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---
base_model: SynamicTechnologies/CYBERT
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Cyber-ThreaD/CyBERT-CyNER
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-CyNER
This model is a fine-tuned version of [SynamicTechnologies/CYBERT](https://huggingface.co/SynamicTechnologies/CYBERT) on the [CyNER](https://github.com/aiforsec/CyNER) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2405
- Precision: 0.4671
- Recall: 0.2810
- F1: 0.3509
- Accuracy: 0.9568
It achieves the following results on the prediction set:
- Loss: 0.2747
- Precision: 0.5442
- Recall: 0.3483
- F1: 0.4248
- Accuracy: 0.9471
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2304 | 1.42 | 500 | 0.2405 | 0.4671 | 0.2810 | 0.3509 | 0.9568 |
| 0.1092 | 2.84 | 1000 | 0.2575 | 0.5426 | 0.2848 | 0.3735 | 0.9601 |
| 0.0797 | 4.26 | 1500 | 0.2454 | 0.4701 | 0.3308 | 0.3883 | 0.9576 |
| 0.0615 | 5.68 | 2000 | 0.2669 | 0.4902 | 0.3180 | 0.3857 | 0.9586 |
| 0.0504 | 7.1 | 2500 | 0.2687 | 0.4885 | 0.3525 | 0.4095 | 0.9580 |
| 0.0379 | 8.52 | 3000 | 0.2752 | 0.4656 | 0.3627 | 0.4078 | 0.9573 |
| 0.0339 | 9.94 | 3500 | 0.2828 | 0.4991 | 0.3499 | 0.4114 | 0.9586 |
### 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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