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license: mit |
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language: |
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- ar |
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metrics: |
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- accuracy |
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datasets: |
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- arbml/CLEANANERCorp |
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pipeline_tag: token-classification |
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--- |
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### Arabic Named Entity Recognition (NER) Model |
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# Overview |
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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: |
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company names, names, cities, etc. |
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The model is trained using TensorFlow and works with a custom dataset split into training, validation, and test sets. |
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# Model Highlights |
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- Language: Arabic |
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- Framework: TensorFlow |
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- Data Format: Text files (txt format) with train, validation, and test splits |
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# Entities Recognized: |
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- ORG: Organizations (e.g., company names) |
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- LOC: Locations (e.g., cities, countries) |
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- PERS: Persons (e.g., names, excluding common/popular names) |
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- MISC: Miscellaneous (e.g., other identifiable private information) |
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-Intended Use: Arabic text processing, personal data anonymization, data extraction. |
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# Dataset and Preprocessing |
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The dataset used in this model is split into three parts: |
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- Training Set: For model training. |
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- Validation Set: For tuning model hyperparameters and monitoring overfitting. |
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- Test Set: For evaluating final model performance. |
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Each sample in the dataset contains labeled entities for efficient supervised learning. |
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Data preprocessing steps include tokenization, normalization, and conversion of entities into a suitable format compatible with TensorFlow. |
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# Model Evaluation |
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The model achieved a Test Accuracy of # 0.9675 on the test set, indicating strong performance in recognizing and classifying entities in Arabic text. |
