File size: 3,441 Bytes
b4daf70 5c31414 067cbaa 6e798b0 067cbaa 321fade 59f34ff 321fade 10710d2 e6bb7d0 321fade 2beed9c b4daf70 067cbaa 928df84 d46c1ba 97ce730 067cbaa 2697bed 81606aa 2697bed 81606aa a453214 11b8e29 11331d7 91c8463 11331d7 91c8463 11331d7 7dc3145 321fade 59f34ff 7dc3145 51685eb 406ed73 7dc3145 59f34ff 406ed73 59f34ff 321fade 51685eb 406ed73 51685eb a453214 406ed73 51685eb 321fade |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 |
---
license: apache-2.0
task_categories:
- text-classification
- token-classification
- zero-shot-classification
size_categories:
- 1M<n<10M
language:
- ar
- es
- pa
- th
- et
- fr
- fi
- hu
- lt
- ur
- so
- pl
- el
- mr
- sk
- gu
- he
- af
- te
- ro
- lv
- sv
- ne
- kn
- it
- mk
- cs
- en
- de
- da
- ta
- bn
- pt
- sq
- tl
- uk
- bg
- ca
- sw
- hi
- zh
- ja
- hr
- ru
- vi
- id
- sl
- cy
- ko
- nl
- ml
- tr
- fa
- 'no'
- multilingual
tags:
- nlp
- moderation
---
**I have decided to release the auto-moderation models all at once sometime in July/August, 2023. The curated datasets for training these models will be avaliable first.**
<br>
This is a large multilingual toxicity dataset with nearly 3M rows of text data from 55 natural languages, all of which are written/sent by humans, not machine translation models.
The preprocessed training data alone consists of 2,880,667 rows of comments, tweets, and messages. Among these rows, 416,529 are classified as toxic, while the remaining 2,463,773 are considered neutral. Below is a table to illustrate the data composition:
| | Toxic | Neutral | Total |
|-------|----------|----------|----------|
| [multilingual-train-deduplicated.csv](./multilingual-train-deduplicated.csv) | 416,529 | 2,464,138 | 2,880,667 |
| [multilingual-validation.csv](./multilingual-validation.csv) | 1,230 | 6,770 | 8,000 |
| [multilingual-test.csv](./multilingual-test.csv) | 14,410 | 49,402 | 63,812 |
Each CSV file has three columns: `text`, `is_toxic`, and `lang`.
Supported types of toxicity:
- Identity Hate/Homophobia
- Hate Speech
- Serious Insults
- Obscene
- Threats
- Harassment
- Racism
- Trolling
- Doxing
- Others
Supported languages:
- Afrikaans
- Albanian
- Arabic
- Bengali
- Bulgarian
- Catalan
- Chinese (Simplified)
- Chinese (Traditional)
- Croatian
- Czech
- Danish
- Dutch
- English
- Estonian
- Finnish
- French
- German
- Greek
- Gujarati
- Hebrew
- Hindi
- Hungarian
- Indonesian
- Italian
- Japanese
- Kannada
- Korean
- Latvian
- Lithuanian
- Macedonian
- Malayalam
- Marathi
- Nepali
- Norwegian
- Persian
- Polish
- Portuguese
- Punjabi
- Romanian
- Russian
- Slovak
- Slovenian
- Somali
- Spanish
- Swahili
- Swedish
- Tagalog
- Tamil
- Telugu
- Thai
- Turkish
- Ukrainian
- Urdu
- Vietnamese
- Welsh
<br>
### Original Source?
Around 11 months ago, I downloaded and preprocessed 2.7M rows of text data, but completely forgot the original source of these datasets...
All I remember is that I downloaded datasets from everywhere I could: HuggingFace, research papers, GitHub, Kaggle, SurgeAI, and Google search. I even fetched 20K+ tweets using the Twitter API.
Recently, I came across two newer HuggingFace datasets, so I remembered to credit them below.
Known datasets:
- tomekkorbak/pile-toxicity-balanced2
- datasets/thai_toxicity_tweet
- inspection-ai/japanese-toxic-dataset (GitHub)
<br>
### Limitations
Limitations include:
- All labels were rounded to the nearest integer. If a text was classified as 46%-54% toxic, the text itself might not be noticeably toxic or neutral.
- There were disagreements among moderators on some labels, due to ambiguity and lack of context.
- When there're only URL(s), emojis, or anything that's unrecognizable as natural language in the "text" column, the corresponding "lang" is "unkown".
- The validation data is not representative of the training data.
Have fun modelling! |