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

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.