| | --- |
| | library_name: transformers |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - wer |
| | model-index: |
| | - name: WMACv2 |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # WMACv2 |
| |
|
| | This model was trained from scratch on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.2606 |
| | - Wer: 41.3106 |
| |
|
| | ## Model description |
| |
|
| | More information needed |
| |
|
| | ## Intended uses & limitations |
| |
|
| | More information needed |
| |
|
| | ## Training and evaluation data |
| |
|
| | More information needed |
| |
|
| | ## Training procedure |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 2.5e-05 |
| | - train_batch_size: 8 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 32 |
| | - 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: cosine |
| | - lr_scheduler_warmup_steps: 650 |
| | - training_steps: 5000 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Wer | |
| | |:-------------:|:------:|:----:|:---------------:|:-------:| |
| | | 1.5465 | 0.4757 | 1000 | 0.4199 | 54.1280 | |
| | | 1.0508 | 0.9514 | 2000 | 0.2911 | 43.9422 | |
| | | 0.6641 | 1.4267 | 3000 | 0.2649 | 44.7884 | |
| | | 0.4569 | 1.9024 | 4000 | 0.2369 | 41.4448 | |
| | | 0.2753 | 2.3777 | 5000 | 0.2606 | 41.3106 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 5.2.0 |
| | - Pytorch 2.10.0+cu128 |
| | - Datasets 3.6.0 |
| | - Tokenizers 0.22.2 |
| | |
