
# 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.
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
{"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}}
TAGS
#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-Turkish #license-cc-by-4.0 #region-us 


# Dataset Card for turkish_shrinked_ner

## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions

## Dataset Description

- Homepage: URL
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: URL

### 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



### Languages

Turkish

## Dataset Structure

### Data Instances



### Data Fields



### Data Splits

There's only the training set.

## Dataset Creation

### Curation Rationale



### Source Data

#### Initial Data Collection and Normalization



#### Who are the source language producers?



### Annotations

#### Annotation process



#### Who are the annotators?



### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators

Behcet Senturk

### Licensing Information

Creative Commons Attribution 4.0 International





### Contributions

Thanks to @bhctsntrk for adding this dataset.
# Dataset Card for turkish_shrinked_ner ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: URL
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: URL ### 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 ### Languages

Turkish ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits

There's only the training set. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators

Behcet Senturk ### Licensing Information

Creative Commons Attribution 4.0 International ### Contributions

Thanks to @bhctsntrk for adding this dataset.
TAGS
#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-Turkish #license-cc-by-4.0 #region-us 
 # Dataset Card for turkish_shrinked_ner ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: URL
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: URL ### 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 ### Languages

Turkish ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits

There's only the training set. ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators

Behcet Senturk ### Licensing Information

Creative Commons Attribution 4.0 International ### Contributions

Thanks to @bhctsntrk for adding this dataset.