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Goader/ner-uk-2.0

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Use restrictions

The declared licence (cc-by-nc-sa-4.0) is classified as having use restrictions.

Declared licence: cc-by-nc-sa-4.0, from the tagEvidence: README.md at b98f906
Licence files: No licence file at the top level of the repositoryEvidence: file list at b98f906
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Tasks

token-classification

Citation text from the card

@inproceedings{chaplynskyi-romanyshyn-2024-introducing,
    title = "Introducing {NER}-{UK} 2.0: A Rich Corpus of Named Entities for {U}krainian",
    author = "Chaplynskyi, Dmytro  and
      Romanyshyn, Mariana",
    editor = "Romanyshyn, Mariana  and
      Romanyshyn, Nataliia  and
      Hlybovets, Andrii  and
      Ignatenko, Oleksii",
    booktitle = "Proceedings of the Third Ukrainian Natural Language Processing Workshop (UNLP) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.unlp-1.4/",
    pages = "23--29",
    abstract = "This paper presents NER-UK 2.0, a corpus of texts in the Ukrainian language manually annotated for the named entity recognition task. The corpus contains 560 texts of multiple genres, boasting 21,993 entities in total. The annotation scheme covers 13 entity types, namely location, person name, organization, artifact, document, job title, date, time, period, money, percentage, quantity, and miscellaneous. Such a rich set of entities makes the corpus valuable for training named-entity recognition models in various domains, including news, social media posts, legal documents, and procurement contracts. The paper presents an updated baseline solution for named entity recognition in Ukrainian with 0.89 F1. The corpus is the largest of its kind for the Ukrainian language and is available for download."
}

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dataset_licence({ "id": "Goader/ner-uk-2.0" })
signal: "restrictive"
stated_licence: "cc-by-nc-sa-4.0"
sources: 0