estudiante_MC318_profesor_MViT_akl_RLVS

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

• Loss: 0.0597
• Accuracy: 0.9803
• F1: 0.9803
• Precision: 0.9803
• Recall: 0.9803
• Roc Auc: 0.9979

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
22.7945	1.0145	79	0.4204	0.8984	0.8984	0.8985	0.8984	0.9730
14.8284	2.0289	158	0.2329	0.9251	0.9251	0.9259	0.9251	0.9836
10.0047	4.0082	237	0.1401	0.9492	0.9492	0.9492	0.9492	0.9905
7.3642	5.0226	316	0.1023	0.9733	0.9733	0.9733	0.9733	0.9932
5.6128	7.0019	395	0.1193	0.9733	0.9733	0.9733	0.9733	0.9947
4.5908	8.0164	474	0.0743	0.9733	0.9733	0.9735	0.9733	0.9958
3.4695	9.0308	553	0.0811	0.9759	0.9759	0.9760	0.9759	0.9968
3.3759	11.0101	632	0.0575	0.9759	0.9759	0.9761	0.9759	0.9975
3.4995	12.0245	711	0.0492	0.9840	0.9840	0.9840	0.9840	0.9982
2.5838	14.0038	790	0.0470	0.9840	0.9840	0.9840	0.9840	0.9980
2.8276	15.0182	869	0.0544	0.9759	0.9759	0.9761	0.9759	0.9986
2.3636	16.0327	948	0.0441	0.9786	0.9786	0.9791	0.9786	0.9989
2.5376	18.0119	1027	0.0431	0.9840	0.9840	0.9842	0.9840	0.9988
2.3264	19.0264	1106	0.0620	0.9706	0.9706	0.9717	0.9706	0.9989

Framework versions

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