---
license: mit
task_categories:
- text-classification
- token-classification
size_categories:
- 1M<n<10M
datasets:
- tomekkorbak/pile-toxicity-balanced2
- datasets/thai_toxicity_tweet
---


About 11 months ago, I downloaded and preprocessed 2.7M rows of text data, but completely forgot the original source of these datasets...
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.
Today (6/28/2023) I came across three newer HuggingFace datasets, so I added them to this dataset.


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:
|       |   Toxic  |  Neutral |   Total  |
|-------|----------|----------|----------|
| [multilingual-train-deduplicated.csv](./multilingual-train-deduplicated.csv) |   416,457   |   2,463,773   |   2,880,230   |
| [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 two columns: `text` and `is_toxic`.

Have fun modelling!