Securebert-froude-website-prediction

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AventIQ-AI

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Quick Summary

AI model with specialized capabilities.

Code Examples

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

Set model to evaluation modepython

def predict_url(url):
    # Tokenize input
    encoding = tokenizer(url, truncation=True, padding=True, max_length=512, return_tensors="pt")
    
    # Perform inference
    with torch.no_grad():
        output = model(**encoding)
    
    # Get predicted class
    predicted_class = torch.argmax(output.logits, dim=1).item()
    
    # Map label
    label = "Phishing" if predicted_class == 1 else "Safe"
    return label

# Example usage
custom_url = "http://example.com/free-gift"
prediction = predict_url(custom_url)
print(f"Predicted label: {prediction}")

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