UberText-NER-Silver is a silver-standard named entity recognition (NER) dataset for the Ukrainian language. It was automatically annotated using a high-performance model trained on NER-UK 2.0 and covers over 2.5 million social media and web sentences. The dataset significantly expands the coverage of underrepresented entity types and informal domains.

Dataset Summary

Total Sentences: 2,573,205
Total Words: 45,489,533
Total Entity Spans: 4,393,316
Entity Types (13): PERS, ORG, LOC, DATE, TIME, JOB, MON, PCT, PERIOD, DOC, QUANT, ART, MISC
Format: IOB-style, token-level annotations

Source

Texts were taken from the UberText 2.0 corpus social media part, filtered and preprocessed for noise reduction and duplication. The dataset includes both entity-rich and entity-free content to improve model generalization.

Example Usage

from datasets import load_dataset

dataset = load_dataset("lang-uk/UberText-NER-Silver", split="train")
print(dataset[0])

Applications

Training large-scale NER models for Ukrainian
Improving performance in low-resource and informal text domains
Cross-lingual or transfer learning experiments

Authors

Vladyslav Radchenko, Nazarii Drushchak

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Total file size:

429 MB

Models trained or fine-tuned on lang-uk/UberText-NER-Silver