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---
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license: mit
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tags:
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- phishing-detection
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- url-classification
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- text-classification
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- roberta
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task: text-classification
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datasets:
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- custom
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---
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# Url Phishing Classifier
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This model is fine-tuned for URL phishing classification. It classifies URLs as phishing (1) or safe (0).
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## Model Description
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This model is based on **roberta-base** and has been fine-tuned for phishing detection tasks.
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## Training Details
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- **Base Model**: roberta-base
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- **Training Samples**: 1629193
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- **Validation Samples**: 325839
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- **Test Samples**: 217226
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- **Epochs**: 5
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- **Batch Size**: 24
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- **Learning Rate**: 2e-05
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- **Max Length**: 256
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## Evaluation Results
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### Test Set Metrics
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- **Loss**: 0.1483
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- **Accuracy**: 0.9463
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- **F1**: 0.9262
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- **Precision**: 0.9259
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- **Recall**: 0.9264
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- **Roc Auc**: 0.9890
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- **True Positives**: 73116.0000
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- **True Negatives**: 132450.0000
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- **False Positives**: 5851.0000
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- **False Negatives**: 5809.0000
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- **Runtime**: 142.5284
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- **Samples Per Second**: 1524.0900
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- **Steps Per Second**: 31.7550
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- **Epoch**: 5.0000
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### Validation Set Metrics
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- **Loss**: 0.1483
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- **Accuracy**: 0.9455
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- **F1**: 0.9250
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- **Precision**: 0.9246
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- **Recall**: 0.9255
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- **Roc Auc**: 0.9888
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- **True Positives**: 109566.0000
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- **True Negatives**: 198511.0000
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- **False Positives**: 8940.0000
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- **False Negatives**: 8822.0000
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- **Runtime**: 195.9861
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- **Samples Per Second**: 1662.5610
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- **Steps Per Second**: 34.6400
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- **Epoch**: 5.0000
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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# Load model and tokenizer
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model_name = "nhellyercreek/url-phishing-classifier"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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# Example inference
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text = "Your email or URL text here"
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inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
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outputs = model(**inputs)
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predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
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# Get prediction
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predicted_class = predictions.argmax().item()
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confidence = predictions[0][predicted_class].item()
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print(f"Predicted class: {predicted_class} (phishing=1, safe=0)")
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print(f"Confidence: {confidence:.4f}")
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```
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## Limitations
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This model was trained on specific datasets and may not generalize to all types of phishing attempts. Always use additional security measures in production environments.
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## Citation
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If you use this model, please cite:
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```bibtex
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@misc{nhellyercreek_url_phishing_classifier,
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title={Url Phishing Classifier},
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author={Your Name},
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year={2024},
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publisher={Hugging Face},
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howpublished={\url{https://huggingface.co/nhellyercreek/url-phishing-classifier}}
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}
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```
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