learning_rate: 0.0001
train_batch_size: 16
eval_batch_size: 16
seed: 1968
gradient_accumulation_steps: 2
total_train_batch_size: 32
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_ratio: 0.1
num_epochs: 15

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Sensitivity
No log	0.8571	3	1.1337	0.2647	0.1108	0.0701	0.2647	0.2647
No log	2.0	7	1.1425	0.2647	0.1108	0.0701	0.2647	0.2647
1.0516	2.8571	10	1.1001	0.5	0.4068	0.3585	0.5	0.5
1.0516	4.0	14	1.1083	0.5294	0.4725	0.5349	0.5294	0.5294
1.0516	4.8571	17	1.4131	0.3235	0.2838	0.5783	0.3235	0.3235
0.6189	6.0	21	1.1835	0.5	0.5036	0.5107	0.5	0.5
0.6189	6.8571	24	1.5920	0.3235	0.3248	0.5662	0.3235	0.3235
0.6189	8.0	28	2.0293	0.3529	0.25	0.2047	0.3529	0.3529
0.1708	8.8571	31	2.1477	0.3529	0.3436	0.5845	0.3529	0.3529
0.1708	10.0	35	2.5696	0.3235	0.2841	0.5607	0.3235	0.3235
0.1708	10.8571	38	2.9175	0.3529	0.2994	0.5809	0.3529	0.3529
0.0173	12.0	42	2.9863	0.2941	0.2676	0.5462	0.2941	0.2941
0.0173	12.8571	45	2.9882	0.2941	0.2676	0.5462	0.2941	0.2941

sarasarasara/whisper-base-finetuned-bmd-20241112_114002

whisper-base-finetuned-bmd-20241112_114002