YAML Metadata Error: "language[0]" must only contain lowercase characters
YAML Metadata Error: "language[0]" with value "nb-NO" is not valid. It must be an ISO 639-1, 639-2 or 639-3 code (two/three letters), or a special value like "code", "multilingual". If you want to use BCP-47 identifiers, you can specify them in language_bcp47.
YAML Metadata Error: "tags[4]" must be a string

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
Downloads last month
17
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train NbAiLab/wav2vec2-xls-r-300m-npsc-bokmaal

Evaluation results