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: 0.7392
  • Accuracy: 0.81

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: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • 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
1.3055 0.97 7 1.2863 0.73
1.2903 1.93 14 1.2504 0.7
1.2118 2.9 21 1.1450 0.77
1.1443 4.0 29 1.1224 0.74
1.006 4.97 36 1.0376 0.79
1.0174 5.93 43 0.9681 0.8
0.9155 6.9 50 0.9322 0.81
0.8781 8.0 58 0.9266 0.78
0.819 8.97 65 0.8473 0.79
0.7984 9.93 72 0.8225 0.77
0.7254 10.9 79 0.8096 0.81
0.6752 12.0 87 0.7801 0.81
0.6132 12.97 94 0.7687 0.8
0.615 13.93 101 0.7603 0.79
0.6162 14.9 108 0.7599 0.82
0.5678 16.0 116 0.7414 0.81
0.548 16.97 123 0.7423 0.81
0.5495 17.93 130 0.7378 0.81
0.5185 18.9 137 0.7396 0.81
0.5544 19.31 140 0.7392 0.81

Framework versions

  • Transformers 4.32.0.dev0
  • Pytorch 2.0.1
  • Datasets 2.13.1
  • Tokenizers 0.13.3
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Dataset used to train Kurokabe/distilhubert-finetuned-gtzan

Evaluation results