profesor_MViT_S_RLVS

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

• Loss: 0.0191
• Accuracy: 0.9947
• F1: 0.9947
• Precision: 0.9947
• Recall: 0.9947
• Roc Auc: 0.9999

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: 20
• eval_batch_size: 20
• 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: 240
• training_steps: 2400
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Roc Auc
0.4099	2.0333	240	0.2282	0.9634	0.9634	0.9639	0.9634	0.9974
0.1424	5.0333	480	0.1349	0.9661	0.9660	0.9676	0.9661	0.9955
0.0659	8.0333	720	0.0890	0.9765	0.9765	0.9768	0.9765	0.9989
0.0582	11.0333	960	0.1153	0.9687	0.9687	0.9700	0.9687	0.9987
0.0319	14.0333	1200	0.0400	0.9896	0.9896	0.9896	0.9896	0.9994
0.0346	17.0333	1440	0.0408	0.9896	0.9896	0.9896	0.9896	0.9993
0.0145	20.0333	1680	0.0314	0.9922	0.9922	0.9922	0.9922	0.9997
0.0302	23.0333	1920	0.0395	0.9896	0.9896	0.9896	0.9896	0.9996
0.0244	26.0333	2160	0.0423	0.9896	0.9896	0.9896	0.9896	0.9998
0.0285	29.0333	2400	0.2273	0.9608	0.9608	0.9637	0.9608	0.9993

Framework versions

• Transformers 4.46.1
• Pytorch 2.0.1+cu118
• Datasets 3.0.2
• Tokenizers 0.20.1