distilhubert-finetuned-gtzan-bs-16

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: 0.5229
• Accuracy: 0.87

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: 16
• eval_batch_size: 16
• 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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.1955	1.0	57	2.1119	0.44
1.6916	2.0	114	1.5973	0.61
1.1805	3.0	171	1.1849	0.74
1.0924	4.0	228	0.9771	0.7
0.7794	5.0	285	0.8201	0.78
0.6335	6.0	342	0.6969	0.82
0.6178	7.0	399	0.6632	0.84
0.4232	8.0	456	0.5841	0.83
0.3135	9.0	513	0.5960	0.82
0.198	10.0	570	0.5557	0.83
0.1651	11.0	627	0.5957	0.84
0.1191	12.0	684	0.5640	0.85
0.1267	13.0	741	0.5604	0.84
0.0784	14.0	798	0.5233	0.85
0.1076	15.0	855	0.5229	0.87

Framework versions

• Transformers 4.32.0.dev0
• Pytorch 2.0.1+cu118
• Datasets 2.14.3
• Tokenizers 0.13.3