--- 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 --- # 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) # Usage The model is available in 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") ``` # Dataset - [Fine-grained Arabic Named Entity Corpora](https://fsalotaibi.kau.edu.sa/Pages-Arabic-NE-Corpora.aspx) # Acknowledgments Thanks for [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 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) -