RoBerta-InterestScoring

# Interest Analysis Model 🎯  

This repository contains a fine-tuned transformer model for **intent analysis**, built on `j-hartmann/emotion-english-distilroberta-base`.  
The model classifies text into three categories:  
✅ **Disinterested**  
✅ **Neutral**  
✅ **Interested**  

This model is useful for analyzing customer feedback, social media interactions, and other text-based user intent scenarios.

## 📂 Repository Structure

. ├── download_model.py # Downloads the model from Hugging Face ├── run_model.py # Loads the model and runs predictions ├── requirements.txt # Required dependencies ├── README.md # Project documentation └── downloaded_model/ # (Automatically created) Directory where the model is saved

## 🚀 Installation  

### **1️⃣ Clone the Repository**  
```bash
git clone https://github.com/Rafay-15/InterestAnalysisModel.git
cd InterestAnalysisModel

2️⃣ Install Dependencies

pip install -r requirements.txt

📥 Download the Model

To download the fine-tuned model from Hugging Face:

python hf.py

This will create a model_final/ directory containing the model and tokenizer.

🔄 Run the Model with Example Texts

Once the model is downloaded, you can test it with sample inputs:

python main.py

Expected Output

--- Model Predictions ---
Text: I absolutely love this! -> Predicted Label: interested
Text: I don't care about this at all. -> Predicted Label: disinterested
Text: It's fine, I guess. -> Predicted Label: neutral

📜 License

This project is released under the MIT License. Feel free to use and modify it for research and commercial purposes.

🤝 Contributing

If you'd like to contribute or improve the model, feel free to fork the repo and submit a pull request.

🔗 Links

• 🤗 Hugging Face Model: https://huggingface.co/rafay-15/Roberta-InterestDetection
• 📂 GitHub Repository: https://github.com/Rafay-15/InterestAnalysisModel

🚀 Happy coding! 🎯