---
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)