estudiante_MC318_profesor_MViT_kl_RLVS

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

• Loss: 0.0617
• Accuracy: 0.9829
• F1: 0.9829
• Precision: 0.9831
• Recall: 0.9829
• Roc Auc: 0.9983

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: 159
• training_steps: 1590
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Roc Auc
1.1707	1.0145	79	0.3903	0.9091	0.9091	0.9091	0.9091	0.9760
0.7543	2.0289	158	0.2225	0.9358	0.9358	0.9359	0.9358	0.9882
0.5018	4.0082	237	0.1346	0.9572	0.9572	0.9577	0.9572	0.9928
0.3639	5.0226	316	0.0938	0.9706	0.9706	0.9706	0.9706	0.9945
0.2751	7.0019	395	0.1042	0.9706	0.9706	0.9707	0.9706	0.9963
0.2243	8.0164	474	0.0650	0.9733	0.9733	0.9735	0.9733	0.9971
0.1755	9.0308	553	0.0690	0.9813	0.9813	0.9813	0.9813	0.9980
0.1681	11.0101	632	0.0532	0.9866	0.9866	0.9866	0.9866	0.9978
0.1581	12.0245	711	0.0432	0.9813	0.9813	0.9813	0.9813	0.9991
0.1245	14.0038	790	0.0446	0.9893	0.9893	0.9894	0.9893	0.9986
0.1291	15.0182	869	0.0467	0.9866	0.9866	0.9868	0.9866	0.9992
0.1057	16.0327	948	0.0427	0.9840	0.9840	0.9842	0.9840	0.9990
0.1109	18.0119	1027	0.0403	0.9840	0.9840	0.9842	0.9840	0.9991
0.1039	19.0264	1106	0.0762	0.9679	0.9679	0.9693	0.9679	0.9989
0.0978	21.0057	1185	0.0393	0.9813	0.9813	0.9813	0.9813	0.9991

Framework versions

• Transformers 4.46.3
• Pytorch 2.0.1+cu118
• Datasets 3.1.0
• Tokenizers 0.20.3