learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.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
0.3844	1.0	174	0.3804	0.0499	0.5137	0.2954
0.3404	2.0	348	0.3427	0.2255	0.5749	0.3379
0.3261	3.0	522	0.3101	0.3609	0.6337	0.4215
0.2787	4.0	696	0.3072	0.3492	0.6277	0.4143
0.2652	5.0	870	0.3143	0.4482	0.6769	0.4467
0.2333	6.0	1044	0.2970	0.4876	0.6852	0.4654
0.2169	7.0	1218	0.3066	0.4911	0.6937	0.4633
0.1875	8.0	1392	0.3103	0.5438	0.7236	0.4813
0.1747	9.0	1566	0.3341	0.5215	0.7157	0.4820
0.1611	10.0	1740	0.3281	0.5332	0.7232	0.4741
0.1371	11.0	1914	0.3279	0.5521	0.7307	0.4841
0.1245	12.0	2088	0.3340	0.5415	0.7246	0.4777
0.1197	13.0	2262	0.3462	0.5388	0.7238	0.4755
0.0978	14.0	2436	0.3530	0.5408	0.7268	0.4733
0.0977	15.0	2610	0.3543	0.5377	0.7250	0.4705

Kuongan/xlm-roberta-base-som-finetuned

xlm-roberta-base-som-finetuned