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Check out the documentation for more information.
# 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! 🎯