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Distilhubert Finetuned Gtzan

This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset.

ArtOtherBasismodellntu-spml/distilhubertVersionRevision eea5b9b

Modellquelle

Art
Other
Dateigröße
90,4 MB
Basismodell
ntu-spml/distilhubert
Version
vRevision eea5b9b
Aktualisiert
19. Sept. 2026
Downloads
8
Quelle
Hugging Face

Quellenbeschreibung

distilhubert-finetuned-gtzan

This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset.

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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Framework versions

  • Transformers 5.1.0
  • Pytorch 2.7.1+cu118
  • Datasets 2.20.0
  • Tokenizers 0.22.2

Quellen

1 Quelle
Hugging FacePrimärVersion eea5b9bd292e3ff0a295b8424500da5ca16f88f1
huggingface.co/axlinwz/distilhubert-finetuned-gtzan/tree/eea5b9bd292e3ff0a295b8424500da5ca16f88f1

Verifiziert 19. Sept.

Modellartefakte

1 Artefakt

model.safetensors

safetensors · 90,4 MB · SHA-256 cc75be99149f…7ff8 · Hugging Face

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Quellenauszüge

2 Auszüge

--- library_name: transformers license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan model-index: - name: distilhubert-finetuned-gtzan results: [] --- <!-- 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. ## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 10 - mixed_precision_training: Native AMP ### Framework versions - Transformers 5.1.0 - Pytorch 2.7.1+cu118 - Datasets 2.20.0 - Tokenizers 0.22.2

Source context: 8 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo axlinwz/distilhubert-finetuned-gtzan