AraBERT_c19 Open-Source Model - Freely Process Arabic Tweets Related to the COVID-19 Pandemic

Arabert C19

Developed by moha

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

Large Language Model Arabic#Arabic COVID-19 Tweet Analysis #Multidialectal Arabic Processing #Pandemic Misinformation Detection

Downloads 197

Release Time : 3/2/2022

Model Overview

This model is a pre-trained version of AraBERT v2, specifically optimized for tasks involving multidialectal Arabic tweets related to COVID-19, excelling in tasks such as fake news detection and hate speech detection.

Model Features

Multidialectal Arabic Support

Specially optimized for multiple Arabic dialects, effectively handling COVID-19-related content in different dialects.

COVID-19 Domain Optimization

Pre-trained on 1.5 million COVID-19-related tweets, outperforming general-purpose models in domain-specific tasks.

Excellent Multitask Performance

Demonstrates outstanding performance in various tasks, including hate speech detection, fake news identification, and treatment discussions.

Model Capabilities

Arabic Text Classification

Hate Speech Detection

Fake News Identification

Medical Content Analysis

Use Cases

Social Media Content Moderation

Hate Speech Detection

Identifying hate speech content in Arabic social media

Achieves an F1 score of 0.8649, outperforming general Arabic language models.

Public Health Information Management

COVID-19 Treatment Discussion Identification

Identifying Arabic content discussing COVID-19 treatments

Achieves an F1 score of 0.9176, demonstrating excellent performance.

News Authenticity Verification

Misinformation Detection

Identifying false pandemic information in Arabic social media

Achieves an F1 score of 0.7739, significantly outperforming general-purpose models.

🚀 arabert_c19: An Arabert model pretrained on 1.5 million COVID-19 multi-dialect Arabic tweets

ARABERT COVID-19 is a powerful tool designed to handle multi - dialect Arabic tweets related to the COVID - 19 pandemic. It's a fine - tuned version of the AraBERT v2 model, trained on a vast dataset of 1.5 million relevant tweets. This enables it to achieve superior results in tasks associated with COVID - 19 Arabic tweets.

🚀 Quick Start

ARABERT COVID - 19 Arxiv URL is a pretrained (fine - tuned) version of the AraBERT v2 model (https://huggingface.co/aubmindlab/bert - base - arabertv02). The pretraining was carried out using 1.5 million multi - dialect Arabic tweets regarding the COVID - 19 pandemic from the “Large Arabic Twitter Dataset on COVID - 19” (https://arxiv.org/abs/2004.04315). This model can deliver better outcomes for tasks dealing with multi - dialect Arabic tweets related to the COVID - 19 pandemic.

✨ Features

Classification Results

The following table shows the 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).

	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/arabert_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

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}
}

📚 Documentation

Contacts

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

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