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update model card README.md

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@@ -32,7 +32,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5481
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  - Accuracy: 0.84
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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: 5e-05
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- - train_batch_size: 8
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- - eval_batch_size: 8
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  - seed: 42
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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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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 1.9865 | 1.0 | 113 | 1.7879 | 0.53 |
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- | 1.2316 | 2.0 | 226 | 1.1546 | 0.59 |
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- | 0.9779 | 3.0 | 339 | 0.8839 | 0.75 |
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- | 0.7637 | 4.0 | 452 | 0.7679 | 0.75 |
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- | 0.6333 | 5.0 | 565 | 0.7052 | 0.78 |
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- | 0.3449 | 6.0 | 678 | 0.5723 | 0.85 |
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- | 0.3993 | 7.0 | 791 | 0.5205 | 0.85 |
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- | 0.1445 | 8.0 | 904 | 0.5066 | 0.84 |
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- | 0.1988 | 9.0 | 1017 | 0.5332 | 0.86 |
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- | 0.1541 | 10.0 | 1130 | 0.5481 | 0.84 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5973
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  - Accuracy: 0.84
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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: 0.0001
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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  - seed: 42
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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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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 1.9359 | 1.0 | 57 | 1.7782 | 0.49 |
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+ | 1.2053 | 2.0 | 114 | 1.1174 | 0.69 |
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+ | 0.8559 | 3.0 | 171 | 0.8714 | 0.79 |
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+ | 0.6889 | 4.0 | 228 | 0.7290 | 0.79 |
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+ | 0.4995 | 5.0 | 285 | 0.5888 | 0.85 |
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+ | 0.2781 | 6.0 | 342 | 0.6412 | 0.83 |
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+ | 0.2084 | 7.0 | 399 | 0.5679 | 0.86 |
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+ | 0.1132 | 8.0 | 456 | 0.5744 | 0.85 |
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+ | 0.088 | 9.0 | 513 | 0.5985 | 0.84 |
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+ | 0.046 | 10.0 | 570 | 0.5973 | 0.84 |
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  ### Framework versions