distilhubert-finetuned-gtzan – AI Model by fffmath | AlphaNeural AI

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fffmath/
distilhubert-finetuned-gtzan

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audio-classification

generated_from_trainer

ntu-spml/distilhubert

endpoints_compatible

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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: 0.5595
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 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
1.8906	1.0	113	1.8154	0.43
1.1888	2.0	226	1.2004	0.67
0.9917	3.0	339	1.0252	0.72
0.5937	4.0	452	0.7423	0.81
0.4669	5.0	565	0.7231	0.77
0.3205	6.0	678	0.6279	0.82
0.3508	7.0	791	0.5861	0.83
0.1254	8.0	904	0.5335	0.84
0.2296	9.0	1017	0.5837	0.84
0.0852	10.0	1130	0.5595	0.85

Framework versions

Transformers 5.13.0
Pytorch 2.12.1+cu130
Datasets 2.21.0
Tokenizers 0.22.2