Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training Results
Table with columns: Step, Training Loss, Validation Loss, WER| Step | Training Loss | Validation Loss | WER |
|---|
| 500 | 1.8105 | 0.7662 | 40.45 |
| 1000 | 0.5350 | 0.7074 | 35.96 |
| 1500 | 0.1668 | 0.7277 | 36.01 |
| 2000 | 0.0645 | 0.7588 | 36.04 |
| 2500 | 0.0332 | 0.7813 | 36.75 |
Best checkpoint: Step 1000 with WER 35.96%.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 500
- training_steps: 3000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 5.0.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2