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| Type: || Algoliterary dataset
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| Type: || Dataset
 
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| Source(s): ||  
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|Number of words: || 1.039.789
 
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| Collectively developed by: || & Algolit
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|Unique words: || 64.136
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| Source: || English Wikipedia
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| Developed by: || Wikimedia Foundation
 
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The [https://meta.wikimedia.org/wiki/Research:Detox Detox dataset] is a project by Wikimedia and [[Crowd Embeddings| Perspective API]] to train a neural network that would detect the level of toxicity of a comment.
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The [https://figshare.com/projects/Wikipedia_Talk/16731 original dataset] consists of:
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*A corpus of all 95 million user and article talk diffs made between 2001 and 2015 scored by the personal attack model.
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*A human annotated dataset of 1m crowd-sourced annotations that cover 100k talk page diffs (with 10 judgements per diff).
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For Algolit, a smaller section of the Detox dataset was used, taken from [https://conversationai.github.io/wikidetox/testdata/tox-sorted/Wikipedia%20Toxicity%20Sorted%20%28Toxicity%405%5BAlpha%5D%29.html Jigsaw's Github], which contains both constructive and vandalist edits.
  
[[Category:Algoliterary-encounters]]
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[[Category:Algoliterary-Encounters]]

Latest revision as of 13:55, 2 November 2017

Type: Dataset
Number of words: 1.039.789
Unique words: 64.136
Source: English Wikipedia
Developed by: Wikimedia Foundation

The Detox dataset is a project by Wikimedia and Perspective API to train a neural network that would detect the level of toxicity of a comment.

The original dataset consists of:

  • A corpus of all 95 million user and article talk diffs made between 2001 and 2015 scored by the personal attack model.
  • A human annotated dataset of 1m crowd-sourced annotations that cover 100k talk page diffs (with 10 judgements per diff).

For Algolit, a smaller section of the Detox dataset was used, taken from Jigsaw's Github, which contains both constructive and vandalist edits.