# BERT Phishing Detection Model

This is a BERT-based model fine-tuned for phishing detection. The model can classify text/URLs as phishing or legitimate.

## Model Details

- **Model Type**: BERT for Sequence Classification
- **Architecture**: BertForSequenceClassification
- **Problem Type**: Single Label Classification
- **Hidden Size**: 768
- **Number of Layers**: 12
- **Number of Attention Heads**: 12
- **Max Position Embeddings**: 512
- **Vocabulary Size**: 30,522

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "th1enq/bert_checkpoint"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example usage
text = "Your text here"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions, dim=-1)
```

## Training

This model was fine-tuned on phishing detection data to classify text as phishing (1) or legitimate (0).

## License

Please refer to the original BERT license and any applicable dataset licenses.