metadata

base_model: SynamicTechnologies/CYBERT
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: Cyber-ThreaD/CyBERT-APTNER
    results: []

Cyber-ThreaD/CyBERT-APTNER

This model is a fine-tuned version of SynamicTechnologies/CYBERT on the 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}
}