Update README.md

README.md

@@ -10,41 +10,45 @@ tags:
 - uzbek_ner
 - ner_for_uzbek_language
 ---
-## O'zbek tili uchun Named Entity Recognition (NER) modeli
-
-### Model haqida
-Ushbu model O'zbek tilidagi matnlarda Named Entity Recognition (NER) ni aniqlash uchun yaratilgan. Model turli xil kategoriyalardagi nomlangan entitetlarni aniqlashga qodir, jumladan shaxslar, joylar, tashkilotlar, sanalar va boshqalar. Ushbu model XLM-RoBERTa large arxitekturasiga asoslangan.
-
-### Diqqat!!!
-Model NEWS datasetda train qilingan va asosan NEWS textlardagi NER ni aniqlay olish aniqligi baland.
-
-### Kategoriyalar
-Model quyidagi NER kategoriyalarini aniqlashga qodir:
-- **LOC (Joy nomlari)**
-- **ORG (Tashkilot nomlari)**
-- **PERSON (Shaxs nomlari)**
-- **DATE (Sana ifodalari)**
-- **MONEY (Pul miqdorlari)**
-- **PERCENT (Foiz qiymatlari)**
-- **QUANTITY (Miqdorlar)**
-- **TIME (Vaqt ifodalari)**
-- **PRODUCT (Mahsulot nomlari)**
-- **EVENT (Voqea nomlari)**
-- **WORK_OF_ART (San'at asarlari nomlari)**
-- **LANGUAGE (Til nomlari)**
-- **CARDINAL (Kardinal raqamlar)**
-- **ORDINAL (Ordinall raqamlar)**
-- **NORP (Millatlar yoki diniy/siyosiy guruhlar)**
-- **FACILITY (Inshoot nomlari)**
-- **LAW (Qonunlar yoki me'yorlar)**
-- **GPE (Davlatlar, shaharlar, shtatlar)**
-
-### Misollar
-Model qanday ishlashini ko'rsatish uchun bir necha misollar:
+Sure! Here is the Hugging Face model card translated into English:
+
+---
+
+## Named Entity Recognition (NER) Model for Uzbek Language
+
+### About the Model
+This model is designed for Named Entity Recognition (NER) in Uzbek text. The model can identify various categories of named entities, including persons, places, organizations, dates, and more. This model is based on the XLM-RoBERTa large architecture.
+
+### Note!!!
+The model is trained on the NEWS dataset and primarily has high accuracy for identifying NER in NEWS texts.
+
+### Categories
+The model can identify the following NER categories:
+- **LOC (Location names)**
+- **ORG (Organization names)**
+- **PERSON (Person names)**
+- **DATE (Date expressions)**
+- **MONEY (Monetary amounts)**
+- **PERCENT (Percentage values)**
+- **QUANTITY (Quantities)**
+- **TIME (Time expressions)**
+- **PRODUCT (Product names)**
+- **EVENT (Event names)**
+- **WORK_OF_ART (Work of art titles)**
+- **LANGUAGE (Language names)**
+- **CARDINAL (Cardinal numbers)**
+- **ORDINAL (Ordinal numbers)**
+- **NORP (Nationalities or religious/political groups)**
+- **FACILITY (Facility names)**
+- **LAW (Laws or regulations)**
+- **GPE (Countries, cities, states)**
+
+### Examples
+To demonstrate how the model works, here are a few examples:
 ```python
 from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification
 
-model_name_or_path = "sizning_model_yolingiz"
+model_name_or_path = "risqaliyevds/xlm-roberta-large-ner"
 tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
 model = AutoModelForTokenClassification.from_pretrained(model_name_or_path).to("cuda")
 
@@ -56,21 +60,21 @@ ner = nlp(text)
 for entity in ner:
     print(entity)
 ```
-Misol matni: "Shavkat Mirziyoyev Rossiyada rasmiy safarda bo'ldi."
+Example text: "Shavkat Mirziyoyev Rossiyada rasmiy safarda bo'ldi."
 
-Natijalar:
+Results:
 ```python
 [{'entity': 'B-PERSON', 'score': 0.88995147, 'index': 1, 'word': '▁Shavkat', 'start': 0, 'end': 7},
  {'entity': 'I-PERSON', 'score': 0.980681, 'index': 2, 'word': '▁Mirziyoyev', 'start': 8, 'end': 18},
  {'entity': 'B-GPE', 'score': 0.8208886, 'index': 3, 'word': '▁Rossiya', 'start': 19, 'end': 26}]
 ```
 
-### Modelni yuklash va ishlatish
-Modelni Hugging Face platformasidan yuklab olish va ishlatish uchun quyidagi koddan foydalanishingiz mumkin:
+### Loading and Using the Model
+To download and use the model from the Hugging Face platform, you can use the following code:
 ```python
 from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification
 
-model_name_or_path = "sizning_model_yolingiz"
+model_name_or_path = "risqaliyevds/xlm-roberta-large-ner"
 
 tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
 model = AutoModelForTokenClassification.from_pretrained(model_name_or_path).to("cuda")
@@ -78,13 +82,13 @@ model = AutoModelForTokenClassification.from_pretrained(model_name_or_path).to("
 nlp = pipeline("ner", model=model, tokenizer=tokenizer)
 ```
 
-## Bog'lanish
+## Contact
 
-Agar savollaringiz bo'lsa yoki qo'shimcha ma'lumot kerak bo'lsa, iltimos biz bilan bog'laning.
+If you have any questions or need more information, please contact us.
 LinkedIn: [Riskaliev Murad](https://www.linkedin.com/in/risqaliyevds/)
 
-### Litsenziya
-Ushbu model ochiq manba sifatida taqdim etiladi va barcha foydalanuvchilar uchun bepul foydalanish imkoniyatiga ega.
+### License
+This model is provided as open source and is available for free use by all users.
 
-### Xulosa
-O'zbek tili uchun NER modeli matnlarda turli xil nomlangan entitetlarni aniqlashda samarali yordam beradi. Modelning yuqori aniqligi va keng qamrovli kategoriyalari uni ilmiy tadqiqotlar, hujjatlarni tahlil qilish va boshqa ko'plab sohalarda qo'llash imkonini beradi.
+### Conclusion
+The NER model for the Uzbek language is effective in identifying various named entities in texts. The high accuracy and wide range of categories make it useful for academic research, document analysis, and many other fields.