estudiante_MC318_VIOPERU

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

• Loss: 0.5975
• Accuracy: 0.75
• F1: 0.7499
• Precision: 0.7503
• Recall: 0.75
• Roc Auc: 0.8064

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
0.6651	1.0095	10	0.7186	0.3214	0.2432	0.1957	0.3214	0.3023
0.6966	2.0190	20	0.7151	0.375	0.3267	0.3247	0.375	0.3724
0.6205	3.0286	30	0.7197	0.4643	0.4385	0.4562	0.4643	0.4554
0.6278	4.0381	40	0.7118	0.4821	0.4614	0.4789	0.4821	0.4994
0.5792	6.0095	50	0.6974	0.5536	0.5465	0.5571	0.5536	0.5357
0.5719	7.0190	60	0.6983	0.5893	0.5860	0.5922	0.5893	0.5638
0.5582	8.0286	70	0.6902	0.6071	0.6026	0.6123	0.6071	0.5912
0.5299	9.0381	80	0.6977	0.5893	0.5828	0.5952	0.5893	0.6059
0.5262	11.0095	90	0.6972	0.6429	0.6424	0.6436	0.6429	0.6224
0.4854	12.0190	100	0.6974	0.625	0.6239	0.6265	0.625	0.6416
0.463	13.0286	110	0.6561	0.5893	0.5892	0.5894	0.5893	0.6556
0.4543	14.0381	120	0.6376	0.625	0.6239	0.6265	0.625	0.6696
0.4127	16.0095	130	0.6716	0.6964	0.6916	0.7095	0.6964	0.6849
0.4125	17.0190	140	0.6945	0.6429	0.6424	0.6436	0.6429	0.6926
0.3985	18.0286	150	0.6841	0.6786	0.6769	0.6823	0.6786	0.7092
0.3954	19.0381	160	0.6239	0.6786	0.6786	0.6786	0.6786	0.7105
0.3474	21.0095	170	0.6424	0.7143	0.7139	0.7154	0.7143	0.7092
0.3339	22.0190	180	0.6594	0.7143	0.7139	0.7154	0.7143	0.7117
0.3295	23.0286	190	0.7352	0.6429	0.6424	0.6436	0.6429	0.7079
0.3323	24.0381	200	0.6903	0.6607	0.6606	0.6609	0.6607	0.7117
0.2761	26.0095	210	0.6788	0.6607	0.6606	0.6609	0.6607	0.7181

Framework versions

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