metadata
license: bigscience-openrail-m
base_model: ehsanaghaei/SecureBERT
tags:
- generated_from_trainer
- cybersecurity
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.
impact-cat-secbert
This model is a fine-tuned version of 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