---
dataset_info:
  features:
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 827986534
    num_examples: 47982455
  download_size: 429416941
  dataset_size: 827986534
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: apache-2.0
task_categories:
- token-classification
language:
- uk
size_categories:
- 10M<n<100M
tags:
- silver-standard
- ukrainian
- NER
---

# UberText-NER-Silver

**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

```python
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](https://huggingface.co/pofce), [Nazarii Drushchak](https://huggingface.co/ndrushchak)

<!-- citekit:start -->
## Citation

If you use UberText-NER-Silver in your work, please cite:

```bibtex
@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/",
    doi       = "10.63317/5mqwb7ftiezv",
    pages     = "23--29",
    ISBN      = "978-2-493814-43-2"
}

@inproceedings{chaplynskyi-2023-introducing,
    title     = "Introducing {U}ber{T}ext 2.0: A Corpus of {M}odern {U}krainian at Scale",
    author    = "Chaplynskyi, Dmytro",
    editor    = "Romanyshyn, Mariana",
    booktitle = "Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)",
    month     = may,
    year      = "2023",
    address   = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url       = "https://aclanthology.org/2023.unlp-1.1/",
    doi       = "10.18653/v1/2023.unlp-1.1",
    pages     = "1--10"
}
```

Paper: https://aclanthology.org/2024.unlp-1.4/
DOI: https://doi.org/10.63317/5mqwb7ftiezv
<!-- citekit:end -->


---

*Mirror created 2026-10-11 by the JoTalbot/ukraine pipeline. Original source: https://huggingface.co/datasets/lang-uk/UberText-NER-Silver — please credit and respect the original dataset's license.*