mangsense/codebert_java

CodeBERT fine-tuned for Java Vulnerability Detection

CodeBERT model fine-tuned for detecting security vulnerabilities in Java code.

Model Description

This model is fine-tuned from microsoft/codebert-base for binary classification of secure/insecure Java code.

Intended Uses

• —Detect security vulnerabilities in Java source code
• —Binary classification: Safe (LABEL0) vs Vulnerable (LABEL1)

How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("mangsense/codebert_java")
model = AutoModelForSequenceClassification.from_pretrained("mangsense/codebert_java")

# run code

from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch import numpy as np tokenizer = AutoTokenizer.frompretrained('mrm8488/codebert-base-finetuned-detect-insecure-code') model = AutoModelForSequenceClassification.frompretrained('mrm8488/codebert-base-finetuned-detect-insecure-code')

inputs = tokenizer("your code here", returntensors="pt", truncation=True, padding='maxlength') labels = torch.tensor([1]).unsqueeze(0) # Batch size 1 outputs = model(**inputs, labels=labels) loss = outputs.loss logits = outputs.logits

print(np.argmax(logits.detach().numpy()))

## Training Data

Trained on CodeXGLUE Defect Detection dataset.

## Limitations

- Focused on Java code only
- May not detect all types of vulnerabilities