Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+language:
+- ar
+metrics:
+- accuracy
+---
+### Arabic Named Entity Recognition (NER) Model
+
+# Overview
+
+This (NER) model specifically designed for the Arabic language. Built from scratch without the use of pretrained models, this model is capable of recognizing entities such as:
+company names, names, cities, etc. 
+
+The model is trained using TensorFlow and works with a custom dataset split into training, validation, and test sets. 
+
+# Model Highlights
+- Language: Arabic
+- Framework: TensorFlow
+- Data Format: Text files (txt format) with train, validation, and test splits
+
+# Entities Recognized:
+- ORG: Organizations (e.g., company names)
+- LOC: Locations (e.g., cities, countries)
+- PERS: Persons (e.g., names, excluding common/popular names)
+- MISC: Miscellaneous (e.g., other identifiable private information)
+
+-Intended Use: Arabic text processing, personal data anonymization, data extraction.
+
+# Dataset and Preprocessing
+The dataset used in this model is split into three parts:
+
+- Training Set: For model training.
+- Validation Set: For tuning model hyperparameters and monitoring overfitting.
+- Test Set: For evaluating final model performance.
+Each sample in the dataset contains labeled entities for efficient supervised learning.
+Data preprocessing steps include tokenization, normalization, and conversion of entities into a suitable format compatible with TensorFlow.
+
+
+# Model Evaluation
+The model achieved a Test Accuracy of # 0.9675#  on the test set, indicating strong performance in recognizing and classifying entities in Arabic text.
+
+