learning_rate: 1.58775582613963e-05
train_batch_size: 8
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.115325565287072
num_epochs: 8
label_smoothing_factor: 0.114373096835144

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.7514	1.0	4250	0.7540	0.8011	0.8113	0.8062	0.9580
0.7317	2.0	8500	0.7358	0.8277	0.8302	0.8289	0.9619
0.7212	3.0	12750	0.7329	0.8183	0.8442	0.8310	0.9635
0.7023	4.0	17000	0.7346	0.8192	0.8459	0.8324	0.9640
0.6935	5.0	21250	0.7343	0.8366	0.8508	0.8436	0.9652
0.6851	6.0	25500	0.7409	0.8319	0.8514	0.8415	0.9646
0.678	7.0	29750	0.7450	0.8299	0.8528	0.8412	0.9645
0.672	8.0	34000	0.7475	0.8349	0.8525	0.8436	0.9646

shellypeng/bert-base-cased-finetuned-ner-final

bert-base-cased-finetuned-ner-final