OmarMaqousi/ner-arabicbert

Arabic Named Entity Recognition (NER) using BERT-Arabic

This model is a fine-tuned version of asafaya/bert-base-arabic for Arabic Named Entity Recognition (NER) on the Arabic subset of the PAN-X (XTREME) dataset.

It predicts three primary entity types: Persons (PER), Organizations (ORG), and Locations (LOC), using the standard IOB2 tagging scheme.

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📊 Model Performance & Test Results

The model was evaluated on an unseen test set split (10% of total stratified data).

Overall Metrics

Metric	Score	Percentage
Test F1-Score (Entity-level)	0.9172	91.72%
Test Accuracy (Token-level)	0.9634	96.34%
Token Error Rate	0.0366	3.66%
Entity Error Rate	0.0828	8.28%

Detailed Classification Report

              precision    recall  f1-score   support

         LOC       0.93      0.93      0.93      1564
         ORG       0.88      0.89      0.89      1438
         PER       0.93      0.94      0.94      1541

   micro avg       0.91      0.92      0.92      4543
   macro avg       0.91      0.92      0.92      4543
weighted avg       0.91      0.92      0.92      4543