jmshd/roberta-ner-uz

NER Model for Uzbek Language (XLM-RoBERTa-based)

This is a Named Entity Recognition (NER) model trained for the Uzbek language based on the XLM-RoBERTa architecture. It is fine-tuned to classify entities into categories such as location, person, organization, and other types.

Model Details

• Model Type: XLM-RoBERTa (Transformer-based)
• Task: Named Entity Recognition (NER)
• Training Data: Custom dataset with labeled named entities for Uzbek language.
• Categories:
  • B-LOC (Location)
  • B-PERSON (Person)
  • B-ORG (Organization)
  • B-PRODUCT (Product)
  • B-DATE (Date)
  • B-TIME
  • B-LANGUAGE
  • B-GPE

Metrics

• Validation accuracy = 0.9793

• val_loss: 0.1141

• Precision: 0.97

• Recall: 0.97

• F1-Score: 0.97

Usage

You can use this model with the Hugging Face Transformers library to perform NER tasks on your own Uzbek language text.

from transformers import pipeline

# Load the NER model
ner_pipeline = pipeline('ner', model='jamshidahmadov/roberta-ner-uz', tokenizer='jamshidahmadov/roberta-ner-uz')

# Example usage
text = "Shvetsiya bosh vaziri Stefan Lyoven Stokholmdagi Spendrups kompaniyasiga tashrif buyurdi."
entities = ner_pipeline(text)

for entity in entities:
    print(entity)