learning_rate: 0.0004
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use 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: 132
num_epochs: 100
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Wer
4.3851	2.3529	200	2.4270	1.0
1.3933	4.7059	400	0.5187	0.6193
0.2417	7.0588	600	0.4328	0.4403
0.1174	9.4118	800	0.4113	0.4186
0.08	11.7647	1000	0.3914	0.3589
0.0509	14.1176	1200	0.4065	0.3665
0.0445	16.4706	1400	0.4714	0.3636
0.0329	18.8235	1600	0.3930	0.3561
0.0315	21.1765	1800	0.5501	0.3655
0.0298	23.5294	2000	0.4462	0.3561
0.0366	25.8824	2200	0.4993	0.3447
0.0259	28.2353	2400	0.5077	0.3561
0.0197	30.5882	2600	0.5029	0.3466
0.0229	32.9412	2800	0.4760	0.3400
0.0174	35.2941	3000	0.5118	0.3475
0.0102	37.6471	3200	0.5630	0.3428
0.0104	40.0	3400	0.5598	0.3400
0.015	42.3529	3600	0.5226	0.3428
0.0102	44.7059	3800	0.5421	0.3513
0.012	47.0588	4000	0.5936	0.3456
0.0101	49.4118	4200	0.5772	0.3485
0.009	51.7647	4400	0.5759	0.3438
0.0104	54.1176	4600	0.5755	0.3400
0.0118	56.4706	4800	0.5868	0.3362
0.0118	58.8235	5000	0.6174	0.3456
0.0109	61.1765	5200	0.6037	0.3390
0.0086	63.5294	5400	0.5903	0.3447
0.0039	65.8824	5600	0.5894	0.3428
0.0058	68.2353	5800	0.6388	0.3428
0.0044	70.5882	6000	0.6378	0.3390
0.0032	72.9412	6200	0.6868	0.3409
0.0057	75.2941	6400	0.6439	0.3419
0.0034	77.6471	6600	0.6888	0.3381
0.0022	80.0	6800	0.7217	0.3381
0.0032	82.3529	7000	0.5946	0.3371
0.0033	84.7059	7200	0.6650	0.3390
0.0018	87.0588	7400	0.6844	0.3371
0.0026	89.4118	7600	0.7199	0.3409
0.0028	91.7647	7800	0.6868	0.3381
0.002	94.1176	8000	0.6752	0.3409
0.0013	96.4706	8200	0.6788	0.3390
0.0011	98.8235	8400	0.6748	0.3390

susmitabhatt/xlsr-a-nomo

xlsr-a-nomo