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README.md
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---
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language:
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- ru
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license: apache-2.0
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library_name: peft
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tags:
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- generated_from_trainer
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base_model: openai/whisper-large-v2
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metrics:
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- wer
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model-index:
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- name: 'Whisper Large Ru ORD 0.9 Peft PEFT 4-bit Q DoRA - Mizoru '
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/mizoru/ORD/runs/te5djaa5)
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# Whisper Large Ru ORD 0.9 Peft PEFT 4-bit Q DoRA - Mizoru
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the ORD_0.9 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9988
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- Wer: 48.4439
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- Cer: 26.5242
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- Clean Wer: 40.8650
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- Clean Cer: 20.9832
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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: 0.001
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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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- 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: 50
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- num_epochs: 4
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Cer | Clean Cer | Clean Wer | Validation Loss | Wer |
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|:-------------:|:-----:|:----:|:-------:|:---------:|:---------:|:---------------:|:-------:|
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| 1.216 | 1.0 | 550 | 27.9352 | 22.0432 | 43.2693 | 1.0350 | 50.7505 |
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| 1.1847 | 2.0 | 1100 | 26.5324 | 20.9303 | 41.2903 | 1.0187 | 49.1670 |
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| 1.055 | 3.0 | 1650 | 26.7141 | 21.0494 | 41.5960 | 0.9889 | 48.8428 |
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| 0.9137 | 4.0 | 2200 | 0.9988 | 48.4439 | 26.5242 | 40.8650 | 20.9832 |
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### Framework versions
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- PEFT 0.11.2.dev0
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- Transformers 4.41.0.dev0
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- Pytorch 2.1.2
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- Datasets 2.18.0
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- Tokenizers 0.19.1
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