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distilhubert-finetuned-gtzan-v2

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: 0.5575
  • 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: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.2975 1.0 14 2.2790 0.26
2.255 1.99 28 2.1863 0.39
2.0948 2.99 42 1.9637 0.43
1.847 3.98 56 1.7093 0.54
1.5798 4.98 70 1.5095 0.62
1.4674 5.97 84 1.3173 0.67
1.2969 6.97 98 1.1894 0.72
1.1472 7.96 112 1.0415 0.77
0.9815 8.96 126 1.0004 0.74
0.8838 9.96 140 0.8808 0.78
0.8294 10.95 154 0.8551 0.78
0.768 11.95 168 0.7939 0.79
0.6499 12.94 182 0.7467 0.81
0.6014 13.94 196 0.6995 0.82
0.5296 14.93 210 0.7152 0.79
0.4478 16.0 225 0.6561 0.83
0.4082 17.0 239 0.6399 0.84
0.374 17.99 253 0.6217 0.86
0.3282 18.99 267 0.5991 0.85
0.28 19.98 281 0.6043 0.84
0.2754 20.98 295 0.5831 0.87
0.2409 21.97 309 0.5680 0.85
0.2172 22.97 323 0.5729 0.85
0.1855 23.96 337 0.5645 0.86
0.1729 24.96 351 0.5576 0.86
0.161 25.96 365 0.5378 0.86
0.1586 26.95 379 0.5662 0.86
0.1452 27.95 393 0.5575 0.87
0.1444 28.94 407 0.5491 0.86
0.1343 29.87 420 0.5528 0.86

Framework versions

  • Transformers 4.39.2
  • Pytorch 1.13.0+cu117
  • Datasets 2.16.1
  • Tokenizers 0.15.1
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Finetuned from

Dataset used to train BanUrsus/distilhubert-finetuned-gtzan-v2