| ### **BERT-Base-Uncased Quantized Model for Disaster SOS Message Classification** |
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| 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. |
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| ## **Model Details** |
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| - **Model Architecture:** BERT Base Uncased |
| - **Task:** Disaster SOS Message Classification |
| - **Dataset:** Disaster Response Messages Dataset |
| - **Quantization:** Float16 |
| - **Fine-tuning Framework:** Hugging Face Transformers |
|
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| ## **Usage** |
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| ### **Installation** |
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| ```sh |
| pip install transformers torch |
| ``` |
|
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| ### **Loading the Model** |
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|
| ```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}") |
| ``` |
|
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| ## **Performance Metrics** |
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| - **Accuracy:** 0.85 |
| - **F1 Score:** 0.83 |
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| ## **Fine-Tuning Details** |
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| ### **Dataset** |
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| The dataset is the **Disaster Response Messages Dataset**, which contains real-life messages from various disaster scenarios. |
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| ### **Training** |
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| - Number of epochs: 3 |
| - Batch size: 8 |
| - Evaluation strategy: epoch |
| - Learning rate: 2e-5 |
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| ### **Quantization** |
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| Post-training quantization was applied using PyTorch’s built-in quantization framework, reducing model size and improving inference speed. |
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| ## **Repository Structure** |
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| ``` |
| . |
| ├── model/ # Contains the quantized model files |
| ├── tokenizer_config/ # Tokenizer configuration and vocabulary files |
| ├── model.safensors/ # Fine-tuned Model |
| ├── README.md # Model documentation |
| ``` |
|
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| ## **Limitations** |
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| - The model may not generalize well to unseen disaster types outside the training data. |
| - Minor accuracy degradation due to quantization. |
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| ## **Contributing** |
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| Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements. |
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| --- |
