Real_Fake_and_Not_News: A Three-Class Bangla Text Classification Dataset

Real_Fake_and_Not_News is a large, balanced Bangla-language corpus designed for three-class text classification. It combines verified real news, fake news, and general Bangla sentences into a single, unified dataset—enabling classification models to learn both news authenticity and the boundary between news-like and non-news text.

The dataset is intended for researchers working on fake news detection, Bangla natural language processing (NLP), and text classification for low-resource languages.

《Real_Fake_and_Not_News》是一个大型均衡的孟加拉语语料库，专为三分类文本分类任务打造。该数据集将经验证的真实新闻、虚假新闻与通用孟加拉语句本整合为统一的数据集，使得分类模型能够同时学习新闻真实性的判别方法，以及类新闻文本与非新闻文本之间的边界。

该数据集面向从事虚假新闻检测、孟加拉语自然语言处理（NLP）以及低资源语言文本分类研究的科研人员。