learning_rate: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.5
num_epochs: 20
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.0167	1.0	3115	0.1945	0.1955	0.5655	0.1004
0.0146	2.0	6230	0.2180	0.1987	0.5679	0.1013
0.0133	3.0	9345	0.2485	0.2006	0.5698	0.0991
0.0114	4.0	12460	0.2969	0.1893	0.5627	0.0950
0.0101	5.0	15575	0.3334	0.1969	0.5684	0.0973
0.0082	6.0	18690	0.4324	0.1971	0.5682	0.0995
0.0065	7.0	21805	0.6038	0.1950	0.5677	0.0959
0.0043	8.0	24920	0.6648	0.2028	0.5731	0.0978
0.0033	9.0	28035	0.6840	0.2052	0.5725	0.1030
0.0015	10.0	31150	0.7048	0.2037	0.5728	0.1016
0.0009	11.0	34265	0.7291	0.1944	0.5667	0.0987
0.0006	12.0	37380	0.7446	0.1990	0.5693	0.0993
0.0004	13.0	40495	0.7531	0.2017	0.5712	0.1004
0.0003	14.0	43610	0.7655	0.1987	0.5684	0.1008
0.0002	15.0	46725	0.7750	0.1976	0.5680	0.0988
0.0002	16.0	49840	0.7800	0.2007	0.5700	0.1008

DayCardoso/valueeval24-modern-bert-cos-initialfreeze-diff-lr-2

valueeval24-modern-bert-cos-initialfreeze-diff-lr-2