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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: 0.9405
  • Accuracy: 0.83

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: 8
  • eval_batch_size: 8
  • 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
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.1782 1.0 113 2.0403 0.46
1.487 2.0 226 1.3902 0.64
1.1634 3.0 339 1.0882 0.71
0.9874 4.0 452 0.9260 0.68
0.7754 5.0 565 0.7265 0.8
0.4512 6.0 678 0.6141 0.84
0.4947 7.0 791 0.8277 0.78
0.1896 8.0 904 0.7220 0.81
0.2142 9.0 1017 0.6393 0.85
0.0413 10.0 1130 0.8113 0.82
0.0105 11.0 1243 0.7368 0.82
0.1392 12.0 1356 0.8139 0.85
0.0051 13.0 1469 0.7893 0.86
0.0041 14.0 1582 0.8515 0.83
0.0041 15.0 1695 0.7707 0.85
0.0033 16.0 1808 0.8931 0.84
0.0772 17.0 1921 0.8411 0.86
0.0028 18.0 2034 0.8884 0.83
0.0025 19.0 2147 0.9094 0.84
0.0027 20.0 2260 0.9405 0.83

Framework versions

  • Transformers 4.36.0.dev0
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0
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Finetuned from

Dataset used to train ghassenhannachi/distilhubert-finetuned-gtzan

Evaluation results