WikiHarass: Difference between revisions
From Algolit
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| Type: || Dataset | | Type: || Dataset | ||
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+ | |Number of words: || 1.039.789 | ||
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+ | |Unique words: || 6.4136 | ||
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| Source: || English Wikipedia | | Source: || English Wikipedia | ||
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*A corpus of all 95 million user and article talk diffs made between 2001–2015 scored by the personal attack model. | *A corpus of all 95 million user and article talk diffs made between 2001–2015 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). | *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. | 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]] | [[Category:Algoliterary-Encounters]] |
Revision as of 20:42, 30 October 2017
Type: | Dataset |
Number of words: | 1.039.789 |
Unique words: | 6.4136 |
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 2001–2015 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.