hubert-base-superb-er-3kfoldfull20-V2-finetuned-bmd-20250824_121113-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: 0.9530
- Accuracy: 0.5833
- F1: 0.5569
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: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 3 | 1.1067 | 0.3333 | 0.1667 |
| No log | 2.0 | 6 | 1.1035 | 0.3333 | 0.1667 |
| No log | 3.0 | 9 | 1.0988 | 0.375 | 0.2445 |
| 1.1017 | 4.0 | 12 | 1.0946 | 0.4583 | 0.3644 |
| 1.1017 | 5.0 | 15 | 1.0908 | 0.4583 | 0.4144 |
| 1.1017 | 6.0 | 18 | 1.0870 | 0.4583 | 0.4273 |
| 1.0899 | 7.0 | 21 | 1.0826 | 0.4583 | 0.4472 |
| 1.0899 | 8.0 | 24 | 1.0774 | 0.4167 | 0.4151 |
| 1.0899 | 9.0 | 27 | 1.0704 | 0.4583 | 0.4750 |
| 1.073 | 10.0 | 30 | 1.0613 | 0.5 | 0.5162 |
| 1.073 | 11.0 | 33 | 1.0502 | 0.5833 | 0.5874 |
| 1.073 | 12.0 | 36 | 1.0368 | 0.5833 | 0.5874 |
| 1.073 | 13.0 | 39 | 1.0211 | 0.625 | 0.6242 |
| 1.0468 | 14.0 | 42 | 1.0042 | 0.6667 | 0.6583 |
| 1.0468 | 15.0 | 45 | 0.9882 | 0.6667 | 0.6631 |
| 1.0468 | 16.0 | 48 | 0.9770 | 0.625 | 0.6114 |
| 1.0055 | 17.0 | 51 | 0.9722 | 0.5833 | 0.5756 |
| 1.0055 | 18.0 | 54 | 0.9654 | 0.625 | 0.6144 |
| 1.0055 | 19.0 | 57 | 0.9563 | 0.5833 | 0.5569 |
| 0.9735 | 20.0 | 60 | 0.9530 | 0.5833 | 0.5569 |
Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.4