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Arabic Question Generation Graduation Project

This project focuses on automatically generating Arabic questions from input context using a fine-tuned AraT5 transformer model. It also includes a complete MERN stack web application for user interaction and evaluation.


🧩 Project Overview

The main goal is to assist Arabic learners and educators by generating high-quality questions automatically from a given Arabic text. The project supports:

  • Natural Language Processing (NLP) for the Arabic language.
  • Educational tools through AI-powered question generation.

🚀 Tech Stack

🔗 Frontend

  • React.js
  • Tailwind CSS (optional)

⚙️ Backend

  • Node.js
  • Express.js
  • MongoDB (for storing user data and chat history)

🤖 Model API

  • Python
  • FastAPI
  • HuggingFace Transformers (AraT5 model)

🗂️ Question and Answer Model Folder Structure

This folder contains all resources related to Arabic question generation and answering using deep learning and NLP techniques. It leverages datasets such as Arabic-SQuAD, ARCD, MLQA, and TydiQA to train and evaluate models for generating high-quality, answerable questions from Arabic context passages.

Structure:

  • notebooks/: Jupyter notebooks for experiments, preprocessing, training, and evaluation.
  • scripts/: Utility scripts for text preprocessing and testing.
  • results/: Output and evaluation reports (e.g., generated questions, model test results).

Refer to the notebooks for step-by-step code, explanations, and example usages. Example context and generated questions are provided in the results files.


📦 Features

  • 💬 Input Arabic context and generate multiple questions.
  • 🧠 Model fine-tuned on Arabic question generation datasets.
  • 💾 Save and access previous chats (context + questions).

🛠️ Getting Started (Development Setup)

# Clone the repo
git clone https://github.com/your-username/arabic-question-generator.git
cd arabic-question-generator

# Install client and server dependencies
cd frontend
npm install

cd backend
npm install

# Start frontend and backend
npm run dev   # or use nodemon / dev script

📑 References

Model References

Data References

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