---
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}}
}
```