readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: XLSR-1B-bokmaal-low
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # XLSR-1B-bokmaal-low
  
  This model was trained from scratch on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1579
  - Wer: 0.0722
  
  ## 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: 1.7e-05
  - train_batch_size: 12
  - eval_batch_size: 12
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 24
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 34.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 0.434         | 0.24  | 500   | 0.1704          | 0.1378 |
  | 0.2833        | 0.48  | 1000  | 0.1638          | 0.1324 |
  | 0.2478        | 0.72  | 1500  | 0.1606          | 0.1240 |
  | 0.2276        | 0.97  | 2000  | 0.1562          | 0.1212 |
  | 0.2208        | 1.21  | 2500  | 0.1576          | 0.1172 |
  | 0.2148        | 1.45  | 3000  | 0.1502          | 0.1119 |
  | 0.1994        | 1.69  | 3500  | 0.1409          | 0.1110 |
  | 0.1932        | 1.93  | 4000  | 0.1432          | 0.1112 |
  | 0.2122        | 2.17  | 4500  | 0.1443          | 0.1098 |
  | 0.2177        | 2.42  | 5000  | 0.1329          | 0.1102 |
  | 0.2058        | 2.66  | 5500  | 0.1403          | 0.1070 |
  | 0.2216        | 2.9   | 6000  | 0.1342          | 0.1067 |
  | 0.1984        | 3.14  | 6500  | 0.1370          | 0.1030 |
  | 0.2056        | 3.38  | 7000  | 0.1371          | 0.1041 |
  | 0.1735        | 3.62  | 7500  | 0.1296          | 0.1003 |
  | 0.203         | 3.87  | 8000  | 0.1301          | 0.1005 |
  | 0.1835        | 4.11  | 8500  | 0.1310          | 0.1004 |
  | 0.178         | 4.35  | 9000  | 0.1300          | 0.0959 |
  | 0.1585        | 4.59  | 9500  | 0.1277          | 0.0966 |
  | 0.1848        | 4.83  | 10000 | 0.1260          | 0.0974 |
  | 0.169         | 5.07  | 10500 | 0.1281          | 0.0969 |
  | 0.1666        | 5.32  | 11000 | 0.1291          | 0.1003 |
  | 0.1552        | 5.56  | 11500 | 0.1271          | 0.0959 |
  | 0.2736        | 5.8   | 12000 | 0.1320          | 0.0935 |
  | 0.2845        | 6.04  | 12500 | 0.1299          | 0.0921 |
  | 0.1536        | 6.28  | 13000 | 0.1282          | 0.0927 |
  | 0.1491        | 6.52  | 13500 | 0.1240          | 0.0906 |
  | 0.1579        | 6.77  | 14000 | 0.1208          | 0.0921 |
  | 0.16          | 7.01  | 14500 | 0.1182          | 0.0903 |
  | 0.1367        | 7.25  | 15000 | 0.1214          | 0.0922 |
  | 0.1499        | 7.49  | 15500 | 0.1232          | 0.0916 |
  | 0.148         | 7.73  | 16000 | 0.1184          | 0.0896 |
  | 0.1426        | 7.97  | 16500 | 0.1201          | 0.0889 |
  | 0.1471        | 8.22  | 17000 | 0.1256          | 0.0882 |
  | 0.1358        | 8.46  | 17500 | 0.1265          | 0.0909 |
  | 0.1245        | 8.7   | 18000 | 0.1263          | 0.0886 |
  | 0.1407        | 8.94  | 18500 | 0.1226          | 0.0885 |
  | 0.1289        | 9.18  | 19000 | 0.1315          | 0.0873 |
  | 0.1326        | 9.42  | 19500 | 0.1233          | 0.0868 |
  | 0.1305        | 9.67  | 20000 | 0.1237          | 0.0870 |
  | 0.1432        | 9.91  | 20500 | 0.1234          | 0.0857 |
  | 0.1205        | 10.15 | 21000 | 0.1303          | 0.0858 |
  | 0.1248        | 10.39 | 21500 | 0.1252          | 0.0858 |
  | 0.1251        | 10.63 | 22000 | 0.1253          | 0.0869 |
  | 0.1143        | 10.87 | 22500 | 0.1266          | 0.0860 |
  | 0.1155        | 11.12 | 23000 | 0.1219          | 0.0862 |
  | 0.1227        | 11.36 | 23500 | 0.1329          | 0.0864 |
  | 0.1229        | 11.6  | 24000 | 0.1244          | 0.0855 |
  | 0.1112        | 11.84 | 24500 | 0.1356          | 0.0851 |
  | 0.2163        | 12.08 | 25000 | 0.1252          | 0.0847 |
  | 0.1146        | 12.32 | 25500 | 0.1211          | 0.0837 |
  | 0.1058        | 12.57 | 26000 | 0.1247          | 0.0843 |
  | 0.1099        | 12.81 | 26500 | 0.1189          | 0.0833 |
  | 0.1028        | 13.05 | 27000 | 0.1303          | 0.0815 |
  | 0.1092        | 13.29 | 27500 | 0.1305          | 0.0838 |
  | 0.1076        | 13.53 | 28000 | 0.1276          | 0.0842 |
  | 0.1074        | 13.77 | 28500 | 0.1268          | 0.0844 |
  | 0.0971        | 14.02 | 29000 | 0.1322          | 0.0839 |
  | 0.1109        | 14.26 | 29500 | 0.1287          | 0.0821 |
  | 0.0991        | 14.5  | 30000 | 0.1289          | 0.0831 |
  | 0.1095        | 14.74 | 30500 | 0.1273          | 0.0822 |
  | 0.1015        | 14.98 | 31000 | 0.1326          | 0.0816 |
  | 0.1051        | 15.22 | 31500 | 0.1337          | 0.0814 |
  | 