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: 0.7713
• Accuracy: 0.87

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: 0.0001
• 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
1.9891	0.99	56	1.9587	0.4
1.5271	2.0	113	1.4658	0.56
1.074	2.99	169	0.9198	0.79
0.8036	4.0	226	0.9191	0.7
0.5017	4.99	282	0.7299	0.8
0.3405	6.0	339	0.6682	0.8
0.2178	6.99	395	0.6877	0.82
0.116	8.0	452	0.6092	0.83
0.0616	8.99	508	0.6579	0.85
0.0229	10.0	565	0.8793	0.8
0.0128	10.99	621	0.6722	0.87
0.0094	12.0	678	0.7586	0.87
0.0073	12.99	734	0.7636	0.87
0.007	14.0	791	0.7728	0.87
0.0073	14.87	840	0.7713	0.87

Framework versions

• Transformers 4.30.2
• Pytorch 1.11.0+cu102
• Datasets 2.4.0
• Tokenizers 0.12.1