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
0.3156	1.0	121	0.2864	0.0	0.5	0.4940
0.3014	2.0	242	0.2773	0.0078	0.5016	0.4940
0.279	3.0	363	0.2487	0.0958	0.5384	0.5261
0.2225	4.0	484	0.2313	0.2855	0.6115	0.5622
0.193	5.0	605	0.2181	0.3816	0.6592	0.6386
0.1321	6.0	726	0.2235	0.3669	0.6665	0.6386
0.1209	7.0	847	0.2270	0.4572	0.7031	0.6386
0.1023	8.0	968	0.2300	0.4030	0.6884	0.6506
0.0924	9.0	1089	0.2392	0.4278	0.6947	0.6667
0.0691	10.0	1210	0.2506	0.4498	0.7062	0.6426
0.0635	11.0	1331	0.2579	0.4643	0.7134	0.6225
0.0537	12.0	1452	0.2558	0.4755	0.7312	0.6345
0.044	13.0	1573	0.2784	0.4893	0.7246	0.6145
0.042	14.0	1694	0.2744	0.4892	0.7298	0.6305
0.0363	15.0	1815	0.2824	0.4976	0.7341	0.6225
0.0356	16.0	1936	0.2892	0.5354	0.7561	0.6225
0.0281	17.0	2057	0.2904	0.5271	0.7479	0.6305
0.0283	18.0	2178	0.2916	0.5071	0.7348	0.6345
0.0261	19.0	2299	0.2935	0.4995	0.7341	0.6305
0.0243	20.0	2420	0.2932	0.4995	0.7341	0.6305

Kuongan/xlm-roberta-base-ukr-finetuned

xlm-roberta-base-ukr-finetuned