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

If you also want to use the learning subpackage, type

pip instal crowd-kit[learning]

If you are interested in contributing to Crowd-Kit, use Pipenv to install the library with its dependencies:

pipenv install --dev

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

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

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)

More usage examples

Implemented aggregation methods

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

Categorical responses

MethodStatus
Majority Vote✅
One-coin Dawid-Skene✅
Dawid-Skene✅
Gold Majority Vote✅
M-MSR✅
Wawa✅
Zero-Based Skill✅
GLAD✅
KOS✅
MACE✅
BCC🟡

Multi-label responses

MethodStatus
Binary Relevance✅

Textual responses

MethodStatus
RASA✅
HRRASA✅
ROVER✅

Image segmentation

MethodStatus
Segmentation MV✅
Segmentation RASA✅
Segmentation EM✅

Pairwise comparisons

MethodStatus
Bradley-Terry✅
Noisy Bradley-Terry✅

Learning from crowds

MethodStatus
CrowdLayer✅
CoNAL✅
TextSummarization✅

Questions and bug reports

  • To report a bug, post an issue on the Toloka/bugreport page.
  • To find answers to common questions or start a new discussion, join our English-speaking Slack community.

Source code

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Crowd-Kit
Overview
Reference
Aggregation
Datasets
Learning
Metrics
Postprocessing