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distilhubert-finetuned-gtzan

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distilhubert-finetuned-gtzan / README.md
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metadata
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
model-index:
  - name: music-genre-classifer-20-finetuned-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: 0.82

music-genre-classifer-20-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: 0.5510
  • Accuracy: 0.82

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: 2e-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: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Accuracy Validation Loss
2.201 1.0 113 0.39 2.1256
1.6789 2.0 226 0.59 1.6543
1.5602 3.0 339 0.64 1.3917
1.1966 4.0 452 0.67 1.1946
1.1131 5.0 565 0.77 1.0492
1.0258 6.0 678 0.76 0.9712
0.988 7.0 791 0.76 0.9160
0.7303 8.0 904 0.8 0.8704
0.8036 9.0 1017 0.8 0.8425
0.742 10.0 1130 0.81 0.8224
0.7463 11.0 1243 0.81 0.8140
0.7428 12.0 1356 0.78 0.8112
0.6081 13.0 1469 0.82 0.6975
0.8154 14.0 1582 0.84 0.6636
0.3758 15.0 1695 0.84 0.6215
0.503 16.0 1808 0.81 0.6251
0.4542 17.0 1921 0.84 0.5869
0.3285 18.0 2034 0.85 0.5830
0.4309 19.0 2147 0.82 0.5844
0.342 20.0 2260 0.85 0.5840
0.3051 21.0 2373 0.83 0.5843
0.3558 22.0 2486 0.6144 0.79
0.3371 23.0 2599 0.5673 0.81
0.2882 24.0 2712 0.5365 0.84
0.2326 25.0 2825 0.5848 0.83
0.192 26.0 2938 0.5406 0.85
0.1528 27.0 3051 0.5482 0.82
0.1937 28.0 3164 0.5448 0.84
0.1264 29.0 3277 0.5487 0.84
0.1356 30.0 3390 0.5510 0.82

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2