Datasets:
turkish_shrinked_ner

	---
	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.