Akash-Sakala
/

bert-phishing-classifier_student

@@ -1,199 +1,118 @@
 ---
-library_name: transformers
-tags: []
 ---
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 ---
+language:
+- en
+license: apache-2.0
+base_model: distilbert/distilbert-base-uncased
+tags:
+- text-classification
+- phishing-detection
+- knowledge-distillation
+- distilbert
+datasets:
+- Akash-Sakala/phishing-site-classification
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: bert-phishing-classifier_student
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Akash-Sakala/phishing-site-classification
+      type: Akash-Sakala/phishing-site-classification
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.9601
+      name: Accuracy
+    - type: f1
+      value: 0.9595
+      name: F1
+    - type: precision
+      value: 0.9710
+      name: Precision
+    - type: recall
+      value: 0.9483
+      name: Recall
 ---
+# DistilBERT Phishing Site Classifier (Student)
+A 4-layer DistilBERT trained via **knowledge distillation** from a fine-tuned BERT teacher
+([Akash-Sakala/bert-phishing-classifier_teacher](https://huggingface.co/Akash-Sakala/bert-phishing-classifier_teacher))
+for binary phishing site URL classification.
 ## Model Details
+| Property        | Value                                     |
+|-----------------|-------------------------------------------|
+| Base model      | distilbert/distilbert-base-uncased        |
+| Architecture    | DistilBertForSequenceClassification       |
+| Layers          | 4 (distilled from 12-layer BERT teacher)  |
+| Attention heads | 8                                         |
+| Task            | Binary classification (phishing / benign) |
+| Parameters      | ~52M                                      |
+## Training — Distillation Setup
+| Hyperparameter    | Value              |
+|-------------------|--------------------|
+| Temperature       | 3.0                |
+| Alpha (KL weight) | 0.6                |
+| Hard label weight | 0.4                |
+| Learning rate     | 2e-5               |
+| Batch size        | 64                 |
+| Epochs            | 4                  |
+| Warmup steps      | 10% of total steps |
+| Weight decay      | 0.01               |
+| Optimizer         | AdamW              |
+| Scheduler         | Linear with warmup |
+| Mixed precision   | fp16 (torch.amp)   |
+## Loss Function
+Combined KL divergence (soft targets) + Cross-Entropy (hard labels):
+    loss = alpha * KL(student_soft || teacher_soft) * T^2 + (1 - alpha) * CrossEntropy(student, labels)
+## Test Set Results
+| Model                  | Accuracy   | Precision  | Recall     | F1         |
+|------------------------|------------|------------|------------|------------|
+| BERT Teacher           | 0.8971     | 0.9136     | 0.8763     | 0.8945     |
+| **DistilBERT Student** | **0.9601** | **0.9710** | **0.9483** | **0.9595** |
+The student **outperforms the teacher** across all metrics while being smaller and faster.
+## Dataset
+- **Dataset:** [Akash-Sakala/phishing-site-classification](https://huggingface.co/datasets/Akash-Sakala/phishing-site-classification)
+- Train: 154,000 | Validation: 33,000 | Test: 33,000
+- Labels: `0` = benign, `1` = phishing
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+tokenizer = AutoTokenizer.from_pretrained('Akash-Sakala/bert-phishing-classifier_student')
+model = AutoModelForSequenceClassification.from_pretrained('Akash-Sakala/bert-phishing-classifier_student')
+url = 'http://suspicious-login.verify-account.com/secure'
+inputs = tokenizer(url, return_tensors='pt', truncation=True, padding='max_length')
+with torch.no_grad():
+    logits = model(**inputs).logits
+pred = torch.argmax(logits, dim=1).item()
+print('Phishing' if pred == 1 else 'Benign')
+```
+## Teacher Model
+[Akash-Sakala/bert-phishing-classifier_teacher](https://huggingface.co/Akash-Sakala/bert-phishing-classifier_teacher)