Question-Answer-chatbot

A chatbot designed to generate accurate responses to user queries using advanced machine learning models like BERT and Encoder-Decoder. The project includes comparative analysis of various approaches to optimize performance and deliver precise results.

Table of Contents

1. Introduction
2. Features
3. Technologies Used
4. Model Performance
5. Setup and Installation
6. Usage
7. Future Enhancements
8. Contributions

Introduction

This chatbot uses state-of-the-art machine learning models to answer user queries accurately. It leverages BERT and Encoder-Decoder architectures, achieving up to 92% accuracy in response generation.

Features

• Supports complex question-answering tasks.
• High response accuracy (up to 92% with BERT).
• Comparative analysis of various ML models (CNN, LSTM, Encoder-Decoder).
• Intuitive and interactive user interface.

Technologies Used

• Programming Language: Python
• Frameworks: TensorFlow
• Models: BERT, Encoder-Decoder (with and without attention)
• Libraries: NumPy, Pandas, Scikit-learn, Flask (optional for web integration)

Model Performance

Model	Accuracy
Encoder-Decoder	85%
BERT	92%
CNN-LSTM	88%

Setup and Installation

1. Clone the repository:

   git clone https://github.com/your-username/question-answer-chatbot.git
   

2. cd question-answer-chatbot

3. pip install -r requirements.txt

4. pip install -r requirements.txt

5. python app.py