Datasets:
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---
license: apache-2.0
task_categories:
- text-classification
language:
- ko
dataset_info:
features:
- name: comment
dtype: string
- name: preference
dtype: int64
- name: profanity
dtype: int64
- name: gender
dtype: int64
- name: politics
dtype: int64
- name: nation
dtype: int64
- name: race
dtype: int64
- name: region
dtype: int64
- name: generation
dtype: int64
- name: social_hierarchy
dtype: int64
- name: appearance
dtype: int64
- name: others
dtype: int64
splits:
- name: train
num_bytes: 7458552
num_examples: 38361
- name: test
num_bytes: 412144
num_examples: 2000
download_size: 2947880
dataset_size: 7870696
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
size_categories:
- 1M<n<10M
---
# KoMultiText: Korean Multi-task Dataset for Classifying Biased Speech
## Dataset Summary
**KoMultiText** is a comprehensive Korean multi-task text dataset designed for classifying biased and harmful speech in online platforms. The dataset focuses on tasks such as **Preference Detection**, **Profanity Identification**, and **Bias Classification** across multiple domains, enabling state-of-the-art language models to perform multi-task learning for socially responsible AI applications.
### Key Features
- **Large-Scale Dataset**: Contains 150,000 comments, including labeled and unlabeled data.
- **Multi-task Annotations**: Covers Preference, Profanity, and nine distinct types of Bias.
- **Human-Labeled**: All labeled data is annotated by **five human experts** to ensure high-quality and unbiased annotations.
- **Real-world Relevance**: Collected from "Real-time Best Gallery" of [DC Inside](https://www.dcinside.com/), a popular online community in South Korea.
### Labels
<img src="https://raw.githubusercontent.com/Dasol-Choi/KoMultiText/main/resources/dataset_configuration.png" width="700">
---
# Dataset Creation
## Source Data
- **Origin**: Comments collected from "Real-time Best Gallery" on [DC Inside](https://www.dcinside.com/).
- **Annotation Process**:
- **Human Annotation**: Five human annotators independently labeled all comments in the dataset to ensure accuracy and minimize bias.
- **Labeling Process**: Annotators followed strict guidelines to classify comments into Preference, Profanity, and nine types of Bias. Discrepancies were resolved through majority voting and discussion.
- **Dataset Composition**:
- **Labeled Data**: 40,361 comments (train/test split).
- **Unlabeled Data**: 110,000 comments for potential pretraining or unsupervised learning. [Veiw Dataset](https://huggingface.co/datasets/Dasool/DC_inside_comments)
## How to Load the Dataset
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("Dasool/KoMultiText")
# Access train and test splits
train_dataset = dataset["train"]
test_dataset = dataset["test"]
```
## Code
- Korean BERT-based fine-tuning code. [Github](https://github.com/Dasol-Choi/KoMultiText?tab=readme-ov-file)
## Citation
<pre>
@misc{choi2023largescale,
title={Large-Scale Korean Text Dataset for Classifying Biased Speech in Real-World Online Services},
author={Dasol Choi and Jooyoung Song and Eunsun Lee and Jinwoo Seo and Heejune Park and Dongbin Na},
year={2023},
eprint={2310.04313},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
</pre>
## Contact
- [email protected] |