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akuzdeuov
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distilhubert-finetuned-gtzan

Audio Classification
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hubert
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  1. README.md +16 -16
  2. model.safetensors +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: 0.86
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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: 0.5679
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- - Accuracy: 0.86
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  ## Model description
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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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- | 1.9516 | 1.0 | 113 | 1.8533 | 0.48 |
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- | 1.234 | 2.0 | 226 | 1.2087 | 0.74 |
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- | 1.0745 | 3.0 | 339 | 1.1431 | 0.65 |
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- | 0.6867 | 4.0 | 452 | 0.8632 | 0.73 |
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- | 0.5575 | 5.0 | 565 | 0.6851 | 0.84 |
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- | 0.3161 | 6.0 | 678 | 0.6355 | 0.83 |
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- | 0.281 | 7.0 | 791 | 0.5866 | 0.84 |
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- | 0.1371 | 8.0 | 904 | 0.5763 | 0.86 |
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- | 0.1433 | 9.0 | 1017 | 0.5654 | 0.86 |
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- | 0.1019 | 10.0 | 1130 | 0.5679 | 0.86 |
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  ### Framework versions
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  - Transformers 4.38.2
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- - Pytorch 2.2.1+cu121
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  - Datasets 2.18.0
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  - Tokenizers 0.15.2
 
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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
 
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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: 0.6028
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+ - Accuracy: 0.83
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  ## Model description
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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: 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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+ | 2.1599 | 1.0 | 57 | 2.0321 | 0.64 |
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+ | 1.5057 | 2.0 | 114 | 1.4337 | 0.58 |
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+ | 1.2107 | 3.0 | 171 | 1.1677 | 0.69 |
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+ | 0.9286 | 4.0 | 228 | 1.0566 | 0.71 |
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+ | 0.8159 | 5.0 | 285 | 0.7997 | 0.81 |
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+ | 0.7071 | 6.0 | 342 | 0.7576 | 0.8 |
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+ | 0.6363 | 7.0 | 399 | 0.6601 | 0.85 |
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+ | 0.4237 | 8.0 | 456 | 0.6692 | 0.78 |
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+ | 0.4457 | 9.0 | 513 | 0.6314 | 0.81 |
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+ | 0.4094 | 10.0 | 570 | 0.6028 | 0.83 |
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  ### Framework versions
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  - Transformers 4.38.2
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+ - Pytorch 2.2.0a0+81ea7a4
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  - Datasets 2.18.0
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  - Tokenizers 0.15.2
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