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---
base_model: SynamicTechnologies/CYBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Cyber-ThreaD/CyBERT-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/CyBERT-APTNER
This model is a fine-tuned version of [SynamicTechnologies/CYBERT](https://huggingface.co/SynamicTechnologies/CYBERT) on the [APTNER](https://github.com/wangxuren/APTNER) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4543
- Precision: 0.4372
- Recall: 0.4293
- F1: 0.4332
- Accuracy: 0.9043
It achieves the following results on the prediction set:
- Loss: 0.3602
- Precision: 0.5489
- Recall: 0.5125
- F1: 0.5301
- Accuracy: 0.9220
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.8182 | 0.59 | 500 | 0.5569 | 0.5219 | 0.2431 | 0.3317 | 0.9014 |
| 0.5357 | 1.19 | 1000 | 0.4980 | 0.4625 | 0.2895 | 0.3561 | 0.9024 |
| 0.4417 | 1.78 | 1500 | 0.4773 | 0.4029 | 0.3572 | 0.3787 | 0.9016 |
| 0.394 | 2.37 | 2000 | 0.4840 | 0.3697 | 0.3943 | 0.3816 | 0.8943 |
| 0.3534 | 2.97 | 2500 | 0.4742 | 0.3586 | 0.4437 | 0.3966 | 0.8914 |
| 0.3048 | 3.56 | 3000 | 0.4543 | 0.4372 | 0.4293 | 0.4332 | 0.9043 |
| 0.2992 | 4.15 | 3500 | 0.4846 | 0.3587 | 0.4392 | 0.3949 | 0.8907 |
| 0.2675 | 4.74 | 4000 | 0.4760 | 0.4100 | 0.4530 | 0.4304 | 0.9000 |
| 0.2454 | 5.34 | 4500 | 0.4702 | 0.4123 | 0.4407 | 0.4260 | 0.9014 |
| 0.2391 | 5.93 | 5000 | 0.4743 | 0.3957 | 0.4638 | 0.4270 | 0.8979 |
| 0.2088 | 6.52 | 5500 | 0.4778 | 0.4224 | 0.4485 | 0.4351 | 0.9038 |
| 0.2076 | 7.12 | 6000 | 0.5050 | 0.3736 | 0.4644 | 0.4140 | 0.8930 |
| 0.1946 | 7.71 | 6500 | 0.4964 | 0.4009 | 0.4599 | 0.4284 | 0.8977 |
| 0.1808 | 8.3 | 7000 | 0.4878 | 0.4226 | 0.4554 | 0.4384 | 0.9028 |
| 0.1683 | 8.9 | 7500 | 0.4947 | 0.3954 | 0.4626 | 0.4264 | 0.8976 |
| 0.1681 | 9.49 | 8000 | 0.4916 | 0.4081 | 0.4662 | 0.4352 | 0.9001 |
### 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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