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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: 0.5738 Accuracy: 0.88

Quellehuggingface.co/bogosla/distilhubert-finetuned-gtzan/tree/19c6328549fa5c067ef172ee82a57811e59a76bb
ArtOtherBasismodellntu-spml/distilhubertVersionRevision 19c6328

Modellquelle

Art
Other
Dateigröße
90,4 MB
Basismodell
ntu-spml/distilhubert
Version
vRevision 19c6328
Aktualisiert
19. Sept. 2026
Downloads
3
Quelle
Hugging Face

Quellenbeschreibung

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: 0.5738
  • Accuracy: 0.88

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: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 25
  • mixed_precision_training: Native AMP

Training results

Training LossEpochStepAccuracyValidation Loss
2.13921.0570.582.0354
1.62582.01140.621.5654
1.35983.01710.711.2824
1.04274.02280.71.1595
0.96955.02850.791.0014
0.85736.03420.790.9319
0.85547.03990.790.8992
0.70558.04560.790.8725
0.67919.05130.810.8256
0.647110.05700.810.7848
0.56611.06270.820.7702
0.567112.06840.820.7426
0.561813.07410.820.7179
0.418714.07980.810.7058
0.410215.08550.830.6610
0.33916.09120.860.6316
0.338917.09690.870.6201
0.321218.010260.840.6248
0.398719.010830.57820.87
0.250820.011400.60460.86
0.223921.011970.57380.88
0.199622.012540.60670.82
0.217823.013110.54930.88
0.178624.013680.54660.88
0.147125.014250.55570.86

Framework versions

  • Transformers 4.57.3
  • Pytorch 2.9.0+cu126
  • Datasets 2.14.0
  • Tokenizers 0.22.1

Quellen

1 Quelle
Hugging FacePrimärVersion 19c6328549fa5c067ef172ee82a57811e59a76bb
huggingface.co/bogosla/distilhubert-finetuned-gtzan/tree/19c6328549fa5c067ef172ee82a57811e59a76bb

Verifiziert 19. Sept.

Modellartefakte

1 Artefakt

model.safetensors

safetensors · 90,4 MB · SHA-256 f08beeac9670…8737 · Hugging Face

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Quellenauszüge

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Readme19. Sept.huggingface.co/bogosla/distilhubert-finetuned-gtzan

--- 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: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.88 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5738 - Accuracy: 0.88 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:----:|:--------:|:---------------:| | 2.1392 | 1.0 | 57 | 0.58 | 2.0354 | | 1.6258 | 2.0 | 114 | 0.62 | 1.5654 | | 1.3598 | 3.0 | 171 | 0.71 | 1.2824 | | 1.0427 | 4.0 | 228 | 0.7 | 1.1595 | | 0.9695 | 5.0 | 285 | 0.79 | 1.0014 | | 0.8573 | 6.0 | 342 | 0.79 | 0.9319 | | 0.8554 | 7.0 | 399 | 0.79 | 0.8992 | | 0.7055 | 8.0 | 456 | 0.79 | 0.8725 | | 0.6791 | 9.0 | 513 | 0.81 | 0.8256 | | 0.6471 | 10.0 | 570 | 0.81 | 0.7848 | | 0.566 | 11.0 | 627 | 0.82 | 0.7702 | | 0.5671 | 12.0 | 684 | 0.82 | 0.7426 | | 0.5618 | 13.0 | 741 | 0.82 | 0.7179 | | 0.4187 | 14.0 | 798 | 0.81 | 0.7058 | | 0.4102 | 15.0 | 855 | 0.83 | 0.6610 | | 0.339 | 16.0 | 912 | 0.86 | 0.6316 | | 0.3389 | 17.0 | 969 | 0.87 | 0.6201 | | 0.3212 | 18.0 | 1026 | 0.84 | 0.6248 |...

Note19. Sept.huggingface.co/bogosla/distilhubert-finetuned-gtzan

Source context: 3 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo bogosla/distilhubert-finetuned-gtzan