distilhubert-finetuned-gtzan-2

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: 0.7203
• 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: 5e-05
• train_batch_size: 10
• eval_batch_size: 10
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.2
• num_epochs: 20
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.2521	1.0	90	2.2219	0.3
1.8502	2.0	180	1.8299	0.54
1.4155	3.0	270	1.4247	0.64
0.9885	4.0	360	1.0313	0.7
0.8111	5.0	450	0.8535	0.78
0.7023	6.0	540	0.7743	0.79
0.5663	7.0	630	0.6618	0.81
0.3577	8.0	720	0.6937	0.77
0.3003	9.0	810	0.6107	0.82
0.1321	10.0	900	0.5648	0.81
0.0488	11.0	990	0.5655	0.84
0.0323	12.0	1080	0.5612	0.86
0.0154	13.0	1170	0.6338	0.85
0.0108	14.0	1260	0.7292	0.84
0.0082	15.0	1350	0.7542	0.84
0.0065	16.0	1440	0.7123	0.86
0.0062	17.0	1530	0.6949	0.86
0.0848	18.0	1620	0.7332	0.85
0.0053	19.0	1710	0.7291	0.85
0.005	20.0	1800	0.7203	0.86

Framework versions

• Transformers 4.38.1
• Pytorch 2.2.1
• Datasets 2.17.1
• Tokenizers 0.15.2