---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: AttackER
  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/DeBERTa-v3-AttackER

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5468
- Precision: 0.4730
- Recall: 0.5569
- F1: 0.5115
- Accuracy: 0.7401

## 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: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.7886        | 0.4   | 500   | 1.5075          | 0.1842    | 0.2103 | 0.1964 | 0.6169   |
| 1.3644        | 0.81  | 1000  | 1.3342          | 0.2364    | 0.3056 | 0.2666 | 0.6492   |
| 1.1181        | 1.21  | 1500  | 1.2655          | 0.2959    | 0.3585 | 0.3242 | 0.6812   |
| 0.9833        | 1.61  | 2000  | 1.2368          | 0.2941    | 0.3902 | 0.3354 | 0.6778   |
| 0.9036        | 2.01  | 2500  | 1.2682          | 0.3551    | 0.4021 | 0.3772 | 0.7023   |
| 0.7102        | 2.42  | 3000  | 1.2176          | 0.3668    | 0.4590 | 0.4078 | 0.7159   |
| 0.6868        | 2.82  | 3500  | 1.2170          | 0.3794    | 0.4683 | 0.4192 | 0.7147   |
| 0.5671        | 3.22  | 4000  | 1.2603          | 0.3951    | 0.4881 | 0.4367 | 0.7259   |
| 0.4878        | 3.63  | 4500  | 1.2460          | 0.3925    | 0.5093 | 0.4433 | 0.7333   |
| 0.4942        | 4.03  | 5000  | 1.3147          | 0.4047    | 0.4802 | 0.4392 | 0.7284   |
| 0.3812        | 4.43  | 5500  | 1.3308          | 0.4205    | 0.5146 | 0.4628 | 0.7351   |
| 0.421         | 4.83  | 6000  | 1.3031          | 0.4275    | 0.5225 | 0.4702 | 0.7386   |
| 0.3157        | 5.24  | 6500  | 1.3943          | 0.4132    | 0.5040 | 0.4541 | 0.7293   |
| 0.3072        | 5.64  | 7000  | 1.4087          | 0.4303    | 0.5185 | 0.4703 | 0.7396   |
| 0.3436        | 6.04  | 7500  | 1.4197          | 0.4461    | 0.5251 | 0.4824 | 0.7363   |
| 0.2774        | 6.45  | 8000  | 1.4249          | 0.4275    | 0.5225 | 0.4702 | 0.7377   |
| 0.2629        | 6.85  | 8500  | 1.4811          | 0.4580    | 0.5344 | 0.4933 | 0.7327   |
| 0.2271        | 7.25  | 9000  | 1.5576          | 0.4733    | 0.5397 | 0.5043 | 0.7415   |
| 0.235         | 7.66  | 9500  | 1.5468          | 0.4730    | 0.5569 | 0.5115 | 0.7401   |
| 0.2415        | 8.06  | 10000 | 1.5956          | 0.4730    | 0.5437 | 0.5058 | 0.7433   |
| 0.1826        | 8.46  | 10500 | 1.6168          | 0.4455    | 0.5410 | 0.4886 | 0.7413   |
| 0.2083        | 8.86  | 11000 | 1.5866          | 0.4505    | 0.5423 | 0.4922 | 0.7413   |
| 0.2169        | 9.27  | 11500 | 1.5974          | 0.4708    | 0.5437 | 0.5046 | 0.7468   |
| 0.1747        | 9.67  | 12000 | 1.6219          | 0.4567    | 0.5437 | 0.4964 | 0.7405   |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0


### 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}
}

```