Roberta-InterestDetection

**📌 README.md **

# Interest Analysis Model 🎯

This is a fine-tuned version of `j-hartmann/emotion-english-distilroberta-base` for **intent analysis**, categorizing text into three classes:  
✅ **Disinterested**  
✅ **Neutral**  
✅ **Interested**  

It is useful for analyzing customer feedback, social media posts, and other text-based interactions to determine user intent.

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## 🚀 Model Details
- **Base Model**: [j-hartmann/emotion-english-distilroberta-base](https://huggingface.co/j-hartmann/emotion-english-distilroberta-base)  
- **Fine-Tuned For**: Intent analysis with **3 labels**  
- **Dataset**: Custom dataset based on user-defined categories  
- **Labels**:  
  - `0`: Disinterested  
  - `1`: Neutral  
  - `2`: Interested  

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## 📥 Installation  
To use this model, install the `transformers` library:  
```bash
pip install transformers

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🔄 Load the Model

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load the model
model_name = "Rafay-15/InterestAnalysisModel"  # Replace with your Hugging Face model name
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Define label mapping
id2label = {0: "disinterested", 1: "neutral", 2: "interested"}

def predict(text):
    """Predicts the intent category of the input text."""
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
    
    with torch.no_grad():
        outputs = model(**inputs)
    
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()
    
    return id2label[predicted_class]

# Test Example
text = "I really love this product!"
print(f"Text: {text} -> Predicted Label: {predict(text)}")

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📊 Example Predictions

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📜 License

This model is released under the MIT License. You are free to use it for research and commercial purposes.

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🤝 Contributing

If you have improvements or suggestions, feel free to open an issue or contribute via GitHub.

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🔗 Links

• 🤗 Model Page: Hugging Face
• 🛠 GitHub Repo: GitHub

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🚀 Enjoy using the model! 🎯