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wav2vec2-xls-r-300m-npsc-bokmaal
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1663
- Wer: 0.0932
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0969 | 0.32 | 500 | 0.1773 | 0.1054 |
| 0.0929 | 0.64 | 1000 | 0.1672 | 0.1061 |
| 0.1018 | 0.97 | 1500 | 0.1770 | 0.1067 |
| 0.0871 | 1.29 | 2000 | 0.1832 | 0.1087 |
| 0.0908 | 1.61 | 2500 | 0.1830 | 0.1101 |
| 0.0975 | 1.93 | 3000 | 0.1848 | 0.1100 |
| 0.0936 | 2.26 | 3500 | 0.1853 | 0.1113 |
| 0.1025 | 2.58 | 4000 | 0.1958 | 0.1149 |
| 0.0989 | 2.9 | 4500 | 0.1776 | 0.1123 |
| 0.0946 | 3.22 | 5000 | 0.1825 | 0.1097 |
| 0.0859 | 3.55 | 5500 | 0.1864 | 0.1072 |
| 0.0867 | 3.87 | 6000 | 0.1886 | 0.1081 |
| 0.0783 | 4.19 | 6500 | 0.1883 | 0.1063 |
| 0.0804 | 4.51 | 7000 | 0.1831 | 0.1063 |
| 0.0797 | 4.84 | 7500 | 0.1884 | 0.1058 |
| 0.0705 | 5.16 | 8000 | 0.1802 | 0.1057 |
| 0.0795 | 5.48 | 8500 | 0.1854 | 0.1038 |
| 0.0711 | 5.8 | 9000 | 0.1766 | 0.1032 |
| 0.0973 | 6.13 | 9500 | 0.1663 | 0.1014 |
| 0.087 | 6.45 | 10000 | 0.1664 | 0.1014 |
| 0.0962 | 6.77 | 10500 | 0.1631 | 0.1009 |
| 0.0857 | 7.09 | 11000 | 0.1659 | 0.1002 |
| 0.0882 | 7.41 | 11500 | 0.1668 | 0.1007 |
| 0.0784 | 7.74 | 12000 | 0.1688 | 0.0996 |
| 0.0838 | 8.06 | 12500 | 0.1675 | 0.0984 |
| 0.0863 | 8.38 | 13000 | 0.1639 | 0.0979 |
| 0.0763 | 8.7 | 13500 | 0.1638 | 0.0980 |
| 0.0822 | 9.03 | 14000 | 0.1709 | 0.0972 |
| 0.0769 | 9.35 | 14500 | 0.1700 | 0.0965 |
| 0.0838 | 9.67 | 15000 | 0.1703 | 0.0974 |
| 0.0799 | 9.99 | 15500 | 0.1667 | 0.0957 |
| 0.0712 | 10.32 | 16000 | 0.1754 | 0.0960 |
| 0.0737 | 10.64 | 16500 | 0.1725 | 0.0968 |
| 0.0851 | 10.96 | 17000 | 0.1733 | 0.0958 |
| 0.076 | 11.28 | 17500 | 0.1682 | 0.0954 |
| 0.0712 | 11.61 | 18000 | 0.1713 | 0.0943 |
| 0.0745 | 11.93 | 18500 | 0.1662 | 0.0951 |
| 0.0864 | 12.25 | 19000 | 0.1692 | 0.0947 |
| 0.0937 | 12.57 | 19500 | 0.1624 | 0.0943 |
| 0.0915 | 12.89 | 20000 | 0.1678 | 0.0942 |
| 0.0926 | 13.22 | 20500 | 0.1641 | 0.0945 |
| 0.0912 | 13.54 | 21000 | 0.1665 | 0.0937 |
| 0.0917 | 13.86 | 21500 | 0.1648 | 0.0936 |
| 0.094 | 14.18 | 22000 | 0.1635 | 0.0935 |
| 0.0864 | 14.51 | 22500 | 0.1678 | 0.0934 |
| 0.0899 | 14.83 | 23000 | 0.1663 | 0.0932 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4.dev0
- Tokenizers 0.11.0
