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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: 0.8818
  • 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: 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: 17

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.5851 1.0 113 1.7243 0.5
1.2937 2.0 226 1.2310 0.68
0.9718 3.0 339 0.8918 0.76
0.6613 4.0 452 0.6837 0.81
0.3693 5.0 565 0.6250 0.82
0.2991 6.0 678 0.5740 0.82
0.1381 7.0 791 0.5874 0.83
0.2047 8.0 904 0.5824 0.86
0.1192 9.0 1017 0.7106 0.83
0.0652 10.0 1130 0.6576 0.87
0.0105 11.0 1243 0.8236 0.84
0.0074 12.0 1356 0.7874 0.85
0.0064 13.0 1469 0.9066 0.84
0.0041 14.0 1582 0.8426 0.85
0.0038 15.0 1695 0.8676 0.84
0.0039 16.0 1808 0.8820 0.85
0.0036 17.0 1921 0.8818 0.85

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1+cu118
  • Datasets 2.13.1
  • Tokenizers 0.13.3