learning_rate: 0.001
train_batch_size: 1
eval_batch_size: 6
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 2
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_steps: 100
num_epochs: 5
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Cer	Wer
0.5519	0.2380	200	1.0491	0.2181	0.7486
0.6695	0.4759	400	1.0794	0.2235	0.7537
0.6281	0.7139	600	0.9872	0.2197	0.7597
1.0204	0.9518	800	1.0147	0.2269	0.8161
0.7552	1.1892	1000	0.9663	0.2169	0.7399
0.5822	1.4271	1200	0.9698	0.2201	0.7370
0.7803	1.6651	1400	0.9582	0.2174	0.7369
0.5241	1.9030	1600	1.0238	0.2145	0.7384
0.6677	2.1404	1800	0.9963	0.2176	0.7454
0.5136	2.3783	2000	0.9709	0.2121	0.7340
0.5906	2.6163	2200	0.9465	0.2165	0.7346
0.4955	2.8543	2400	0.9719	0.2153	0.7393
0.5493	3.0916	2600	1.0170	0.2098	0.7257
0.5162	3.3296	2800	1.0054	0.2112	0.7302
0.5153	3.5675	3000	0.9765	0.2103	0.7255
0.4576	3.8055	3200	0.9748	0.2113	0.7238
0.5859	4.0428	3400	0.9813	0.2115	0.7242
0.5492	4.2808	3600	0.9603	0.2117	0.7283
0.5105	4.5187	3800	0.9731	0.2130	0.7323
0.5012	4.7567	4000	0.9905	0.2090	0.7310
0.4848	4.9946	4200	0.9833	0.2078	0.7198

ctaguchi/ssc-koo-mms-model-mix-adapt-max2

ssc-koo-mms-model-mix-adapt-max2