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
0.3933	1.0	180	0.3696	0.6352	0.7378	0.4288
0.2905	2.0	360	0.2958	0.7524	0.8079	0.5587
0.2139	3.0	540	0.2298	0.8538	0.8845	0.6769
0.1505	4.0	720	0.1996	0.8757	0.9030	0.7429
0.1218	5.0	900	0.1745	0.9027	0.9200	0.7880
0.0737	6.0	1080	0.1556	0.9199	0.9356	0.8290
0.0604	7.0	1260	0.1541	0.9241	0.9366	0.8485
0.0353	8.0	1440	0.1538	0.9294	0.9468	0.8652
0.024	9.0	1620	0.1561	0.9312	0.9459	0.8638
0.0235	10.0	1800	0.1638	0.9283	0.9459	0.8624
0.0159	11.0	1980	0.1615	0.9310	0.9467	0.8694
0.0158	12.0	2160	0.1625	0.9307	0.9461	0.8687

Kuongan/CS221-roberta-base-finetuned-augmentation

CS221-roberta-base-finetuned-augmentation