hubert-base-superb-er-3kfoldfull30-V3-finetuned-bmd-20250824_123804-LOSO-section-out1
This model is a fine-tuned version of superb/hubert-base-superb-er on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9340
- Accuracy: 0.5417
- F1: 0.5530
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: 8
- seed: 1968
- 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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 3 | 1.1069 | 0.3333 | 0.1667 |
| No log | 2.0 | 6 | 1.1057 | 0.3333 | 0.1667 |
| No log | 3.0 | 9 | 1.1031 | 0.375 | 0.2461 |
| 1.1006 | 4.0 | 12 | 1.1003 | 0.2917 | 0.2169 |
| 1.1006 | 5.0 | 15 | 1.0982 | 0.3333 | 0.2626 |
| 1.1006 | 6.0 | 18 | 1.0964 | 0.25 | 0.1778 |
| 1.0878 | 7.0 | 21 | 1.0944 | 0.3333 | 0.2775 |
| 1.0878 | 8.0 | 24 | 1.0911 | 0.375 | 0.3017 |
| 1.0878 | 9.0 | 27 | 1.0875 | 0.3333 | 0.2296 |
| 1.0708 | 10.0 | 30 | 1.0825 | 0.3333 | 0.2296 |
| 1.0708 | 11.0 | 33 | 1.0752 | 0.375 | 0.3017 |
| 1.0708 | 12.0 | 36 | 1.0615 | 0.375 | 0.3333 |
| 1.0708 | 13.0 | 39 | 1.0489 | 0.5417 | 0.5462 |
| 1.0439 | 14.0 | 42 | 1.0394 | 0.5417 | 0.5462 |
| 1.0439 | 15.0 | 45 | 1.0201 | 0.5417 | 0.5587 |
| 1.0439 | 16.0 | 48 | 1.0012 | 0.5417 | 0.5587 |
| 0.9911 | 17.0 | 51 | 0.9850 | 0.5833 | 0.5940 |
| 0.9911 | 18.0 | 54 | 0.9805 | 0.5417 | 0.5543 |
| 0.9911 | 19.0 | 57 | 0.9663 | 0.5833 | 0.5940 |
| 0.9186 | 20.0 | 60 | 0.9603 | 0.5833 | 0.5940 |
| 0.9186 | 21.0 | 63 | 0.9539 | 0.5417 | 0.5530 |
| 0.9186 | 22.0 | 66 | 0.9497 | 0.5417 | 0.5530 |
| 0.9186 | 23.0 | 69 | 0.9460 | 0.5417 | 0.5530 |
| 0.8535 | 24.0 | 72 | 0.9458 | 0.5417 | 0.5530 |
| 0.8535 | 25.0 | 75 | 0.9438 | 0.5417 | 0.5530 |
| 0.8535 | 26.0 | 78 | 0.9382 | 0.5417 | 0.5530 |
| 0.7935 | 27.0 | 81 | 0.9371 | 0.5417 | 0.5530 |
| 0.7935 | 28.0 | 84 | 0.9357 | 0.5417 | 0.5530 |
| 0.7935 | 29.0 | 87 | 0.9346 | 0.5417 | 0.5530 |
| 0.7683 | 30.0 | 90 | 0.9340 | 0.5417 | 0.5530 |
Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.4