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dviro/
urlbert-onnx-mini

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URLBert Phishing Detector (ONNX)

This is an ONNX version of the CrabInHoney/urlbert-tiny-v4-phishing-classifier model, which is designed to detect phishing URLs.

Usage with Hugging Face Transformers

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import onnxruntime as ort
import numpy as np

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("dviro/urlbert-onnx-mini")

# Load ONNX model
ort_session = ort.InferenceSession("phishing_detector.onnx")

# Example URL
url = "example.com"

# Tokenize
inputs = tokenizer(url, return_tensors="np", padding="max_length", truncation=True)
input_ids = inputs["input_ids"].astype(np.int64)
attention_mask = inputs["attention_mask"].astype(np.int64)
token_type_ids = inputs.get("token_type_ids", np.zeros_like(input_ids)).astype(np.int64)

# Run inference
outputs = ort_session.run(
    None, 
    {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
        "token_type_ids": token_type_ids
    }
)

# Process results
logits = outputs[0][0]
scores = np.exp(logits) / np.sum(np.exp(logits))
labels = ["Safe", "Phishing"]
prediction = labels[np.argmax(scores)]
confidence = np.max(scores)

print(f"URL: {url}")
print(f"Prediction: {prediction}")
print(f"Confidence: {confidence:.4f}")

Usage with ONNX Runtime Web

This model can also be used in browser applications with ONNX Runtime Web. See the example code in the accompanying GitHub repository.

Model Details

Original model: CrabInHoney/urlbert-tiny-v4-phishing-classifier
Task: Binary classification (phishing detection)
Framework: ONNX
Model size: Small (BERT-tiny architecture)