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license: mit
tags:
- phishing-detection
- url-classification
- text-classification
- roberta
task: text-classification
datasets:
- custom
---
# Url Phishing Classifier
This model is fine-tuned for URL phishing classification. It classifies URLs as phishing (1) or safe (0).
## Model Description
This model is based on **roberta-base** and has been fine-tuned for phishing detection tasks.
## Training Details
- **Base Model**: roberta-base
- **Training Samples**: 1629193
- **Validation Samples**: 325839
- **Test Samples**: 217226
- **Epochs**: 5
- **Batch Size**: 24
- **Learning Rate**: 2e-05
- **Max Length**: 256
## Evaluation Results
### Test Set Metrics
- **Loss**: 0.1483
- **Accuracy**: 0.9463
- **F1**: 0.9262
- **Precision**: 0.9259
- **Recall**: 0.9264
- **Roc Auc**: 0.9890
- **True Positives**: 73116.0000
- **True Negatives**: 132450.0000
- **False Positives**: 5851.0000
- **False Negatives**: 5809.0000
- **Runtime**: 142.5284
- **Samples Per Second**: 1524.0900
- **Steps Per Second**: 31.7550
- **Epoch**: 5.0000
### Validation Set Metrics
- **Loss**: 0.1483
- **Accuracy**: 0.9455
- **F1**: 0.9250
- **Precision**: 0.9246
- **Recall**: 0.9255
- **Roc Auc**: 0.9888
- **True Positives**: 109566.0000
- **True Negatives**: 198511.0000
- **False Positives**: 8940.0000
- **False Negatives**: 8822.0000
- **Runtime**: 195.9861
- **Samples Per Second**: 1662.5610
- **Steps Per Second**: 34.6400
- **Epoch**: 5.0000
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model and tokenizer
model_name = "nhellyercreek/url-phishing-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Example inference
text = "Your email or URL text here"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
# Get prediction
predicted_class = predictions.argmax().item()
confidence = predictions[0][predicted_class].item()
print(f"Predicted class: {predicted_class} (phishing=1, safe=0)")
print(f"Confidence: {confidence:.4f}")
```
## Limitations
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.
## Citation
If you use this model, please cite:
```bibtex
@misc{nhellyercreek_url_phishing_classifier,
title={Url Phishing Classifier},
author={Your Name},
year={2024},
publisher={Hugging Face},
howpublished={\url{https://huggingface.co/nhellyercreek/url-phishing-classifier}}
}
```
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