metadata

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.

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