---
license: bigscience-openrail-m
base_model: ehsanaghaei/SecureBERT
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: impact-cat-secbert
  results: []
widget:
- text: "This flaw allows an attacker who has access to a virtual machine guest on a node with DownwardMetrics enabled to cause a denial of service."
---

<!-- 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. -->

# impact-cat-secbert

This model is a fine-tuned version of [ehsanaghaei/SecureBERT](https://huggingface.co/ehsanaghaei/SecureBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1585
- Accuracy: 0.9434

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 128  | 0.2407          | 0.9023   |
| No log        | 2.0   | 256  | 0.1664          | 0.9258   |
| No log        | 3.0   | 384  | 0.1614          | 0.9434   |
| 0.423         | 4.0   | 512  | 0.1585          | 0.9434   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2