CodeBERT-Base-Solidity-Vulnerability Open-source Model - Free Detection of Solidity Smart Contract Vulnerabilities

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Codebert Base Solidity Vulnerability

Developed by angusleung100

A Solidity smart contract vulnerability detection model fine-tuned based on CodeBERT-base

Text Classification

Transformers

#Solidity Vulnerability Detection #Smart Contract Security #Code Static Analysis

Downloads 244

Release Time : 10/3/2024

Model Overview

This model is a fine-tuned version of microsoft/codebert-base, specifically designed to detect security vulnerabilities in Solidity smart contracts. It performs exceptionally well on the evaluation set, with all metrics reaching 1.0.

Model Features

High-Precision Vulnerability Detection

Achieved 1.0 accuracy, precision, recall, and F1 score on the evaluation set

Fine-Tuned Based on CodeBERT

Utilizes the powerful CodeBERT pre-trained model for domain adaptation

Specialized for Solidity

Optimized specifically for Ethereum's smart contract language Solidity

Model Capabilities

Smart Contract Code Analysis

Security Vulnerability Detection

Code Classification

Use Cases

Blockchain Security

Smart Contract Auditing

Automatically detects security vulnerabilities in Solidity smart contracts

Can identify common vulnerability patterns with 100% accuracy

Development Assistance Tool

Real-time detection of potential security issues during development

Helps developers identify and fix vulnerabilities early

🚀 CodeBERT-Base-Solidity-Vulnerability

This model is a fine - tuned version of CodeBERT base, designed to address Solidity vulnerability detection, offering high - precision results.

🚀 Quick Start

This model is a fine - tuned version of microsoft/codebert-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0000
Accuracy: 1.0
Precision: 1.0
Recall: 1.0
F1: 1.0

✨ Features

This fine - tuned model can potentially offer more accurate vulnerability detection in Solidity code compared to the base model, leveraging the pre - trained knowledge of CodeBERT.

📦 Installation

No installation steps were provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples were provided in the original document, so this section is skipped.

📚 Documentation

Model Information

Property	Details
Library Name	transformers
Base Model	microsoft/codebert - base
Tags	generated_from_trainer
Metrics	accuracy, precision, recall, f1
Model Name	CodeBERT - Base - Solidity - Vulnerability

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e - 05
train_batch_size: 1
eval_batch_size: 1
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Accuracy	Precision	Recall	F1
0.0195	1.0	4713	1.0	1.0	1.0	1.0
0.0	2.0	9426	1.0	1.0	1.0	1.0
0.0	3.0	14139	1.0	1.0	1.0	1.0

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.19.1

🔧 Technical Details

No detailed technical implementation information was provided in the original document, so this section is skipped.

📄 License

No license information was provided in the original document, so this section is skipped.

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