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metadata
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
  - generated_from_trainer
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 None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5086
  • Accuracy: 0.89

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: 4e-05
  • train_batch_size: 6
  • eval_batch_size: 6
  • seed: 42
  • gradient_accumulation_steps: 7
  • total_train_batch_size: 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: 25
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.2912 0.98 21 2.2667 0.19
2.2263 1.96 42 2.1460 0.48
1.9552 2.99 64 1.8067 0.44
1.5982 3.97 85 1.5912 0.54
1.5182 4.99 107 1.4077 0.61
1.2855 5.97 128 1.2654 0.69
1.1649 7.0 150 1.1915 0.69
1.0742 7.98 171 1.0769 0.75
1.0495 8.96 192 1.0011 0.77
0.8827 9.99 214 0.9062 0.79
0.7886 10.97 235 0.8333 0.83
0.7019 11.99 257 0.7801 0.83
0.6642 12.97 278 0.7691 0.79
0.5982 14.0 300 0.6984 0.82
0.5002 14.98 321 0.6526 0.84
0.4789 15.96 342 0.5980 0.88
0.3908 16.99 364 0.5874 0.86
0.3892 17.97 385 0.5570 0.86
0.3675 18.99 407 0.5634 0.87
0.303 19.97 428 0.5387 0.87
0.3017 21.0 450 0.5086 0.89
0.2469 21.98 471 0.4969 0.89
0.2542 22.96 492 0.4972 0.88
0.2651 23.99 514 0.4947 0.89
0.2591 24.5 525 0.4929 0.89

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0
  • Datasets 2.14.6
  • Tokenizers 0.14.1