| # 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. |