f0ghedgeh0g/distilhubert-finetuned-gtzan

distilhubert-finetuned-hyperparam-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: 1.2045
• Accuracy: 0.86

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: 8
• eval_batch_size: 8
• seed: 42
• 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: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.7469	1.0	113	1.3737	0.57
0.7973	2.0	226	1.5247	0.57
0.6831	3.0	339	0.8961	0.74
0.4573	4.0	452	0.8638	0.76
0.1874	5.0	565	0.7839	0.81
0.0829	6.0	678	1.0174	0.79
0.0306	7.0	791	0.9393	0.81
0.004	8.0	904	0.9737	0.85
0.1209	9.0	1017	1.0625	0.8
0.0237	10.0	1130	1.3653	0.8
0.0164	11.0	1243	1.3065	0.81
0.0007	12.0	1356	1.1272	0.83
0.0004	13.0	1469	1.3226	0.83
0.0001	14.0	1582	1.6092	0.82
0.0001	15.0	1695	1.2045	0.86
0.0002	16.0	1808	1.1312	0.85
0.0003	17.0	1921	1.0911	0.86
0.0	18.0	2034	1.1983	0.84
0.0001	19.0	2147	1.1363	0.85
0.0	20.0	2260	1.2547	0.85
0.0002	21.0	2373	1.2394	0.84
0.0001	22.0	2486	1.5508	0.85
0.0	23.0	2599	1.2689	0.83
0.0	24.0	2712	1.2343	0.83
0.0003	25.0	2825	1.2313	0.81
0.0	26.0	2938	1.2217	0.83
0.0	27.0	3051	1.1596	0.84
0.0	28.0	3164	1.1081	0.85
0.0001	29.0	3277	1.1394	0.85
0.0	30.0	3390	1.1215	0.85

Framework versions

• Transformers 4.48.0.dev0
• Pytorch 2.6.0+cu126
• Datasets 3.2.0
• Tokenizers 0.21.0