README.md

metadata

license: mit
datasets:
  - facebook/natural_reasoning
language:
  - en
base_model:
  - distilbert/distilgpt2
pipeline_tag: text2text-generation
tags:
  - Resoning
  - Maths
  - physics

Natural Reasoning Bot 🤖

A lightweight QA chatbot built using Hugging Face's facebook/natural_questions dataset and a fine-tuned distilgpt2 model. This project provides a simple Streamlit interface to interact with the model in a clean, animated UI.

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✨ Features

• Reasoning-based natural language answers
• Animated Streamlit UI with gradient layout
• Fine-tuned GPT-style language model
• Clean prompt formatting with context handling
• Ready for deployment on GitHub or Streamlit Cloud

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🎓 Example Questions That Work Best

Use questions that require factual understanding, calculation, or reasoning:

Question Example	Why It Works
What is the total work done on an object lifted 5m?	Physics-based factual reasoning
Why is work zero in circular motion?	Conceptual explanation
If a car moves 60km/h for 2 hours, what is distance?	Simple arithmetic reasoning

Avoid vague or one-word questions like "gravity" or "work".

Always format your prompt like this:

### Question: <your question>
### Answer:

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📊 Accuracy Evaluation (Optional)

To evaluate model performance, you can measure accuracy or BLEU/ROUGE scores if using a validation dataset. Here's a simple accuracy graph generation using matplotlib:

import matplotlib.pyplot as plt

# Sample accuracy values per epoch
epochs = [1, 2, 3, 4, 5]
train_acc = [0.52, 0.65, 0.72, 0.78, 0.81]
val_acc = [0.50, 0.63, 0.70, 0.75, 0.79]

plt.plot(epochs, train_acc, label='Train Accuracy', marker='o')
plt.plot(epochs, val_acc, label='Validation Accuracy', marker='x')
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.title("Training vs Validation Accuracy")
plt.legend()
plt.grid(True)
plt.show()

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🚀 How to Run

1. Install requirements

pip install -r requirements.txt

2. Run Streamlit App

streamlit run app.py

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🌐 Folder Structure

project/
├── app.py               # Streamlit UI app
├── model/               # Fine-tuned model directory
├── utils.py             # Utility functions for prompt formatting
├── requirements.txt    # Dependencies
└── README.md            # Project readme

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💪 Credits

• Hugging Face Transformers
• Streamlit
• Dataset: facebook/natural_questions
• Model: distilgpt2

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For deployment on GitHub/Streamlit Cloud, keep model size small and test on CPU mode.

Made with ❤️ by [Vardaan Shukla]