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Test-demo-colab
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9479
- Wer: 0.6856
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: 0.0001
- train_batch_size: 8
- 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: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 4.2676 | 1.0 | 500 | 2.2725 | 1.0013 |
| 2.0086 | 2.01 | 1000 | 1.2788 | 0.8053 |
| 1.6389 | 3.01 | 1500 | 1.1333 | 0.7458 |
| 1.4908 | 4.02 | 2000 | 1.0369 | 0.7356 |
| 1.4137 | 5.02 | 2500 | 0.9894 | 0.7111 |
| 1.3507 | 6.02 | 3000 | 0.9394 | 0.7098 |
| 1.3101 | 7.03 | 3500 | 0.9531 | 0.6966 |
| 1.2682 | 8.03 | 4000 | 0.9255 | 0.6892 |
| 1.239 | 9.04 | 4500 | 0.9222 | 0.6818 |
| 1.2161 | 10.04 | 5000 | 0.9079 | 0.6911 |
| 1.1871 | 11.04 | 5500 | 0.9100 | 0.7033 |
| 1.1688 | 12.05 | 6000 | 0.9080 | 0.6924 |
| 1.1383 | 13.05 | 6500 | 0.9097 | 0.6910 |
| 1.1304 | 14.06 | 7000 | 0.9052 | 0.6810 |
| 1.1181 | 15.06 | 7500 | 0.9025 | 0.6847 |
| 1.0905 | 16.06 | 8000 | 0.9296 | 0.6832 |
| 1.0744 | 17.07 | 8500 | 0.9120 | 0.6912 |
| 1.0675 | 18.07 | 9000 | 0.9039 | 0.6864 |
| 1.0511 | 19.08 | 9500 | 0.9157 | 0.7004 |
| 1.0401 | 20.08 | 10000 | 0.9259 | 0.6792 |
| 1.0319 | 21.08 | 10500 | 0.9478 | 0.6976 |
| 1.0194 | 22.09 | 11000 | 0.9438 | 0.6820 |
| 1.0117 | 23.09 | 11500 | 0.9577 | 0.6891 |
| 1.0038 | 24.1 | 12000 | 0.9670 | 0.6918 |
| 0.9882 | 25.1 | 12500 | 0.9579 | 0.6884 |
| 0.9979 | 26.1 | 13000 | 0.9502 | 0.6869 |
| 0.9767 | 27.11 | 13500 | 0.9537 | 0.6833 |
| 0.964 | 28.11 | 14000 | 0.9525 | 0.6880 |
| 0.9867 | 29.12 | 14500 | 0.9479 | 0.6856 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
