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: 0.5528
• Accuracy: 0.84

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
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.1578	0.99	56	2.1203	0.55
1.6815	2.0	113	1.6607	0.57
1.2921	2.99	169	1.2421	0.64
1.0324	4.0	226	1.0260	0.7
0.8661	4.99	282	0.8973	0.7
0.6192	6.0	339	0.7420	0.79
0.5437	6.99	395	0.6951	0.8
0.4917	8.0	452	0.6996	0.78
0.3868	8.99	508	0.6648	0.81
0.3816	10.0	565	0.6584	0.79
0.1935	10.99	621	0.6101	0.84
0.128	12.0	678	0.5445	0.85
0.1144	12.99	734	0.5703	0.84
0.0828	14.0	791	0.5632	0.83
0.0928	14.87	840	0.5528	0.84

Framework versions

• Transformers 4.30.1
• Pytorch 2.0.0
• Datasets 2.1.0
• Tokenizers 0.13.3