Create README.md

README.md

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+ ### **BERT-Base-Uncased Quantized Model for Disaster SOS Message Classification**  
+
+This repository hosts a quantized version of the BERT model, fine-tuned for **Disaster SOS Message Classification**. The model efficiently classifies emergency messages related to disasters, helping prioritize urgent cases. It has been optimized for deployment in resource-constrained environments while maintaining high accuracy.  
+
+## **Model Details**  
+
+- **Model Architecture:** BERT Base Uncased  
+- **Task:** Disaster SOS Message Classification  
+- **Dataset:** Disaster Response Messages Dataset  
+- **Quantization:** Float16  
+- **Fine-tuning Framework:** Hugging Face Transformers  
+
+## **Usage**  
+
+### **Installation**  
+
+```sh
+pip install transformers torch
+```  
+
+### **Loading the Model**  
+
+```python
+from transformers import BertForSequenceClassification, BertTokenizer
+import torch
+
+# Load quantized model
+quantized_model_path = "/kaggle/working/bert_finetuned_fp16"
+quantized_model = BertForSequenceClassification.from_pretrained(quantized_model_path)
+quantized_model.eval()  # Set to evaluation mode
+quantized_model.half()  # Convert model to FP16
+
+# Load tokenizer
+tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+
+# Define a test SOS message
+test_message = "There is a massive earthquake, and people need help immediately!"
+
+# Tokenize input
+inputs = tokenizer(test_message, return_tensors="pt", padding=True, truncation=True, max_length=128)
+
+# Ensure input tensors are in correct dtype
+inputs["input_ids"] = inputs["input_ids"].long()
+inputs["attention_mask"] = inputs["attention_mask"].long()
+
+# Make prediction
+with torch.no_grad():
+    outputs = quantized_model(**inputs)
+
+# Get predicted categories
+probs = torch.sigmoid(outputs.logits).cpu().numpy().flatten()
+predictions = (probs > 0.5).astype(int)
+
+# Category mapping (Example)
+category_names = ["Earthquake", "Flood", "Medical Emergency", "Infrastructure Damage", "General Help"]
+predicted_labels = [category_names[i] for i in range(len(predictions)) if predictions[i] == 1]
+
+print(f"Message: {test_message}")
+print(f"Predicted Categories: {predicted_labels}")
+print(f"Confidence Scores: {probs}")
+```  
+
+## **Performance Metrics**  
+
+- **Accuracy:** 0.85  
+- **F1 Score:** 0.83  
+
+## **Fine-Tuning Details**  
+
+### **Dataset**  
+
+The dataset is the **Disaster Response Messages Dataset**, which contains real-life messages from various disaster scenarios.  
+
+### **Training**  
+
+- Number of epochs: 3  
+- Batch size: 8  
+- Evaluation strategy: epoch  
+- Learning rate: 2e-5  
+
+### **Quantization**  
+
+Post-training quantization was applied using PyTorch’s built-in quantization framework, reducing model size and improving inference speed.  
+
+## **Repository Structure**  
+
+```
+.
+├── model/               # Contains the quantized model files
+├── tokenizer_config/    # Tokenizer configuration and vocabulary files
+├── model.safensors/     # Fine-tuned Model
+├── README.md            # Model documentation
+```  
+
+## **Limitations**  
+
+- The model may not generalize well to unseen disaster types outside the training data.  
+- Minor accuracy degradation due to quantization.  
+
+## **Contributing**  
+
+Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.  
+
+---