Crowd-Kit

Computational quality control for crowdsourcing

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;
• 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
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If you also want to use the learning subpackage, type

pip instal crowd-kit[learning]
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If you are interested in contributing to Crowd-Kit, use Pipenv to install the library with its dependencies:

pipenv install --dev
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We use pytest for testing.

Getting started

This example shows how to use Crowd-Kit for categorical aggregation using the classical Dawid-Skene algorithm.

First, let us do all the necessary imports.

from crowdkit.aggregation import DawidSkene
from crowdkit.datasets import load_dataset
import pandas as pd
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Then, you need to read your annotations into Pandas DataFrame with columns task, worker, label. Alternatively, you can download an example dataset:

df = pd.read_csv('results.csv')  # should contain columns: task, worker, label
# df, ground_truth = load_dataset('relevance-2')  # or download an example dataset
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Then, you can aggregate the workers' responses using the fit_predict method from the scikit-learn library:

aggregated_labels = DawidSkene(n_iter=100).fit_predict(df)
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More usage examples

Implemented aggregation methods

Below is the list of currently implemented methods, including the already available (✅) and in progress (🟡).

Categorical responses

Multi-label responses

Textual responses

Image segmentation

Pairwise comparisons

Learning from crowds

Questions and bug reports

• To report a bug, post an issue on the Toloka/bugreport page.

Source code

• Crowd-Kit on GitHub

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Last updated: February 6, 2023