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ukr-detect/ukr-emotions-binary

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Permissive or share-alike

The declared licence (cc-by-4.0) is classified as permissive.

Declared licence: cc-by-4.0, from the tagEvidence: README.md at 93188a2
Licence files: No licence file at the top level of the repositoryEvidence: file list at 93188a2
Declared sources: None listed in the card metadata
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Tasks

text-classification

Citation text from the card

@inproceedings{dementieva-etal-2025-emobench,
    title = "{E}mo{B}ench-{UA}: A Benchmark Dataset for Emotion Detection in {U}krainian",
    author = "Dementieva, Daryna  and
      Babakov, Nikolay  and
      Fraser, Alexander",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-emnlp.107/",
    doi = "10.18653/v1/2025.findings-emnlp.107",
    pages = "2025--2048",
    ISBN = "979-8-89176-335-7",
    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

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dataset_licence({ "id": "ukr-detect/ukr-emotions-binary" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0