learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 128
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 2

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1861	0.1013	400	0.1766	0.4918	0.3811	0.4294	0.9014
0.1197	0.2025	800	0.1510	0.6989	0.6392	0.6677	0.9399
0.0630	0.3038	1200	0.1601	0.7535	0.8117	0.7815	0.9534
0.0395	0.4051	1600	0.1506	0.8215	0.8770	0.8483	0.9652
0.0288	0.5063	2000	0.1764	0.8406	0.8582	0.8493	0.9668
0.0210	0.6076	2400	0.1632	0.8554	0.8785	0.8668	0.9702
0.0177	0.7089	2800	0.1504	0.8677	0.8755	0.8715	0.9717
0.0133	0.8101	3200	0.1946	0.8719	0.8785	0.8752	0.9730
0.0132	0.9114	3600	0.1760	0.8484	0.8777	0.8628	0.9703
0.0112	1.0127	4000	0.2094	0.8468	0.8792	0.8627	0.9687
0.0087	1.1139	4400	0.2003	0.8700	0.8837	0.8768	0.9730
0.0085	1.2152	4800	0.1958	0.8688	0.8942	0.8813	0.9730
0.0070	1.3165	5200	0.2149	0.8495	0.8852	0.8670	0.9706
0.0064	1.4177	5600	0.2139	0.8743	0.8822	0.8783	0.9712
0.0055	1.5190	6000	0.2070	0.8662	0.8890	0.8775	0.9726
0.0053	1.6203	6400	0.2201	0.8603	0.8822	0.8711	0.9710
0.0045	1.7215	6800	0.2325	0.8611	0.8882	0.8744	0.9717
0.0052	1.8228	7200	0.2173	0.8691	0.8867	0.8778	0.9728
0.0043	1.9241	7600	0.2287	0.8662	0.8837	0.8749	0.9715
0.0043	2.0	7900	0.2271	0.8671	0.8860	0.8764	0.9717

YarBar/bert-finetuned-ner-17-2epochs_even_more_data

bert-finetuned-ner-17-2epochs_even_more_data