ntu-spml/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: 0.7428
• 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: 3.992986714871485e-05
• train_batch_size: 16
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9807885777224674,0.996064720140604) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 509
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	57	2.2832	0.3
No log	2.0	114	2.2273	0.28
No log	3.0	171	2.0861	0.46
No log	4.0	228	1.8473	0.5
No log	5.0	285	1.5146	0.6
No log	6.0	342	1.2140	0.69
No log	7.0	399	0.9856	0.74
No log	8.0	456	0.8056	0.79
1.6591	9.0	513	0.7135	0.8
1.6591	10.0	570	0.7642	0.75
1.6591	11.0	627	0.6344	0.79
1.6591	12.0	684	0.5982	0.83
1.6591	13.0	741	0.5369	0.86
1.6591	14.0	798	0.7501	0.79
1.6591	15.0	855	0.7493	0.78
1.6591	16.0	912	0.6891	0.83
1.6591	17.0	969	0.7492	0.8
0.2402	18.0	1026	0.6663	0.88
0.2402	19.0	1083	0.5750	0.89
0.2402	20.0	1140	0.8215	0.81
0.2402	21.0	1197	0.7435	0.79
0.2402	22.0	1254	0.8305	0.86
0.2402	23.0	1311	0.7636	0.83
0.2402	24.0	1368	0.9786	0.77
0.2402	25.0	1425	0.7082	0.88
0.2402	26.0	1482	0.7698	0.85
0.0206	27.0	1539	0.7360	0.87
0.0206	28.0	1596	0.8575	0.84
0.0206	29.0	1653	0.7428	0.87

Framework versions

• Transformers 4.32.0.dev0
• Pytorch 2.0.1
• Datasets 2.13.2.dev1
• Tokenizers 0.13.3