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: 0.8249
• Accuracy: 0.87

The initial model trained for 20 epochs and overfit, so I recovered the best epoch (10) and pushed to hub. The metrics above reflect the latest model from epoch 10/checkpoint 2250.

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: 4
• eval_batch_size: 4
• 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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.9486	1.0	225	1.8744	0.54
1.0616	2.0	450	1.2196	0.66
1.0193	3.0	675	0.7841	0.78
0.81	4.0	900	0.7212	0.8
0.2171	5.0	1125	0.7194	0.77
0.0458	6.0	1350	0.8966	0.81
0.3485	7.0	1575	0.7960	0.81
0.09	8.0	1800	1.0860	0.82
0.0031	9.0	2025	0.7744	0.84
0.0026	10.0	2250	0.8249	0.87

Framework versions

• Transformers 4.33.0.dev0
• Pytorch 2.0.1+cu118
• Datasets 2.14.4.dev0
• Tokenizers 0.13.3