kingabzpro
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update model card README.md
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README.md
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@@ -15,9 +15,9 @@ 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-urdu-urm-60](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-urdu-urm-60) on the common_voice dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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- Wer: 0.
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- Cer: 0.
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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: 0.
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- train_batch_size: 16
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- eval_batch_size: 8
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- seed: 42
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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:
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- num_epochs:
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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 | Cer |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
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| 1.0751 | 29.17 | 350 | 8.8609 | 0.5948 | 0.3176 |
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### Framework versions
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This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-urdu-urm-60](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-urdu-urm-60) on the common_voice dataset.
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It achieves the following results on the evaluation set:
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- Loss: 6.4496
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- Wer: 0.5913
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- Cer: 0.3310
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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: 0.0003
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- train_batch_size: 16
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- eval_batch_size: 8
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- seed: 42
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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: 200
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- num_epochs: 50
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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 | Cer |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
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| 12.6045 | 8.33 | 100 | 8.4997 | 0.6978 | 0.3923 |
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| 1.3367 | 16.67 | 200 | 5.0015 | 0.6515 | 0.3556 |
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| 0.5344 | 25.0 | 300 | 9.3687 | 0.6393 | 0.3625 |
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| 0.2922 | 33.33 | 400 | 9.2381 | 0.6236 | 0.3432 |
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| 0.1867 | 41.67 | 500 | 6.2150 | 0.6035 | 0.3448 |
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| 0.1166 | 50.0 | 600 | 6.4496 | 0.5913 | 0.3310 |
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### Framework versions
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