profesor_MViT_N_VIOPERU

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

• Loss: 0.4752
• Accuracy: 0.8929
• F1: 0.8927
• Precision: 0.8949
• Recall: 0.8929
• Roc Auc: 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: 1e-05
• train_batch_size: 22
• eval_batch_size: 22
• 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: 90
• training_steps: 900
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Roc Auc
0.6465	7.0067	90	0.6553	0.7679	0.7672	0.7710	0.7679	0.8010
0.5617	14.0133	180	0.5770	0.8036	0.8030	0.8071	0.8036	0.8673
0.4403	22.0067	270	0.4678	0.8929	0.8927	0.8949	0.8929	0.9031
0.3043	29.0133	360	0.4475	0.8393	0.8380	0.8505	0.8393	0.9069
0.196	37.0067	450	0.4182	0.8393	0.8380	0.8505	0.8393	0.9247
0.131	44.0133	540	0.3250	0.875	0.8746	0.8794	0.875	0.9515
0.0945	52.0067	630	0.3444	0.8929	0.8927	0.8949	0.8929	0.9605
0.0736	59.0133	720	0.3662	0.875	0.8746	0.8794	0.875	0.9554
0.0535	67.0067	810	0.4566	0.8571	0.8564	0.8646	0.8571	0.9681
0.0513	74.0133	900	0.4785	0.875	0.8746	0.8794	0.875	0.9554

Framework versions

• Transformers 4.46.1
• Pytorch 2.0.1+cu118
• Datasets 3.1.0
• Tokenizers 0.20.1