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AK-12
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my_awesome_model

Audio Classification
Transformers
PyTorch
TensorBoard
hubert
Generated from Trainer
Eval Results
Inference Endpoints
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AK-12 commited on
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End of training

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Files changed (2) hide show
  1. README.md +21 -23
  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: 0.86
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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: 0.7337
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- - Accuracy: 0.86
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  ## Model description
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@@ -53,11 +53,9 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 6e-05
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- - train_batch_size: 6
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- - eval_batch_size: 6
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  - seed: 42
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- - gradient_accumulation_steps: 2
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- - total_train_batch_size: 12
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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
@@ -67,26 +65,26 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 2.1397 | 1.0 | 75 | 2.0011 | 0.45 |
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- | 1.4889 | 2.0 | 150 | 1.3599 | 0.66 |
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- | 1.0109 | 3.0 | 225 | 1.0052 | 0.74 |
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- | 0.7499 | 4.0 | 300 | 0.8884 | 0.77 |
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- | 0.5627 | 5.0 | 375 | 0.6333 | 0.86 |
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- | 0.4138 | 6.0 | 450 | 0.5492 | 0.81 |
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- | 0.2909 | 7.0 | 525 | 0.6417 | 0.81 |
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- | 0.1475 | 8.0 | 600 | 0.5900 | 0.84 |
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- | 0.0845 | 9.0 | 675 | 0.6959 | 0.84 |
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- | 0.0619 | 10.0 | 750 | 0.6587 | 0.86 |
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- | 0.0233 | 11.0 | 825 | 0.7675 | 0.82 |
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- | 0.0168 | 12.0 | 900 | 0.7352 | 0.83 |
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- | 0.0152 | 13.0 | 975 | 0.7293 | 0.87 |
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- | 0.0136 | 14.0 | 1050 | 0.7490 | 0.86 |
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- | 0.0123 | 15.0 | 1125 | 0.7337 | 0.86 |
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  ### Framework versions
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- - Transformers 4.32.0
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  - Pytorch 2.0.1+cu118
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  - Datasets 2.14.4
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  - Tokenizers 0.13.3
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.85
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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: 0.8372
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+ - Accuracy: 0.85
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 6e-05
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+ - train_batch_size: 7
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+ - eval_batch_size: 7
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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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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 1.9696 | 1.0 | 129 | 1.8571 | 0.55 |
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+ | 1.4269 | 2.0 | 258 | 1.2394 | 0.61 |
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+ | 1.0166 | 3.0 | 387 | 1.0173 | 0.74 |
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+ | 0.7446 | 4.0 | 516 | 0.8103 | 0.75 |
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+ | 0.4953 | 5.0 | 645 | 0.7800 | 0.77 |
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+ | 0.3973 | 6.0 | 774 | 0.7359 | 0.81 |
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+ | 0.2831 | 7.0 | 903 | 0.6434 | 0.84 |
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+ | 0.2147 | 8.0 | 1032 | 0.6592 | 0.84 |
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+ | 0.1287 | 9.0 | 1161 | 0.6988 | 0.85 |
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+ | 0.014 | 10.0 | 1290 | 0.7569 | 0.83 |
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+ | 0.0073 | 11.0 | 1419 | 0.8282 | 0.84 |
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+ | 0.0049 | 12.0 | 1548 | 0.8531 | 0.84 |
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+ | 0.0053 | 13.0 | 1677 | 0.8584 | 0.84 |
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+ | 0.0044 | 14.0 | 1806 | 0.8707 | 0.84 |
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+ | 0.0038 | 15.0 | 1935 | 0.8372 | 0.85 |
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  ### Framework versions
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+ - Transformers 4.32.1
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  - Pytorch 2.0.1+cu118
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  - Datasets 2.14.4
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  - Tokenizers 0.13.3
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