
# Dataset Card for Jigsaw Unintended Bias in Toxicity Classification

## Table of Contents
- [Table of Contents](#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:** https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification
- **Repository:**
- **Paper:**
- **Leaderboard:** https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/leaderboard
- **Point of Contact:**

### Dataset Summary

The Jigsaw Unintended Bias in Toxicity Classification dataset comes from the eponymous Kaggle competition.

Please see the original [data](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data)
 description for more information.

### Supported Tasks and Leaderboards

The main target for this dataset is toxicity prediction. Several toxicity subtypes are also available, so the dataset 
can be used for multi-attribute prediction.

See the original [leaderboard](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/leaderboard)
for reference.

### Languages

English

## Dataset Structure

### Data Instances

A data point consists of an id, a comment, the main target, the other toxicity subtypes as well as identity attributes.

For instance, here's the first train example.
```
{
    "article_id": 2006,
    "asian": NaN,
    "atheist": NaN,
    "bisexual": NaN,
    "black": NaN,
    "buddhist": NaN,
    "christian": NaN,
    "comment_text": "This is so cool. It's like, 'would you want your mother to read this??' Really great idea, well done!",
    "created_date": "2015-09-29 10:50:41.987077+00",
    "disagree": 0,
    "female": NaN,
    "funny": 0,
    "heterosexual": NaN,
    "hindu": NaN,
    "homosexual_gay_or_lesbian": NaN,
    "identity_annotator_count": 0,
    "identity_attack": 0.0,
    "insult": 0.0,
    "intellectual_or_learning_disability": NaN,
    "jewish": NaN,
    "latino": NaN,
    "likes": 0,
    "male": NaN,
    "muslim": NaN,
    "obscene": 0.0,
    "other_disability": NaN,
    "other_gender": NaN,
    "other_race_or_ethnicity": NaN,
    "other_religion": NaN,
    "other_sexual_orientation": NaN,
    "parent_id": NaN,
    "physical_disability": NaN,
    "psychiatric_or_mental_illness": NaN,
    "publication_id": 2,
    "rating": 0,
    "sad": 0,
    "severe_toxicity": 0.0,
    "sexual_explicit": 0.0,
    "target": 0.0,
    "threat": 0.0,
    "toxicity_annotator_count": 4,
    "transgender": NaN,
    "white": NaN,
    "wow": 0
}
```

### Data Fields

- `id`: id of the comment
- `target`: value between 0(non-toxic) and 1(toxic) classifying the comment
- `comment_text`: the text of the comment
- `severe_toxicity`: value between 0(non-severe_toxic) and 1(severe_toxic) classifying the comment
- `obscene`: value between 0(non-obscene) and 1(obscene) classifying the comment
- `identity_attack`: value between 0(non-identity_hate) or 1(identity_hate) classifying the comment
- `insult`: value between 0(non-insult) or 1(insult) classifying the comment
- `threat`: value between 0(non-threat) and 1(threat) classifying the comment
- For a subset of rows, columns containing whether the comment mentions the entities (they may contain NaNs): 
  - `male`
  - `female`
  - `transgender`
  - `other_gender`
  - `heterosexual`
  - `homosexual_gay_or_lesbian`
  - `bisexual`
  - `other_sexual_orientation`
  - `christian`
  - `jewish`
  - `muslim`
  - `hindu`
  - `buddhist`
  - `atheist`
  - `other_religion`
  - `black`
  - `white`
  - `asian`
  - `latino`
  - `other_race_or_ethnicity`
  - `physical_disability`
  - `intellectual_or_learning_disability`
  - `psychiatric_or_mental_illness`
  - `other_disability`
- Other metadata related to the source of the comment, such as creation date, publication id, number of likes,
number of annotators, etc:
  - `created_date`
  - `publication_id`
  - `parent_id`
  - `article_id`
  - `rating`
  - `funny`
  - `wow`
  - `sad`
  - `likes`
  - `disagree`
  - `sexual_explicit`
  - `identity_annotator_count`
  - `toxicity_annotator_count`

### Data Splits

There are four splits:
- train: The train dataset as released during the competition. Contains labels and identity information for a 
subset of rows.
- test: The train dataset as released during the competition. Does not contain labels nor identity information.
- test_private_expanded: The private leaderboard test set, including toxicity labels and subgroups. The competition target was a binarized version of the toxicity column, which can be easily reconstructed using a >=0.5 threshold.
- test_public_expanded: The public leaderboard test set, including toxicity labels and subgroups. The competition target was a binarized version of the toxicity column, which can be easily reconstructed using a >=0.5 threshold.

