mcamara/distilhubert-finetuned-gtzan-xP

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: 1.0379
• Accuracy: 0.81

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.0307	1.0	113	2.0561	0.41
1.4208	2.0	226	1.4850	0.63
1.1959	3.0	339	1.0617	0.66
0.6929	4.0	452	0.8228	0.74
0.5104	5.0	565	0.6969	0.77
0.4735	6.0	678	0.7412	0.79
0.2185	7.0	791	0.6586	0.76
0.3087	8.0	904	0.8234	0.78
0.1066	9.0	1017	0.8210	0.8
0.0841	10.0	1130	1.0040	0.8
0.0387	11.0	1243	0.9195	0.81
0.0091	12.0	1356	0.9208	0.82
0.006	13.0	1469	0.9190	0.81
0.0051	14.0	1582	0.9796	0.8
0.0038	15.0	1695	0.9823	0.8
0.0035	16.0	1808	1.0252	0.8
0.0032	17.0	1921	1.0172	0.8
0.0032	18.0	2034	1.0433	0.81
0.0029	19.0	2147	1.0577	0.81
0.0029	20.0	2260	1.0379	0.81

Framework versions

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