README.md

---
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.