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  ---
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  # Uzbek NER Dataset
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- ## Ma'lumotlar to'plami haqida
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- Ushbu ma'lumotlar to'plami O'zbek tilidagi matnlardan Named Entity Recognition (NER) ni aniqlash uchun yaratilgan. Ma'lumotlar to'plami turli xil kategoriyalardagi (shaxslar, joylar, tashkilotlar, sanalar va boshqalar) nomlangan entitetlarni o'z ichiga oladi.
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- ## Ma'lumotlar tuzilishi
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- Ma'lumotlar quyidagi formatda JSON tuzilishida keltirilgan:
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  ```json
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  {
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- "LOC": ["Joy nomlari"],
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- "ORG": ["Tashkilot nomlari"],
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- "PERSON": ["Shaxs nomlari"],
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- "DATE": ["Sana ifodalari"],
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- "MONEY": ["Pul miqdorlari"],
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- "PERCENT": ["Foiz qiymatlari"],
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- "QUANTITY": ["Miqdorlar"],
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- "TIME": ["Vaqt ifodalari"],
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- "PRODUCT": ["Mahsulot nomlari"],
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- "EVENT": ["Voqea nomlari"],
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- "WORK_OF_ART": ["San'at asarlari nomlari"],
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- "LANGUAGE": ["Til nomlari"],
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- "CARDINAL": ["Kardinal raqamlar"],
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- "ORDINAL": ["Ordinall raqamlar"],
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- "NORP": ["Millatlar yoki diniy/siyosiy guruhlar"],
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- "FACILITY": ["Inshoot nomlari"],
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- "LAW": ["Qonunlar yoki me'yorlar"],
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- "GPE": ["Davlatlar, shaharlar, shtatlar"]
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  }
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  ```
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- ## Tayyorlanish yo'riqnomasi
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- Ushbu ma'lumotlar to'plamidan tayyorlashda quyidagi yo'riqnomalarga rioya qilingan:
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- 1. Matndan barcha mumkin bo'lgan NERlar ajratilingan (deyarli 80%-90%).
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- 2. NERlar qo'shimcha izoh yoki tarjimalarsiz asl holatida keltirilingan.
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- ## Misollar
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- Quyida NER kategoriyalari uchun ba'zi misollar keltirilgan:
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- - **LOC (Joy nomlari):** ["Toshkent", "O‘zbekiston"]
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- - **ORG (Tashkilot nomlari):** ["XDP", "O‘zbekiston Respublikasi Madaniyat vazirligi"]
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- - **PERSON (Shaxs nomlari):** ["Ozodbek Nazarbekov", "Ibrat Safo"]
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- - **DATE (Sana ifodalari):** ["2022 yil", "2024 yil"]
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- - **MONEY (Pul miqdorlari):** ["2 mlrd so‘m", "25 trln so‘m"]
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- - **PERCENT (Foiz qiymatlari):** ["3%", "5%"]
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- - **QUANTITY (Miqdorlar):** ["100 tonna", "200 litr"]
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- - **TIME (Vaqt ifodalari):** ["5 daqiqa", "2 soat"]
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- - **PRODUCT (Mahsulot nomlari):** ["Toyota", "iPhone 13"]
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- - **EVENT (Voqea nomlari):** ["O‘zbekiston chempionati", "O‘zbekiston kubogi"]
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- - **WORK_OF_ART (San'at asarlari nomlari):** ["Qo‘shiq", "Kitob"]
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- - **LANGUAGE (Til nomlari):** ["O‘zbek tili", "Ingliz tili"]
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- - **CARDINAL (Kardinal raqamlar):** ["bir", "ikki"]
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- - **ORDINAL (Ordinall raqamlar):** ["birinchi", "ikkinchi"]
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- - **NORP (Millatlar yoki diniy/siyosiy guruhlar):** ["o‘zbeklar", "ruslar"]
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- - **FACILITY (Inshoot nomlari):** ["Bolalar sog‘liqni saqlash poliklinikasi", "Toshkent aviatsiya instituti"]
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- - **LAW (Qonunlar yoki me'yorlar):** ["Konstitutsiya", "Qonun"]
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- - **GPE (Davlatlar, shaharlar, shtatlar):** ["O‘zbekiston", "Qo‘qon"]
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- ## Yuklab olish
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- Ushbu ma'lumotlar to'plamini quyidagi kod yordamida yuklab olishingiz mumkin:
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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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- ## Litsenziya
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- Ushbu ma'lumotlar to'plami ochiq manba sifatida taqdim etiladi va barcha foydalanuvchilar uchun bepul foydalanish imkoniyatiga ega.
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- ## Bog'lanish
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- Agar savollaringiz bo'lsa yoki qo'shimcha ma'lumot kerak bo'lsa, iltimos biz bilan bog'laning.
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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/)