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Training in progress, epoch 1
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metadata
library_name: transformers
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: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.86

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: 1.1749
  • Accuracy: 0.86

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: 4
  • eval_batch_size: 4
  • 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: 25
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.0517 1.0 225 2.0004 0.47
1.3283 2.0 450 1.3458 0.57
0.729 3.0 675 0.8563 0.76
0.4007 4.0 900 0.6748 0.8
0.3923 5.0 1125 0.7340 0.78
0.2193 6.0 1350 0.8712 0.76
0.2383 7.0 1575 0.7414 0.79
0.3 8.0 1800 0.7387 0.86
0.006 9.0 2025 0.9203 0.85
0.002 10.0 2250 0.8956 0.85
0.0014 11.0 2475 0.9831 0.86
0.001 12.0 2700 0.9406 0.86
0.0009 13.0 2925 1.0288 0.86
0.0007 14.0 3150 1.0172 0.86
0.0007 15.0 3375 0.9912 0.89
0.0005 16.0 3600 1.0282 0.86
0.0006 17.0 3825 1.3495 0.83
0.2453 18.0 4050 1.0340 0.87
0.0004 19.0 4275 1.1048 0.86
0.0004 20.0 4500 1.3051 0.85
0.0003 21.0 4725 1.2280 0.85
0.0003 22.0 4950 1.2530 0.85
0.0003 23.0 5175 1.1992 0.85
0.0003 24.0 5400 1.1881 0.85
0.0003 25.0 5625 1.1749 0.86

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.3.1
  • Datasets 2.21.0
  • Tokenizers 0.19.1