learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
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: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 20

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.5508	1.0	139	0.4985	0.1682	0.5518	0.1643
0.4245	2.0	278	0.4152	0.6348	0.7259	0.3881
0.3521	3.0	417	0.3579	0.7279	0.7918	0.4747
0.2247	4.0	556	0.3752	0.7206	0.7825	0.4675
0.2045	5.0	695	0.3588	0.7561	0.8109	0.5181
0.1466	6.0	834	0.4355	0.7353	0.7928	0.4765
0.0911	7.0	973	0.4347	0.7574	0.8115	0.5036
0.0731	8.0	1112	0.4792	0.7233	0.7869	0.4639
0.0576	9.0	1251	0.5001	0.7505	0.8056	0.5054
0.0376	10.0	1390	0.5599	0.7404	0.7973	0.4928
0.0248	11.0	1529	0.5667	0.7416	0.7982	0.4964
0.0147	12.0	1668	0.5936	0.7442	0.8033	0.4982
0.0148	13.0	1807	0.5873	0.7531	0.8133	0.5
0.009	14.0	1946	0.6078	0.7465	0.8029	0.5072
0.0122	15.0	2085	0.6217	0.7475	0.8045	0.4946
0.0057	16.0	2224	0.6321	0.7479	0.8037	0.5072
0.0072	17.0	2363	0.6359	0.7451	0.8021	0.5036
0.0061	18.0	2502	0.6397	0.7480	0.8044	0.5018
0.0063	19.0	2641	0.6387	0.7466	0.8031	0.5
0.0063	20.0	2780	0.6394	0.7470	0.8034	0.5018

sercetexam9/UIT-deberta-v3-base-finetuned

UIT-deberta-v3-base-finetuned