NLP Hallucination Detection Pipeline

This repository contains the codebase, notebooks, and deliverables for the NLP Assignment (CS F429) focused on Hallucination Detection in Natural Language Processing.

📂 Project Structure

The project is structured into a sequence of Jupyter Notebooks that document the end-to-end machine learning pipeline:

• 01_data_exploration.ipynb: Loading and preprocessing datasets (HaluEval and RAGTruth), analyzing label distributions, and data cleaning.
• 02_core_pipeline.ipynb: Implementation of the core modeling pipeline and feature engineering.
• 03_baselines.ipynb: Establishing baseline model performance for hallucination detection.
• 04_experiments.ipynb: Advanced experimentation, hyperparameter tuning, and detailed evaluation metrics.
• 05_demo .ipynb: A demonstration notebook showcasing the final model's capabilities on custom text inputs.

📄 Deliverables

The repository also includes the final project reports and presentations submitted by Team 8:

• NLP_Project_Report.pdf / NLP_Report_Team_8.docx
• NLP_PPT_TEAM_8.pptx
• Presentation_Script_NLP.pdf
• Team_Prep_Guide_NLP.pdf

🚀 Setup & Installation

To run this project locally, it is recommended to use a virtual environment.

1. Clone this repository:

   git clone https://github.com/aaqibnp971/NLP_Assignment.git
   cd NLP_Assignment

2. Install the necessary dependencies (ensure you have Jupyter installed):

   pip install datasets pandas scikit-learn jupyterlab

   (Note: Adjust the installed packages based on your specific environment requirements).

3. Run Jupyter Notebook or Jupyter Lab:

   jupyter lab

⚠️ Notes on Data

Due to GitHub's 100MB file size limits, large datasets (such as ragtruth.csv) are not tracked in this repository. The 01_data_exploration.ipynb notebook contains the code to pull and process the dataset directly from the Hugging Face Hub (wandb/RAGTruth-processed).