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--- |
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license: apache-2.0 |
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base_model: NbAiLab/nb-whisper-medium-verbatim |
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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: nb-whisper-medium-karelian-CodeSwitching |
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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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# nb-whisper-medium-karelian-CodeSwitching |
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This model is a fine-tuned version of [NbAiLab/nb-whisper-medium-verbatim](https://huggingface.co/NbAiLab/nb-whisper-medium-verbatim) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5439 |
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- Wer: 0.2585 |
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- Cer: 0.0714 |
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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: 16 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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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- training_steps: 10000 |
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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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| 0.2467 | 1.1351 | 500 | 0.5664 | 0.3488 | 0.0895 | |
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| 0.0718 | 2.2701 | 1000 | 0.5562 | 0.3166 | 0.0819 | |
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| 0.0513 | 3.4052 | 1500 | 0.5366 | 0.2997 | 0.0798 | |
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| 0.0377 | 4.5403 | 2000 | 0.5430 | 0.2815 | 0.0730 | |
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| 0.0339 | 5.6754 | 2500 | 0.5444 | 0.2906 | 0.0755 | |
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| 0.0263 | 6.8104 | 3000 | 0.5439 | 0.2757 | 0.0735 | |
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| 0.0182 | 7.9455 | 3500 | 0.5474 | 0.2754 | 0.0741 | |
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| 0.0141 | 9.0806 | 4000 | 0.5625 | 0.2808 | 0.0758 | |
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| 0.0117 | 10.2157 | 4500 | 0.5537 | 0.2662 | 0.0716 | |
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| 0.0122 | 11.3507 | 5000 | 0.5610 | 0.2703 | 0.0726 | |
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| 0.0118 | 12.4858 | 5500 | 0.5557 | 0.2686 | 0.0720 | |
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| 0.0075 | 13.6209 | 6000 | 0.5522 | 0.2673 | 0.0711 | |
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| 0.0069 | 14.7560 | 6500 | 0.5576 | 0.2764 | 0.0745 | |
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| 0.0072 | 15.8910 | 7000 | 0.5562 | 0.2676 | 0.0705 | |
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| 0.0085 | 17.0261 | 7500 | 0.5474 | 0.2713 | 0.0868 | |
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| 0.0041 | 18.1612 | 8000 | 0.5493 | 0.2639 | 0.0716 | |
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| 0.0041 | 19.2963 | 8500 | 0.5493 | 0.2612 | 0.0712 | |
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| 0.0041 | 20.4313 | 9000 | 0.5449 | 0.2554 | 0.0699 | |
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| 0.004 | 21.5664 | 9500 | 0.5444 | 0.2591 | 0.0708 | |
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| 0.0028 | 22.7015 | 10000 | 0.5439 | 0.2585 | 0.0714 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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