---
license: bigscience-openrail-m
base_model: ehsanaghaei/SecureBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Cyber-ThreaD/SecureBERT-APTNER
  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. -->

# Cyber-ThreaD/SecureBERT-APTNER

This model is a fine-tuned version of [ehsanaghaei/SecureBERT](https://huggingface.co/ehsanaghaei/SecureBERT) on the [APTNER](https://github.com/wangxuren/APTNER) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2915
- Precision: 0.5392
- Recall: 0.5871
- F1: 0.5621
- Accuracy: 0.9211

It achieves the following results on the prediction set:
- Loss: 0.2404
- Precision: 0.6277
- Recall: 0.6450
- F1: 0.6362
- Accuracy: 0.9367

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.8252        | 0.59  | 500  | 0.3771          | 0.4383    | 0.4413 | 0.4398 | 0.9112   |
| 0.3593        | 1.19  | 1000 | 0.2915          | 0.5392    | 0.5871 | 0.5621 | 0.9211   |
| 0.2704        | 1.78  | 1500 | 0.2949          | 0.5480    | 0.6201 | 0.5818 | 0.9203   |
| 0.2308        | 2.37  | 2000 | 0.2988          | 0.5524    | 0.6269 | 0.5873 | 0.9187   |
| 0.1934        | 2.97  | 2500 | 0.3123          | 0.5365    | 0.6515 | 0.5884 | 0.9152   |
| 0.1567        | 3.56  | 3000 | 0.3128          | 0.5702    | 0.6404 | 0.6033 | 0.9210   |
| 0.1471        | 4.15  | 3500 | 0.3651          | 0.5379    | 0.6243 | 0.5779 | 0.9117   |
| 0.1249        | 4.74  | 4000 | 0.3771          | 0.5363    | 0.6566 | 0.5904 | 0.9125   |
| 0.1106        | 5.34  | 4500 | 0.3866          | 0.5624    | 0.6341 | 0.5961 | 0.9156   |
| 0.1063        | 5.93  | 5000 | 0.3754          | 0.5731    | 0.6371 | 0.6034 | 0.9191   |
| 0.0835        | 6.52  | 5500 | 0.4015          | 0.5551    | 0.6428 | 0.5957 | 0.9165   |
| 0.0854        | 7.12  | 6000 | 0.4325          | 0.5461    | 0.6425 | 0.5904 | 0.9138   |
| 0.0743        | 7.71  | 6500 | 0.4184          | 0.5642    | 0.6473 | 0.6029 | 0.9179   |
| 0.0704        | 8.3   | 7000 | 0.4315          | 0.5613    | 0.6323 | 0.5947 | 0.9172   |
| 0.06          | 8.9   | 7500 | 0.4354          | 0.5635    | 0.6401 | 0.5994 | 0.9176   |
| 0.0612        | 9.49  | 8000 | 0.4452          | 0.5643    | 0.6452 | 0.6020 | 0.9179   |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1


### Citing & Authors

If you use the model kindly cite the following work

```
@inproceedings{deka2024attacker,
  title={AttackER: Towards Enhancing Cyber-Attack Attribution with a Named Entity Recognition Dataset},
  author={Deka, Pritam and Rajapaksha, Sampath and Rani, Ruby and Almutairi, Amirah and Karafili, Erisa},
  booktitle={International Conference on Web Information Systems Engineering},
  pages={255--270},
  year={2024},
  organization={Springer}
}

```