distilhubert-finetuned-gtzan

This model is a fine-tuned version of sm226/distilhubert-finetuned-gtzan on the GTZAN dataset. It achieves the following results on the evaluation set:

• Loss: 0.8792
• Accuracy: 0.87

Model description

Base model used for fine tuning: ntu-spml/distilhubert

Intended uses & limitations

More information needed

Training and evaluation data

marsyas/gtzan

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0005
• 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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.5395	1.0	113	1.7428	0.39
1.317	2.0	226	1.3209	0.53
1.3358	3.0	339	1.0134	0.7
0.9406	4.0	452	1.3402	0.53
0.5655	5.0	565	0.8318	0.74
0.3066	6.0	678	0.8744	0.8
0.2673	7.0	791	1.0217	0.78
0.2582	8.0	904	1.0037	0.78
0.0319	9.0	1017	0.9541	0.85
0.0011	10.0	1130	0.8792	0.87

Framework versions

• Transformers 4.33.2
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.13.3