metadata
language: ar
license: apache-2.0
tags:
- arabic
- ner
- named-entity-recognition
- bert
- token-classification
datasets:
- custom
metrics:
- f1
- precision
- recall
widget:
- text: أحمد محمد يعمل في شركة جوجل في الرياض
example_title: Arabic NER Example
MutazYoune/Arabic-NER-PII2
Model Description
This is an Arabic Named Entity Recognition (NER) model fine-tuned on BERT architecture specifically for Arabic text processing. The model is based on MutazYoune/ARAB_BERT and has been trained to identify and classify named entities in Arabic text.
Model Details
- Model Type: Token Classification (NER)
- Language: Arabic (ar)
- Base Model: MutazYoune/ARAB_BERT
- Dataset: augmented_pattern2
- Task: Named Entity Recognition
Training Configuration
- Epochs: 30
- Batch Size: 16
- Learning Rate: 3e-05
Supported Entity Types
- CONTACT
- IDENTIFIER
- NETWORK
- NUMERIC_ID
- PII
Usage
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("MutazYoune/Arabic-NER-PII2")
model = AutoModelForTokenClassification.from_pretrained("MutazYoune/Arabic-NER-PII2")
# Create NER pipeline
ner_pipeline = pipeline("ner",
model=model,
tokenizer=tokenizer,
aggregation_strategy="simple")
# Example usage
text = "أحمد محمد يعمل في شركة جوجل في الرياض"
entities = ner_pipeline(text)
print(entities)
Model Performance
This model was trained on the complete dataset without validation split for final production use.
Training Data
The model was trained on custom Arabic NER dataset:
- Dataset type: augmented_pattern2
- Combined training and test data for final model
Citation
@misc{arabic-ner-bert,
title={Arabic BERT NER Model},
author={Trained on Kaggle},
year={2025},
publisher={Hugging Face},
url={https://huggingface.co/MutazYoune/Arabic-NER-PII2}
}