Training Loss	Epoch	Step	Accuracy	Validation Loss
4.6198	0.46	200	0.35	nan
4.1243	0.91	400	0.3244	nan
3.7837	1.37	600	0.3273	nan
3.7335	1.82	800	0.3293	nan
3.4681	2.28	1000	0.3963	nan
3.4469	2.73	1200	0.4186	nan
3.3005	3.19	1400	0.4307	3.0076
3.2722	3.64	1600	0.4257	nan
3.3514	4.1	1800	0.4832	2.5834
3.0384	4.56	2000	0.4397	2.9867
3.0971	5.01	2200	0.3799	nan
2.903	5.47	2400	0.5107	2.6798
3.006	5.92	2600	0.4504	2.9205
2.7999	6.38	2800	0.4809	nan
2.8268	6.83	3000	0.4321	2.5767
2.8814	7.29	3200	0.4706	2.7337
2.6975	7.74	3400	0.4831	nan
2.7642	8.2	3600	0.4669	2.8202
2.8996	8.66	3800	0.5187	2.7733
2.6657	9.11	4000	0.4697	nan
2.7318	9.57	4200	0.4532	nan
2.7065	10.02	4400	0.4785	2.5715
2.5635	10.48	4600	0.4969	2.8287
2.5543	10.93	4800	0.4909	2.3697
2.5284	11.39	5000	0.4706	nan
2.5401	11.85	5200	0.4679	nan
2.4722	12.3	5400	0.4983	2.3692
2.5367	12.76	5600	0.5663	nan
2.5331	13.21	5800	0.525	nan
2.183	13.67	6000	0.5137	2.3417
2.4319	14.12	6200	0.5409	2.2783
2.4168	14.58	6400	0.4828	2.7092

soBeauty/V3_20230929-4-xlm-roberta-base-new

V3_20230929-4-xlm-roberta-base-new