---
language:
- uz
license: cc-by-4.0
pretty_name: Uzbek NER Gold
task_categories:
- token-classification
task_ids:
- named-entity-recognition
size_categories:
- 10K<n<100K
tags:
- uzbek
- named-entity-recognition
- ner
- token-classification
- bio-tagging
- nlp
configs:
- config_name: default
  data_files:
  - split: train
    path: Uzbek_NER_Gold.tsv
---

# Uzbek NER Gold

Uzbek NER Gold is a token-level named entity recognition dataset for Uzbek. The dataset is distributed as a UTF-8 TSV file and uses BIO tagging for named entities.

## Dataset Summary

- Dataset ID: `uznlp-uz/uzbek_NER`
- Language: Uzbek (`uz`)
- Rows: 59,569 token rows
- Columns: 5
- Sentences: 4,176
- Split: `train`
- Format: UTF-8 TSV
- Data file: `Uzbek_NER_Gold.tsv`
- License: CC BY 4.0

## Data Fields

| Field | Description |
| --- | --- |
| `Sentence` | Sentence identifier. Tokens with the same value belong to the same sentence. |
| `TokenOrder` | 1-based token position inside the sentence. |
| `Token` | Token text. |
| `NER_Tag` | BIO named entity tag. `O` marks tokens outside named entities. |
| `pos` | Source POS or token category label. |

## Tagset

The dataset uses BIO labels over the following named entity types.

| Entity | Tag | Gold v1.0 guideline |
| --- | --- | --- |
| Person | `PER` | Inson ismi yoki aniq shaxs nomi; lavozim, kasb, umumiy guruh va olmoshlar PER emas. |
| Organization | `ORG` | Tashkilot, vazirlik, universitet, agentlik, kompaniya, qo‘mita, fond/jamg‘arma va boshqalar. |
| Location | `LOC` | Davlat, respublika, viloyat, shahar, tuman, geografik joy nomlari. |
| Miscellaneous | `MISC` | Qolgan maxsus nomlangan obyektlar; aniq sinfga tushmaydigan nomlar. |
| Money | `MONEY` | Pul birliklari va pul miqdorlari. |
| Number | `NUMERIC` | Son, raqam, miqdor ifodalari, money/time bo‘lmagan raqamli birliklar. |
| Date/Time | `TEMPORAL` | Sana, vaqt, davr, yil, oy, kun va vaqt oralig‘i. |
| Work | `WORK` | Asar, kitob, film, loyiha, badiiy yoki ilmiy ish nomlari; standart yozilishi WORK. |

## Label Values

NER labels: `O`, `B-PER`, `I-PER`, `B-ORG`, `I-ORG`, `B-LOC`, `I-LOC`, `B-MISC`, `I-MISC`, `B-MONEY`, `I-MONEY`, `B-NUMERIC`, `I-NUMERIC`, `B-TEMPORAL`, `I-TEMPORAL`, `B-WORK`, `I-WORK`

POS/category labels: `C`, `IB`, `II`, `JJ`, `MD`, `N`, `NER`, `NUM`, `P`, `PUNCT`, `Prt`, `RR`, `UH`, `UNK`, `VB`

## Statistics

### Overview

| Metric | Value |
| --- | ---: |
| Token rows | 59,569 |
| Columns | 5 |
| Sentences | 4,176 |
| Unique tokens, case-sensitive | 14,791 |
| Unique tokens, case-folded | 13,969 |
| Duplicate rows | 0 |
| Empty values | 0 |
| Minimum sentence length | 1 |
| Maximum sentence length | 253 |
| Mean sentence length | 14.26 |
| Median sentence length | 12 |

### Entity Span Distribution

| Entity type | Gold v1.0 spans | Original spans | Difference |
| --- | ---: | ---: | ---: |
| `LOC` | 2,302 | 2,262 | 40 |
| `ORG` | 2,148 | 2,207 | -59 |
| `PER` | 1,653 | 2,845 | -1,192 |
| `MISC` | 1,400 | 1,498 | -98 |
| `TEMPORAL` | 591 | 615 | -24 |
| `NUMERIC` | 581 | 591 | -10 |
| `WORK` | 441 | 465 | -24 |
| `MONEY` | 134 | 140 | -6 |
| **Total** | **9,250** | **10,623** | **-1,373** |

### NER Tag Distribution

| NER tag | Count |
| --- | ---: |
| `O` | 43,445 |
| `B-LOC` | 2,302 |
| `I-ORG` | 2,191 |
| `B-ORG` | 2,148 |
| `B-PER` | 1,653 |
| `B-MISC` | 1,400 |
| `I-MISC` | 1,258 |
| `I-PER` | 1,161 |
| `I-LOC` | 842 |
| `B-TEMPORAL` | 591 |
| `B-NUMERIC` | 581 |
| `I-WORK` | 448 |
| `B-WORK` | 441 |
| `I-NUMERIC` | 437 |
| `I-TEMPORAL` | 323 |
| `I-MONEY` | 214 |
| `B-MONEY` | 134 |

### POS/Category Distribution

| POS/category | Count |
| --- | ---: |
| `N` | 17,733 |
| `NER` | 12,914 |
| `VB` | 9,746 |
| `PUNCT` | 4,628 |
| `JJ` | 4,096 |
| `IB` | 2,676 |
| `II` | 2,137 |
| `C` | 1,478 |
| `P` | 1,328 |
| `RR` | 1,207 |
| `NUM` | 956 |
| `MD` | 417 |
| `Prt` | 225 |
| `UNK` | 26 |
| `UH` | 2 |

## Normalization

- Text is stored as UTF-8.
- Uzbek text is normalized to Latin script.
- Uzbek apostrophes were normalized to distinct Unicode characters: `‘` for `o‘/g‘` and `’` for the tutuq sign.
- Non-standard apostrophe variants were removed from the released TSV.
- Each token is stored on a separate TSV row.
- No field contains empty values in the released file.

## Loading

```python
from datasets import load_dataset

dataset = load_dataset("uznlp-uz/uzbek_NER", split="train")
print(dataset[0])
```

The TSV file can also be loaded directly:

```python
from datasets import load_dataset

dataset = load_dataset(
    "csv",
    data_files="Uzbek_NER_Gold.tsv",
    delimiter="\t",
    split="train",
)
```

## Intended Use

This dataset can be used for Uzbek named entity recognition research and development, including token classification, NER model training, NER evaluation, information extraction, and benchmarking Uzbek language models.

## Notes

The dataset is sentence-aware through the `Sentence` and `TokenOrder` fields. To reconstruct sentences, group rows by `Sentence` and sort them by `TokenOrder`.

The `NER_Tag` field follows BIO notation. A tag beginning with `B-` starts an entity span, and a tag beginning with `I-` continues an entity span of the same type.

## Citation

If you use Uzbek NER Gold, cite the dataset repository:

```bibtex
@misc{uzbek_ner_gold,
  title = {Uzbek NER Gold},
  author = {{Elov B.B., Alaev R.H.}},
  year = {2026},
  howpublished = {\url{https://huggingface.co/datasets/uznlp-uz/uzbek_NER}},
  license = {CC BY 4.0}
}
```