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wav2vec2-xls-r-1b-scandinavian-E2-100h-30-epochs-20250123

@@ -1,199 +1,116 @@
 ---
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 ---
 library_name: transformers
+license: apache-2.0
+base_model: facebook/wav2vec2-xls-r-1b
+tags:
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: wav2vec2-xls-r-1b-scandinavian-E2-100h-30-epochs-20250123
+  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. -->
+# wav2vec2-xls-r-1b-scandinavian-E2-100h-30-epochs-20250123
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1348
+- Wer: 21.6270
+- Cer: 4.5904
+## 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: 16
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 6000
+- num_epochs: 30
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch   | Step  | Validation Loss | Wer     | Cer     |
+|:-------------:|:-------:|:-----:|:---------------:|:-------:|:-------:|
+| 1.0569        | 0.5831  | 1000  | 0.5118          | 63.8799 | 16.0725 |
+| 0.4643        | 1.1662  | 2000  | 0.2647          | 38.1428 | 9.2101  |
+| 0.3797        | 1.7493  | 3000  | 0.2158          | 33.0390 | 7.8553  |
+| 0.3558        | 2.3324  | 4000  | 0.1996          | 31.8993 | 7.4365  |
+| 0.3784        | 2.9155  | 5000  | 0.2039          | 31.7718 | 7.5216  |
+| 0.4332        | 3.4985  | 6000  | 0.2181          | 33.0704 | 7.8748  |
+| 0.3112        | 4.0816  | 7000  | 0.2070          | 32.4627 | 7.6624  |
+| 0.3189        | 4.6647  | 8000  | 0.1970          | 31.6000 | 7.4421  |
+| 0.2768        | 5.2478  | 9000  | 0.1998          | 31.2380 | 7.4350  |
+| 0.2924        | 5.8309  | 10000 | 0.1871          | 30.2479 | 7.0815  |
+| 0.2971        | 6.4140  | 11000 | 0.1874          | 30.3957 | 7.2106  |
+| 0.3326        | 6.9971  | 12000 | 0.1860          | 29.7972 | 7.0494  |
+| 0.2833        | 7.5802  | 13000 | 0.1908          | 30.0964 | 7.1554  |
+| 0.2283        | 8.1633  | 14000 | 0.1775          | 29.1211 | 6.8593  |
+| 0.2145        | 8.7464  | 15000 | 0.1768          | 28.5836 | 6.6787  |
+| 0.2556        | 9.3294  | 16000 | 0.1785          | 28.7831 | 6.7674  |
+| 0.2567        | 9.9125  | 17000 | 0.1738          | 28.3009 | 6.5702  |
+| 0.2418        | 10.4956 | 18000 | 0.1719          | 28.5466 | 6.6845  |
+| 0.1733        | 11.0787 | 19000 | 0.1619          | 27.0467 | 6.2287  |
+| 0.1734        | 11.6618 | 20000 | 0.1624          | 26.6052 | 6.1501  |
+| 0.2026        | 12.2449 | 21000 | 0.1614          | 26.8417 | 6.1794  |
+| 0.1898        | 12.8280 | 22000 | 0.1541          | 26.6902 | 6.1292  |
+| 0.2149        | 13.4111 | 23000 | 0.1570          | 26.3780 | 6.0525  |
+| 0.1751        | 13.9942 | 24000 | 0.1494          | 25.8756 | 5.8931  |
+| 0.1488        | 14.5773 | 25000 | 0.1468          | 25.5819 | 5.7224  |
+| 0.1283        | 15.1603 | 26000 | 0.1477          | 25.3380 | 5.6983  |
+| 0.1363        | 15.7434 | 27000 | 0.1463          | 24.8430 | 5.5717  |
+| 0.1324        | 16.3265 | 28000 | 0.1453          | 24.9095 | 5.5905  |
+| 0.143         | 16.9096 | 29000 | 0.1434          | 24.6915 | 5.5245  |
+| 0.1241        | 17.4927 | 30000 | 0.1436          | 24.1965 | 5.3843  |
+| 0.0976        | 18.0758 | 31000 | 0.1475          | 24.1429 | 5.3458  |
+| 0.0945        | 18.6589 | 32000 | 0.1405          | 23.4871 | 5.1769  |
+| 0.1085        | 19.2420 | 33000 | 0.1393          | 23.6515 | 5.1972  |
+| 0.1165        | 19.8251 | 34000 | 0.1350          | 23.2599 | 5.1165  |
+| 0.1195        | 20.4082 | 35000 | 0.1435          | 23.1196 | 5.0626  |
+| 0.101         | 20.9913 | 36000 | 0.1366          | 22.9219 | 4.9904  |
+| 0.0764        | 21.5743 | 37000 | 0.1359          | 22.6892 | 4.9498  |
+| 0.0806        | 22.1574 | 38000 | 0.1372          | 22.5506 | 4.8653  |
+| 0.084         | 22.7405 | 39000 | 0.1350          | 22.3530 | 4.8049  |
+| 0.0825        | 23.3236 | 40000 | 0.1380          | 22.1812 | 4.7661  |
+| 0.0788        | 23.9067 | 41000 | 0.1407          | 22.1221 | 4.7596  |
+| 0.0632        | 24.4898 | 42000 | 0.1385          | 22.0463 | 4.7328  |
+| 0.0502        | 25.0729 | 43000 | 0.1393          | 21.9761 | 4.6974  |
+| 0.0482        | 25.6560 | 44000 | 0.1384          | 21.9946 | 4.6878  |
+| 0.0612        | 26.2391 | 45000 | 0.1364          | 21.7619 | 4.6330  |
+| 0.0708        | 26.8222 | 46000 | 0.1346          | 21.7378 | 4.6234  |
+| 0.0609        | 27.4052 | 47000 | 0.1353          | 21.7101 | 4.6142  |
+| 0.0632        | 27.9883 | 48000 | 0.1361          | 21.7064 | 4.6071  |
+| 0.0528        | 28.5714 | 49000 | 0.1350          | 21.6769 | 4.6064  |
+| 0.0696        | 29.1545 | 50000 | 0.1347          | 21.6381 | 4.5898  |
+| 0.0564        | 29.7376 | 51000 | 0.1348          | 21.6270 | 4.5904  |
+### Framework versions
+- Transformers 4.48.1
+- Pytorch 2.5.1+cu124
+- Datasets 3.2.0
+- Tokenizers 0.21.0