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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
model-index:
  - name: distilhubert-finetuned-gtzan
    results: []

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: 0.8042
  • Accuracy: 0.86

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: 8
  • 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
2.0168 1.0 113 2.0642 0.45
1.4374 2.0 226 1.4358 0.64
1.1551 3.0 339 0.9743 0.74
0.7756 4.0 452 0.7805 0.81
0.4436 5.0 565 0.6117 0.81
0.3047 6.0 678 0.7366 0.79
0.2288 7.0 791 0.5297 0.86
0.2728 8.0 904 0.5677 0.87
0.1072 9.0 1017 0.6887 0.86
0.137 10.0 1130 0.9238 0.8
0.021 11.0 1243 0.7738 0.84
0.007 12.0 1356 0.7002 0.86
0.0047 13.0 1469 0.7805 0.86
0.0039 14.0 1582 0.7624 0.85
0.0034 15.0 1695 0.7892 0.85
0.0031 16.0 1808 0.7806 0.85
0.0029 17.0 1921 0.8005 0.85
0.0028 18.0 2034 0.7942 0.85
0.0025 19.0 2147 0.8138 0.86
0.0025 20.0 2260 0.8042 0.86

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1
  • Datasets 2.13.1
  • Tokenizers 0.13.3