---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: tokens
    sequence: string
  - name: pos_tags
    sequence:
      class_label:
        names:
          '0': ADJ
          '1': ADP
          '2': ADV
          '3': AUX
          '4': CCONJ
          '5': DET
          '6': INTJ
          '7': NOUN
          '8': NUM
          '9': PART
          '10': PRON
          '11': PROPN
          '12': PUNCT
          '13': SCONJ
          '14': SYM
          '15': VERB
          '16': X
  splits:
  - name: latin
    num_bytes: 114634
    num_examples: 500
  - name: cyrillic
    num_bytes: 143553
    num_examples: 500
  download_size: 99179
  dataset_size: 258187
configs:
- config_name: default
  data_files:
  - split: latin
    path: data/latin-*
  - split: cyrillic
    path: data/cyrillic-*
license: apache-2.0
task_categories:
- token-classification
language:
- uz
tags:
- pos
- uz
pretty_name: uzbekpos
size_categories:
- n<1K
---

# Dataset Card for UzbekPos

### Dataset Summary

This dataset is an annotated dataset for POS tagging. It contains 250 sample sentences collected from news outlets and fictional books respectively.
The dataset is presented in both Uzbek scripts i.e., Latin and Cyrillic. The annotation was done manually according to [UPOS tagset](https://universaldependencies.org/u/pos/).

## Dataset Structure

An example of 'latin' looks as follows.
```
{
  'id': 0,
  'tokens': "['Doimiy', 'g‘ala-g‘ovur', ',', 'to‘lib-toshgan', 'peshtaxtalar', ',', 'mahsulotlarning', 'o‘ziga', 'xos', 'qorishiq', 'isi', '…']",
  'pos_tags': '[0, 7, 12, 15, 7, 12, 7, 10, 0, 0, 7, 12]'
}
```

### Data Splits
| name            |         |
|-----------------|--------:|
| latin           | 500     |
| cyrillic        | 500     |

### Data Fields
The data fields are the same among all splits:
- `id` (`string`): ID of the example.
- `tokens` (`list` of `string`): Tokens of the example text.
- `pos_tags` (`list` of class labels): POS tags of the tokens, with possible values:
  - 0: `ADJ`
  - 1: `ADP`
  - 2: `ADV`
  - 3: `AUX`
  - 4: `CCONJ`
  - 5: `DET`
  - 6: `INTJ`
  - 7: `NOUN`
  - 8: `NUM`
  - 9: `PART`
  - 10: `PRON`
  - 11: `PROPN`
  - 12: `PUNCT`
  - 13: `SCONJ`
  - 14: `SYM`
  - 15: `VERB`
  - 16: `X`
  
### Source Data
* news articles
* fictional books