| Property | Value |
|---|---|
| Base Model | CodeBERT |
| Author | Zaib |
| Framework | PyTorch 1.12.0+cu113 |
| Evaluation Loss | 0.5778 |
What is Vulnerability-detection?
Vulnerability-detection is a specialized model fine-tuned from mrm8488/codebert-base-finetuned-detect-insecure-code, designed to identify security vulnerabilities in source code. This model leverages the power of CodeBERT architecture to analyze code patterns and detect potential security risks.
Implementation Details
The model implements a carefully tuned training procedure using the Adam optimizer with specific hyperparameters (β1=0.9, β2=0.999, ε=1e-08). Training was conducted over 3 epochs with a linear learning rate scheduler and 500 warmup steps, using a learning rate of 5e-05.
- Batch sizes: Training (16), Evaluation (64)
- Transformer version: 4.21.1
- Dataset integration using Datasets 2.4.0
- Tokenization through Tokenizers 0.12.1
Core Capabilities
- Automated detection of insecure code patterns
- Code vulnerability analysis
- Security risk assessment in source code
- Batch processing of code segments
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in security vulnerability detection by building upon the established CodeBERT architecture, utilizing a carefully optimized training process to achieve a loss of 0.5778 on the evaluation set.
Q: What are the recommended use cases?
The model is particularly suited for automated code review processes, security audits, and continuous integration pipelines where identifying potential security vulnerabilities in code is crucial.