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update model card README.md

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@@ -5,9 +5,24 @@ tags:
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  - generated_from_trainer
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  datasets:
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  - marsyas/gtzan
 
 
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  model-index:
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  - name: distilhubert-finetuned-gtzan
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- results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -17,13 +32,8 @@ 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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- - eval_loss: 0.9333
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- - eval_accuracy: 0.87
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- - eval_runtime: 21.6309
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- - eval_samples_per_second: 4.623
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- - eval_steps_per_second: 0.185
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- - epoch: 1.0
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- - step: 29
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  ## Model description
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@@ -49,7 +59,43 @@ The following hyperparameters were used during training:
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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_ratio: 0.1
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- - num_epochs: 50
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  - generated_from_trainer
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  datasets:
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  - marsyas/gtzan
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+ metrics:
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+ - accuracy
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  model-index:
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  - name: distilhubert-finetuned-gtzan
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+ results:
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+ - task:
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+ name: Audio Classification
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+ type: audio-classification
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+ dataset:
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+ name: GTZAN
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+ type: marsyas/gtzan
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+ config: all
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+ split: train
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+ args: all
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.81
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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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: 1.1842
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+ - Accuracy: 0.81
 
 
 
 
 
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  ## Model description
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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_ratio: 0.1
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+ - num_epochs: 30
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 2.1069 | 1.0 | 29 | 2.0003 | 0.46 |
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+ | 1.8026 | 2.0 | 58 | 1.6073 | 0.59 |
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+ | 1.3938 | 3.0 | 87 | 1.2140 | 0.72 |
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+ | 1.0295 | 4.0 | 116 | 1.0740 | 0.64 |
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+ | 0.8339 | 5.0 | 145 | 0.9243 | 0.71 |
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+ | 0.6347 | 6.0 | 174 | 0.8837 | 0.72 |
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+ | 0.4137 | 7.0 | 203 | 0.8274 | 0.78 |
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+ | 0.3162 | 8.0 | 232 | 0.7596 | 0.82 |
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+ | 0.2055 | 9.0 | 261 | 0.8541 | 0.77 |
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+ | 0.2237 | 10.0 | 290 | 0.7220 | 0.78 |
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+ | 0.0601 | 11.0 | 319 | 0.7765 | 0.81 |
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+ | 0.0817 | 12.0 | 348 | 0.7603 | 0.86 |
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+ | 0.0196 | 13.0 | 377 | 0.8611 | 0.8 |
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+ | 0.0641 | 14.0 | 406 | 0.9281 | 0.8 |
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+ | 0.0253 | 15.0 | 435 | 1.2051 | 0.77 |
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+ | 0.0079 | 16.0 | 464 | 1.1073 | 0.81 |
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+ | 0.0055 | 17.0 | 493 | 1.0920 | 0.81 |
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+ | 0.012 | 18.0 | 522 | 1.1882 | 0.82 |
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+ | 0.0051 | 19.0 | 551 | 1.0023 | 0.81 |
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+ | 0.0047 | 20.0 | 580 | 1.2339 | 0.79 |
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+ | 0.0036 | 21.0 | 609 | 1.1471 | 0.79 |
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+ | 0.0033 | 22.0 | 638 | 1.1924 | 0.8 |
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+ | 0.0032 | 23.0 | 667 | 1.1064 | 0.81 |
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+ | 0.0028 | 24.0 | 696 | 1.1140 | 0.8 |
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+ | 0.0026 | 25.0 | 725 | 1.1344 | 0.81 |
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+ | 0.0163 | 26.0 | 754 | 1.1551 | 0.8 |
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+ | 0.0027 | 27.0 | 783 | 1.1843 | 0.81 |
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+ | 0.0025 | 28.0 | 812 | 1.1824 | 0.81 |
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+ | 0.0104 | 29.0 | 841 | 1.1636 | 0.8 |
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+ | 0.0047 | 30.0 | 870 | 1.1842 | 0.81 |
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+
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  ### Framework versions
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