metadata
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vulnerability-severity-classification-roberta-base
results: []
datasets:
- CIRCL/vulnerability-scores
vulnerability-severity-classification-roberta-base
This model is a fine-tuned version of RoBERTa-base on the dataset CIRCL/vulnerability-scores.
It achieves the following results on the evaluation set:
- Loss: 0.6501
- Accuracy: 0.7607
Model description
It is a classification model and is aimed to assist in classifying vulnerabilities by severity based on their descriptions.
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5993 | 1.0 | 14930 | 0.6907 | 0.7245 |
| 0.5952 | 2.0 | 29860 | 0.6572 | 0.7416 |
| 0.6602 | 3.0 | 44790 | 0.6146 | 0.7513 |
| 0.4305 | 4.0 | 59720 | 0.6159 | 0.7615 |
| 0.3855 | 5.0 | 74650 | 0.6501 | 0.7607 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0