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
0.3372	1.0	123	0.3504	0.0	0.5	0.2959
0.3383	2.0	246	0.3199	0.0	0.5	0.2959
0.3208	3.0	369	0.2941	0.1868	0.5728	0.3480
0.2913	4.0	492	0.2852	0.2785	0.6222	0.3980
0.2613	5.0	615	0.2767	0.4047	0.6659	0.4531
0.2449	6.0	738	0.2649	0.4176	0.6791	0.4816
0.2226	7.0	861	0.2636	0.4390	0.6889	0.4939
0.2054	8.0	984	0.2459	0.4594	0.7017	0.5449
0.1972	9.0	1107	0.2435	0.4724	0.7094	0.5592
0.1667	10.0	1230	0.2430	0.4667	0.7068	0.5541
0.1566	11.0	1353	0.2261	0.4980	0.7253	0.6031
0.1404	12.0	1476	0.2213	0.4952	0.7210	0.6
0.149	13.0	1599	0.2167	0.5080	0.7317	0.6265
0.1228	14.0	1722	0.2144	0.5381	0.7443	0.6316
0.1327	15.0	1845	0.2115	0.5507	0.7487	0.6439
0.1236	16.0	1968	0.2092	0.5568	0.7512	0.65
0.118	17.0	2091	0.2075	0.5539	0.7503	0.6480
0.1201	18.0	2214	0.2072	0.5657	0.7535	0.6490
0.1068	19.0	2337	0.2068	0.5673	0.7554	0.65
0.1035	20.0	2460	0.2067	0.5672	0.7552	0.6520

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

xlm-roberta-base-kin-finetuned-RC