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update model card README.md
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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- common_voice_13_0
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metrics:
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- wer
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model-index:
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- name: b30-wav2vec2-large-xls-r-romansh-colab
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: common_voice_13_0
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type: common_voice_13_0
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config: rm-vallader
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split: test
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args: rm-vallader
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metrics:
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- name: Wer
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type: wer
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value: 0.20470423847228691
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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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# b30-wav2vec2-large-xls-r-romansh-colab
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_13_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2906
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- Wer: 0.2047
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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: 0.0001
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- train_batch_size: 4
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 8
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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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- num_epochs: 30
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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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| 6.1887 | 3.05 | 400 | 2.9441 | 1.0 |
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| 2.3896 | 6.11 | 800 | 0.5913 | 0.5021 |
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| 0.368 | 9.16 | 1200 | 0.3131 | 0.2834 |
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| 0.1647 | 12.21 | 1600 | 0.2876 | 0.2531 |
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| 0.1111 | 15.27 | 2000 | 0.2965 | 0.2494 |
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| 0.0831 | 18.32 | 2400 | 0.2891 | 0.2264 |
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| 0.0688 | 21.37 | 2800 | 0.2970 | 0.2259 |
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| 0.0551 | 24.43 | 3200 | 0.2867 | 0.2075 |
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| 0.0447 | 27.48 | 3600 | 0.2906 | 0.2047 |
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### Framework versions
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- Transformers 4.26.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.4
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- Tokenizers 0.13.3
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