ArabicFraudDetection

A machine learning project to detect fraudulent comments in ride-hailing services using Arabic text data. The project leverages pre-trained language models like AraBERT to classify user comments as fraudulent or non-fraudulent.

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Overview

Detecting fraudulent activities in ride-hailing services is critical for ensuring reliability and customer trust. This project focuses on processing Arabic text data, fine-tuning a pre-trained language model, and deploying an effective system for fraud detection.

Key Features:

• Custom Preprocessing: Automatically label comments based on fraud-related keywords.
• Fine-Tuning Pre-trained Models: Fine-tune AraBERT for binary classification tasks.
• Evaluation Metrics: Calculate metrics like accuracy, precision, recall, and F1-score.
• Modular Code: Organized and reusable Python modules for data processing, training, and evaluation.
• Docker Support: Easily run the project in a containerized environment.

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Directory Structure

ArabicFraudDetection/
├── data/                    # Store data files or sample datasets
├── src/                     # Python scripts for preprocessing, training, evaluation, etc.
│   ├── preprocessing.py     # Data cleaning and preparation
│   ├── train.py             # Model fine-tuning script
│   ├── evaluate.py          # Evaluation and metrics calculation
│   ├── utils.py             # Helper functions
├── models/                  # Save trained models and checkpoints
├── notebooks/               # Jupyter notebooks for exploratory work
├── tests/                   # Unit and integration tests
├── tmp_trainer/             # 
├── requirements.txt         # Python dependencies
├── Dockerfile               # Dockerfile to containerize the project
├── README.md                # Documentation
├── LICENSE                  # License file
├── .gitignore               # Ignore unnecessary files (e.g., data, logs)
└── setup.py                 # Package installation script

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Getting Started

Prerequisites

Ensure you have the following installed:

• Python 3.8+
• Pip
• Docker (if using containerized setup)
• Virtual environment (optional but recommended)

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Installation

Option 1: Run Locally

1. Clone the repository:

   git clone https://github.com/smasoudrezvani/ArabicFraudDetection_LLM.git
   cd ArabicFraudDetection

2. Install dependencies:

   pip install -r requirements.txt

3. Prepare your dataset:

   • Place your raw dataset (e.g., df_rating&comment.xlsx) in the data/ folder.

Option 2: Run with Docker

1. Build the Docker image:

   docker build -t arabic-fraud-detection-LLM .

2. Run the Docker container:

   docker run --rm -it arabic-fraud-detection-LLM

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Usage (Not using Docker)

1. Preprocess Data

Run the preprocessing script to clean and label the data:

python3 ./src/preprocessing.py --input "data/df_rating&comment.xlsx" --output "data/processed"

2. Train the Model

Fine-tune the pre-trained language model:

python3 ./src/train.py --model aubmindlab/bert-base-arabertv2 --train data/processed/train.json --test data/processed/test.json --output models/fraud_detector

3. Evaluate the Model

Evaluate the model's performance on the test set:

python3 ./src/evaluate.py --model ./models/fraud_detector --test ./data/processed/test.json

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Using Docker

1. Run Preprocessing with Docker

You can modify the Dockerfile command or use the container interactively:

docker run -v $(pwd)/data:/app/data arabic-fraud-detection python src/preprocessing.py --input data/df_rating&comment.xlsx --output data/processed

2. Train the Model with Docker

docker run -v $(pwd)/models:/app/models arabic-fraud-detection python src/train.py --model aubmindlab/bert-base-arabertv2 --train data/processed/train.json --test data/processed/test.json --output models/fraud_detector

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Results

Metric	Value
Accuracy	0.92
Precision	0.90
Recall	0.89
F1 Score	0.89

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Advanced Features

• Hyperparameter Tuning: Use the Trainer API's hyperparameter search functionality to optimize the model.
• Data Augmentation: Extend the dataset using techniques like back-translation or synonym replacement.
• Deployment: Deploy the model as a REST API using FastAPI or a user-friendly interface with Streamlit.

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Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a feature branch: git checkout -b feature-name.
3. Commit changes: git commit -m 'Add feature-name'.
4. Push to the branch: git push origin feature-name.
5. Open a pull request.

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License

This project is licensed under the Apache License.

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Acknowledgments

Special thanks to Hugging Face for providing the tools and pre-trained models that make this project possible.

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This version includes details about how to use the `Dockerfile` for preprocessing, training, and evaluation. Let me know if you'd like further changes!