Ir para o conteúdo
ModelListadosExterno
Baixar

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

Fontehuggingface.co/bogosla/distilhubert-finetuned-gtzan/tree/19c6328549fa5c067ef172ee82a57811e59a76bb
TipoOtherModelo basentu-spml/distilhubertVersãoRevision 19c6328

Fonte do modelo

Tipo
Other
Tamanho do arquivo
90,4 MB
Modelo base
ntu-spml/distilhubert
Versão
vRevision 19c6328
Atualizado
19 de set. de 2026
Downloads
3
Fonte
Hugging Face

Descrição da fonte

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

Fontes

1 fonte
Hugging FacePrimárioVersão 19c6328549fa5c067ef172ee82a57811e59a76bb
huggingface.co/bogosla/distilhubert-finetuned-gtzan/tree/19c6328549fa5c067ef172ee82a57811e59a76bb

Verificado 19 de set.

Artefatos de modelo

1 artefato

model.safetensors

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

Baixar

Trechos de fonte

2 trechos
Readme19 de set.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 de set.huggingface.co/bogosla/distilhubert-finetuned-gtzan

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