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: 0.7127
• 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: 0.2
• num_epochs: 16

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.2072	0.99	56	2.1364	0.37
1.6502	2.0	113	1.5282	0.63
1.2965	2.99	169	1.1371	0.69
1.0407	4.0	226	0.9643	0.74
0.6558	4.99	282	0.7303	0.76
0.3615	6.0	339	0.7688	0.78
0.3705	6.99	395	0.5905	0.85
0.2165	8.0	452	0.6988	0.81
0.1098	8.99	508	0.4604	0.9
0.0647	10.0	565	0.6756	0.87
0.0179	10.99	621	0.8108	0.83
0.0278	12.0	678	0.6674	0.87
0.0075	12.99	734	0.8230	0.83
0.0061	14.0	791	0.8155	0.85
0.0056	14.99	847	0.7233	0.87
0.0055	15.86	896	0.7127	0.87

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.1
• Tokenizers 0.13.3