task_categories:token-classification task_ids:named-entity-recognition annotations_creators:machine-generated language_creators:expert-generated multilinguality:monolingual size_categories:100K<n<1M source_datasets:extended|other-turkish_ner language:tr license:cc-by-4.0 region:us
Shrinked version (48 entity type) of the turkish_ner.

Original turkish_ner dataset: Automatically annotated Turkish corpus for named entity recognition and text categorization using large-scale gazetteers. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 25 different domains.

Shrinked entity types are: academic, academic_person, aircraft, album_person, anatomy, animal, architect_person, capital, chemical, clothes, country, culture, currency, date, food, genre, government, government_person, language, location, material, measure, medical, military, military_person, nation, newspaper, organization, organization_person, person, production_art_music, production_art_music_person, quantity, religion, science, shape, ship, software, space, space_person, sport, sport_name, sport_person, structure, subject, tech, train, vehicle
\
---
annotations_creators:
- machine-generated
language_creators:
- expert-generated
language:
- tr
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|other-turkish_ner
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: TurkishShrinkedNer
dataset_info:
  features:
  - name: id
    dtype: string
  - name: tokens
    sequence: string
  - name: ner_tags
    sequence:
      class_label:
        names:
          '0': O
          '1': B-academic
          '2': I-academic
          '3': B-academic_person
          '4': I-academic_person
          '5': B-aircraft
          '6': I-aircraft
          '7': B-album_person
          '8': I-album_person
          '9': B-anatomy
          '10': I-anatomy
          '11': B-animal
          '12': I-animal
          '13': B-architect_person
          '14': I-architect_person
          '15': B-capital
          '16': I-capital
          '17': B-chemical
          '18': I-chemical
          '19': B-clothes
          '20': I-clothes
          '21': B-country
          '22': I-country
          '23': B-culture
          '24': I-culture
          '25': B-currency
          '26': I-currency
          '27': B-date
          '28': I-date
          '29': B-food
          '30': I-food
          '31': B-genre
          '32': I-genre
          '33': B-government
          '34': I-government
          '35': B-government_person
          '36': I-government_person
          '37': B-language
          '38': I-language
          '39': B-location
          '40': I-location
          '41': B-material
          '42': I-material
          '43': B-measure
          '44': I-measure
          '45': B-medical
          '46': I-medical
          '47': B-military
          '48': I-military
          '49': B-military_person
          '50': I-military_person
          '51': B-nation
          '52': I-nation
          '53': B-newspaper
          '54': I-newspaper
          '55': B-organization
          '56': I-organization
          '57': B-organization_person
          '58': I-organization_person
          '59': B-person
          '60': I-person
          '61': B-production_art_music
          '62': I-production_art_music
          '63': B-production_art_music_person
          '64': I-production_art_music_person
          '65': B-quantity
          '66': I-quantity
          '67': B-religion
          '68': I-religion
          '69': B-science
          '70': I-science
          '71': B-shape
          '72': I-shape
          '73': B-ship
          '74': I-ship
          '75': B-software
          '76': I-software
          '77': B-space
          '78': I-space
          '79': B-space_person
          '80': I-space_person
          '81': B-sport
          '82': I-sport
          '83': B-sport_name
          '84': I-sport_name
          '85': B-sport_person
          '86': I-sport_person
          '87': B-structure
          '88': I-structure
          '89': B-subject
          '90': I-subject
          '91': B-tech
          '92': I-tech
          '93': B-train
          '94': I-train
          '95': B-vehicle
          '96': I-vehicle
  splits:
  - name: train
    num_bytes: 200728389
    num_examples: 614515
  download_size: 0
  dataset_size: 200728389
---

# Dataset Card for turkish_shrinked_ner

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://www.kaggle.com/behcetsenturk/shrinked-twnertc-turkish-ner-data-by-kuzgunlar
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** https://www.kaggle.com/behcetsenturk

### Dataset Summary

Shrinked processed version (48 entity type) of the turkish_ner.

Original turkish_ner dataset: Automatically annotated Turkish corpus for named entity recognition and text categorization using large-scale gazetteers. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 25 different domains.

Shrinked entity types are: academic, academic_person, aircraft, album_person, anatomy, animal, architect_person, capital, chemical, clothes, country, culture, currency, date, food, genre, government, government_person, language, location, material, measure, medical, military, military_person, nation, newspaper, organization, organization_person, person, production_art_music, production_art_music_person, quantity, religion, science, shape, ship, software, space, space_person, sport, sport_name, sport_person, structure, subject, tech, train, vehicle

### Supported Tasks and Leaderboards

[Needs More Information]

### Languages

Turkish

## Dataset Structure

### Data Instances

[Needs More Information]

### Data Fields

[Needs More Information]

### Data Splits

There's only the training set.

## Dataset Creation

### Curation Rationale

[Needs More Information]

### Source Data

#### Initial Data Collection and Normalization

[Needs More Information]

#### Who are the source language producers?

[Needs More Information]

### Annotations

#### Annotation process

[Needs More Information]

#### Who are the annotators?

[Needs More Information]

### Personal and Sensitive Information

[Needs More Information]

## Considerations for Using the Data

### Social Impact of Dataset

[Needs More Information]

### Discussion of Biases

[Needs More Information]

### Other Known Limitations

[Needs More Information]

## Additional Information

### Dataset Curators

Behcet Senturk

### Licensing Information

Creative Commons Attribution 4.0 International

### Citation Information

[Needs More Information]

### Contributions

Thanks to [@bhctsntrk](https://github.com/bhctsntrk) for adding this dataset.