MelanieKoe
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README.md
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---
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license: apache-2.0
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base_model: facebook/wav2vec2-base
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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: w2v2-base-pretrained_lr5e-5_at0.8_da0.4
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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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# w2v2-base-pretrained_lr5e-5_at0.8_da0.4
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.0085
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- Wer: 0.1892
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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-05
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- train_batch_size: 32
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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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- training_steps: 4000
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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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| 17.5459 | 13.16 | 250 | 3.5740 | 1.0 |
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| 3.1637 | 26.32 | 500 | 3.2268 | 1.0 |
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| 2.4912 | 39.47 | 750 | 1.4668 | 0.8590 |
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| 0.2685 | 52.63 | 1000 | 1.2848 | 0.2469 |
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| 0.0883 | 65.79 | 1250 | 1.4088 | 0.2302 |
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| 0.0559 | 78.95 | 1500 | 1.5663 | 0.2196 |
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| 0.0386 | 92.11 | 1750 | 1.7690 | 0.2063 |
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| 0.0289 | 105.26 | 2000 | 1.7150 | 0.2114 |
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| 0.0244 | 118.42 | 2250 | 1.8159 | 0.2106 |
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| 0.018 | 131.58 | 2500 | 1.8855 | 0.2038 |
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| 0.0147 | 144.74 | 2750 | 1.8758 | 0.2042 |
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| 0.0121 | 157.89 | 3000 | 2.1326 | 0.2012 |
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| 0.0105 | 171.05 | 3250 | 1.9975 | 0.1897 |
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| 0.0093 | 184.21 | 3500 | 1.9094 | 0.1905 |
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| 0.0079 | 197.37 | 3750 | 2.0668 | 0.1909 |
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| 0.0082 | 210.53 | 4000 | 2.0085 | 0.1892 |
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### Framework versions
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- Transformers 4.35.0
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- Pytorch 2.0.0
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- Datasets 2.14.6
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- Tokenizers 0.14.1
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model.safetensors
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