Solidity Smart Contract Vulnerability Detection
Collection
A collection of experimental fine-tuned models fine-tuned for vulnerability detection for Solidity smart contracts. • 5 items • Updated
• 2
GraphCodeBERT-Base-Solidity-Vulnerability
This model is a fine-tuned version of microsoft/graphcodebert-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
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 | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.0522 | 1.0 | 4713 | 0.0101 | 0.9992 | 0.9992 | 0.9992 | 0.9992 |
| 0.0563 | 2.0 | 9426 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0 | 3.0 | 14139 | 0.0000 | 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
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Model tree for angusleung100/GraphCodeBERT-Base-Solidity-Vulnerability
Base model
microsoft/graphcodebert-base