🧠 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.

✨ 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

🧠 Intended Uses

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

🚫 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)

🏋️‍♂️ 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

📊 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.

🔎 Label Mapping

Label ID	Sentiment
0	Negative
1	Positive

🚀 Usage

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
python
Copy
Edit
.
├── 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.