README.md

---
annotations_creators:
- crowdsourced
language_creators:
- other
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
pretty_name: JigsawToxicityPred
dataset_info:
  features:
  - name: comment_text
    dtype: string
  - name: toxic
    dtype:
      class_label:
        names:
          '0': 'false'
          '1': 'true'
  - name: severe_toxic
    dtype:
      class_label:
        names:
          '0': 'false'
          '1': 'true'
  - name: obscene
    dtype:
      class_label:
        names:
          '0': 'false'
          '1': 'true'
  - name: threat
    dtype:
      class_label:
        names:
          '0': 'false'
          '1': 'true'
  - name: insult
    dtype:
      class_label:
        names:
          '0': 'false'
          '1': 'true'
  - name: identity_hate
    dtype:
      class_label:
        names:
          '0': 'false'
          '1': 'true'
  splits:
  - name: train
    num_bytes: 71282358
    num_examples: 159571
  - name: test
    num_bytes: 28241991
    num_examples: 63978
  download_size: 0
  dataset_size: 99524349
train-eval-index:
- config: default
  task: text-classification
  task_id: binary_classification
  splits:
    train_split: train
    eval_split: test
  col_mapping:
    comment_text: text
    toxic: target
  metrics:
  - type: accuracy
    name: Accuracy
  - type: f1
    name: F1 macro
    args:
      average: macro
  - type: f1
    name: F1 micro
    args:
      average: micro
  - type: f1
    name: F1 weighted
    args:
      average: weighted
  - type: precision
    name: Precision macro
    args:
      average: macro
  - type: precision
    name: Precision micro
    args:
      average: micro
  - type: precision
    name: Precision weighted
    args:
      average: weighted
  - type: recall
    name: Recall macro
    args:
      average: macro
  - type: recall
    name: Recall micro
    args:
      average: micro
  - type: recall
    name: Recall weighted
    args:
      average: weighted
---

# Dataset Card for [Dataset Name]

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [Jigsaw Comment Toxicity Classification Kaggle Competition](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**

### Dataset Summary

Discussing things you care about can be difficult. The threat of abuse and harassment online means that many people stop expressing themselves and give up on seeking different opinions. Platforms struggle to effectively facilitate conversations, leading many communities to limit or completely shut down user comments. This dataset consists of a large number of Wikipedia comments which have been labeled by human raters for toxic behavior.

### Supported Tasks and Leaderboards

The dataset support multi-label classification

### Languages

The comments are in English

## Dataset Structure

### Data Instances

A data point consists of a comment followed by multiple labels that can be associated with it.
{'id': '02141412314',
 'comment_text': 'Sample comment text',
 'toxic': 0,
 'severe_toxic': 0,
 'obscene': 0,
 'threat': 0,
 'insult': 0,
 'identity_hate': 1,
}

### Data Fields

- `id`: id of the comment
- `comment_text`: the text of the comment
- `toxic`: value of 0(non-toxic) or 1(toxic) classifying the comment
- `severe_toxic`: value of 0(non-severe_toxic) or 1(severe_toxic) classifying the comment
- `obscene`: value of 0(non-obscene) or 1(obscene) classifying the comment
- `threat`: value of 0(non-threat) or 1(threat) classifying the comment
- `insult`: value of 0(non-insult) or 1(insult) classifying the comment
- `identity_hate`: value of 0(non-identity_hate) or 1(identity_hate) classifying the comment

### Data Splits

The data is split into a training and testing set.

## Dataset Creation

### Curation Rationale

The dataset was created to help in efforts to identify and curb instances of toxicity online.

### Source Data

#### Initial Data Collection and Normalization

The dataset is a collection of Wikipedia comments.

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

If words that are associated with swearing, insults or profanity are present in a comment, it is likely that it will be classified as toxic, regardless of the tone or the intent of the author e.g. humorous/self-deprecating. This could present some biases towards already vulnerable minority groups.

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

The "Toxic Comment Classification" dataset is released under [CC0], with the underlying comment text being governed by Wikipedia\'s [CC-SA-3.0].

### Citation Information

No citation information.

### Contributions

Thanks to [@Tigrex161](https://github.com/Tigrex161) for adding this dataset.