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update model card README.md

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@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the OPENSLR_SLR66 - NA dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6547
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- - Wer: 0.7824
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 2e-05
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- - train_batch_size: 4
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  - eval_batch_size: 4
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  - seed: 42
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- - gradient_accumulation_steps: 8
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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: 300
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- - num_epochs: 5.0
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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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- | 1.3883 | 4.81 | 500 | 0.6559 | 0.7844 |
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the OPENSLR_SLR66 - NA dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5929
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+ - Wer: 0.7180
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  ## Model description
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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: 4
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  - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 64
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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: 300
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+ - num_epochs: 100.0
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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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+ | 1.5794 | 9.61 | 500 | 0.9860 | 0.9582 |
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+ | 1.0985 | 19.23 | 1000 | 0.5948 | 0.7534 |
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+ | 0.9551 | 28.84 | 1500 | 0.5892 | 0.7392 |
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+ | 0.8699 | 38.46 | 2000 | 0.5927 | 0.72 |
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+ | 0.8372 | 48.08 | 2500 | 0.5939 | 0.7385 |
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+ | 0.7794 | 57.69 | 3000 | 0.5801 | 0.7388 |
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+ | 0.7565 | 67.31 | 3500 | 0.5827 | 0.7184 |
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+ | 0.7303 | 76.92 | 4000 | 0.5800 | 0.7297 |
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+ | 0.7128 | 86.54 | 4500 | 0.5977 | 0.7150 |
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+ | 0.6972 | 96.15 | 5000 | 0.5901 | 0.7177 |
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  ### Framework versions