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README.md
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language:
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thumbnail:
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tags:
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metrics:
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widget:
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datasets:
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# Arabic Named Entity Recognition
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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.
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- [ANER Paper](https://drive.google.com/file/d/1jJn3iWqOeLzaNvO-6aKfgidzJlWOtvti/view?usp=sharing)
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# Usage
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The model is available
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### Use in python:
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model = AutoModelForTokenClassification.from_pretrained("boda/ANER")
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```
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# Dataset
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- [Fine-grained Arabic Named Entity Corpora](https://fsalotaibi.kau.edu.sa/Pages-Arabic-NE-Corpora.aspx)
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# Acknowledgments
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Thanks
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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.
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# Contacts
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- [LinkedIn](linkedin.com/in/boda-sadalla)
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- [Github](https://github.com/BodaSadalla98)
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- <
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---
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language:
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- ar
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- fr
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thumbnail: url to a thumbnail used in social sharing
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tags:
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- ner
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- token-classification
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- Arabic-NER
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metrics:
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- accuracy
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- f1
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- precision
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- recall
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widget:
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- text: النجم محمد صلاح لاعب المنتخب المصري يعيش في مصر بالتحديد من نجريج, الشرقية
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example_title: Mohamed Salah
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- text: انا ساكن في حدايق الزتون و بدرس في جامعه عين شمس
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example_title: Egyptian Dialect
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- text: يقع نهر الأمازون في قارة أمريكا الجنوبية
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example_title: Standard Arabic
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datasets:
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- Fine-grained-Arabic-Named-Entity-Corpora
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pipeline_tag: token-classification
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---
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# Arabic Named Entity Recognition
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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.
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- [ANER Paper](https://drive.google.com/file/d/1jJn3iWqOeLzaNvO-6aKfgidzJlWOtvti/view?usp=sharing)
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# Dataset
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- [Fine-grained Arabic Named Entity Corpora](https://fsalotaibi.kau.edu.sa/Pages-Arabic-NE-Corpora.aspx)
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# Evaluation results
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The model achieves the following results:
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| Dataset | WikiFANE Gold | WikiFANE Gold | WikiFANE Gold | NewsFANE Gold | NewsFANE Gold | NewsFANE Gold
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|:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|
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| (metric) | (Recall) | (Precision) | (F1) | (Recall) | (Precision) | (F1)
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| | 87.0 | 90.5 | 88.7 | 78.1 | 77.4 | 77.7
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# Usage
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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.
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### Use in python:
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model = AutoModelForTokenClassification.from_pretrained("boda/ANER")
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```
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# Acknowledgments
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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.
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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.
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# Contacts
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- [LinkedIn](linkedin.com/in/boda-sadalla)
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- [Github](https://github.com/BodaSadalla98)
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- <bodasadallah@yahoo.com>
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