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AdonaiHS
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distilhubert-finetuned-gtzan

Audio Classification
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PyTorch
TensorBoard
hubert
Generated from Trainer
Eval Results
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End of training

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  1. README.md +21 -43
  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: 0.83
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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: 0.9791
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- - Accuracy: 0.83
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  ## Model description
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@@ -59,49 +59,27 @@ 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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- - training_steps: 4000
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  ### Training results
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- | Training Loss | Epoch | Step | Accuracy | Validation Loss |
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- |:-------------:|:-----:|:----:|:--------:|:---------------:|
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- | 2.0825 | 0.88 | 100 | 0.47 | 1.8392 |
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- | 1.4043 | 1.77 | 200 | 0.67 | 1.2675 |
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- | 1.0686 | 2.65 | 300 | 0.71 | 1.0186 |
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- | 0.8037 | 3.54 | 400 | 0.74 | 0.9198 |
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- | 0.6215 | 4.42 | 500 | 0.78 | 0.7636 |
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- | 0.5106 | 5.31 | 600 | 0.76 | 0.7937 |
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- | 0.3844 | 6.19 | 700 | 0.78 | 0.6909 |
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- | 0.3043 | 7.08 | 800 | 0.77 | 0.7279 |
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- | 0.2453 | 7.96 | 900 | 0.82 | 0.6447 |
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- | 0.211 | 8.85 | 1000 | 0.84 | 0.6404 |
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- | 0.2268 | 9.73 | 1100 | 0.77 | 0.7198 |
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- | 0.1565 | 10.62 | 1200 | 0.83 | 0.6704 |
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- | 0.0694 | 11.5 | 1300 | 0.83 | 0.8017 |
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- | 0.0568 | 12.39 | 1400 | 0.8 | 0.7841 |
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- | 0.0441 | 13.27 | 1500 | 0.81 | 0.7757 |
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- | 0.0302 | 14.16 | 1600 | 0.84 | 0.7819 |
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- | 0.0116 | 15.04 | 1700 | 0.83 | 0.7949 |
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- | 0.0289 | 15.93 | 1800 | 0.85 | 0.8057 |
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- | 0.0115 | 16.81 | 1900 | 0.83 | 0.8271 |
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- | 0.0081 | 17.7 | 2000 | 0.86 | 0.8005 |
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- | 0.0124 | 18.58 | 2100 | 0.8 | 0.8927 |
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- | 0.0219 | 19.47 | 2200 | 0.85 | 0.8126 |
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- | 0.0161 | 20.35 | 2300 | 0.85 | 0.8464 |
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- | 0.0157 | 21.24 | 2400 | 0.86 | 0.8459 |
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- | 0.0039 | 22.12 | 2500 | 0.8 | 1.0282 |
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- | 0.0157 | 23.01 | 2600 | 0.86 | 0.8649 |
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- | 0.0119 | 23.89 | 2700 | 0.85 | 0.8894 |
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- | 0.0129 | 24.78 | 2800 | 0.87 | 0.8624 |
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- | 0.0124 | 25.66 | 2900 | 0.85 | 0.8862 |
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- | 0.0025 | 26.55 | 3000 | 0.84 | 0.9097 |
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- | 0.0197 | 27.43 | 3100 | 0.9150 | 0.85 |
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- | 0.0193 | 28.32 | 3200 | 0.9986 | 0.83 |
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- | 0.0119 | 29.2 | 3300 | 0.9001 | 0.87 |
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- | 0.0017 | 30.09 | 3400 | 0.9599 | 0.83 |
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- | 0.015 | 30.97 | 3500 | 0.9442 | 0.84 |
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- | 0.0015 | 31.86 | 3600 | 0.9813 | 0.83 |
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- | 0.0056 | 32.74 | 3700 | 0.9791 | 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: 0.85
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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: 0.9971
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+ - Accuracy: 0.85
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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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+ - training_steps: 3000
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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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+ | 2.2793 | 0.88 | 100 | 2.1792 | 0.41 |
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+ | 1.992 | 1.77 | 200 | 1.6741 | 0.56 |
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+ | 1.4928 | 2.65 | 300 | 1.2795 | 0.56 |
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+ | 1.1156 | 3.54 | 400 | 0.9983 | 0.69 |
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+ | 0.9162 | 4.42 | 500 | 0.8222 | 0.73 |
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+ | 0.6785 | 5.31 | 600 | 0.8422 | 0.78 |
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+ | 0.4695 | 6.19 | 700 | 0.7034 | 0.8 |
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+ | 0.3362 | 7.08 | 800 | 0.9594 | 0.72 |
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+ | 0.2051 | 7.96 | 900 | 0.6157 | 0.84 |
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+ | 0.1242 | 8.85 | 1000 | 0.6059 | 0.86 |
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+ | 0.0678 | 9.73 | 1100 | 0.7626 | 0.86 |
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+ | 0.0479 | 10.62 | 1200 | 0.7886 | 0.84 |
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+ | 0.0216 | 11.5 | 1300 | 0.8302 | 0.85 |
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+ | 0.0202 | 12.39 | 1400 | 0.8921 | 0.86 |
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+ | 0.0155 | 13.27 | 1500 | 0.9971 | 0.85 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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