estudiante_MC318_profesor_MViT_akl_VIOPERU

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

• Loss: 0.6486
• Accuracy: 0.6696
• F1: 0.6636
• Precision: 0.6828
• Recall: 0.6696
• Roc Auc: 0.7280

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: 1e-05
• train_batch_size: 30
• eval_batch_size: 30
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 21
• training_steps: 210
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Roc Auc
3.1309	1.0095	10	0.6797	0.5536	0.4805	0.6224	0.5536	0.6633
2.7485	2.0190	20	0.6672	0.5357	0.4944	0.5530	0.5357	0.6747
2.7139	3.0286	30	0.6622	0.6429	0.6410	0.6458	0.6429	0.6709
2.5183	4.0381	40	0.6592	0.6607	0.6597	0.6626	0.6607	0.6747
2.3888	6.0095	50	0.6528	0.6786	0.6782	0.6795	0.6786	0.6760
2.2645	7.0190	60	0.6537	0.6786	0.6748	0.6872	0.6786	0.6722
2.1218	8.0286	70	0.6496	0.6786	0.6748	0.6872	0.6786	0.6735
1.8439	9.0381	80	0.6484	0.6607	0.6580	0.6660	0.6607	0.6607
1.8092	11.0095	90	0.6502	0.6607	0.6553	0.6714	0.6607	0.6518
1.667	12.0190	100	0.6523	0.6607	0.6580	0.6660	0.6607	0.6582

Framework versions

• Transformers 4.46.1
• Pytorch 2.0.1+cu118
• Datasets 3.1.0
• Tokenizers 0.20.2