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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: 0.5247 Accuracy: 0.85

Sourcehuggingface.co/willtherebenough/distilhubert-finetuned-gtzan/tree/763226322acf11f230e58fe64c6c97bd6a20bdeb
TypeOtherModèle de basentu-spml/distilhubertVersionRevision 7632263BClasse

Source du modèle

Type
Other
Taille du fichier
90,4 MB
Modèle de base
ntu-spml/distilhubert
Version
vRevision 7632263
Mis à jour
31 oct. 2026
Téléchargements
15
Source
Hugging Face

Description de la source

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: 0.5247
  • Accuracy: 0.85

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: 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_steps: 100
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training LossEpochStepValidation LossAccuracy
1.90931.01131.83910.54
1.14292.02261.22750.63
0.95933.03391.01760.72
0.67694.04520.93330.74
0.52835.05650.62170.8
0.38496.06780.55580.83
0.26757.07910.48730.86
0.09858.09040.56830.81
0.13079.010170.53440.83
0.102410.011300.52470.85

Framework versions

  • Transformers 5.10.0.dev0
  • Pytorch 2.9.0+cu129
  • Datasets 2.21.0
  • Tokenizers 0.22.2

Sources

1 source
Hugging FacePrincipalVersion 763226322acf11f230e58fe64c6c97bd6a20bdeb
huggingface.co/willtherebenough/distilhubert-finetuned-gtzan/tree/763226322acf11f230e58fe64c6c97bd6a20bdeb

Vérifié 31 oct.

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Artefacts du modèle

1 artefact

model.safetensors

safetensors · FP32 (unverified) · 90,4 MB · SHA-256 774c9a74d816…5968 · Hugging Face

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Extraits de sources

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Readme15 aoûthuggingface.co/willtherebenough/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: 0.85 --- <!-- 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: 0.5247 - Accuracy: 0.85 ## 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: 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_steps: 100 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9093 | 1.0 | 113 | 1.8391 | 0.54 | | 1.1429 | 2.0 | 226 | 1.2275 | 0.63 | | 0.9593 | 3.0 | 339 | 1.0176 | 0.72 | | 0.6769 | 4.0 | 452 | 0.9333 | 0.74 | | 0.5283 | 5.0 | 565 | 0.6217 | 0.8 | | 0.3849 | 6.0 | 678 | 0.5558 | 0.83 | | 0.2675 | 7.0 | 791 | 0.4873 | 0.86 | | 0.0985 | 8.0 | 904 | 0.5683 | 0.81 | | 0.1307 | 9.0 | 1017 | 0.5344 | 0.83 | | 0.1024 | 10.0 | 1130 | 0.5247 | 0.85 | ### Framework versions - Transformers 5.10.0.dev0 - Pytorch 2.9.0+cu129 - Datasets 2.21.0 - Tokenizers 0.22.2

Note31 oct.huggingface.co/willtherebenough/distilhubert-finetuned-gtzan

Source context: 15 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo willtherebenough/distilhubert-finetuned-gtzan

Note15 aoûthuggingface.co/willtherebenough/distilhubert-finetuned-gtzan

Source context: 9 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo willtherebenough/distilhubert-finetuned-gtzan