




# 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: 

This is the paper for the system, where you can find all the details: https://arxiv.org/abs/2308.14669


# 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>
{"language": ["ar"], "tags": ["ner", "token-classification", "Arabic-NER"], "datasets": ["Fine-grained-Arabic-Named-Entity-Corpora"], "metrics": ["accuracy", "f1", "precision", "recall"], "thumbnail": "url to a thumbnail used in social sharing", "widget": [{"text": "\u0627\u0644\u0646\u062c\u0645 \u0645\u062d\u0645\u062f \u0635\u0644\u0627\u062d \u0644\u0627\u0639\u0628 \u0627\u0644\u0645\u0646\u062a\u062e\u0628 \u0627\u0644\u0645\u0635\u0631\u064a \u064a\u0639\u064a\u0634 \u0641\u064a \u0645\u0635\u0631 \u0628\u0627\u0644\u062a\u062d\u062f\u064a\u062f \u0645\u0646 \u0646\u062c\u0631\u064a\u062c, \u0627\u0644\u0634\u0631\u0642\u064a\u0629", "example_title": "Mohamed Salah"}, {"text": "\u0627\u0646\u0627 \u0633\u0627\u0643\u0646 \u0641\u064a \u062d\u062f\u0627\u064a\u0642 \u0627\u0644\u0632\u062a\u0648\u0646 \u0648 \u0628\u062f\u0631\u0633 \u0641\u064a \u062c\u0627\u0645\u0639\u0647 \u0639\u064a\u0646 \u0634\u0645\u0633", "example_title": "Egyptian Dialect"}, {"text": "\u064a\u0642\u0639 \u0646\u0647\u0631 \u0627\u0644\u0623\u0645\u0627\u0632\u0648\u0646 \u0641\u064a \u0642\u0627\u0631\u0629 \u0623\u0645\u0631\u064a\u0643\u0627 \u0627\u0644\u062c\u0646\u0648\u0628\u064a\u0629", "example_title": "Standard Arabic"}], "pipeline_tag": "token-classification"}
transformers pytorch safetensors bert token-classification ner Arabic-NER ar dataset:Fine-grained-Arabic-Named-Entity-Corpora arxiv:2308.14669 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #token-classification #ner #Arabic-NER #ar #dataset-Fine-grained-Arabic-Named-Entity-Corpora #arxiv-2308.14669 #autotrain_compatible #endpoints_compatible #region-us 

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:
======


This is the paper for the system, where you can find all the details: URL


Dataset
=======


* Fine-grained Arabic Named Entity Corpora


Evaluation results
==================


The model achieves the following results:



Usage
=====


The model is available on the HuggingFace model page under the name: boda/ANER. Checkpoints are available only in PyTorch at the time.


### Use in python:


Acknowledgments
===============


Thanks to 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 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
* Github
* [bodasadallah@URL](mailto:bodasadallah@URL)



### Use in python:


Acknowledgments
===============


Thanks to 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 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
* Github
* [bodasadallah@URL](mailto:bodasadallah@URL)
TAGS
#transformers #pytorch #safetensors #bert #token-classification #ner #Arabic-NER #ar #dataset-Fine-grained-Arabic-Named-Entity-Corpora #arxiv-2308.14669 #autotrain_compatible #endpoints_compatible #region-us 
 ### Use in python:


Acknowledgments
===============


Thanks to 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 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
* Github
* [bodasadallah@URL](mailto:bodasadallah@URL)
TAGS
#transformers #pytorch #safetensors #bert #token-classification #ner #Arabic-NER #ar #dataset-Fine-grained-Arabic-Named-Entity-Corpora #arxiv-2308.14669 #autotrain_compatible #endpoints_compatible #region-us 
### Use in python:


Acknowledgments
===============


Thanks to 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 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
* Github
* [bodasadallah@URL](mailto:bodasadallah@URL)