Open-source mBERT_ar_c19 Model - Efficiently Process Multidialectal Arabic COVID-19-related Tweets

Mbert Ar C19

Developed by moha

An mBERT model pre-trained on 1.5 million multidialectal Arabic tweets about COVID-19, optimized for processing pandemic-related Arabic tweets.

Large Language Model Arabic#Arabic COVID-19 Tweet Analysis #Multidialectal Arabic Processing #Fake News Detection

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Release Time : 3/2/2022

Model Overview

This model is a multidialectal Arabic version of mBERT, specifically pre-trained on COVID-19 related content, delivering superior performance when handling pandemic-related Arabic text.

Model Features

Multidialectal Support

Specifically optimized for multiple Arabic dialects, effectively handling regional variants of Arabic.

Pandemic Domain Optimization

Pre-trained on 1.5 million COVID-19 related tweets, excelling in pandemic-related tasks.

Multitask Capability

Capable of handling multiple tasks simultaneously, including fake news detection, hate speech identification, and content classification.

Model Capabilities

Arabic Text Classification

Fake News Detection

Hate Speech Identification

Pandemic-related Content Analysis

Use Cases

Social Media Monitoring

Pandemic Fake News Detection

Identifying misinformation about COVID-19 on social media.

F1 score 0.7739 (outperforming baseline models)

Hate Speech Monitoring

Detecting hate speech in pandemic-related discussions.

F1 score 0.8649 (outperforming baseline models)

Public Health

Treatment Method Discussion Analysis

Classifying tweets discussing COVID-19 treatment methods.

F1 score 0.9176 (outperforming baseline models)

🚀 mbert_c19: An mbert model pretrained on 1.5 million COVID-19 multi-dialect Arabic tweets

This project presents mBERT COVID-19, a pre - trained (fine - tuned) version of the mBERT model. It leverages 1.5 million multi - dialect Arabic tweets related to the COVID - 19 pandemic, enabling better performance in tasks dealing with such tweets.

🚀 Quick Start

The mBERT COVID-19 Arxiv URL is a pre - trained (fine - tuned) version of the mBERT model (https://huggingface.co/bert-base-multilingual-cased). The pre - training was carried out using 1.5 million multi - dialect Arabic tweets about the COVID - 19 pandemic from the “Large Arabic Twitter Dataset on COVID - 19” (https://arxiv.org/abs/2004.04315). This model can achieve better results for tasks related to multi - dialect Arabic tweets regarding the COVID - 19 pandemic.

📚 Documentation

Classification results for multiple tasks including fake - news and hate speech detection when using arabert_c19 and mbert_ar_c19:

For more details, refer to the paper (link).

Property	arabert	mbert	distilbert multi	arabert Covid - 19	mbert Covid - 19
Contains hate (Binary)	0.8346	0.6675	0.7145	`0.8649`	0.8492
Talk about a cure (Binary)	0.8193	0.7406	0.7127	0.9055	`0.9176`
News or opinion (Binary)	0.8987	0.8332	0.8099	`0.9163`	0.9116
Contains fake information (Binary)	0.6415	0.5428	0.4743	`0.7739`	0.7228

💻 Usage Examples

Basic Usage

from arabert.preprocess import ArabertPreprocessor
model_name="moha/mbert_ar_c19"
arabert_prep = ArabertPreprocessor(model_name=model_name)
text = "للوقايه من عدم انتشار كورونا عليك اولا غسل اليدين بالماء والصابون وتكون عملية الغسل دقيقه تشمل راحة اليد الأصابع التركيز على الإبهام"
arabert_prep.preprocess(text)

📄 License

Citation

Please cite as:

@misc{ameur2021aracovid19mfh,
      title={AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset}, 
      author={Mohamed Seghir Hadj Ameur and Hassina Aliane},
      year={2021},
      eprint={2105.03143},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

📞 Contacts

Hadj Ameur: Github | mohamedhadjameur@gmail.com | mhadjameur@cerist.dz

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