Small Language Model Chatbot

Project Overview

The Small Language Model Chatbot is a Python-based interactive chatbot built using Hugging Face's distilGPT-2 model. The chatbot is fun, lightweight, and ideal for demonstrating the capabilities of small-scale language models. It includes interactive widgets for user input and a visual element that tracks and displays the word count of user inputs over time.

This project is designed to run seamlessly in Google Colab.

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Features

🤖 Interactive Chatbot

• Accepts user input and generates meaningful responses using distilGPT-2.
• Incorporates randomness and creativity in responses through fine-tuned parameters.

📊 Visualization

• Displays a bar chart to track the word count of user inputs for each interaction.

⚙️ Enhanced Parameters for Text Generation

• temperature=0.7: Balances creativity and determinism.
• top_p=0.9: Uses nucleus sampling to generate high-quality responses.
• min_length=20: Ensures meaningful replies.

🛡️ Fallback Mechanism

• If the chatbot generates a very short or meaningless response, it provides a fallback message:

  I couldn't think of anything interesting to say! Let's try again.
  

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Installation Instructions

1. Clone the Repository

git clone https://github.com/thekartikeyamishra/small-language-model-chatbot.git
cd small-language-model-chatbot

2. Install Required Libraries

Make sure to install the necessary Python libraries:

pip install transformers ipywidgets

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How to Use

1. Open Google Colab:

   • Copy and paste the code into a new Google Colab notebook.

2. Run the Script:

   • Execute the code cells to initialize the chatbot.

3. Interact with the Chatbot:

   • Enter your message in the text input field.
   • Click the Send button to receive the chatbot's reply.

4. View Visualizations:

   • The chatbot displays a bar chart showing the word count of user inputs after each interaction.

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Visualization:

A bar chart shows the number of words in the user's message for each interaction.

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Potential Enhancements

1. Sentiment Analysis:

   • Add a sentiment analysis layer to analyze the tone of user messages.

2. Multilingual Support:

   • Integrate pre-trained multilingual models for global audiences.

3. Save Conversations:

   • Allow users to save chat history for later review.

4. Fine-Tuned Models:

   • Use domain-specific datasets to fine-tune the chatbot for specific applications (e.g., customer support, education).

5. Voice Input/Output:

   • Add support for voice-based interactions using libraries like SpeechRecognition and pyttsx3.

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Use Cases

• Learning Tool:

  • Explore the capabilities of small language models.
  • Understand text generation and visualization in NLP.

• Prototyping:

  • Use this chatbot as a starting point for building advanced conversational AI.

• Fun and Engagement:

  • Interact with a lightweight chatbot for entertainment or quick inspiration.

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Contribute

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Add new feature"

4. Push to the branch:

   git push origin feature-name

5. Open a pull request.

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Support

If you enjoy using this project, please:

• 🌟 Star this repository on GitHub.
• 🗣️ Share it with your network.
• 💬 Submit feedback or feature requests.

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Acknowledgments

• Hugging Face Transformers: For providing pre-trained models and an intuitive API.
• Matplotlib: For data visualization.
• IPyWidgets: For building an interactive interface.

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Let me know if you'd like further refinements or additional sections! 🚀