Dataset Card for Wiki Toxic

Dataset Summary

The Wiki Toxic dataset is a modified, cleaned version of the dataset used in the Kaggle Toxic Comment Classification challenge from 2017/18. The dataset contains comments collected from Wikipedia forums and classifies them into two categories, toxic and non-toxic .

The Kaggle dataset was cleaned using the included clean.py file.

Supported Tasks and Leaderboards

• Text Classification: the dataset can be used for training a model to recognise toxicity in sentences and classify them accordingly.

Languages

The sole language used in the dataset is English.

Dataset Structure

Data Instances

For each data point, there is an id, the comment_text itself, and a label (0 for non-toxic, 1 for toxic).

{'id': 'a123a58f610cffbc',
 'comment_text': '"This article SUCKS. It may be poorly written, poorly formatted, or full of pointless crap that no one cares about, and probably all of the above.  If it can be rewritten into something less horrible, please, for the love of God, do so, before the vacuum caused by its utter lack of quality drags the rest of Wikipedia down into a bottomless pit of mediocrity."',
 'label': 1}

Data Fields

• id : A unique identifier string for each comment
• comment_text : A string containing the text of the comment
• label : An integer, either 0 if the comment is non-toxic, or 1 if the comment is toxic

Data Splits

The Wiki Toxic dataset has three splits: train , validation , and test . The statistics for each split are below:

Dataset Split	Number of data points in split
Train	127,656
Validation	31,915
Test	63,978

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

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Contributions

Thanks to @github-username for adding this dataset.