Chatbot with BERT-based Sentiment Analysis

This project implements a chatbot with sentiment analysis using the BERT model. The chatbot interacts with users, providing answers and assistance based on the input text. It utilizes the Hugging Face Transformers library for BERT model loading and inference.

Requirements

• Python 3
• Hugging Face Transformers
• PyTorch
• Tensorflow (optional, for accelerated training)

Install the required dependencies using:

pip install transformers torch

Usage

Data Preparation: Ensure you have the dataset in JSON format containing intents and responses (intents.json in this case).
Model Training: Run the code cells to train the BERT model for sentiment analysis.
Chatbot Initialization: After training, save the trained model and tokenizer.
Chatbot Interaction: Interact with the chatbot using the chat() function provided in the code.

Training the Model

Load the intents data from the intents.json file.
Preprocess the data and tokenize text using the BERT tokenizer.
Split the data into training and testing sets.
Fine-tune the BERT model for sentiment analysis.
Evaluate the trained model on the test dataset.

Chatbot Initialization

Save the trained BERT model and tokenizer to the specified directory.

Chatbot Interaction

Initialize the chatbot pipeline using the trained model and tokenizer.
Start the conversation with the chatbot using the chat() function.
Enter text inputs, and the chatbot will provide responses based on the trained sentiment analysis model.

Folder Structure

intents.json: JSON file containing intents and responses for training the chatbot.
README.md: Markdown file containing information about the project and instructions for usage.
chatbot.ipynb: Jupyter Notebook containing the Python code for training the chatbot and interacting with it.
output/: Directory to store the trained model and tokenizer.

Additional Notes

This project can be extended by training the chatbot on larger datasets or integrating it with other NLP tasks.
Experiment with different BERT model variants and hyperparameters for better performance.
Customize the chatbot responses and intents for specific applications or domains.

Feel free to explore and modify the code according to your requirements!