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# 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.
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