learning_rate: 0.0001
train_batch_size: 64
eval_batch_size: 64
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
lr_scheduler_warmup_steps: 500
num_epochs: 8

Training Loss	Epoch	Step	Validation Loss	F1 Micro	Precision Micro	Recall Micro	Accuracy
No log	1.0	123	0.2308	0.0320	0.0164	0.5905	0.0
No log	2.0	246	0.0527	0.1882	0.5469	0.1136	0.1233
No log	3.0	369	0.0476	0.2757	0.3968	0.2113	0.1459
No log	4.0	492	0.0409	0.4130	0.6102	0.3121	0.2533
0.1778	5.0	615	0.0365	0.4977	0.6169	0.4170	0.2671
0.1778	6.0	738	0.0346	0.5304	0.6461	0.4499	0.2820
0.1778	7.0	861	0.0328	0.5450	0.6405	0.4743	0.2815
0.1778	8.0	984	0.0324	0.5528	0.6494	0.4813	0.2932

ahmedfarazsyk/bert-base-uncased-pandas-github-issues

bert-base-uncased-pandas-github-issues