| # 🧠 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 | |
| ```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 | |
| 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. | |