distilhubert-finetuned-gtzan_accuracy_93

This model is a fine-tuned version of yuval6967/distilhubert-finetuned-gtzan on the GTZAN dataset.

It achieves the following results on the evaluation set:

• Loss: 0.5121
• Accuracy: 0.93

Model description

• Fine-tuned model to demonstrate > 87% accuracy for the Huggingface Audio course

Intended uses & limitations

• Model is built to identify the genre of music based on a ~30 sec clip

Training and evaluation data

More information needed

Training procedure

• test_size = 0.20 was used for the split

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• 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
0.0316	1.0	100	0.4338	0.895
0.0031	2.0	200	0.7039	0.86
0.0069	3.0	300	0.4526	0.925
0.1799	4.0	400	0.7071	0.88
0.1783	5.0	500	0.5923	0.92
0.0011	6.0	600	0.5498	0.92
0.0005	7.0	700	0.4927	0.925
0.0005	8.0	800	0.6172	0.915
0.0004	9.0	900	0.4988	0.925
0.0004	10.0	1000	0.5121	0.93

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.1+cu118
• Datasets 2.13.1
• Tokenizers 0.13.3