learning_rate: 2e-05
train_batch_size: 16
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: 3

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1814	0.2278	200	0.1767	0.6484	0.7109	0.6782	0.9195
0.1096	0.4556	400	0.2426	0.6404	0.5734	0.6050	0.8985
0.0783	0.6834	600	0.3193	0.5905	0.5639	0.5769	0.8911
0.0559	0.9112	800	0.3580	0.6168	0.5982	0.6074	0.8928
0.0357	1.1390	1000	0.5001	0.5797	0.5159	0.5460	0.8760
0.0308	1.3667	1200	0.4609	0.6015	0.5663	0.5834	0.8793
0.0279	1.5945	1400	0.4791	0.6488	0.6248	0.6366	0.8895
0.0276	1.8223	1600	0.5009	0.6211	0.5760	0.5977	0.8825
0.0248	2.0501	1800	0.4665	0.6462	0.6228	0.6343	0.8901
0.0173	2.2779	2000	0.5199	0.6712	0.6930	0.6819	0.9013
0.0147	2.5057	2200	0.5416	0.6328	0.6001	0.6160	0.8867
0.0146	2.7335	2400	0.5662	0.6233	0.5721	0.5966	0.8801
0.0146	2.9613	2600	0.5514	0.6403	0.6024	0.6208	0.8865
0.0146	3.0	2634	0.5523	0.6396	0.6004	0.6194	0.8862

YarBar/bert-finetuned-ner-13-more-q-variations

bert-finetuned-ner-13-more-q-variations