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Librarian Bot: Add base_model information to model (#1)
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
base_model: ntu-spml/distilhubert
model-index:
  - name: distilhubert-finetuned-gtzan
    results: []

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: 0.4989
  • Accuracy: 0.91

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-06
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.2359 1.0 112 0.4776 0.87
0.1235 2.0 225 0.4872 0.84
0.2083 3.0 337 0.4910 0.85
0.19 4.0 450 0.4953 0.87
0.1128 5.0 562 0.4801 0.87
0.1644 6.0 675 0.4703 0.87
0.0699 7.0 787 0.4692 0.85
0.1082 8.0 900 0.4708 0.87
0.0898 9.0 1012 0.4347 0.89
0.1071 10.0 1125 0.5310 0.85
0.0727 11.0 1237 0.4765 0.87
0.0338 12.0 1350 0.4859 0.87
0.0233 13.0 1462 0.4713 0.87
0.0248 14.0 1575 0.5068 0.88
0.0263 15.0 1687 0.4874 0.88
0.0185 16.0 1800 0.4925 0.88
0.0142 17.0 1912 0.4766 0.89
0.0178 18.0 2025 0.4850 0.89
0.0153 19.0 2137 0.4660 0.88
0.012 20.0 2250 0.4831 0.88
0.0113 21.0 2362 0.4965 0.89
0.0106 22.0 2475 0.5098 0.89
0.011 23.0 2587 0.5093 0.89
0.009 24.0 2700 0.4989 0.91
0.0094 25.0 2812 0.4999 0.89
0.0441 26.0 2925 0.5197 0.88
0.0079 27.0 3037 0.5115 0.89
0.0072 28.0 3150 0.5136 0.88
0.007 29.0 3262 0.5394 0.88
0.0068 30.0 3375 0.5374 0.88
0.0061 31.0 3487 0.5221 0.88
0.0533 32.0 3600 0.5775 0.87
0.0055 33.0 3712 0.5632 0.88
0.0059 34.0 3825 0.5584 0.87
0.0051 35.0 3937 0.5444 0.88
0.0051 36.0 4050 0.5373 0.88
0.0045 37.0 4162 0.5723 0.87
0.0058 38.0 4275 0.5773 0.87
0.0043 39.0 4387 0.5455 0.88
0.0044 40.0 4500 0.5686 0.88
0.004 41.0 4612 0.5622 0.87
0.004 42.0 4725 0.5797 0.88
0.0042 43.0 4837 0.5621 0.88
0.0037 44.0 4950 0.5734 0.87
0.0048 45.0 5062 0.5774 0.88
0.0039 46.0 5175 0.5901 0.87
0.0043 47.0 5287 0.5743 0.88
0.0043 48.0 5400 0.5757 0.87
0.0037 49.0 5512 0.5710 0.88
0.0036 49.78 5600 0.5759 0.87

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.13.1
  • Tokenizers 0.13.3