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distilhubert-finetuned-gtzan

Audio Classification
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hubert
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End of training

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Files changed (2) hide show
  1. README.md +16 -16
  2. pytorch_model.bin +1 -1
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: 0.79
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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: 0.6862
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- - Accuracy: 0.79
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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: 20
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- - eval_batch_size: 20
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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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- | 2.1654 | 1.0 | 45 | 2.0737 | 0.46 |
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- | 1.6926 | 2.0 | 90 | 1.5669 | 0.55 |
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- | 1.2793 | 3.0 | 135 | 1.1986 | 0.77 |
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- | 1.0386 | 4.0 | 180 | 1.0169 | 0.78 |
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- | 0.9172 | 5.0 | 225 | 0.9652 | 0.71 |
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- | 0.7859 | 6.0 | 270 | 0.7929 | 0.79 |
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- | 0.7468 | 7.0 | 315 | 0.7860 | 0.75 |
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- | 0.6594 | 8.0 | 360 | 0.7388 | 0.79 |
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- | 0.471 | 9.0 | 405 | 0.6955 | 0.79 |
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- | 0.5886 | 10.0 | 450 | 0.6862 | 0.79 |
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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: 0.82
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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: 0.9462
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+ - Accuracy: 0.82
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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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+ | 0.5455 | 1.0 | 57 | 0.7144 | 0.76 |
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+ | 0.5342 | 2.0 | 114 | 0.8039 | 0.75 |
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+ | 0.1636 | 3.0 | 171 | 0.6388 | 0.83 |
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+ | 0.1868 | 4.0 | 228 | 0.6027 | 0.81 |
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+ | 0.0643 | 5.0 | 285 | 0.6728 | 0.83 |
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+ | 0.0418 | 6.0 | 342 | 0.6726 | 0.82 |
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+ | 0.0925 | 7.0 | 399 | 0.9795 | 0.81 |
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+ | 0.0047 | 8.0 | 456 | 1.0072 | 0.82 |
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+ | 0.0296 | 9.0 | 513 | 0.9450 | 0.82 |
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+ | 0.0031 | 10.0 | 570 | 0.9462 | 0.82 |
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  ### Framework versions
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