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distilhubert-finetuned-gtzan

Audio Classification
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hubert
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  1. README.md +21 -17
  2. model.safetensors +1 -1
  3. runs/Nov13_17-44-43_a60a690ed401/events.out.tfevents.1699897488.a60a690ed401.365.1 +2 -2
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.83
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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
@@ -32,8 +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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- - Loss: 0.5474
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- - Accuracy: 0.83
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  ## Model description
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@@ -52,30 +52,34 @@ More information needed
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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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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 10
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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 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 2.0195 | 1.0 | 113 | 1.8110 | 0.49 |
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- | 1.3352 | 2.0 | 226 | 1.2507 | 0.65 |
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- | 1.0411 | 3.0 | 339 | 0.9619 | 0.77 |
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- | 0.8179 | 4.0 | 452 | 0.8647 | 0.71 |
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- | 0.699 | 5.0 | 565 | 0.7015 | 0.79 |
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- | 0.4359 | 6.0 | 678 | 0.6897 | 0.78 |
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- | 0.4689 | 7.0 | 791 | 0.5793 | 0.84 |
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- | 0.2185 | 8.0 | 904 | 0.5124 | 0.85 |
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- | 0.2557 | 9.0 | 1017 | 0.5593 | 0.83 |
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- | 0.1715 | 10.0 | 1130 | 0.5474 | 0.83 |
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.84
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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: 0.7931
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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: 4e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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  - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 16
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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: 12
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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 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.2107 | 1.0 | 56 | 0.4744 | 0.89 |
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+ | 0.0867 | 1.99 | 112 | 0.7316 | 0.8 |
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+ | 0.1117 | 2.99 | 168 | 0.6942 | 0.81 |
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+ | 0.1024 | 4.0 | 225 | 0.6151 | 0.85 |
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+ | 0.0141 | 5.0 | 281 | 0.7542 | 0.83 |
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+ | 0.0089 | 5.99 | 337 | 0.7236 | 0.85 |
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+ | 0.007 | 6.99 | 393 | 0.7115 | 0.84 |
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+ | 0.0477 | 8.0 | 450 | 0.7334 | 0.85 |
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+ | 0.0048 | 9.0 | 506 | 0.7772 | 0.85 |
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+ | 0.0348 | 9.99 | 562 | 0.7465 | 0.85 |
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+ | 0.0035 | 10.99 | 618 | 0.8011 | 0.84 |
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+ | 0.004 | 11.95 | 672 | 0.7931 | 0.84 |
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  ### Framework versions
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