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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: distilhubert-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: 0.81

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: 0.7392
  • Accuracy: 0.81

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: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • 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
1.3055 0.97 7 1.2863 0.73
1.2903 1.93 14 1.2504 0.7
1.2118 2.9 21 1.1450 0.77
1.1443 4.0 29 1.1224 0.74
1.006 4.97 36 1.0376 0.79
1.0174 5.93 43 0.9681 0.8
0.9155 6.9 50 0.9322 0.81
0.8781 8.0 58 0.9266 0.78
0.819 8.97 65 0.8473 0.79
0.7984 9.93 72 0.8225 0.77
0.7254 10.9 79 0.8096 0.81
0.6752 12.0 87 0.7801 0.81
0.6132 12.97 94 0.7687 0.8
0.615 13.93 101 0.7603 0.79
0.6162 14.9 108 0.7599 0.82
0.5678 16.0 116 0.7414 0.81
0.548 16.97 123 0.7423 0.81
0.5495 17.93 130 0.7378 0.81
0.5185 18.9 137 0.7396 0.81
0.5544 19.31 140 0.7392 0.81

Framework versions

  • Transformers 4.32.0.dev0
  • Pytorch 2.0.1
  • Datasets 2.13.1
  • Tokenizers 0.13.3