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OmarMaqousi/ner-arabicbert
ner-arabicbert is a token classification model from OmarMaqousi. Use it when you need labels on individual words, such as names. The card lists the license as apache-2.0.
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.
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From the Hugging Face model README
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.
The model was evaluated on an unseen test set split (10% of total stratified data).
| 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% |
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