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:

• eval_loss: 0.4572
• eval_accuracy: 0.89
• eval_runtime: 49.0273
• eval_samples_per_second: 2.04
• eval_steps_per_second: 0.51
• epoch: 7.0
• step: 791

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_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: 0.2
• num_epochs: 10
• mixed_precision_training: Native AMP

Framework versions

• Transformers 5.0.0.dev0
• Pytorch 2.8.0+cu126
• Datasets 3.6.0
• Tokenizers 0.22.1