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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: 1.7615
  • Accuracy: 0.85

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: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.2914 1.0 57 2.2595 0.25
2.1225 2.0 114 2.0265 0.57
1.7631 3.0 171 1.6482 0.59
1.3445 4.0 228 1.3380 0.62
1.1548 5.0 285 1.0589 0.72
0.9289 6.0 342 0.8541 0.76
0.708 7.0 399 0.7628 0.79
0.4497 8.0 456 0.7088 0.82
0.4061 9.0 513 0.6118 0.85
0.286 10.0 570 0.6684 0.79
0.1739 11.0 627 0.5965 0.83
0.1103 12.0 684 0.8414 0.81
0.0922 13.0 741 0.5937 0.87
0.0166 14.0 798 0.5786 0.86
0.0075 15.0 855 0.7950 0.84
0.0014 16.0 912 0.8492 0.87
0.0006 17.0 969 1.2642 0.82
0.0815 18.0 1026 1.1173 0.87
0.0 19.0 1083 1.2181 0.86
0.0 20.0 1140 1.6673 0.85
0.0 21.0 1197 1.4749 0.86
0.0611 22.0 1254 2.2533 0.82
0.0978 23.0 1311 2.0092 0.86
0.0 24.0 1368 2.3586 0.83
0.0 25.0 1425 1.7617 0.86
0.0 26.0 1482 1.7425 0.86
0.0 27.0 1539 1.8418 0.85
0.0 28.0 1596 1.6987 0.87
0.0 29.0 1653 1.9399 0.85
0.0 30.0 1710 2.4230 0.81
0.0 31.0 1767 1.4312 0.88
0.1807 32.0 1824 1.5278 0.87
0.0 33.0 1881 1.3795 0.88
0.0 34.0 1938 1.5051 0.88
0.0 35.0 1995 1.6587 0.85
0.0 36.0 2052 1.6256 0.86
0.0 37.0 2109 1.7290 0.85
0.0 38.0 2166 1.8676 0.87
0.0 39.0 2223 1.8963 0.86
0.166 40.0 2280 1.7057 0.85
0.1293 41.0 2337 1.4235 0.87
0.1491 42.0 2394 1.7916 0.85
0.1416 43.0 2451 1.8634 0.85
0.0 44.0 2508 1.6286 0.86
0.0526 45.0 2565 1.6242 0.86
0.0 46.0 2622 1.7576 0.85
0.0 47.0 2679 1.7897 0.85
0.0 48.0 2736 1.7571 0.85
0.0018 49.0 2793 1.6993 0.85
0.0 50.0 2850 1.7615 0.85

Framework versions

  • Transformers 4.42.3
  • Pytorch 2.1.2
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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Dataset used to train nithin04/distilhubert-finetuned-gtzan

Evaluation results