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--- |
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library_name: transformers |
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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: shuff_100 |
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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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# shuff_100 |
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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: 322.2777 |
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- Wer: 0.6311 |
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- Cer: 0.2590 |
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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: 0.0005 |
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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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.15 |
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- num_epochs: 60.0 |
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- mixed_precision_training: Native AMP |
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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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| 2829.8126 | 1.0 | 3265 | 346.9778 | 0.7165 | 0.2613 | |
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| 761.7557 | 2.0 | 6530 | 242.2776 | 0.5436 | 0.2114 | |
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| 672.5873 | 3.0 | 9795 | 339.8657 | 0.5724 | 0.2624 | |
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| 668.1343 | 4.0 | 13060 | 278.7672 | 0.5458 | 0.2289 | |
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| 674.2274 | 5.0 | 16325 | 305.6685 | 0.6026 | 0.2488 | |
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| 707.3097 | 6.0 | 19590 | 285.1494 | 0.5852 | 0.2355 | |
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| 741.8663 | 7.0 | 22855 | 350.9450 | 0.6435 | 0.2659 | |
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| 764.1505 | 8.0 | 26120 | 320.6730 | 0.6421 | 0.2611 | |
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| 814.4658 | 9.0 | 29385 | 319.7635 | 0.6572 | 0.2670 | |
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| 842.7828 | 10.0 | 32650 | 375.5847 | 0.6620 | 0.2833 | |
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| 827.2463 | 11.0 | 35915 | 329.4790 | 0.6426 | 0.2613 | |
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| 812.5209 | 12.0 | 39180 | 322.2777 | 0.6311 | 0.2590 | |
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### Framework versions |
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- Transformers 4.48.3 |
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- Pytorch 2.5.1 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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