---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vulnerability-severity-classification-roberta-base
  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. -->

# vulnerability-severity-classification-roberta-base

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1088
- Accuracy: 0.4592

## 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: 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 |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 1.2002        | 1.0   | 27520  | 1.1105          | 0.4592   |
| 1.0249        | 2.0   | 55040  | 1.1088          | 0.4592   |
| 1.1372        | 3.0   | 82560  | 1.1086          | 0.4592   |
| 1.0043        | 4.0   | 110080 | 1.1090          | 0.4592   |
| 1.0767        | 5.0   | 137600 | 1.1088          | 0.4592   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1