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---
library_name: peft
base_model: Vulnerability-Detection/codet5_merged
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: results_v3
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. -->
# results_v3
This model is a fine-tuned version of [Vulnerability-Detection/codet5_merged](https://huggingface.co/Vulnerability-Detection/codet5_merged) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5477
- Accuracy: 0.7452
- Precision: 0.1235
- Recall: 0.7658
- F1 Score: 0.2128
- F2 Score: 0.3754
- Gmean: 0.7549
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score | F2 Score | Gmean |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:--------:|:------:|
| 0.4724 | 1.0 | 197 | 0.5542 | 0.7347 | 0.1212 | 0.7838 | 0.2099 | 0.3744 | 0.7577 |
| 0.4822 | 2.0 | 394 | 0.5152 | 0.7566 | 0.1265 | 0.7477 | 0.2164 | 0.3773 | 0.7524 |
| 0.4859 | 3.0 | 591 | 0.5477 | 0.7452 | 0.1235 | 0.7658 | 0.2128 | 0.3754 | 0.7549 |
### Framework versions
- PEFT 0.14.0
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0 |