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  1. README.md +8 -5
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@@ -5,9 +5,6 @@ task_categories:
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  - token-classification
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  size_categories:
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  - 1M<n<10M
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- datasets:
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- - tomekkorbak/pile-toxicity-balanced2
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- - datasets/thai_toxicity_tweet
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  language:
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  - ar
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  - es
@@ -27,7 +24,7 @@ language:
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  - sk
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  - gu
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  - he
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- - af=
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  - te
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  - ro
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  - lv
@@ -71,7 +68,7 @@ tags:
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  About 11 months ago, I downloaded and preprocessed 2.7M rows of text data, but completely forgot the original source of these datasets...
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  All I know is that I looked everywhere: HuggingFace, research papers, GitHub, Kaggle, and Google search. I even fetched 20K+ tweets using the Twitter API.
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- Today (6/28/2023) I came across three newer HuggingFace datasets, so I added them to this dataset.
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  The deduplicated training data alone consists of 2,880,230 rows of comments and messages. Among these rows, 416,457 are classified as toxic, while the remaining 2,463,773 are considered neutral. Below is a table to illustrate the data composition:
@@ -140,6 +137,12 @@ Each CSV file has two columns: `text` and `is_toxic`.
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  - Welsh
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  <br>
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  Have fun modelling!
 
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  - token-classification
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  size_categories:
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  - 1M<n<10M
 
 
 
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  language:
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  - ar
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  - es
 
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  - sk
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  - gu
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  - he
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+ - af
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  - te
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  - ro
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  - lv
 
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  About 11 months ago, I downloaded and preprocessed 2.7M rows of text data, but completely forgot the original source of these datasets...
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  All I know is that I looked everywhere: HuggingFace, research papers, GitHub, Kaggle, and Google search. I even fetched 20K+ tweets using the Twitter API.
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+ Today (6/28/2023) I came across two newer HuggingFace datasets, so I remembered to credit them at the bottom of the page.
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  The deduplicated training data alone consists of 2,880,230 rows of comments and messages. Among these rows, 416,457 are classified as toxic, while the remaining 2,463,773 are considered neutral. Below is a table to illustrate the data composition:
 
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  - Welsh
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+ <br>
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+
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+ Known datasets:
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+ - tomekkorbak/pile-toxicity-balanced2
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+ - datasets/thai_toxicity_tweet
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+
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  <br>
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  Have fun modelling!