Training Loss	Epoch	Step	Accuracy	Validation Loss
4.4076	0.46	200	0.2641	nan
4.0232	0.91	400	0.3172	nan
3.9061	1.37	600	0.3025	nan
3.6669	1.82	800	0.3241	3.5105
3.5761	2.28	1000	0.3492	3.2617
3.4949	2.73	1200	0.3894	nan
3.3705	3.19	1400	0.4269	nan
3.3901	3.64	1600	0.4191	3.3508
3.1971	4.1	1800	0.3889	nan
3.1646	4.56	2000	0.4204	3.3370
2.9748	5.01	2200	0.4468	2.8025
3.0047	5.47	2400	0.4327	3.0098
2.9395	5.92	2600	0.4673	2.7967
2.8467	6.38	2800	0.4675	nan
2.7492	6.83	3000	0.4613	2.8956
2.802	7.29	3200	0.4766	2.5544
2.8111	7.74	3400	0.4925	nan
2.6549	8.2	3600	0.5013	nan
2.7367	8.66	3800	0.4842	2.7689
2.6375	9.11	4000	0.5303	nan
2.616	9.57	4200	0.5157	2.6867
2.711	10.02	4400	0.5357	2.2537
2.4239	10.48	4600	0.4976	nan
2.7012	10.93	4800	0.5105	2.4325
2.5152	11.39	5000	0.5372	nan
2.5798	11.85	5200	0.5320	nan
2.4845	12.3	5400	0.5553	nan
2.4842	12.76	5600	0.5206	nan
2.4412	13.21	5800	0.5398	2.3403
2.4967	13.67	6000	0.5285	2.2010
2.4451	14.12	6200	0.4803	2.5469
2.485	14.58	6400	0.5469	nan

soBeauty/V3_20230929-2-xlm-roberta-base-new

V3_20230929-2-xlm-roberta-base-new