| --- |
| library_name: transformers |
| tags: |
| - generated_from_trainer |
| metrics: |
| - f1 |
| - accuracy |
| model-index: |
| - name: wav2vec-bert-ser-standard |
| 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. --> |
|
|
| # wav2vec-bert-ser-standard |
|
|
| This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 2.3597 |
| - F1: 0.5549 |
| - Accuracy: 0.564 |
|
|
| ## 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: 2e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - gradient_accumulation_steps: 8 |
| - total_train_batch_size: 64 |
| - 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 |
| - num_epochs: 10 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| |
| | 30.8905 | 1.0 | 16 | 3.6367 | 0.1464 | 0.24 | |
| | 28.7614 | 2.0 | 32 | 3.5061 | 0.1679 | 0.256 | |
| | 27.0469 | 3.0 | 48 | 3.3160 | 0.3390 | 0.388 | |
| | 27.3445 | 4.0 | 64 | 3.0776 | 0.3525 | 0.396 | |
| | 24.3884 | 5.0 | 80 | 2.9147 | 0.4089 | 0.452 | |
| | 24.4721 | 6.0 | 96 | 2.7240 | 0.4445 | 0.472 | |
| | 22.5651 | 7.0 | 112 | 2.6093 | 0.5077 | 0.532 | |
| | 21.9695 | 8.0 | 128 | 2.6026 | 0.4392 | 0.476 | |
| | 21.3548 | 9.0 | 144 | 2.3849 | 0.5656 | 0.584 | |
| | 18.9157 | 10.0 | 160 | 2.3597 | 0.5549 | 0.564 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 5.0.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 4.8.3 |
| - Tokenizers 0.22.2 |
|
|
