shogun-the-great
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finetuned-bert-phishing-site-classification

@@ -1,199 +1,86 @@
 ---
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 ---
 library_name: transformers
+tags:
+  - phishing-detection
+  - binary-classification
+  - bert
+  - nlp
 ---
+# Model Card for Fine-tuned BERT-Base-Uncased on Phishing Site Classification
 ## Model Details
 ### Model Description
+This model is a fine-tuned version of [BERT-Base-Uncased](https://huggingface.co/google-bert/bert-base-uncased) for phishing site classification. The model predicts whether a website is classified as "Safe" or "Not Safe" based on textual input.
+- **Developed by:** [shogun-the-great](https://huggingface.co/shogun-the-great)
+- **Model type:** Binary Classification (Safe vs Not Safe)
+- **Language(s):** English
+- **License:** Apache-2.0 (or specify your license)
+- **Finetuned from model:** `google/bert-base-uncased`
+- ** **
+### Model Sources
+- **Dataset:** [shawhin/phishing-site-classification](https://huggingface.co/datasets/shawhin/phishing-site-classification)
 ## Uses
 ### Direct Use
+This model can be directly used for phishing detection by classifying text into two categories: "Safe" and "Not Safe." Typical use cases include:
+- Integrating with browser extensions for real-time website classification.
+- Analyzing textual data for phishing indicators.
+### Downstream Use
+Users can fine-tune the model further for specific binary classification tasks or for datasets with similar domains.
 ### Out-of-Scope Use
+This model might not perform well for:
+- Non-English text.
+- Adversarial phishing attacks or heavily obfuscated text.
+- Tasks unrelated to text-based classification.
 ## Bias, Risks, and Limitations
+### Bias
+The model's predictions are influenced by the dataset used during fine-tuning. If the training data contains biases, these may reflect in the predictions.
+### Risks
+- False positives: Legitimate websites flagged as phishing.
+- False negatives: Some phishing sites might not be detected.
+- Potential vulnerabilities to adversarial examples.
+### Recommendations
+- Regularly update the dataset and model to stay aligned with emerging phishing patterns.
+- Use in combination with other security measures for robust phishing detection.
+## How to Get Started with the Model
+You can load the fine-tuned model directly from the Hugging Face Hub:
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+# Load the tokenizer and model from Hugging Face Hub
+model_name = "shogun-the-great/finetuned-bert-phishing-site-classification"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+# Example usage
+text = "Enter your login credentials to claim a free reward!"
+inputs = tokenizer(text, return_tensors="pt", truncation=True)
+outputs = model(**inputs)
+# Get the predicted label
+logits = outputs.logits
+prediction = logits.argmax(dim=-1).item()
+print("Prediction:", "Not Safe" if prediction == 1 else "Safe")