metadata

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

Cyber-ThreaD/DeBERTa-v3-AttackER

This model is a fine-tuned version of 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}
}