baoule_stt_2 API & Inference Endpoint

More information needed

More information needed

More information needed

The following hyperparameters were used during training:

learning_rate: 3e-05
train_batch_size: 12
eval_batch_size: 12
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 24
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: 50
training_steps: 48000
mixed_precision_training: Native AMP

Table with columns: Training Loss, Epoch, Step, Validation Loss, Wer
Training Loss	Epoch	Step	Validation Loss	Wer
0.3006	4.2105	2000	0.2106	0.1831
0.0347	8.4211	4000	0.2498	0.1819
0.0197	12.6316	6000	0.2616	0.1627
0.015	16.8421	8000	0.2623	0.1588
0.0108	21.0526	10000	0.2774	0.1574

More information needed

More information needed

More information needed

The following hyperparameters were used during training:

learning_rate: 3e-05
train_batch_size: 12
eval_batch_size: 12
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 24
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: 50
training_steps: 48000
mixed_precision_training: Native AMP

Table with columns: Training Loss, Epoch, Step, Validation Loss, Wer
Training Loss	Epoch	Step	Validation Loss	Wer
0.3006	4.2105	2000	0.2106	0.1831
0.0347	8.4211	4000	0.2498	0.1819
0.0197	12.6316	6000	0.2616	0.1627
0.015	16.8421	8000	0.2623	0.1588
0.0108	21.0526	10000	0.2774	0.1574

baoule_stt_2