
# Dataset Card for ToxiGen

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Fields](#data-instances)
- [Additional Information](#additional-information)
  - [Citation Information](#citation-information)

## Sign up for Data Access
To access ToxiGen, first fill out [this form](https://forms.office.com/r/r6VXX8f8vh).

## Dataset Description

- **Repository:** https://github.com/microsoft/toxigen
- **Paper:** https://arxiv.org/abs/2203.09509
- **Point of Contact #1:** [Tom Hartvigsen](tomh@mit.edu)
- **Point of Contact #2:** [Saadia Gabriel](skgabrie@cs.washington.edu)

### Dataset Summary

This dataset is for implicit hate speech detection. All instances were generated using GPT-3 and the methods described in [our paper](https://arxiv.org/abs/2203.09509).

### Languages

All text is written in English.

## Dataset Structure

### Data Fields

We release TOXIGEN as a dataframe with the following fields:
- **prompt** is the prompt used for **generation**.
- **generation** is the TOXIGEN generated text.
- **generation_method** denotes whether or not ALICE was used to generate the corresponding generation. If this value is ALICE, then ALICE was used, if it is TopK, then ALICE was not used.
- **prompt_label** is the binary value indicating whether or not the prompt is toxic (1 is toxic, 0 is benign).
- **group** indicates the target group of the prompt.
- **roberta_prediction** is the probability predicted by our corresponding RoBERTa model for each instance.

### Citation Information

```bibtex
@inproceedings{hartvigsen2022toxigen,
  title={ToxiGen: A Large-Scale Machine-Generated Dataset for Implicit and Adversarial Hate Speech Detection},
  author={Hartvigsen, Thomas and Gabriel, Saadia and Palangi, Hamid and Sap, Maarten and Ray, Dipankar and Kamar, Ece},
  booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
  year={2022}
}
```

task_categories:text-classification task_ids:hate-speech-detection annotations_creators:expert-generated language_creators:machine-generated multilinguality:monolingual size_categories:100K<n<1M source_datasets:original arxiv:2203.09509 region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["machine-generated"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["hate-speech-detection"], "pretty_name": "ToxiGen", "languages": ["en-US"], "licenses": []}
TAGS
#task_categories-text-classification #task_ids-hate-speech-detection #annotations_creators-expert-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #arxiv-2203.09509 #region-us 


# Dataset Card for ToxiGen

## Table of Contents
- Dataset Description
  - Dataset Summary
  - Languages
- Dataset Structure
  - Data Fields
- Additional Information
  - Citation Information

## Sign up for Data Access
To access ToxiGen, first fill out this form.

## Dataset Description

- Repository: URL
- Paper: URL
- Point of Contact #1: Tom Hartvigsen
- Point of Contact #2: Saadia Gabriel

### Dataset Summary

This dataset is for implicit hate speech detection. All instances were generated using GPT-3 and the methods described in our paper.

### Languages

All text is written in English.

## Dataset Structure

### Data Fields

We release TOXIGEN as a dataframe with the following fields:
- prompt is the prompt used for generation.
- generation is the TOXIGEN generated text.
- generation_method denotes whether or not ALICE was used to generate the corresponding generation. If this value is ALICE, then ALICE was used, if it is TopK, then ALICE was not used.
- prompt_label is the binary value indicating whether or not the prompt is toxic (1 is toxic, 0 is benign).
- group indicates the target group of the prompt.
- roberta_prediction is the probability predicted by our corresponding RoBERTa model for each instance.





# Dataset Card for ToxiGen ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Languages
- Dataset Structure
  - Data Fields
- Additional Information
  - Citation Information ## Sign up for Data Access
To access ToxiGen, first fill out this form. ## Dataset Description

- Repository: URL
- Paper: URL
- Point of Contact #1: Tom Hartvigsen
- Point of Contact #2: Saadia Gabriel ### Dataset Summary

This dataset is for implicit hate speech detection. All instances were generated using GPT-3 and the methods described in our paper. ### Languages

All text is written in English. ## Dataset Structure ### Data Fields

We release TOXIGEN as a dataframe with the following fields:
- prompt is the prompt used for generation.
- generation is the TOXIGEN generated text.
- generation_method denotes whether or not ALICE was used to generate the corresponding generation. If this value is ALICE, then ALICE was used, if it is TopK, then ALICE was not used.
- prompt_label is the binary value indicating whether or not the prompt is toxic (1 is toxic, 0 is benign).
- group indicates the target group of the prompt.
- roberta_prediction is the probability predicted by our corresponding RoBERTa model for each instance.
TAGS
#task_categories-text-classification #task_ids-hate-speech-detection #annotations_creators-expert-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #arxiv-2203.09509 #region-us 
 # Dataset Card for ToxiGen ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Languages
- Dataset Structure
  - Data Fields
- Additional Information
  - Citation Information ## Sign up for Data Access
To access ToxiGen, first fill out this form. ## Dataset Description

- Repository: URL
- Paper: URL
- Point of Contact #1: Tom Hartvigsen
- Point of Contact #2: Saadia Gabriel ### Dataset Summary

This dataset is for implicit hate speech detection. All instances were generated using GPT-3 and the methods described in our paper. ### Languages

All text is written in English. ## Dataset Structure ### Data Fields

We release TOXIGEN as a dataframe with the following fields:
- prompt is the prompt used for generation.
- generation is the TOXIGEN generated text.
- generation_method denotes whether or not ALICE was used to generate the corresponding generation. If this value is ALICE, then ALICE was used, if it is TopK, then ALICE was not used.
- prompt_label is the binary value indicating whether or not the prompt is toxic (1 is toxic, 0 is benign).
- group indicates the target group of the prompt.
- roberta_prediction is the probability predicted by our corresponding RoBERTa model for each instance.