ft-hubert-on-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: 0.6574
• Accuracy: 0.825

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: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	100	1.5408	0.58
No log	2.0	200	1.1600	0.615
No log	3.0	300	0.9942	0.705
No log	4.0	400	0.8390	0.77
1.0814	5.0	500	0.8495	0.745
1.0814	6.0	600	0.6807	0.79
1.0814	7.0	700	0.7361	0.78
1.0814	8.0	800	0.6250	0.815
1.0814	9.0	900	0.6308	0.83
0.2344	10.0	1000	0.6574	0.825

Framework versions

• Transformers 4.48.1
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0