## 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

[More Information Needed]

#### 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

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

This dataset is released under CC0, as is the underlying comment text.

### Citation Information

No citation is available for this dataset, though you may link to the [kaggle](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification) competition

### Contributions

Thanks to [@iwontbecreative](https://github.com/iwontbecreative) for adding this dataset.
task_categories:text-classification task_ids:text-scoring annotations_creators:crowdsourced language_creators:crowdsourced multilinguality:monolingual size_categories:1M<n<10M source_datasets:original language:en license:cc0-1.0 toxicity-prediction region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc0-1.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["text-scoring"], "pretty_name": "Jigsaw Unintended Bias in Toxicity Classification", "tags": ["toxicity-prediction"], "dataset_info": {"features": [{"name": "target", "dtype": "float32"}, {"name": "comment_text", "dtype": "string"}, {"name": "severe_toxicity", "dtype": "float32"}, {"name": "obscene", "dtype": "float32"}, {"name": "identity_attack", "dtype": "float32"}, {"name": "insult", "dtype": "float32"}, {"name": "threat", "dtype": "float32"}, {"name": "asian", "dtype": "float32"}, {"name": "atheist", "dtype": "float32"}, {"name": "bisexual", "dtype": "float32"}, {"name": "black", "dtype": "float32"}, {"name": "buddhist", "dtype": "float32"}, {"name": "christian", "dtype": "float32"}, {"name": "female", "dtype": "float32"}, {"name": "heterosexual", "dtype": "float32"}, {"name": "hindu", "dtype": "float32"}, {"name": "homosexual_gay_or_lesbian", "dtype": "float32"}, {"name": "intellectual_or_learning_disability", "dtype": "float32"}, {"name": "jewish", "dtype": "float32"}, {"name": "latino", "dtype": "float32"}, {"name": "male", "dtype": "float32"}, {"name": "muslim", "dtype": "float32"}, {"name": "other_disability", "dtype": "float32"}, {"name": "other_gender", "dtype": "float32"}, {"name": "other_race_or_ethnicity", "dtype": "float32"}, {"name": "other_religion", "dtype": "float32"}, {"name": "other_sexual_orientation", "dtype": "float32"}, {"name": "physical_disability", "dtype": "float32"}, {"name": "psychiatric_or_mental_illness", "dtype": "float32"}, {"name": "transgender", "dtype": "float32"}, {"name": "white", "dtype": "float32"}, {"name": "created_date", "dtype": "string"}, {"name": "publication_id", "dtype": "int32"}, {"name": "parent_id", "dtype": "float32"}, {"name": "article_id", "dtype": "int32"}, {"name": "rating", "dtype": {"class_label": {"names": {"0": "rejected", "1": "approved"}}}}, {"name": "funny", "dtype": "int32"}, {"name": "wow", "dtype": "int32"}, {"name": "sad", "dtype": "int32"}, {"name": "likes", "dtype": "int32"}, {"name": "disagree", "dtype": "int32"}, {"name": "sexual_explicit", "dtype": "float32"}, {"name": "identity_annotator_count", "dtype": "int32"}, {"name": "toxicity_annotator_count", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 914264058, "num_examples": 1804874}, {"name": "test_private_leaderboard", "num_bytes": 49188921, "num_examples": 97320}, {"name": "test_public_leaderboard", "num_bytes": 49442360, "num_examples": 97320}], "download_size": 0, "dataset_size": 1012895339}}
TAGS
#task_categories-text-classification #task_ids-text-scoring #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc0-1.0 #toxicity-prediction #region-us 


# Dataset Card for Jigsaw Unintended Bias in Toxicity Classification

## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - 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
  - Citation Information
  - Contributions

## Dataset Description

- Homepage: URL
- Repository:
- Paper:
- Leaderboard: URL
- Point of Contact:

### Dataset Summary

The Jigsaw Unintended Bias in Toxicity Classification dataset comes from the eponymous Kaggle competition.

Please see the original data
 description for more information.

### Supported Tasks and Leaderboards

The main target for this dataset is toxicity prediction. Several toxicity subtypes are also available, so the dataset 
can be used for multi-attribute prediction.

See the original leaderboard
for reference.

### Languages

English

## Dataset Structure

### Data Instances

A data point consists of an id, a comment, the main target, the other toxicity subtypes as well as identity attributes.

For instance, here's the first train example.


