Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
json
Languages:
Uzbek
Size:
10K - 100K
License:
risqaliyevds
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README.md
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---
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# Uzbek NER Dataset
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##
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##
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```json
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{
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"LOC": ["
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"ORG": ["
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"PERSON": ["
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"DATE": ["
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"MONEY": ["
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"PERCENT": ["
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"QUANTITY": ["
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"TIME": ["
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"PRODUCT": ["
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"EVENT": ["
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"WORK_OF_ART": ["
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"LANGUAGE": ["
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"CARDINAL": ["
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"ORDINAL": ["
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"NORP": ["
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"FACILITY": ["
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"LAW": ["
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"GPE": ["
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}
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```
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##
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##
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- **LOC (
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- **ORG (
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- **PERSON (
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- **DATE (
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- **MONEY (
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- **PERCENT (
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- **QUANTITY (
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- **TIME (
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- **PRODUCT (
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- **EVENT (
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- **WORK_OF_ART (
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- **LANGUAGE (
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- **CARDINAL (
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- **ORDINAL (
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- **NORP (
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##
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```python
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from datasets import load_dataset
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dataset = load_dataset("risqaliyevds/uzbek_ner")
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```
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##
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LinkedIn: [Riskaliev Murad](https://www.linkedin.com/in/risqaliyevds/)
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---
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# Uzbek NER Dataset
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## About the Dataset
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This dataset is created for Named Entity Recognition (NER) in Uzbek texts. The dataset includes named entities from various categories such as persons, places, organizations, dates, and more.
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## Data Structure
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The data is provided in JSON format with the following structure:
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```json
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{
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"LOC": ["Location names"],
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"ORG": ["Organization names"],
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"PERSON": ["Person names"],
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"DATE": ["Date expressions"],
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"MONEY": ["Monetary amounts"],
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"PERCENT": ["Percentage values"],
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"QUANTITY": ["Quantities"],
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"TIME": ["Time expressions"],
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"PRODUCT": ["Product names"],
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"EVENT": ["Event names"],
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"WORK_OF_ART": ["Work of art titles"],
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"LANGUAGE": ["Language names"],
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"CARDINAL": ["Cardinal numbers"],
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"ORDINAL": ["Ordinal numbers"],
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"NORP": ["Nationalities or religious/political groups"],
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"FACILITY": ["Facility names"],
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"LAW": ["Laws or regulations"],
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"GPE": ["Countries, cities, states"]
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}
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```
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## Preparation Guidelines
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The following guidelines were followed in preparing this dataset:
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1. All possible NERs were extracted from the text (approximately 80%-90%).
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2. NERs are provided in their original form without additional annotations or translations.
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## Examples
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Here are some examples of NER categories:
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- **LOC (Location names):** ["Tashkent", "Uzbekistan"]
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- **ORG (Organization names):** ["XDP", "Uzbekistan Ministry of Culture"]
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- **PERSON (Person names):** ["Ozodbek Nazarbekov", "Ibrat Safo"]
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- **DATE (Date expressions):** ["2022", "2024"]
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- **MONEY (Monetary amounts):** ["2 billion som", "25 trillion som"]
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- **PERCENT (Percentage values):** ["3%", "5%"]
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- **QUANTITY (Quantities):** ["100 tons", "200 liters"]
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- **TIME (Time expressions):** ["5 minutes", "2 hours"]
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- **PRODUCT (Product names):** ["Toyota", "iPhone 13"]
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- **EVENT (Event names):** ["Uzbekistan Championship", "Uzbekistan Cup"]
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- **WORK_OF_ART (Work of art titles):** ["Song", "Book"]
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- **LANGUAGE (Language names):** ["Uzbek", "English"]
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- **CARDINAL (Cardinal numbers):** ["one", "two"]
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- **ORDINAL (Ordinal numbers):** ["first", "second"]
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- **NORP (Nationalities or religious/political groups):** ["Uzbeks", "Russians"]
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- **FACILITY (Facility names):** ["Children's Health Clinic", "Tashkent Aviation Institute"]
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- **LAW (Laws or regulations):** ["Constitution", "Law"]
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- **GPE (Countries, cities, states):** ["Uzbekistan", "Kokand"]
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## Download
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You can download this dataset using the following code:
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```python
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from datasets import load_dataset
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dataset = load_dataset("risqaliyevds/uzbek_ner")
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```
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## License
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This dataset is provided as open source and is available for free use by all users.
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## Contact
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If you have any questions or need more information, please contact us.
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LinkedIn: [Riskaliev Murad](https://www.linkedin.com/in/risqaliyevds/)
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