learning_rate: 0.0003
train_batch_size: 8
eval_batch_size: 6
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
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: 100
num_epochs: 5
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Cer	Wer
1.6201	0.3487	200	0.7838	0.1619	0.6789
1.2566	0.6975	400	0.7509	0.1552	0.6508
1.1404	1.0453	600	0.7600	0.1584	0.6621
1.1198	1.3941	800	0.7692	0.1588	0.6555
1.114	1.7428	1000	0.7009	0.1490	0.6146
1.157	2.0907	1200	0.7033	0.1468	0.6002
1.0393	2.4394	1400	0.7133	0.1468	0.5936
1.0582	2.7881	1600	0.7062	0.1450	0.5921
1.0315	3.1360	1800	0.7120	0.1481	0.6084
1.04	3.4847	2000	0.7125	0.1454	0.5897
1.0979	3.8335	2200	0.7051	0.1448	0.5917
0.9606	4.1813	2400	0.7085	0.1451	0.5827
0.9512	4.5301	2600	0.7061	0.1459	0.5893
1.07	4.8788	2800	0.7049	0.1452	0.5905

ctaguchi/ssc-top-mms-model-mix-adapt-max-lowlr

ssc-top-mms-model-mix-adapt-max-lowlr