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--- |
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license: mit |
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datasets: |
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- facebook/natural_reasoning |
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language: |
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- en |
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base_model: |
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- distilbert/distilgpt2 |
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pipeline_tag: text2text-generation |
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tags: |
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- Resoning |
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- Maths |
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- physics |
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--- |
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# Natural Reasoning Bot 🤖 |
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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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--- |
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## ✨ Features |
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* Reasoning-based natural language answers |
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* Animated Streamlit UI with gradient layout |
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* Fine-tuned GPT-style language model |
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* Clean prompt formatting with context handling |
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* Ready for deployment on GitHub or Streamlit Cloud |
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--- |
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## 🎓 Example Questions That Work Best |
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Use questions that require factual understanding, calculation, or reasoning: |
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| Question Example | Why It Works | |
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| ---------------------------------------------------- | ------------------------------- | |
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| What is the total work done on an object lifted 5m? | Physics-based factual reasoning | |
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| Why is work zero in circular motion? | Conceptual explanation | |
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| If a car moves 60km/h for 2 hours, what is distance? | Simple arithmetic reasoning | |
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> Avoid vague or one-word questions like "gravity" or "work". |
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Always format your prompt like this: |
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```txt |
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### Question: <your question> |
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### Answer: |
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``` |
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--- |
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## 📊 Accuracy Evaluation (Optional) |
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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: |
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```python |
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import matplotlib.pyplot as plt |
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# Sample accuracy values per epoch |
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epochs = [1, 2, 3, 4, 5] |
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train_acc = [0.52, 0.65, 0.72, 0.78, 0.81] |
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val_acc = [0.50, 0.63, 0.70, 0.75, 0.79] |
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plt.plot(epochs, train_acc, label='Train Accuracy', marker='o') |
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plt.plot(epochs, val_acc, label='Validation Accuracy', marker='x') |
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plt.xlabel("Epoch") |
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plt.ylabel("Accuracy") |
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plt.title("Training vs Validation Accuracy") |
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plt.legend() |
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plt.grid(True) |
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plt.show() |
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``` |
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--- |
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## 🚀 How to Run |
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1. **Install requirements** |
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```bash |
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pip install -r requirements.txt |
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``` |
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2. **Run Streamlit App** |
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```bash |
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streamlit run app.py |
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``` |
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--- |
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## 🌐 Folder Structure |
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``` |
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project/ |
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├── app.py # Streamlit UI app |
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├── model/ # Fine-tuned model directory |
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├── utils.py # Utility functions for prompt formatting |
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├── requirements.txt # Dependencies |
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└── README.md # Project readme |
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``` |
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--- |
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## 💪 Credits |
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* Hugging Face Transformers |
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* Streamlit |
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* Dataset: `facebook/natural_questions` |
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* Model: `distilgpt2` |
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--- |
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For deployment on GitHub/Streamlit Cloud, keep model size small and test on CPU mode. |
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**Made with ❤️ by \[Vardaan Shukla]** |
