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README.md

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+---
+language: en
+tags:
+  - code
+  - security
+  - vulnerability-detection
+  - codebert
+  - classification
+license: mit
+---
+
+# PolyGuard — Code Vulnerability Scanner
+
+A fine-tuned [CodeBERT](https://huggingface.co/microsoft/codebert-base) model
+for detecting security vulnerabilities in source code.
+
+## Supported Languages
+Python, JavaScript, SQL, PHP, Java, C, C++, Go, Ruby, Rust
+
+## Performance
+- **F1 Score**: 0.6698
+- **Training samples**: 16681
+- **Base model**: microsoft/codebert-base
+- **Trained at**: 2026-04-29
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_id = "MUHAMMADSAADAMIN/PolyGuard"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model     = AutoModelForSequenceClassification.from_pretrained(model_id)
+model.eval()
+
+code = "eval(input())"
+inputs = tokenizer(code, return_tensors="pt", truncation=True, max_length=512)
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+probs = torch.softmax(logits, dim=1).squeeze().tolist()
+print(f"Clean: {probs[0]*100:.1f}%  Vulnerable: {probs[1]*100:.1f}%")
+```
+
+## Labels
+- 0 = Clean / Safe
+- 1 = Vulnerable
+
+## Training Data
+Fine-tuned on CrossVUL dataset (~9,300 real-world CVE pairs) with
+curated augmentation examples covering common CWEs.