Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
1.7927	0.4	500	1.5607	0.0956	0.0780	0.0859	0.6139
1.3551	0.81	1000	1.3530	0.2064	0.2381	0.2211	0.6495
1.0432	1.21	1500	1.3107	0.2269	0.3082	0.2614	0.6740
0.8468	1.61	2000	1.2497	0.2447	0.3373	0.2836	0.6767
0.7775	2.01	2500	1.2710	0.2895	0.3730	0.3260	0.6939
0.5374	2.42	3000	1.3020	0.3006	0.4048	0.3450	0.7044
0.5071	2.82	3500	1.2614	0.2959	0.4048	0.3419	0.7081
0.4237	3.22	4000	1.3251	0.3367	0.4405	0.3817	0.7166
0.3597	3.63	4500	1.3853	0.3423	0.4524	0.3897	0.7125
0.3632	4.03	5000	1.4156	0.3559	0.4524	0.3984	0.7127
0.2589	4.43	5500	1.4472	0.3504	0.4709	0.4018	0.7173
0.323	4.83	6000	1.3997	0.3452	0.4603	0.3946	0.7222
0.2167	5.24	6500	1.5194	0.3467	0.4590	0.3950	0.7233
0.2363	5.64	7000	1.5585	0.3507	0.4722	0.4025	0.7222
0.2721	6.04	7500	1.5420	0.3715	0.4854	0.4209	0.7210
0.2073	6.45	8000	1.5878	0.3536	0.4854	0.4091	0.7147
0.2021	6.85	8500	1.6637	0.3722	0.4854	0.4214	0.7197
0.1648	7.25	9000	1.6724	0.3795	0.4788	0.4234	0.7255
0.1927	7.66	9500	1.6891	0.3801	0.4947	0.4299	0.7245
0.1958	8.06	10000	1.6774	0.3937	0.4974	0.4395	0.7281
0.1508	8.46	10500	1.7379	0.3815	0.4854	0.4272	0.7259
0.184	8.86	11000	1.7001	0.3863	0.5013	0.4364	0.7277
0.1696	9.27	11500	1.6932	0.3931	0.4987	0.4397	0.7295
0.1425	9.67	12000	1.7137	0.3824	0.5013	0.4339	0.7276

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}
}