Question Answering System Using DistilBERT-NQ

This is a web-based Question Answering (QA) system built using the pretrained DistilBERT model fine-tuned on the Natural Questions dataset. It allows users to input a passage and ask a question related to it, and returns the most probable answer using deep learning.

🧠 Features

• Uses AsmaAwad/distilbert-base-uncased-NaturalQuestions transformer model
• Accepts user-provided paragraph and question inputs
• Predicts and highlights the most relevant answer
• Flask backend for inference API
• Simple, user-friendly web interface

🔧 Tech Stack

• Frontend: HTML, CSS, Bootstrap
• Backend: Python, Flask
• AI Engine: Hugging Face Transformers, DistilBERT
• Model: AsmaAwad/distilbert-base-uncased-NaturalQuestions

🚀 Getting Started

1. Clone the Repository

git clone https://github.com/yourusername/Question-Answering-System-Using-DistilBERT-NQ.git
cd Question-Answering-System-Using-DistilBERT-NQ
python -m venv venv
source venv/bin/activate  # For Linux/macOS
venv\Scripts\activate     # For Windows
pip install -r requirements.txt
pip install flask transformers torch
python app.py
QA System/
│
├── app.py                # Flask application
├── templates/
│   └── index.html        # HTML form for user input
├── static/
│   └── styles.css        # Styling
├── model/
│   └── [optional model loading or tokenizer cache]
└── README.md             # Project overview