learning_rate: 2e-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: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 20

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.4847	1.0	187	0.4784	0.1137	0.5005	0.2160
0.4397	2.0	374	0.4546	0.2391	0.5679	0.2750
0.3848	3.0	561	0.3969	0.3759	0.6366	0.3018
0.3585	4.0	748	0.3665	0.4574	0.6711	0.3508
0.3049	5.0	935	0.3598	0.4761	0.6802	0.3823
0.295	6.0	1122	0.3421	0.5135	0.7011	0.3883
0.2641	7.0	1309	0.3280	0.5639	0.7292	0.4359
0.2289	8.0	1496	0.3214	0.6291	0.7614	0.4769
0.2105	9.0	1683	0.3074	0.6173	0.7549	0.4997
0.176	10.0	1870	0.2993	0.6614	0.7754	0.5359
0.1623	11.0	2057	0.2809	0.6977	0.7964	0.5634
0.1563	12.0	2244	0.2689	0.7166	0.8055	0.5909
0.1172	13.0	2431	0.2609	0.7312	0.8152	0.6030
0.0996	14.0	2618	0.2608	0.7422	0.8224	0.6043
0.1021	15.0	2805	0.2588	0.7606	0.8375	0.6231
0.0942	16.0	2992	0.2623	0.7595	0.8366	0.6264
0.0906	17.0	3179	0.2535	0.7752	0.8434	0.6425
0.0773	18.0	3366	0.2555	0.7702	0.8404	0.6345
0.0746	19.0	3553	0.2533	0.7726	0.8419	0.6372
0.0738	20.0	3740	0.2538	0.7739	0.8423	0.6405

Kuongan/xlm-roberta-base-pcm-finetuned-RC

xlm-roberta-base-pcm-finetuned-RC