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vapari
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distilhubert-finetuned-gtzan

Audio Classification
Transformers
Safetensors
hubert
Generated from Trainer
Eval Results
Inference Endpoints
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End of training

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  1. README.md +16 -15
  2. model.safetensors +1 -1
README.md CHANGED
@@ -23,7 +23,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.7692307692307693
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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
@@ -33,8 +33,8 @@ 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: 1.2256
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- - Accuracy: 0.7692
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  ## Model description
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@@ -53,30 +53,31 @@ 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: 9e-05
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  - train_batch_size: 10
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  - eval_batch_size: 10
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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: 18
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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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- | 1.9893 | 1.0 | 70 | 1.9671 | 0.4615 |
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- | 1.1923 | 2.0 | 140 | 1.3634 | 0.5256 |
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- | 1.1937 | 3.0 | 210 | 1.0865 | 0.6154 |
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- | 0.5684 | 4.0 | 280 | 0.9352 | 0.6795 |
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- | 0.4571 | 5.0 | 350 | 0.7889 | 0.7564 |
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- | 0.1854 | 6.0 | 420 | 0.8209 | 0.7308 |
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- | 0.0688 | 7.0 | 490 | 0.9835 | 0.7692 |
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- | 0.087 | 8.0 | 560 | 1.1710 | 0.7179 |
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- | 0.0109 | 9.0 | 630 | 1.0900 | 0.7692 |
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- | 0.0049 | 10.0 | 700 | 1.2256 | 0.7692 |
 
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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: 0.7435897435897436
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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: 0.9861
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+ - Accuracy: 0.7436
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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: 10
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  - eval_batch_size: 10
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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: 20
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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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+ | 2.1171 | 1.0 | 70 | 2.1232 | 0.2308 |
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+ | 1.534 | 2.0 | 140 | 1.6014 | 0.5128 |
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+ | 1.4328 | 3.0 | 210 | 1.2896 | 0.5641 |
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+ | 0.8631 | 4.0 | 280 | 1.1275 | 0.5897 |
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+ | 0.6448 | 5.0 | 350 | 1.0679 | 0.6667 |
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+ | 0.482 | 6.0 | 420 | 0.8798 | 0.7051 |
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+ | 0.2458 | 7.0 | 490 | 0.8290 | 0.7564 |
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+ | 0.2264 | 8.0 | 560 | 0.8350 | 0.7564 |
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+ | 0.1661 | 9.0 | 630 | 0.8284 | 0.7179 |
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+ | 0.0286 | 10.0 | 700 | 0.9681 | 0.7179 |
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+ | 0.0155 | 11.0 | 770 | 0.9861 | 0.7436 |
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  ### Framework versions
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