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

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Cyber-ThreaD/
RoBERTa-DNRTI

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token-classification

generated_from_trainer

FacebookAI/roberta-base

autotrain_compatible

endpoints_compatible

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dnrti_our

This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2414
Precision: 0.7221
Recall: 0.7683
F1: 0.7445
Accuracy: 0.9283

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
0.5955	0.76	500	0.3862	0.5271	0.6278	0.5731	0.8741
0.3197	1.52	1000	0.3042	0.6336	0.6674	0.6501	0.9003
0.2565	2.28	1500	0.2859	0.6474	0.7315	0.6869	0.9095
0.2067	3.04	2000	0.2631	0.6955	0.7605	0.7265	0.9218
0.1657	3.81	2500	0.2414	0.7221	0.7683	0.7445	0.9283
0.1311	4.57	3000	0.2424	0.7239	0.7812	0.7514	0.9307
0.1178	5.33	3500	0.2639	0.7366	0.7830	0.7591	0.9333
0.099	6.09	4000	0.2692	0.7321	0.8070	0.7677	0.9328
0.0838	6.85	4500	0.2505	0.7663	0.7913	0.7786	0.9376
0.0728	7.61	5000	0.2731	0.7392	0.8093	0.7726	0.9341
0.0654	8.37	5500	0.2725	0.7601	0.8056	0.7822	0.9370
0.0589	9.13	6000	0.2770	0.7588	0.8158	0.7862	0.9386
0.0536	9.89	6500	0.2766	0.7610	0.8171	0.7881	0.9390

Framework versions

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