Update README.md

README.md

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-### Load the Model
+# 🧠 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   | 0.94   |
+| Precision  | 0.93   |
+| Recall     | 0.95   |
+| F1 Score   | 0.94   |
+
+> 📌 Replace with your final values after complete training if these were updated.
+
+---
+
+## 🔎 Label Mapping
+
+| Label ID | Sentiment |
+|----------|-----------|
+| 0        | Negative  |
+| 1        | Positive  |
+
+---
+
+## 🚀 Usage
 
 ```python
-import torch
-from torch.utils.data import Dataset, DataLoader
-from torch.optim import AdamW
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 import torch.nn.functional as F
 
-MODEL = "AventIQ-AI/sentiment-analysis-for-user-reviews-sentiment"
-tokenizer = AutoTokenizer.from_pretrained(MODEL)
-model = AutoModelForSequenceClassification.from_pretrained(MODEL,num_labels=2,ignore_mismatched_sizes=True)
-
+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 = quantize_model(**inputs)
+        outputs = model(**inputs)
         probs = F.softmax(outputs.logits, dim=1)
         pred = torch.argmax(probs, dim=1).item()
-        label_map = {0: "Negative", 1: "Neutral", 2: "Positive"}
-    return f"Sentiment: {label_map[pred]} (Confidence: {probs[0][pred]:.2f})"
+        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 ideas to improve this model or documentation.
+
+
+
+
+
 
-# Test predictions
-print("\nTest Predictions:")
-print(predict("the product quality is just so good"))