learning_rate: 8.142490647628966e-05
train_batch_size: 32
eval_batch_size: 32
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
num_epochs: 20

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.1045	1.0	219	0.8521	0.7039	0.7122	0.7143	0.7090
0.8	2.0	438	0.8836	0.6902	0.7060	0.6914	0.6933
0.5912	3.0	657	0.8122	0.7267	0.7370	0.7304	0.7310
0.4065	4.0	876	1.0771	0.6879	0.7203	0.6934	0.7021
0.3133	5.0	1095	1.3608	0.6765	0.6949	0.6805	0.6780
0.258	6.0	1314	1.4317	0.6651	0.6939	0.6802	0.6760
0.1839	7.0	1533	1.5627	0.6651	0.6734	0.6800	0.6664
0.1641	8.0	1752	1.5399	0.6856	0.6829	0.7021	0.6892

Alfanatasya/results_indobert-base-p2_preprocessing_aug_tuning

results_indobert-base-p2_preprocessing_aug_tuning