Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- text-classification
+language:
+- ko
+---
+
+# 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.
+
+---
+
+## 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.
+
+## 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}
+}