References:
Use the following command to load this dataset in TFDS:
ds = tfds.load('huggingface:turkish_shrinked_ner')
- Description:
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
- License: Attribution 4.0 International (CC BY 4.0)
- Version: 0.0.0
- Splits:
| Split | Examples |
|---|---|
'train' |
614515 |
- Features:
{
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"tokens": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"ner_tags": {
"feature": {
"num_classes": 97,
"names": [
"O",
"B-academic",
"I-academic",
"B-academic_person",
"I-academic_person",
"B-aircraft",
"I-aircraft",
"B-album_person",
"I-album_person",
"B-anatomy",
"I-anatomy",
"B-animal",
"I-animal",
"B-architect_person",
"I-architect_person",
"B-capital",
"I-capital",
"B-chemical",
"I-chemical",
"B-clothes",
"I-clothes",
"B-country",
"I-country",
"B-culture",
"I-culture",
"B-currency",
"I-currency",
"B-date",
"I-date",
"B-food",
"I-food",
"B-genre",
"I-genre",
"B-government",
"I-government",
"B-government_person",
"I-government_person",
"B-language",
"I-language",
"B-location",
"I-location",
"B-material",
"I-material",
"B-measure",
"I-measure",
"B-medical",
"I-medical",
"B-military",
"I-military",
"B-military_person",
"I-military_person",
"B-nation",
"I-nation",
"B-newspaper",
"I-newspaper",
"B-organization",
"I-organization",
"B-organization_person",
"I-organization_person",
"B-person",
"I-person",
"B-production_art_music",
"I-production_art_music",
"B-production_art_music_person",
"I-production_art_music_person",
"B-quantity",
"I-quantity",
"B-religion",
"I-religion",
"B-science",
"I-science",
"B-shape",
"I-shape",
"B-ship",
"I-ship",
"B-software",
"I-software",
"B-space",
"I-space",
"B-space_person",
"I-space_person",
"B-sport",
"I-sport",
"B-sport_name",
"I-sport_name",
"B-sport_person",
"I-sport_person",
"B-structure",
"I-structure",
"B-subject",
"I-subject",
"B-tech",
"I-tech",
"B-train",
"I-train",
"B-vehicle",
"I-vehicle"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}