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distilhubert-finetuned-gtzan

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metadata
library_name: transformers
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
model-index:
  - name: distilhubert-finetuned-hyperparam-gtzan
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: GTZAN
          type: marsyas/gtzan
          config: all
          split: train
          args: all
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.86

distilhubert-finetuned-hyperparam-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: 1.2045
  • 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: 3e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.7469 1.0 113 1.3737 0.57
0.7973 2.0 226 1.5247 0.57
0.6831 3.0 339 0.8961 0.74
0.4573 4.0 452 0.8638 0.76
0.1874 5.0 565 0.7839 0.81
0.0829 6.0 678 1.0174 0.79
0.0306 7.0 791 0.9393 0.81
0.004 8.0 904 0.9737 0.85
0.1209 9.0 1017 1.0625 0.8
0.0237 10.0 1130 1.3653 0.8
0.0164 11.0 1243 1.3065 0.81
0.0007 12.0 1356 1.1272 0.83
0.0004 13.0 1469 1.3226 0.83
0.0001 14.0 1582 1.6092 0.82
0.0001 15.0 1695 1.2045 0.86
0.0002 16.0 1808 1.1312 0.85
0.0003 17.0 1921 1.0911 0.86
0.0 18.0 2034 1.1983 0.84
0.0001 19.0 2147 1.1363 0.85
0.0 20.0 2260 1.2547 0.85
0.0002 21.0 2373 1.2394 0.84
0.0001 22.0 2486 1.5508 0.85
0.0 23.0 2599 1.2689 0.83
0.0 24.0 2712 1.2343 0.83
0.0003 25.0 2825 1.2313 0.81
0.0 26.0 2938 1.2217 0.83
0.0 27.0 3051 1.1596 0.84
0.0 28.0 3164 1.1081 0.85
0.0001 29.0 3277 1.1394 0.85
0.0 30.0 3390 1.1215 0.85

Framework versions

  • Transformers 4.48.0.dev0
  • Pytorch 2.6.0+cu126
  • Datasets 3.2.0
  • Tokenizers 0.21.0