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update model card README.md

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@@ -16,7 +16,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-tamil-tam-250](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-tamil-tam-250) on the common_voice dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 3.3440
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  - Wer: 1.0
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  ## Model description
@@ -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: 30
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  - mixed_precision_training: Native AMP
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@@ -52,13 +52,23 @@ 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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- | 4.568 | 4.21 | 400 | 3.3715 | 1.0 |
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- | 3.2873 | 8.42 | 800 | 3.3552 | 1.0 |
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- | 3.2812 | 12.63 | 1200 | 3.3399 | 1.0 |
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- | 3.2754 | 16.84 | 1600 | 3.3670 | 1.0 |
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- | 3.2751 | 21.05 | 2000 | 3.3369 | 1.0 |
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- | 3.2747 | 25.26 | 2400 | 3.3417 | 1.0 |
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- | 3.2738 | 29.47 | 2800 | 3.3440 | 1.0 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-tamil-tam-250](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-tamil-tam-250) on the common_voice dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 3.3415
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  - Wer: 1.0
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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: 400
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  - num_epochs: 30
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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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+ | 5.384 | 1.69 | 200 | 3.3400 | 1.0 |
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+ | 3.3085 | 3.39 | 400 | 3.3609 | 1.0 |
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+ | 3.3008 | 5.08 | 600 | 3.3331 | 1.0 |
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+ | 3.2852 | 6.78 | 800 | 3.3492 | 1.0 |
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+ | 3.2908 | 8.47 | 1000 | 3.3318 | 1.0 |
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+ | 3.2865 | 10.17 | 1200 | 3.3501 | 1.0 |
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+ | 3.2826 | 11.86 | 1400 | 3.3403 | 1.0 |
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+ | 3.2875 | 13.56 | 1600 | 3.3335 | 1.0 |
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+ | 3.2899 | 15.25 | 1800 | 3.3311 | 1.0 |
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+ | 3.2755 | 16.95 | 2000 | 3.3617 | 1.0 |
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+ | 3.2877 | 18.64 | 2200 | 3.3317 | 1.0 |
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+ | 3.2854 | 20.34 | 2400 | 3.3560 | 1.0 |
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+ | 3.2878 | 22.03 | 2600 | 3.3332 | 1.0 |
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+ | 3.2766 | 23.73 | 2800 | 3.3317 | 1.0 |
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+ | 3.2943 | 25.42 | 3000 | 3.3737 | 1.0 |
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+ | 3.2845 | 27.12 | 3200 | 3.3347 | 1.0 |
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+ | 3.2765 | 28.81 | 3400 | 3.3415 | 1.0 |
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  ### Framework versions