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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: 0.6333
  • 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: 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

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.2417 1.0 57 2.1896 0.42
1.8003 2.0 114 1.6369 0.52
1.3938 3.0 171 1.2560 0.72
1.2724 4.0 228 1.1942 0.68
0.9682 5.0 285 0.8864 0.8
0.7111 6.0 342 0.7542 0.82
0.6339 7.0 399 0.7712 0.81
0.4599 8.0 456 0.6080 0.84
0.3261 9.0 513 0.5998 0.84
0.2991 10.0 570 0.6767 0.79
0.1615 11.0 627 0.5817 0.87
0.0854 12.0 684 0.5859 0.83
0.0752 13.0 741 0.5681 0.85
0.0341 14.0 798 0.5916 0.88
0.0331 15.0 855 0.6028 0.87
0.02 16.0 912 0.6283 0.85
0.0175 17.0 969 0.6103 0.88
0.0151 18.0 1026 0.6244 0.88
0.014 19.0 1083 0.6293 0.86
0.0181 20.0 1140 0.6333 0.87

Framework versions

  • Transformers 4.31.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.13.1
  • Tokenizers 0.13.3
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Dataset used to train Apocalypse-19/distilhubert-finetuned-gtzan

Evaluation results