Create README.md

README.md

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+# 💬 Sentiment-Analysis-for-Product-Release-Sentiment
+
+A **BERT-based sentiment analysis model** fine-tuned on a product review dataset. It predicts the sentiment of a text as **Positive**, **Neutral**, or **Negative** with a confidence score. This model is ideal for analyzing customer feedback, reviews, or user comments.
+
+---
+
+## ✨ Model Highlights
+
+- 📌 **Architecture**: Based on [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) by Google
+- 🔧 **Fine-tuned** on labeled product review data
+- 🔍 **3-way sentiment classification**: `Negative (0)`, `Neutral (1)`, `Positive (2)`
+- 💾 **Quantized version available** for faster inference
+
+---
+
+## 🧠 Intended Uses
+
+- ✅ Classifying product feedback and user reviews
+- ✅ Sentiment analysis for e-commerce platforms
+- ✅ Social media monitoring and customer opinion mining
+
+---
+
+## 🚫 Limitations
+
+- ❌ Designed for English texts only
+- ❌ May not perform well on sarcastic or ironic inputs
+- ❌ May struggle with domains very different from product reviews
+- ❌ Input texts longer than 128 tokens are truncated
+
+---
+
+## 🏋️‍♂️ Training Details
+
+- **Base Model**: `bert-base-uncased`
+- **Dataset**: Custom-labeled product review dataset
+- **Epochs**: 5
+- **Batch Size**: 8
+- **Max Length**: 128 tokens
+- **Optimizer**: AdamW
+- **Loss Function**: CrossEntropyLoss (with class balancing)
+- **Hardware**: Trained on NVIDIA GPU (CUDA-enabled)
+
+---
+
+## 📊 Evaluation Metrics
+
+| Metric     | Score |
+|------------|-------|
+| Accuracy   | 0.90  |
+| F1         | 0.90  |
+| Precision  | 0.90  |
+| Recall     | 0.90  |
+
+---
+
+## 🔎 Label Mapping
+
+| Label ID | Sentiment |
+|----------|-----------|
+| 0        | Negative  |
+| 1        | Neutral   |
+| 2        | Positive  |
+
+---
+
+## 🚀 Usage Example
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
+from transformers import DataCollatorWithPadding
+import torch
+import torch.nn.functional as F
+
+# Load model and tokenizer
+model_name = "AventIQ-AI/Sentiment-Analysis-for-Product-Release-Sentiment"
+tokenizer = BertTokenizer.from_pretrained(model_name)
+model = BertForSequenceClassification.from_pretrained(model_name)
+model.eval()
+
+# Inference
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    inputs = {k: v.to(quantized_model.device) for k, v in inputs.items()}
+    with torch.no_grad():
+        logits = quantized_model(**inputs).logits
+        probs = F.softmax(logits, dim=1)
+
+    predicted_class_id = torch.argmax(probs, dim=1).item()
+    confidence = probs[0][predicted_class_id].item()
+
+    label_map = {0: "Negative", 1: "Positive"}
+    label = label_map[predicted_class_id]
+    confidence_str = f"confidence : {confidence * 100:.1f}%"
+
+    return label, confidence_str
+
+# Example
+print(predict_sentiment(
+"The service was excellent and the staff was friendly.")
+)
+```
+
+---
+
+## 🧪 Quantization
+
+- Applied **post-training dynamic quantization** using PyTorch to reduce model size and speed up inference.
+- Quantized model supports CPU-based deployments.
+
+---
+
+## 📁 Repository Structure
+
+```
+.
+├── model/               # Quantized model files
+├── tokenizer/           # Tokenizer config and vocabulary
+├── model.safetensors/   # Fine-tuned full-precision model
+├── README.md            # Model documentation
+```
+
+---
+
+## 📌 Limitations
+
+- May not generalize to completely different domains (e.g., medical, legal)
+- Quantized version may show slight drop in accuracy compared to full-precision model
+
+---
+
+## 🤝 Contributing
+
+We welcome contributions! Please feel free to raise an issue or submit a pull request if you find a bug or have a suggestion.