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: 0.6191
• Accuracy: 0.88

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: 16
• eval_batch_size: 16
• 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_ratio: 0.1
• num_epochs: 20
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.2418	1.0	57	2.1887	0.41
1.7963	2.0	114	1.7322	0.47
1.375	3.0	171	1.3294	0.67
1.0205	4.0	228	1.0478	0.7
0.8203	5.0	285	0.8415	0.76
0.699	6.0	342	0.7388	0.8
0.5515	7.0	399	0.7179	0.8
0.359	8.0	456	0.7102	0.83
0.3362	9.0	513	0.5565	0.87
0.2396	10.0	570	0.5104	0.86
0.1479	11.0	627	0.4885	0.87
0.1418	12.0	684	0.5929	0.85
0.1281	13.0	741	0.6748	0.83
0.049	14.0	798	0.6507	0.85
0.0401	15.0	855	0.6423	0.84
0.0251	16.0	912	0.5937	0.86
0.0147	17.0	969	0.5809	0.88
0.0541	18.0	1026	0.5991	0.88
0.0123	19.0	1083	0.6127	0.88
0.0079	20.0	1140	0.6191	0.88

Framework versions

• Transformers 4.47.0
• Pytorch 2.5.1+cu121
• Datasets 2.15.0
• Tokenizers 0.21.0