YAML Metadata
Warning:
The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
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.
✨ 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
🎓 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:
📊 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()
🚀 How to Run
- Install requirements
pip install -r requirements.txt
- Run Streamlit App
streamlit run app.py
🌐 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
💪 Credits
- Hugging Face Transformers
- Streamlit
- Dataset:
facebook/natural_questions - Model:
distilgpt2
For deployment on GitHub/Streamlit Cloud, keep model size small and test on CPU mode.
Made with ❤️ by [Vardaan Shukla]
Model tree for Vardaan98/Reasoning_model
Base model
distilbert/distilgpt2