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
0.4959	1.0	139	0.4214	0.4807	0.6598	0.3375
0.3785	2.0	278	0.3560	0.7080	0.7697	0.4314
0.2713	3.0	417	0.3301	0.7451	0.8055	0.4910
0.1539	4.0	556	0.3920	0.7364	0.7963	0.4603
0.1307	5.0	695	0.4197	0.7320	0.7892	0.4819
0.0868	6.0	834	0.4604	0.7280	0.7891	0.4819
0.0449	7.0	973	0.4778	0.7450	0.8063	0.4838
0.064	8.0	1112	0.4934	0.7654	0.8248	0.4964
0.0379	9.0	1251	0.5534	0.7490	0.8148	0.4801
0.0141	10.0	1390	0.5641	0.7397	0.8048	0.4801
0.0143	11.0	1529	0.5763	0.7517	0.8166	0.4928
0.0067	12.0	1668	0.5987	0.7448	0.8035	0.5036
0.0045	13.0	1807	0.5933	0.7576	0.8155	0.5162
0.004	14.0	1946	0.6058	0.7558	0.8132	0.5054
0.0025	15.0	2085	0.6091	0.7531	0.8138	0.5018
0.0024	16.0	2224	0.6189	0.7540	0.8124	0.5054
0.0025	17.0	2363	0.6259	0.7532	0.8122	0.5054
0.0027	18.0	2502	0.6255	0.7568	0.8138	0.5108
0.0022	19.0	2641	0.6267	0.7553	0.8130	0.5072
0.0022	20.0	2780	0.6263	0.7549	0.8127	0.5072

sercetexam9/UIT-roberta-large-finetuned

UIT-roberta-large-finetuned