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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: 0.80 Accuracy: 0.89 Macro F1: 0.8957

Sourcehuggingface.co/sanyam3/distilhubert-finetuned-gtzan/tree/1b41dd20c2fc0c0984e0d71c103eea6f2f849063
KindOtherBase modelntu-spml/distilhubertVersionRevision 1b41dd2

Model source

Kind
Other
File size
90.4 MB
Base model
ntu-spml/distilhubert
Version
vRevision 1b41dd2
Updated
Sep 19, 2026
Downloads
8
Source
Hugging Face

Source description

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: 0.80
  • Accuracy: 0.89
  • Macro F1: 0.8957

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: 3e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training LossEpochStepValidation LossAccuracyMacro F1
2.26411.04502.22280.450.3545
1.88572.09001.85270.560.4640
1.60083.013501.41080.680.6543
1.17424.018001.11910.720.6910
0.92575.022500.87380.810.8026
0.77666.027000.68430.820.8216
0.45577.031500.62630.820.8170
0.32788.036000.55550.840.8285
0.32629.040500.54710.870.8641
0.254710.045000.76850.810.8043
0.271311.049500.61070.890.8782
0.155512.054000.67960.880.8767
0.198313.058500.82510.860.8466
0.114614.063000.83320.850.8341
0.116415.067500.80600.890.8957
0.010316.072001.19650.840.8345
0.024717.076501.12320.860.8539
0.003718.081001.01720.870.8665
0.002119.085500.98680.880.8734
0.059420.090001.40430.860.8591

Framework versions

  • Transformers 4.53.3
  • Pytorch 2.9.1+cu126
  • Datasets 3.6.0
  • Tokenizers 0.21.1

Sources

1 source
Hugging FacePrimaryVersion 1b41dd20c2fc0c0984e0d71c103eea6f2f849063
huggingface.co/sanyam3/distilhubert-finetuned-gtzan/tree/1b41dd20c2fc0c0984e0d71c103eea6f2f849063

Verified Sep 19

Model artifacts

1 artifact

model.safetensors

safetensors · 90.4 MB · SHA-256 d4400177d8e8…0aab · Hugging Face

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Source excerpts

2 excerpts
ReadmeSep 19huggingface.co/sanyam3/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: 0.80 - Accuracy: 0.89 - Macro F1: 0.8957 ## 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: 3e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| | 2.2641 | 1.0 | 450 | 2.2228 | 0.45 | 0.3545 | | 1.8857 | 2.0 | 900 | 1.8527 | 0.56 | 0.4640 | | 1.6008 | 3.0 | 1350 | 1.4108 | 0.68 | 0.6543 | | 1.1742 | 4.0 | 1800 | 1.1191 | 0.72 | 0.6910 | | 0.9257 | 5.0 | 2250 | 0.8738 | 0.81 | 0.8026 | | 0.7766 | 6.0 | 2700 | 0.6843 | 0.82 | 0.8216 | | 0.4557 | 7.0 | 3150 | 0.6263 | 0.82 | 0.8170 | | 0.3278 | 8.0 | 3600 | 0.5555 | 0.84 | 0.8285 | | 0.3262 | 9.0 | 4050 | 0.5471 | 0.87 | 0.8641 | | 0.2547 | 10.0 | 4500 | 0.7685 | 0.81 | 0.8043 | | 0.2713 | 11.0 | 4950 | 0.6107 | 0.89 | 0.8782 | | 0.1555 | 12.0 | 5400 | 0.6796 | 0.88 | 0.8767 | | 0.1983 | 13.0 | 5850 | 0.8251 | 0.86 | 0.8466 | | 0.1146 | 14.0 | 6300 | 0.8332 | 0...

NoteSep 19huggingface.co/sanyam3/distilhubert-finetuned-gtzan

Source context: 8 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo sanyam3/distilhubert-finetuned-gtzan