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---
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}
}
```
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