MostafaAhmed98

Model Card for Arabic Named Entity Recognition with AraBERT Model Type: AraBERT (Pre-trained on Arabic text and fine-tuned on Arabic Named Entity Recognition task) AraBERT-NER is a fine-tuned version of the AraBERT model specifically designed for Named Entity Recognition (NER) tasks in Arabic. The model has been trained to identify and classify named entities such as persons, organizations, locations and MISC and more within Arabic text. This makes it suitable for various applications such as information extraction, document categorization, and data annotation in Arabic. - Named Entity Recognition systems for Arabic - Information extraction from Arabic text - Document categorization and annotation - Arabic language processing research The model was fine-tuned on the CoNLL-NER-AR dataset. - CoNLL-NER-AR: A dataset for named entity recognition tasks in Arabic. The model was trained using the Hugging Face `transformers` library. The training process involved: - Preprocessing the CoNLL-NER-AR to format the text and entity annotations for NER. - Fine-tuning the pre-trained AraBERT model on the Arabic NER dataset. - Evaluating the model's performance using standard NER metrics (e.g., Precision, Recall, F1 Score). The model was evaluated on a held-out test set from the CoNLL-NER-AR dataset. Here are the key performance metrics: - Precision: 0.8547 - Recall: 0.8633 - F1 Score: 0.8590 - Accuracy: 0.9542 These metrics indicate the model's effectiveness in accurately identifying and classifying named entities in Arabic text. You can load and use the model with the Hugging Face `transformers` library as follows: