{
 "id": "Oni279/BanglaRQA",
 "owner": "Oni279",
 "name": "BanglaRQA",
 "downloads": 30,
 "likes": 0,
 "last_modified": "2026-03-28T20:11:51.000Z",
 "stated_licence": "cc-by-nc-sa-4.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [
  "question-answering"
 ],
 "citation": "@inproceedings{ekram-etal-2022-banglarqa,\n    title = \"{B}angla{RQA}: A Benchmark Dataset for Under-resourced {B}angla Language Reading Comprehension-based Question Answering with Diverse Question-Answer Types\",\n    author = \"Ekram, Syed Mohammed Sartaj  and\n      Rahman, Adham Arik  and\n      Altaf, Md. Sajid  and\n      Islam, Mohammed Saidul  and\n      Rahman, Mehrab Mustafy  and\n      Rahman, Md Mezbaur  and\n      Hossain, Md Azam  and\n      Kamal, Abu Raihan Mostofa\",\n    booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2022\",\n    month = dec,\n    year = \"2022\",\n    address = \"Abu Dhabi, United Arab Emirates\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2022.findings-emnlp.186\",\n    pages = \"2518--2532\",\n    abstract = \"High-resource languages, such as English, have access to a plethora of datasets with various question-answer types resembling real-world reading comprehension. However, there is a severe lack of diverse and comprehensive question-answering datasets in under-resourced languages like Bangla. The ones available are either translated versions of English datasets with a niche answer format or created by human annotations focusing on a specific domain, question type, or answer type. To address these limitations, this paper introduces BanglaRQA, a reading comprehension-based Bangla question-answering dataset with various question-answer types. BanglaRQA consists of 3,000 context pas",
 "signal": "restrictive",
 "reason": "The declared licence (cc-by-nc-sa-4.0) is classified as having use restrictions.",
 "revision": "2de71c0cfc7f47cd640384c94895c5de5f15d3cc",
 "stated_evidence": "https://huggingface.co/datasets/Oni279/BanglaRQA/blob/2de71c0cfc7f47cd640384c94895c5de5f15d3cc/README.md",
 "file_evidence": "https://huggingface.co/datasets/Oni279/BanglaRQA/tree/2de71c0cfc7f47cd640384c94895c5de5f15d3cc",
 "card_nc": false
}