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: 0.4989
• Accuracy: 0.91

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: 4e-06
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2359	1.0	112	0.4776	0.87
0.1235	2.0	225	0.4872	0.84
0.2083	3.0	337	0.4910	0.85
0.19	4.0	450	0.4953	0.87
0.1128	5.0	562	0.4801	0.87
0.1644	6.0	675	0.4703	0.87
0.0699	7.0	787	0.4692	0.85
0.1082	8.0	900	0.4708	0.87
0.0898	9.0	1012	0.4347	0.89
0.1071	10.0	1125	0.5310	0.85
0.0727	11.0	1237	0.4765	0.87
0.0338	12.0	1350	0.4859	0.87
0.0233	13.0	1462	0.4713	0.87
0.0248	14.0	1575	0.5068	0.88
0.0263	15.0	1687	0.4874	0.88
0.0185	16.0	1800	0.4925	0.88
0.0142	17.0	1912	0.4766	0.89
0.0178	18.0	2025	0.4850	0.89
0.0153	19.0	2137	0.4660	0.88
0.012	20.0	2250	0.4831	0.88
0.0113	21.0	2362	0.4965	0.89
0.0106	22.0	2475	0.5098	0.89
0.011	23.0	2587	0.5093	0.89
0.009	24.0	2700	0.4989	0.91
0.0094	25.0	2812	0.4999	0.89
0.0441	26.0	2925	0.5197	0.88
0.0079	27.0	3037	0.5115	0.89
0.0072	28.0	3150	0.5136	0.88
0.007	29.0	3262	0.5394	0.88
0.0068	30.0	3375	0.5374	0.88
0.0061	31.0	3487	0.5221	0.88
0.0533	32.0	3600	0.5775	0.87
0.0055	33.0	3712	0.5632	0.88
0.0059	34.0	3825	0.5584	0.87
0.0051	35.0	3937	0.5444	0.88
0.0051	36.0	4050	0.5373	0.88
0.0045	37.0	4162	0.5723	0.87
0.0058	38.0	4275	0.5773	0.87
0.0043	39.0	4387	0.5455	0.88
0.0044	40.0	4500	0.5686	0.88
0.004	41.0	4612	0.5622	0.87
0.004	42.0	4725	0.5797	0.88
0.0042	43.0	4837	0.5621	0.88
0.0037	44.0	4950	0.5734	0.87
0.0048	45.0	5062	0.5774	0.88
0.0039	46.0	5175	0.5901	0.87
0.0043	47.0	5287	0.5743	0.88
0.0043	48.0	5400	0.5757	0.87
0.0037	49.0	5512	0.5710	0.88
0.0036	49.78	5600	0.5759	0.87

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.1+cu117
• Datasets 2.13.1
• Tokenizers 0.13.3