angusleung100/GraphCodeBERT-Base-Solidity-Vulnerability

1---2library_name: transformers3base_model: microsoft/graphcodebert-base4tags:5- generated_from_trainer6metrics:7- accuracy8- precision9- recall10- f111model-index:12- name: GraphCodeBERT-Base-Solidity-Vulnerability13  results: []14---15 16<!-- This model card has been generated automatically according to the information the Trainer had access to. You17should probably proofread and complete it, then remove this comment. -->18 19# GraphCodeBERT-Base-Solidity-Vulnerability20 21This model is a fine-tuned version of [microsoft/graphcodebert-base](https://huggingface.co/microsoft/graphcodebert-base) on an unknown dataset.22It achieves the following results on the evaluation set:23- Loss: 0.000024- Accuracy: 1.025- Precision: 1.026- Recall: 1.027- F1: 1.028 29## Model description30 31More information needed32 33## Intended uses & limitations34 35More information needed36 37## Training and evaluation data38 39More information needed40 41## Training procedure42 43### Training hyperparameters44 45The following hyperparameters were used during training:46- learning_rate: 2e-0547- train_batch_size: 148- eval_batch_size: 149- seed: 4250- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0851- lr_scheduler_type: linear52- num_epochs: 353 54### Training results55 56| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |57|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|58| 0.0522        | 1.0   | 4713  | 0.0101          | 0.9992   | 0.9992    | 0.9992 | 0.9992 |59| 0.0563        | 2.0   | 9426  | 0.0000          | 1.0      | 1.0       | 1.0    | 1.0    |60| 0.0           | 3.0   | 14139 | 0.0000          | 1.0      | 1.0       | 1.0    | 1.0    |61 62 63### Framework versions64 65- Transformers 4.44.266- Pytorch 2.4.1+cu12167- Datasets 3.0.168- Tokenizers 0.19.169