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README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: openai/whisper-large-v3
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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: whisper-small-medical
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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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# whisper-small-medical
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6113
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- Wer: 51.2340
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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: 5e-07
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- train_batch_size: 32
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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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- optimizer: Use 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_steps: 50
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- training_steps: 100
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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 |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|
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| 7.4337 | 25.0 | 50 | 0.6201 | 44.2553 |
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| 5.7447 | 50.0 | 100 | 0.6113 | 51.2340 |
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### Framework versions
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- Transformers 4.48.0.dev0
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- Pytorch 2.1.0+cu118
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- Datasets 3.1.1.dev0
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- Tokenizers 0.21.0
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