aggregate;
  • loaders for popular crowdsourced datasets.

Installing

To install Crowd-Kit, run the following command: pip install crowd-kit. If you also want to use the learning subpackage, type pip install crowd-kit[learning].

Getting Started

Crowd-Kit's API resembles the one of scikit-learn. We recommend checking out our examples at https://github.com/Toloka/crowd-kit/tree/main/examples.

Citation

@misc{CrowdKit,
  author    = {Ustalov, Dmitry and Pavlichenko, Nikita and Tseitlin, Boris},
  title     = {{Learning from Crowds with Crowd-Kit}},
  year      = {2023},
  publisher = {arXiv},
  eprint    = {2109.08584},
  eprinttype = {arxiv},
  eprintclass = {cs.HC},
  url       = {https://arxiv.org/abs/2109.08584},
  language  = {english},
}