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update model card README.md

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@@ -16,8 +16,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the common_voice_1_0 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.4176
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- - Wer: 0.5532
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  ## Model description
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@@ -44,7 +44,7 @@ The following hyperparameters were used during training:
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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: 500
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  - num_epochs: 20
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  - mixed_precision_training: Native AMP
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@@ -52,11 +52,17 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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- | 3.7147 | 3.64 | 400 | 2.0524 | 0.9310 |
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- | 0.5925 | 7.27 | 800 | 1.3358 | 0.6120 |
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- | 0.225 | 10.91 | 1200 | 1.3472 | 0.5807 |
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- | 0.1444 | 14.55 | 1600 | 1.4857 | 0.5672 |
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- | 0.1072 | 18.18 | 2000 | 1.4176 | 0.5532 |
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the common_voice_1_0 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.4036
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+ - Wer: 0.5090
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  ## Model description
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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: 20
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  - mixed_precision_training: Native AMP
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 4.4355 | 1.82 | 200 | 3.0307 | 1.0 |
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+ | 2.1744 | 3.64 | 400 | 1.5661 | 0.7449 |
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+ | 0.5535 | 5.45 | 600 | 1.3005 | 0.5914 |
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+ | 0.3248 | 7.27 | 800 | 1.2481 | 0.5690 |
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+ | 0.2297 | 9.09 | 1000 | 1.2810 | 0.5366 |
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+ | 0.18 | 10.91 | 1200 | 1.3481 | 0.5378 |
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+ | 0.1499 | 12.73 | 1400 | 1.3124 | 0.5350 |
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+ | 0.1283 | 14.55 | 1600 | 1.3668 | 0.5161 |
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+ | 0.1089 | 16.36 | 1800 | 1.3833 | 0.5109 |
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+ | 0.0973 | 18.18 | 2000 | 1.3876 | 0.5075 |
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+ | 0.0897 | 20.0 | 2200 | 1.4036 | 0.5090 |
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  ### Framework versions