Home
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.
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
- Ustalov D., Pavlichenko N., Tseitlin B. Learning from Crowds with Crowd-Kit. 2023. arXiv: 2109.08584 [cs.HC].
@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},
}