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
0.2156	1.0	93	0.1656	0.9273	0.4389	0.4389	0.3873	0.3873	0.5064	0.5064
0.136	2.0	186	0.1196	0.9611	0.5396	0.5396	0.8129	0.8129	0.4038	0.4038
0.0885	3.0	279	0.0983	0.9673	0.7120	0.7120	0.7031	0.7031	0.7212	0.7212
0.0585	4.0	372	0.0908	0.9760	0.7898	0.7898	0.8381	0.8381	0.7468	0.7468
0.0406	5.0	465	0.0952	0.9723	0.7365	0.7365	0.8084	0.8084	0.6763	0.6763
0.0323	6.0	558	0.0755	0.9826	0.8529	0.8529	0.87	0.87	0.8365	0.8365
0.0228	7.0	651	0.0682	0.9858	0.8724	0.8724	0.8795	0.8795	0.8654	0.8654
0.0127	8.0	744	0.0822	0.9866	0.8799	0.8799	0.9319	0.9319	0.8333	0.8333
0.0107	9.0	837	0.0802	0.9879	0.9008	0.9008	0.9142	0.9142	0.8878	0.8878
0.007	10.0	930	0.0866	0.9878	0.9042	0.9042	0.9505	0.9505	0.8622	0.8622
0.0049	11.0	1023	0.0815	0.9884	0.9005	0.9005	0.9169	0.9169	0.8846	0.8846
0.0045	12.0	1116	0.0931	0.9886	0.9082	0.9082	0.9477	0.9477	0.8718	0.8718
0.003	13.0	1209	0.0926	0.9891	0.9195	0.9195	0.9648	0.9648	0.8782	0.8782
0.0015	14.0	1302	0.0840	0.9900	0.9208	0.9208	0.9490	0.9490	0.8942	0.8942
0.0009	15.0	1395	0.0895	0.9907	0.9280	0.9280	0.9719	0.9719	0.8878	0.8878
0.0007	16.0	1488	0.0898	0.9907	0.9282	0.9282	0.9686	0.9686	0.8910	0.8910
0.0005	17.0	1581	0.0903	0.9907	0.9282	0.9282	0.9686	0.9686	0.8910	0.8910
0.0006	18.0	1674	0.0907	0.9907	0.9282	0.9282	0.9686	0.9686	0.8910	0.8910

codymd/xlnet-base-cased-grammar-ner-generic

xlnet-base-cased-grammar-ner-generic