learning_rate: 0.001
train_batch_size: 8
eval_batch_size: 8
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_steps: 100
num_epochs: 4
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Wer
0.6132	0.3040	200	0.4195	0.6846
0.4931	0.6079	400	0.3299	0.5413
0.3934	0.9119	600	0.2873	0.5070
0.3771	1.2158	800	0.2497	0.4569
0.3445	1.5198	1000	0.2516	0.4304
0.3724	1.8237	1200	0.2439	0.4079
0.3265	2.1277	1400	0.2083	0.4065
0.2983	2.4316	1600	0.2082	0.3693
0.3222	2.7356	1800	0.2073	0.3688
0.3461	3.0395	2000	0.1854	0.3438
0.2745	3.3435	2200	0.1813	0.3329
0.2867	3.6474	2400	0.1784	0.3258
0.2715	3.9514	2600	0.1748	0.3268

MatricariaV/Kabardian-ASR-kaggle

Kabardian-ASR-kaggle