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README.md

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+# Duplicate Sentence Detection with ALBERT-base-v2
+
+## 📌 Overview
+
+This repository hosts the quantized version of the ALBERT-base-v2 model for Duplicate Sentence Detection. The model is designed to determine whether two sentences convey the same meaning. If they are similar, the model outputs "duplicate" with a confidence score; otherwise, it outputs "not duplicate" with a confidence score. The model has been optimized for efficient deployment while maintaining reasonable accuracy, making it suitable for real-time applications.
+
+## 🏗 Model Details
+
+- **Model Architecture:** ALBERT-base-v2  
+- **Task:** Duplicate Sentence Detection  
+- **Dataset:** Hugging Face's `quora-question-pairs`  
+- **Quantization:** Float16 (FP16) for optimized inference  
+- **Fine-tuning Framework:** Hugging Face Transformers  
+
+## 🚀 Usage
+
+### Installation
+
+```bash
+pip install transformers torch
+```
+
+### Loading the Model
+
+```python
+from transformers import AlbertTokenizer, AlbertForSequenceClassification
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model_name = "AventIQ-AI/albert-duplicate-sentence-detection"
+model = AlbertForSequenceClassification.from_pretrained(model_name).to(device)
+tokenizer = AlbertTokenizer.from_pretrained(model_name)
+```
+
+### Paraphrase Detection Inference
+
+```python
+def predict_duplicate(question1, question2, model):
+    inputs = tokenizer(question1, question2, truncation=True, padding="max_length", max_length=128, return_tensors="pt")
+    
+    # ✅ Move inputs to the same device as the model
+    inputs = {key: value.to(device) for key, value in inputs.items()}
+    
+    with torch.no_grad():  # Disable gradient calculation
+        outputs = model(**inputs)
+        logits = outputs.logits
+    
+    # ✅ Get prediction
+    probs = torch.softmax(logits, dim=1)
+    prediction = torch.argmax(probs, dim=1).item()
+ 
+    # ✅ Output the results
+    label_map = {0: "Not Duplicate", 1: "Duplicate"}
+    print(f"Q1: {question1}")
+    print(f"Q2: {question2}")
+    print(f"Prediction: {label_map[prediction]} (Confidence: {probs.max().item():.4f})\n")
+
+# 🔍 Test Example
+test_samples = [
+    ("How can I learn Python quickly?", "What is the fastest way to learn Python?"),  # Duplicate
+    ("What is the capital of India?", "Where is New Delhi located?"),  # Duplicate
+    ("How to lose weight fast?", "What is the best programming language to learn?"),  # Not Duplicate
+    ("Who is the CEO of Tesla?", "What is the net worth of Elon Musk?"),  # Not Duplicate
+    ("What is machine learning?", "How does AI work?"),  # Duplicate
+]
+for q1, q2 in test_samples:
+    predict_duplicate(q1, q2, model)
+```
+
+## 📊 Quantized Model Evaluation Results
+
+### 🔥 Evaluation Metrics 🔥
+
+- ✅ **Accuracy:**  0.7215  
+- ✅ **Precision:** 0.6497  
+- ✅ **Recall:**    0.5440  
+- ✅ **F1-score:**  0.5922  
+
+## ⚡ Quantization Details
+
+Post-training quantization was applied using PyTorch's built-in quantization framework. The model was quantized to Float16 (FP16) to reduce model size and improve inference efficiency while balancing accuracy.
+
+## 📂 Repository Structure
+
+```
+.
+├── model/               # Contains the quantized model files
+├── tokenizer_config/    # Tokenizer configuration and vocabulary files
+├── model.safetensors/   # Quantized Model
+├── README.md            # Model documentation
+```
+
+## ⚠️ Limitations
+
+- The model may struggle with highly nuanced paraphrases.
+- Quantization may lead to slight degradation in accuracy compared to full-precision models.
+- Performance may vary across different domains and sentence structures.
+
+## 🤝 Contributing
+
+Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.