### Data Fields

- 'id': id of the comment
- 'target': value between 0(non-toxic) and 1(toxic) classifying the comment
- 'comment_text': the text of the comment
- 'severe_toxicity': value between 0(non-severe_toxic) and 1(severe_toxic) classifying the comment
- 'obscene': value between 0(non-obscene) and 1(obscene) classifying the comment
- 'identity_attack': value between 0(non-identity_hate) or 1(identity_hate) classifying the comment
- 'insult': value between 0(non-insult) or 1(insult) classifying the comment
- 'threat': value between 0(non-threat) and 1(threat) classifying the comment
- For a subset of rows, columns containing whether the comment mentions the entities (they may contain NaNs): 
  - 'male'
  - 'female'
  - 'transgender'
  - 'other_gender'
  - 'heterosexual'
  - 'homosexual_gay_or_lesbian'
  - 'bisexual'
  - 'other_sexual_orientation'
  - 'christian'
  - 'jewish'
  - 'muslim'
  - 'hindu'
  - 'buddhist'
  - 'atheist'
  - 'other_religion'
  - 'black'
  - 'white'
  - 'asian'
  - 'latino'
  - 'other_race_or_ethnicity'
  - 'physical_disability'
  - 'intellectual_or_learning_disability'
  - 'psychiatric_or_mental_illness'
  - 'other_disability'
- Other metadata related to the source of the comment, such as creation date, publication id, number of likes,
number of annotators, etc:
  - 'created_date'
  - 'publication_id'
  - 'parent_id'
  - 'article_id'
  - 'rating'
  - 'funny'
  - 'wow'
  - 'sad'
  - 'likes'
  - 'disagree'
  - 'sexual_explicit'
  - 'identity_annotator_count'
  - 'toxicity_annotator_count'

### Data Splits

There are four splits:
- train: The train dataset as released during the competition. Contains labels and identity information for a 
subset of rows.
- test: The train dataset as released during the competition. Does not contain labels nor identity information.
- test_private_expanded: The private leaderboard test set, including toxicity labels and subgroups. The competition target was a binarized version of the toxicity column, which can be easily reconstructed using a >=0.5 threshold.
- test_public_expanded: The public leaderboard test set, including toxicity labels and subgroups. The competition target was a binarized version of the toxicity column, which can be easily reconstructed using a >=0.5 threshold.

## 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



#### 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, as is the underlying comment text.



No citation is available for this dataset, though you may link to the kaggle competition

### Contributions

Thanks to @iwontbecreative for adding this dataset.
# Dataset Card for Jigsaw Unintended Bias in Toxicity Classification ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - 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
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: URL
- Repository:
- Paper:
- Leaderboard: URL
- Point of Contact: ### Dataset Summary

The Jigsaw Unintended Bias in Toxicity Classification dataset comes from the eponymous Kaggle competition.

Please see the original data
 description for more information. ### Supported Tasks and Leaderboards

The main target for this dataset is toxicity prediction. Several toxicity subtypes are also available, so the dataset 
can be used for multi-attribute prediction.

See the original leaderboard
for reference. ### Languages

English ## Dataset Structure ### Data Instances

A data point consists of an id, a comment, the main target, the other toxicity subtypes as well as identity attributes.

For instance, here's the first train example. ### Data Fields

- 'id': id of the comment
- 'target': value between 0(non-toxic) and 1(toxic) classifying the comment
- 'comment_text': the text of the comment
- 'severe_toxicity': value between 0(non-severe_toxic) and 1(severe_toxic) classifying the comment
- 'obscene': value between 0(non-obscene) and 1(obscene) classifying the comment
- 'identity_attack': value between 0(non-identity_hate) or 1(identity_hate) classifying the comment
- 'insult': value between 0(non-insult) or 1(insult) classifying the comment
- 'threat': value between 0(non-threat) and 1(threat) classifying the comment
- For a subset of rows, columns containing whether the comment mentions the entities (they may contain NaNs): 
  - 'male'
  - 'female'
  - 'transgender'
  - 'other_gender'
  - 'heterosexual'
  - 'homosexual_gay_or_lesbian'
  - 'bisexual'
  - 'other_sexual_orientation'
  - 'christian'
  - 'jewish'
  - 'muslim'
  - 'hindu'
  - 'buddhist'
  - 'atheist'
  - 'other_religion'
  - 'black'
  - 'white'
  - 'asian'
  - 'latino'
  - 'other_race_or_ethnicity'
  - 'physical_disability'
  - 'intellectual_or_learning_disability'
  - 'psychiatric_or_mental_illness'
  - 'other_disability'
- Other metadata related to the source of the comment, such as creation date, publication id, number of likes,
number of annotators, etc:
  - 'created_date'
  - 'publication_id'
  - 'parent_id'
  - 'article_id'
  - 'rating'
  - 'funny'
  - 'wow'
  - 'sad'
  - 'likes'
  - 'disagree'
  - 'sexual_explicit'
  - 'identity_annotator_count'
  - 'toxicity_annotator_count' ### Data Splits

