{
 "id": "ukr-detect/ukr-emotions-binary",
 "owner": "ukr-detect",
 "name": "ukr-emotions-binary",
 "downloads": 131,
 "likes": 0,
 "last_modified": "2026-08-05T08:26:37.000Z",
 "stated_licence": "cc-by-4.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [
  "text-classification"
 ],
 "citation": "@inproceedings{dementieva-etal-2025-emobench,\n    title = \"{E}mo{B}ench-{UA}: A Benchmark Dataset for Emotion Detection in {U}krainian\",\n    author = \"Dementieva, Daryna  and\n      Babakov, Nikolay  and\n      Fraser, Alexander\",\n    editor = \"Christodoulopoulos, Christos  and\n      Chakraborty, Tanmoy  and\n      Rose, Carolyn  and\n      Peng, Violet\",\n    booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2025\",\n    month = nov,\n    year = \"2025\",\n    address = \"Suzhou, China\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.findings-emnlp.107/\",\n    doi = \"10.18653/v1/2025.findings-emnlp.107\",\n    pages = \"2025--2048\",\n    ISBN = \"979-8-89176-335-7\",\n    abstract = \"While Ukrainian NLP has seen progress in many texts processing tasks, emotion classification remains an underexplored area with no publicly available benchmark to date. In this work, we introduce \\textbf{EmoBench-UA}, the first annotated dataset for emotion detection in Ukrainian texts. Our annotation schema is adapted from the previous English-centric works on emotion detection (Mohammad et al., 2018; Mohammad, 2022) guidelines. The dataset was created through crowdsourcing using the Toloka.ai platform ensuring high-quality of the annotation process. Then, we evaluate a range of approaches on the collected dataset, starting from linguistic-based baselines, synthetic data translated from English, to large language models (LLMs). Our ",
 "signal": "permissive",
 "reason": "The declared licence (cc-by-4.0) is classified as permissive.",
 "revision": "93188a2db49abbb01bda467d6e51201d1f1debbb",
 "stated_evidence": "https://huggingface.co/datasets/ukr-detect/ukr-emotions-binary/blob/93188a2db49abbb01bda467d6e51201d1f1debbb/README.md",
 "file_evidence": "https://huggingface.co/datasets/ukr-detect/ukr-emotions-binary/tree/93188a2db49abbb01bda467d6e51201d1f1debbb",
 "card_nc": false
}