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: 0.6501
• Accuracy: 0.87
• Precision: 0.8803
• Recall: 0.87
• F1: 0.8627

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: 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: 20
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
2.1743	1.0	113	2.0604	0.38	0.5273	0.38	0.3101
1.6179	2.0	226	1.4299	0.62	0.6136	0.62	0.5877
1.0981	3.0	339	1.0223	0.79	0.8516	0.79	0.7669
0.9785	4.0	452	0.8722	0.71	0.7748	0.71	0.6733
0.8834	5.0	565	0.8363	0.76	0.7691	0.76	0.7449
0.4936	6.0	678	0.6241	0.82	0.8313	0.82	0.8193
0.2772	7.0	791	0.5648	0.85	0.8623	0.85	0.8459
0.1213	8.0	904	0.6919	0.81	0.8429	0.81	0.7997
0.0958	9.0	1017	0.5527	0.86	0.8682	0.86	0.8541
0.0194	10.0	1130	0.6840	0.85	0.8645	0.85	0.8420
0.0151	11.0	1243	0.6214	0.86	0.8642	0.86	0.8542
0.1239	12.0	1356	0.6501	0.87	0.8803	0.87	0.8627
0.0049	13.0	1469	0.6651	0.87	0.8803	0.87	0.8627
0.0043	14.0	1582	0.7188	0.87	0.8803	0.87	0.8627
0.0035	15.0	1695	0.6808	0.87	0.8803	0.87	0.8627

Framework versions

• Transformers 4.42.3
• Pytorch 2.1.2
• Datasets 2.20.0
• Tokenizers 0.19.1