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- ---
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- language:
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- - uk
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- license: cc-by-nc-sa-4.0
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- dataset_info:
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- features:
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- - name: document_id
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- dtype: string
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- - name: tokens
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- sequence: string
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- - name: ner_tags
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- sequence:
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- class_label:
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- names:
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- '0': O
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- '1': B-ORG
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- '2': I-ORG
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- '3': B-PERS
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- '4': I-PERS
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- '5': B-LOC
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- '6': I-LOC
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- '7': B-MON
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- '8': I-MON
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- '9': B-PCT
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- '10': I-PCT
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- '11': B-DATE
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- '12': I-DATE
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- '13': B-TIME
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- '14': I-TIME
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- '15': B-PERIOD
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- '16': I-PERIOD
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- '17': B-JOB
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- '18': I-JOB
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- '19': B-DOC
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- '20': I-DOC
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- '21': B-QUANT
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- '22': I-QUANT
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- '23': B-ART
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- '24': I-ART
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- '25': B-MISC
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- '26': I-MISC
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- - name: source
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- dtype: string
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- splits:
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- - name: train
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- num_bytes: 4426002
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- num_examples: 10980
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- - name: validation
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- num_bytes: 472074
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- num_examples: 1206
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- - name: test
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- num_bytes: 2307876
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- num_examples: 5593
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- download_size: 1855779
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- dataset_size: 7205952
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- configs:
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- - config_name: default
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- data_files:
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- - split: train
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- path: data/train-*
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- - split: validation
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- path: data/validation-*
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- - split: test
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- path: data/test-*
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ language:
3
+ - uk
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+ license: cc-by-nc-sa-4.0
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+ dataset_info:
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+ features:
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+ - name: document_id
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+ dtype: string
9
+ - name: tokens
10
+ sequence: string
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+ - name: ner_tags
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+ sequence:
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+ class_label:
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+ names:
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+ '0': O
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+ '1': B-ORG
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+ '2': I-ORG
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+ '3': B-PERS
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+ '4': I-PERS
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+ '5': B-LOC
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+ '6': I-LOC
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+ '7': B-MON
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+ '8': I-MON
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+ '9': B-PCT
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+ '10': I-PCT
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+ '11': B-DATE
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+ '12': I-DATE
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+ '13': B-TIME
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+ '14': I-TIME
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+ '15': B-PERIOD
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+ '16': I-PERIOD
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+ '17': B-JOB
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+ '18': I-JOB
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+ '19': B-DOC
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+ '20': I-DOC
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+ '21': B-QUANT
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+ '22': I-QUANT
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+ '23': B-ART
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+ '24': I-ART
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+ '25': B-MISC
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+ '26': I-MISC
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+ - name: source
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+ dtype: string
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+ splits:
45
+ - name: train
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+ num_bytes: 4426002
47
+ num_examples: 10980
48
+ - name: validation
49
+ num_bytes: 472074
50
+ num_examples: 1206
51
+ - name: test
52
+ num_bytes: 2307876
53
+ num_examples: 5593
54
+ download_size: 1855779
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+ dataset_size: 7205952
56
+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: data/train-*
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+ - split: validation
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+ path: data/validation-*
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+ - split: test
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+ path: data/test-*
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+ task_categories:
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+ - token-classification
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+ ---
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+
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+ # NER-UK 2.0
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+
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+
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+ Second version of the Named Entity Recognition for Ukrainian dataset.
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+
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+ All the credit belongs to [lang-uk](https://github.com/lang-uk).
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+
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+ This repository is merely made to simplify the workflow. Previous version of the dataset was published in a similar fashion at [benjamin/ner-uk](https://huggingface.co/datasets/benjamin/ner-uk).
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+
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+ ## License
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+
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+ <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a>
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+
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+ "Корпус NER-анотацій українських текстів" by [lang-uk](https://github.com/lang-uk) is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
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+ Based on a work at [https://github.com/lang-uk/ner-uk](https://github.com/lang-uk/ner-uk).
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+
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+ ## Citation
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+
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+ ```
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+ @inproceedings{chaplynskyi-romanyshyn-2024-introducing,
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+ title = "Introducing {NER}-{UK} 2.0: A Rich Corpus of Named Entities for {U}krainian",
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+ author = "Chaplynskyi, Dmytro and
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+ Romanyshyn, Mariana",
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+ editor = "Romanyshyn, Mariana and
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+ Romanyshyn, Nataliia and
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+ Hlybovets, Andrii and
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+ Ignatenko, Oleksii",
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+ booktitle = "Proceedings of the Third Ukrainian Natural Language Processing Workshop (UNLP) @ LREC-COLING 2024",
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+ month = may,
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+ year = "2024",
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+ address = "Torino, Italia",
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+ publisher = "ELRA and ICCL",
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+ url = "https://aclanthology.org/2024.unlp-1.4/",
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+ pages = "23--29",
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+ 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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+ }
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+ ```