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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
base_model: ntu-spml/distilhubert
model-index:
  - name: distilhubert-finetuned-gtzan
    results:
      - task:
          type: audio-classification
          name: Audio Classification
        dataset:
          name: GTZAN
          type: marsyas/gtzan
          config: all
          split: train
          args: all
        metrics:
          - type: accuracy
            value: 0.87
            name: Accuracy

distilhubert-finetuned-gtzan

This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5647
  • Accuracy: 0.87

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.2278 1.0 57 2.1709 0.44
1.7173 2.0 114 1.6084 0.57
1.1979 3.0 171 1.1897 0.67
1.1177 4.0 228 1.0003 0.72
0.8526 5.0 285 0.8854 0.73
0.6463 6.0 342 0.7791 0.79
0.5461 7.0 399 0.7468 0.78
0.3953 8.0 456 0.7352 0.75
0.3054 9.0 513 0.6757 0.79
0.18 10.0 570 0.5711 0.76
0.1526 11.0 627 0.6026 0.85
0.0812 12.0 684 0.5876 0.82
0.0578 13.0 741 0.5815 0.85
0.0318 14.0 798 0.5828 0.85
0.0283 15.0 855 0.5960 0.85
0.0393 16.0 912 0.5674 0.85
0.018 17.0 969 0.5647 0.87

Framework versions

  • Transformers 4.31.0.dev0
  • Pytorch 1.13.0
  • Datasets 2.1.0
  • Tokenizers 0.13.3