Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0013	21.74	500	0.9018	0.6843	0.7475	0.7145	0.8599
0.012	43.48	1000	1.0791	0.7115	0.7623	0.7360	0.8561
0.0002	65.22	1500	1.0060	0.7360	0.7623	0.7489	0.8565
0.03	86.96	2000	1.1521	0.7282	0.6700	0.6979	0.8313
0.0013	108.7	2500	1.1517	0.7240	0.7463	0.7350	0.8579
0.0016	130.43	3000	0.9393	0.7319	0.7697	0.7503	0.8732
0.0021	152.17	3500	0.9972	0.7249	0.7562	0.7402	0.8635
0.0001	173.91	4000	1.0485	0.7049	0.7796	0.7404	0.8583
0.0002	195.65	4500	1.0827	0.7055	0.7315	0.7183	0.8433
0.0	217.39	5000	1.0528	0.7354	0.7599	0.7474	0.8586
0.0001	239.13	5500	1.1183	0.7001	0.7131	0.7065	0.8465
0.0002	260.87	6000	1.1749	0.7231	0.7685	0.7451	0.8520
0.0	282.61	6500	1.1206	0.7315	0.7685	0.7495	0.8611
0.0	304.35	7000	1.2037	0.7417	0.7709	0.7560	0.8558
0.0	326.09	7500	1.3737	0.7391	0.75	0.7445	0.8513
0.0	347.83	8000	1.2926	0.7221	0.7648	0.7428	0.8475
0.0	369.57	8500	1.4108	0.6966	0.7549	0.7246	0.8293
0.0	391.3	9000	1.4346	0.7222	0.7586	0.7399	0.8303
0.0	413.04	9500	1.4146	0.7225	0.7599	0.7407	0.8363
0.0	434.78	10000	1.4097	0.7121	0.7586	0.7346	0.8346

kavg/LiLT-SER-ZH-SIN

LiLT-SER-ZH-SIN