learning_rate: 5e-05
train_batch_size: 24
eval_batch_size: 24
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: linear
num_epochs: 18

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	F1 Micro	Precision Macro	Precision Micro	Recall Macro	Recall Micro
0.3654	1.0	93	0.2315	0.9329	0.2241	0.4091	0.2338	0.5016	0.2436	0.3454
0.1834	2.0	186	0.1603	0.9627	0.2982	0.6748	0.4007	0.8441	0.2733	0.5621
0.1161	3.0	279	0.1472	0.9649	0.4016	0.7249	0.4585	0.7494	0.3910	0.7020
0.0764	4.0	372	0.1233	0.9739	0.4844	0.7783	0.5824	0.8346	0.4487	0.7291
0.0561	5.0	465	0.1224	0.9738	0.4467	0.7792	0.5923	0.8650	0.4044	0.7088
0.0423	6.0	558	0.1135	0.9799	0.6021	0.8375	0.6885	0.8825	0.5619	0.7968
0.0319	7.0	651	0.0987	0.9820	0.6386	0.8541	0.6928	0.8841	0.6342	0.8262
0.0221	8.0	744	0.1034	0.9836	0.6463	0.8623	0.7605	0.9184	0.6045	0.8126
0.0175	9.0	837	0.0984	0.9852	0.6852	0.8794	0.7154	0.9045	0.6849	0.8555
0.0094	10.0	930	0.0985	0.9865	0.6985	0.8936	0.7434	0.9272	0.6914	0.8623
0.0078	11.0	1023	0.0987	0.9858	0.7314	0.8876	0.7563	0.9119	0.7406	0.8646
0.0056	12.0	1116	0.1051	0.9868	0.7501	0.8923	0.7955	0.9270	0.7410	0.8600
0.0047	13.0	1209	0.1027	0.9866	0.7606	0.8936	0.8116	0.9272	0.7469	0.8623
0.0031	14.0	1302	0.1009	0.9866	0.7762	0.8953	0.8034	0.9233	0.7769	0.8691
0.0027	15.0	1395	0.1008	0.9873	0.7763	0.8995	0.8073	0.9322	0.7769	0.8691
0.002	16.0	1488	0.1034	0.9884	0.7939	0.9067	0.8590	0.9505	0.7591	0.8668
0.0015	17.0	1581	0.1020	0.9881	0.7925	0.9059	0.8362	0.9459	0.7769	0.8691
0.0016	18.0	1674	0.1027	0.9879	0.7899	0.9048	0.8321	0.9436	0.7769	0.8691

codymd/xlnet-base-cased-grammar-ner

xlnet-base-cased-grammar-ner