---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: AttackVectorClassifier
  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. -->

# AttackVectorClassifier

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2148
- Accuracy: 0.9548
- F1: 0.9577
- Precision: 0.9646
- Recall: 0.9509
- Roc Auc: 0.9898

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| 0.1444        | 1.0   | 2393 | 0.1713          | 0.9517   | 0.9542 | 0.9768    | 0.9325 | 0.9887  |
| 0.1601        | 2.0   | 4786 | 0.1616          | 0.9487   | 0.9529 | 0.9439    | 0.9620 | 0.9904  |
| 0.0945        | 3.0   | 7179 | 0.2148          | 0.9548   | 0.9577 | 0.9646    | 0.9509 | 0.9898  |


### Framework versions

- Transformers 5.13.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2