Datasets:
language:
- en
license: cc0-1.0
paperswithcode_id: civil-comments
pretty_name: Civil Comments
tags:
- toxic-comment-classification
task_categories:
- text-classification
task_ids:
- multi-label-classification
dataset_info:
features:
- name: text
dtype: string
- name: toxicity
dtype: float32
- name: severe_toxicity
dtype: float32
- name: obscene
dtype: float32
- name: threat
dtype: float32
- name: insult
dtype: float32
- name: identity_attack
dtype: float32
- name: sexual_explicit
dtype: float32
splits:
- name: train
num_bytes: 594805164
num_examples: 1804874
- name: validation
num_bytes: 32216880
num_examples: 97320
- name: test
num_bytes: 31963524
num_examples: 97320
download_size: 422061071
dataset_size: 658985568
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
Dataset Card for "civil_comments"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data
- Repository: https://github.com/conversationai/unintended-ml-bias-analysis
- Paper: https://arxiv.org/abs/1903.04561
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 414.95 MB
- Size of the generated dataset: 661.23 MB
- Total amount of disk used: 1.08 GB
Dataset Summary
The comments in this dataset come from an archive of the Civil Comments platform, a commenting plugin for independent news sites. These public comments were created from 2015 - 2017 and appeared on approximately 50 English-language news sites across the world. When Civil Comments shut down in 2017, they chose to make the public comments available in a lasting open archive to enable future research. The original data, published on figshare, includes the public comment text, some associated metadata such as article IDs, timestamps and commenter-generated "civility" labels, but does not include user ids. Jigsaw extended this dataset by adding additional labels for toxicity and identity mentions. This data set is an exact replica of the data released for the Jigsaw Unintended Bias in Toxicity Classification Kaggle challenge. This dataset is released under CC0, as is the underlying comment text.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
default
- Size of downloaded dataset files: 414.95 MB
- Size of the generated dataset: 661.23 MB
- Total amount of disk used: 1.08 GB
An example of 'validation' looks as follows.
{
"identity_attack": 0.0,
"insult": 0.0,
"obscene": 0.0,
"severe_toxicity": 0.0,
"sexual_explicit": 0.0,
"text": "The public test.",
"threat": 0.0,
"toxicity": 0.0
}
Data Fields
The data fields are the same among all splits.
default
text
: astring
feature.toxicity
: afloat32
feature.severe_toxicity
: afloat32
feature.obscene
: afloat32
feature.threat
: afloat32
feature.insult
: afloat32
feature.identity_attack
: afloat32
feature.sexual_explicit
: afloat32
feature.
Data Splits
name | train | validation | test |
---|---|---|---|
default | 1804874 | 97320 | 97320 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
This dataset is released under CC0 1.0.
Citation Information
@article{DBLP:journals/corr/abs-1903-04561,
author = {Daniel Borkan and
Lucas Dixon and
Jeffrey Sorensen and
Nithum Thain and
Lucy Vasserman},
title = {Nuanced Metrics for Measuring Unintended Bias with Real Data for Text
Classification},
journal = {CoRR},
volume = {abs/1903.04561},
year = {2019},
url = {http://arxiv.org/abs/1903.04561},
archivePrefix = {arXiv},
eprint = {1903.04561},
timestamp = {Sun, 31 Mar 2019 19:01:24 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1903-04561},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Contributions
Thanks to @lewtun, @patrickvonplaten, @thomwolf for adding this dataset.