Upload README.md

README.md

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-license: other
-license_name: vnu-uet
-license_link: LICENSE
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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.