Arabic Dialect Classifier

This project is a classifier of arabic dialects at a country level:
Given some arabic text, the goal is to predict the country of the text's dialect.

You can use the "/classify" endpoint through a POST request with a json input of the form: '{"text": "Your arabic text"}'

curl -X POST -H "Content-Type: application/json" -d '{"text": "Your arabic text"}' http://localhost:8080/classify

Run the app locally with Docker:

Clone the repository with Git:

git clone https://github.com/zaidmehdi/arabic-dialect-classifier.git

Build the Docker image:

docker build -t adc .

Run the Docker Container:

docker run -p 8080:80 adc

Now you can try sending a POST request:

curl -X POST -H "Content-Type: application/json" -d '{"text": "Your Arabic text"}' http://localhost:8080/classify

The response should be a json with the following fields:

{
    "class": "country_name"
}

How I built this project:

The data used to train the classifier comes from the NADI 2021 dataset for Arabic Dialect Identification (Abdul-Mageed et al., 2021).
It is a corpus of tweets collected using Twitter's API and labeled thanks to the users' locations with the country and region.

I used the language model https://huggingface.co/moussaKam/AraBART to extract features from the input text by taking the output of its last hidden layer. I used these vector embeddings as the input for a Multinomial Logistic Regression to classify the input text into one of the 21 dialects (Countries).

For more details, you can refer to the docs directory.

References:

Abdul-Mageed et al., 2021
Title: NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task
Authors: Abdul-Mageed, Muhammad; Zhang, Chiyu; Elmadany, AbdelRahim; Bouamor, Houda; Habash, Nizar
Year: 2021
Conference/Book Title: Proceedings of the Sixth Arabic Natural Language Processing Workshop (WANLP 2021)