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: 0.3539
  • Accuracy: 0.91

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: 2
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 18

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.2281 1.0 112 2.1128 0.26
1.7082 2.0 225 1.6252 0.52
1.267 3.0 337 1.3100 0.54
1.1791 4.0 450 1.0496 0.71
1.1765 5.0 562 0.8928 0.74
0.5714 6.0 675 0.8298 0.77
0.4869 7.0 787 0.7145 0.79
0.4967 8.0 900 0.6990 0.82
0.8314 9.0 1012 0.5657 0.83
0.4633 10.0 1125 0.4589 0.89
0.5547 11.0 1237 0.4919 0.86
0.4827 12.0 1350 0.4069 0.92
0.324 13.0 1462 0.4634 0.87
0.5224 14.0 1575 0.4419 0.86
0.1873 15.0 1687 0.3988 0.89
0.2852 16.0 1800 0.3788 0.9
0.3169 17.0 1912 0.3526 0.89
0.4491 17.92 2016 0.3539 0.91

Framework versions

  • Transformers 4.32.0.dev0
  • Pytorch 2.0.1+cu117
  • Datasets 2.13.1
  • Tokenizers 0.13.3
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Dataset used to train NicolasDenier/distilhubert-finetuned-gtzan

Evaluation results