Hubert-base-superb
This model is a fine-tuned version of ntu-spml/distilhubert on the superb dataset.
It achieves the following results on the evaluation set:
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: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 7
Training results
| Training Loss |
Epoch |
Step |
Validation Loss |
Wer |
| 1.7884 |
0.8 |
500 |
0.8900 |
0.6940 |
| 0.6603 |
1.6 |
1000 |
0.7378 |
0.6103 |
| 0.5401 |
2.4 |
1500 |
0.7107 |
0.5762 |
| 0.4604 |
3.2 |
2000 |
0.6563 |
0.5320 |
| 0.3936 |
4.0 |
2500 |
0.6315 |
0.5244 |
| 0.3186 |
4.8 |
3000 |
0.6525 |
0.5007 |
| 0.2727 |
5.6 |
3500 |
0.6553 |
0.4855 |
| 0.2296 |
6.4 |
4000 |
0.6712 |
0.4781 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
