---
library_name: transformers
base_model: microsoft/graphcodebert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: GraphCodeBERT-Base-Solidity-Vulnerability
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# GraphCodeBERT-Base-Solidity-Vulnerability

This model is a fine-tuned version of [microsoft/graphcodebert-base](https://huggingface.co/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