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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: tun_msa_wav2vec2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# tun_msa_wav2vec2 |
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This model was trained from scratch on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5373 |
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- Wer: 0.5598 |
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- Cer: 0.1779 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
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|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:| |
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| 4.1696 | 2.2727 | 700 | 1.2685 | 0.8366 | 0.3108 | |
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| 1.4028 | 4.5455 | 1400 | 0.7872 | 0.7136 | 0.2375 | |
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| 1.0872 | 6.8182 | 2100 | 0.6787 | 0.6778 | 0.2198 | |
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| 0.9573 | 9.0909 | 2800 | 0.6569 | 0.6605 | 0.2121 | |
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| 0.939 | 11.3636 | 3500 | 0.6115 | 0.6409 | 0.2040 | |
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| 0.8378 | 13.6364 | 4200 | 0.6097 | 0.6288 | 0.2000 | |
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| 0.8026 | 15.9091 | 4900 | 0.5843 | 0.6125 | 0.1949 | |
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| 0.7712 | 18.1818 | 5600 | 0.5830 | 0.6080 | 0.1942 | |
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| 0.7561 | 20.4545 | 6300 | 0.5637 | 0.5951 | 0.1889 | |
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| 0.7255 | 22.7273 | 7000 | 0.5630 | 0.5888 | 0.1869 | |
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| 0.7051 | 25.0 | 7700 | 0.5573 | 0.5852 | 0.1848 | |
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| 0.6873 | 27.2727 | 8400 | 0.5577 | 0.5799 | 0.1841 | |
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| 0.6722 | 29.5455 | 9100 | 0.5460 | 0.5748 | 0.1827 | |
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| 0.6673 | 31.8182 | 9800 | 0.5495 | 0.5725 | 0.1827 | |
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| 0.6359 | 34.0909 | 10500 | 0.5509 | 0.5720 | 0.1831 | |
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| 0.6513 | 36.3636 | 11200 | 0.5455 | 0.5676 | 0.1804 | |
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| 0.6402 | 38.6364 | 11900 | 0.5373 | 0.5649 | 0.1797 | |
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| 0.6302 | 40.9091 | 12600 | 0.5399 | 0.5643 | 0.1797 | |
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| 0.617 | 43.1818 | 13300 | 0.5368 | 0.5628 | 0.1793 | |
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| 0.6353 | 45.4545 | 14000 | 0.5369 | 0.5596 | 0.1786 | |
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| 0.6277 | 47.7273 | 14700 | 0.5361 | 0.5597 | 0.1783 | |
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| 0.5956 | 50.0 | 15400 | 0.5373 | 0.5598 | 0.1779 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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