There are four splits:
- train: The train dataset as released during the competition. Contains labels and identity information for a 
subset of rows.
- test: The train dataset as released during the competition. Does not contain labels nor identity information.
- test_private_expanded: The private leaderboard test set, including toxicity labels and subgroups. The competition target was a binarized version of the toxicity column, which can be easily reconstructed using a >=0.5 threshold.
- test_public_expanded: The public leaderboard test set, including toxicity labels and subgroups. The competition target was a binarized version of the toxicity column, which can be easily reconstructed using a >=0.5 threshold. ## 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 #### 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, as is the underlying comment text.



No citation is available for this dataset, though you may link to the kaggle competition ### Contributions

Thanks to @iwontbecreative for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-text-scoring #annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc0-1.0 #toxicity-prediction #region-us 
 # Dataset Card for Jigsaw Unintended Bias in Toxicity Classification ## Table of Contents
- Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - 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
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: URL
- Repository:
- Paper:
- Leaderboard: URL
- Point of Contact: ### Dataset Summary

The Jigsaw Unintended Bias in Toxicity Classification dataset comes from the eponymous Kaggle competition.

Please see the original data
 description for more information. ### Supported Tasks and Leaderboards

The main target for this dataset is toxicity prediction. Several toxicity subtypes are also available, so the dataset 
can be used for multi-attribute prediction.

See the original leaderboard
for reference. ### Languages

English ## Dataset Structure ### Data Instances

A data point consists of an id, a comment, the main target, the other toxicity subtypes as well as identity attributes.

For instance, here's the first train example. ### Data Fields

- 'id': id of the comment
- 'target': value between 0(non-toxic) and 1(toxic) classifying the comment
- 'comment_text': the text of the comment
- 'severe_toxicity': value between 0(non-severe_toxic) and 1(severe_toxic) classifying the comment
- 'obscene': value between 0(non-obscene) and 1(obscene) classifying the comment
- 'identity_attack': value between 0(non-identity_hate) or 1(identity_hate) classifying the comment
- 'insult': value between 0(non-insult) or 1(insult) classifying the comment
- 'threat': value between 0(non-threat) and 1(threat) classifying the comment
- For a subset of rows, columns containing whether the comment mentions the entities (they may contain NaNs): 
  - 'male'
  - 'female'
  - 'transgender'
  - 'other_gender'
  - 'heterosexual'
  - 'homosexual_gay_or_lesbian'
  - 'bisexual'
  - 'other_sexual_orientation'
  - 'christian'
  - 'jewish'
  - 'muslim'
  - 'hindu'
  - 'buddhist'
  - 'atheist'
  - 'other_religion'
  - 'black'
  - 'white'
  - 'asian'
  - 'latino'
  - 'other_race_or_ethnicity'
  - 'physical_disability'
  - 'intellectual_or_learning_disability'
  - 'psychiatric_or_mental_illness'
  - 'other_disability'
- Other metadata related to the source of the comment, such as creation date, publication id, number of likes,
number of annotators, etc:
  - 'created_date'
  - 'publication_id'
  - 'parent_id'
  - 'article_id'
  - 'rating'
  - 'funny'
  - 'wow'
  - 'sad'
  - 'likes'
  - 'disagree'
  - 'sexual_explicit'
  - 'identity_annotator_count'
  - 'toxicity_annotator_count' ### Data Splits

There are four splits:
- train: The train dataset as released during the competition. Contains labels and identity information for a 
subset of rows.
- test: The train dataset as released during the competition. Does not contain labels nor identity information.
- test_private_expanded: The private leaderboard test set, including toxicity labels and subgroups. The competition target was a binarized version of the toxicity column, which can be easily reconstructed using a >=0.5 threshold.
- test_public_expanded: The public leaderboard test set, including toxicity labels and subgroups. The competition target was a binarized version of the toxicity column, which can be easily reconstructed using a >=0.5 threshold. ## 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 #### 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, as is the underlying comment text.



No citation is available for this dataset, though you may link to the kaggle competition ### Contributions

Thanks to @iwontbecreative for adding this dataset.