distilhubert-finetuned-gtzan – AI Model by reichenbach | AlphaNeural AI

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reichenbach/
distilhubert-finetuned-gtzan

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audio-classification

generated_from_trainer

ntu-spml/distilhubert

endpoints_compatible

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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: 0.8933
Accuracy: 0.83

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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.1059	1.0	113	1.9709	0.39
1.4561	2.0	226	1.2865	0.59
1.03	3.0	339	0.9918	0.75
0.8979	4.0	452	0.8700	0.77
0.6697	5.0	565	0.7090	0.79
0.3289	6.0	678	0.6646	0.77
0.3612	7.0	791	0.6384	0.83
0.068	8.0	904	0.5989	0.85
0.1159	9.0	1017	0.7136	0.83
0.0228	10.0	1130	0.8329	0.84
0.0484	11.0	1243	0.8401	0.84
0.0283	12.0	1356	0.8522	0.84
0.008	13.0	1469	0.8865	0.84
0.0066	14.0	1582	0.9048	0.85
0.0067	15.0	1695	0.8933	0.83

Framework versions

Transformers 4.31.0
Pytorch 2.0.0
Datasets 2.14.4
Tokenizers 0.13.3