readme: ---
  language:
  - ar
  thumbnail: url to a thumbnail used in social sharing
  tags:
  - ner
  - token-classification
  - Arabic-NER
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  widget:
  - text: النجم محمد صلاح لاعب المنتخب المصري يعيش في مصر بالتحديد من نجريج, الشرقية
    example_title: Mohamed Salah
  - text: انا ساكن في حدايق الزتون و بدرس في جامعه عين شمس
    example_title: Egyptian Dialect
  - text: يقع نهر الأمازون في قارة أمريكا الجنوبية
    example_title: Standard Arabic
  datasets:
  - Fine-grained-Arabic-Named-Entity-Corpora
  pipeline_tag: token-classification
  ---
  
  
  
  
  
  # Arabic Named Entity Recognition
  
  This project is made to enrich the Arabic Named Entity Recognition(ANER). Arabic is a tough language to deal with and has alot of difficulties.
  We managed to made a model based on Arabert to support 50 entities.
  
  ## Paper
  
  Here's the paper that contains all the details for our model, our approach, and the training results
  
  - [ANER Paper](https://drive.google.com/file/d/1jJn3iWqOeLzaNvO-6aKfgidzJlWOtvti/view?usp=sharing)
  
  
  
  # Dataset
  
  - [Fine-grained Arabic Named Entity Corpora](https://fsalotaibi.kau.edu.sa/Pages-Arabic-NE-Corpora.aspx)
  
  
  # Evaluation results
  
  The model achieves the following results:
  
  | Dataset  | WikiFANE Gold | WikiFANE Gold | WikiFANE Gold | NewsFANE Gold | NewsFANE Gold | NewsFANE Gold  
  |:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|
  | (metric) | (Recall)   | (Precision)   | (F1)  | (Recall)  | (Precision)     | (F1) 
  |          | 87.0  | 90.5   | 88.7 | 78.1   | 77.4     | 77.7  
  
  
  # Usage
  
  The model is available on the HuggingFace model page under the name: [boda/ANER](https://huggingface.co/boda/ANER). Checkpoints are available only in PyTorch at the time.
  
  ### Use in python:
  
  ```python
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  
  tokenizer = AutoTokenizer.from_pretrained("boda/ANER")
  
  model = AutoModelForTokenClassification.from_pretrained("boda/ANER")
  ```
  
  
  # Acknowledgments
  
  Thanks to [Arabert](https://github.com/aub-mind/arabert) for providing the Arabic Bert model, which we used as a base model for our work.
  
  We also would like to thank [Prof. Fahd Saleh S Alotaibi](https://fsalotaibi.kau.edu.sa/Pages-Arabic-NE-Corpora.aspx) at the Faculty of Computing and Information Technology King Abdulaziz University, for providing the dataset which we used to train our model with.
  
  # Contacts
  
  **Abdelrahman Atef**
  
  - [LinkedIn](linkedin.com/in/boda-sadalla)
  - [Github](https://github.com/BodaSadalla98)
  - <bodasadallah@yahoo.com>