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End of training
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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: Speech_command_RK
          type: marsyas/gtzan
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9975728155339806

distilhubert-finetuned-gtzan

This model is a fine-tuned version of ntu-spml/distilhubert on the Speech_command_RK dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2480
  • Accuracy: 0.9976

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: 264
  • eval_batch_size: 264
  • 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: 5

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.4512 1.0 25 2.2018 0.6638
1.2836 2.0 50 1.0664 0.9636
0.6447 3.0 75 0.5056 0.9891
0.3833 4.0 100 0.2985 0.9964
0.3167 5.0 125 0.2480 0.9976

Framework versions

  • Transformers 4.40.0
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.0
  • Tokenizers 0.19.1