davanstrien / distilhubert-finetuned-AESDD

huggingface.co
Total runs: 9
24-hour runs: 0
7-day runs: -4
30-day runs: 4
Model's Last Updated: August 02 2023
audio-classification

Introduction of distilhubert-finetuned-AESDD

Model Details of distilhubert-finetuned-AESDD

distilhubert-finetuned-AESDD

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

  • Loss: 0.4389
  • Accuracy: 0.9016
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: 8
  • eval_batch_size: 8
  • 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: 10
Training results
Training Loss Epoch Step Validation Loss Accuracy
1.1249 1.0 68 1.1905 0.5082
0.7441 2.0 136 0.8850 0.6721
0.5941 3.0 204 0.6579 0.8361
0.4349 4.0 272 0.9638 0.6721
0.2612 5.0 340 0.5081 0.8689
0.1883 6.0 408 0.6223 0.8197
0.0978 7.0 476 0.4671 0.8689
0.0425 8.0 544 0.4338 0.8852
0.0264 9.0 612 0.4488 0.8525
0.0219 10.0 680 0.4389 0.9016
Framework versions
  • Transformers 4.31.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.2
  • Tokenizers 0.13.3

Runs of davanstrien distilhubert-finetuned-AESDD on huggingface.co

9
Total runs
0
24-hour runs
-4
3-day runs
-4
7-day runs
4
30-day runs

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