# 🧠 SentimentClassifier-RoBERTa-UserReviews

A RoBERTa-based sentiment analysis model fine-tuned on user review data. This model classifies reviews as **Positive** or **Negative**, making it ideal for analyzing product feedback, customer reviews, and other short user-generated content.

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## ✨ Model Highlights

📌 Based on `cardiffnlp/twitter-roberta-base-sentiment` (from Cardiff NLP)  
🔍 Fine-tuned on binary-labeled user reviews (positive vs. negative)  
⚡ Supports prediction of 2 classes: Positive, Negative  
🧠 Built using Hugging Face 🤗 Transformers and PyTorch  

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## 🧠 Intended Uses

- ✅ Customer review sentiment classification  
- ✅ E-commerce product feedback analysis  
- ✅ App review categorization  

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## 🚫 Limitations

- ❌ Not optimized for multi-class sentiment (Neutral, Sarcasm, etc.)  
- 🌍 Trained primarily on English-language reviews  
- 📏 Performance may degrade for texts >128 tokens (due to max length truncation)  
- 🤔 Not designed for domain-specific jargon (e.g., legal or medical texts)  

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## 🏋️‍♂️ Training Details

| Attribute         | Value                                 |
|-------------------|----------------------------------------|
| Base Model        | cardiffnlp/twitter-roberta-base-sentiment |
| Dataset           | Filtered user reviews (binary labeled) |
| Labels            | Positive (1), Negative (0)             |
| Max Token Length  | 128                                    |
| Epochs            | 3                                      |
| Batch Size        | 8                                      |
| Optimizer         | AdamW                                  |
| Loss Function     | CrossEntropyLoss                       |
| Framework         | PyTorch + Hugging Face Transformers    |
| Hardware          | CUDA-enabled GPU                       |

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## 📊 Evaluation Metrics

| Metric     | Score  |
|------------|--------|
| Accuracy   | 0.97  |
| Precision  | 0.96   |
| Recall     | 1.00  |
| F1 Score   | 0.98   |

> 📌 Replace with your final values after complete training if these were updated.

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## 🔎 Label Mapping

| Label ID | Sentiment |
|----------|-----------|
| 0        | Negative  |
| 1        | Positive  |

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## 🚀 Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import torch.nn.functional as F

model_name = "your-username/sentiment-roberta-user-reviews"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()

def predict(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = F.softmax(outputs.logits, dim=1)
        pred = torch.argmax(probs, dim=1).item()
        label_map = {0: "Negative", 1: "Positive"}
        return f"Sentiment: {label_map[pred]} (Confidence: {probs[0][pred]:.2f})"

# Example
print(predict("I really love this product, works great!"))



📁 Repository Structure
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├── model/               # Contains fine-tuned model files
├── tokenizer/           # Tokenizer config and vocab
├── config.json          # Model configuration
├── pytorch_model.bin    # Fine-tuned model weights
├── README.md            # Model card



🤝 Contributing
Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.