DeBERTa-DNRTI – AI Model by Cyber-ThreaD | AlphaNeural AI

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Cyber-ThreaD/
DeBERTa-DNRTI

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token-classification

generated_from_trainer

microsoft/deberta-v3-base

autotrain_compatible

endpoints_compatible

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dnrti_deberta

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: 0.2586
Precision: 0.7684
Recall: 0.7978
F1: 0.7828
Accuracy: 0.9373

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
0.5854	0.76	500	0.4097	0.5198	0.6403	0.5738	0.8704
0.3165	1.52	1000	0.3137	0.6396	0.6891	0.6634	0.9029
0.2535	2.28	1500	0.2948	0.6330	0.7379	0.6814	0.9087
0.2034	3.04	2000	0.2660	0.7062	0.7494	0.7272	0.9210
0.1596	3.81	2500	0.2682	0.7071	0.7729	0.7386	0.9232
0.1269	4.57	3000	0.2595	0.7285	0.7959	0.7607	0.9292
0.1122	5.33	3500	0.2642	0.7468	0.7826	0.7643	0.9321
0.0923	6.09	4000	0.2677	0.7513	0.8070	0.7781	0.9352
0.0804	6.85	4500	0.2586	0.7684	0.7978	0.7828	0.9373
0.0687	7.61	5000	0.2791	0.7522	0.8236	0.7863	0.9368
0.0626	8.37	5500	0.2789	0.7614	0.8204	0.7898	0.9379
0.0552	9.13	6000	0.2774	0.7674	0.8208	0.7932	0.9405
0.0488	9.89	6500	0.2808	0.7742	0.8213	0.7970	0.9415

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1