distilhubert-finetuned-gtzan – AI Model by timothy-geiger | AlphaNeural AI

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timothy-geiger/
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: 1.0924
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: 2
eval_batch_size: 2
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
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.7495	1.0	450	1.7168	0.52
1.1633	2.0	900	1.0515	0.66
0.3792	3.0	1350	0.7312	0.73
0.5365	4.0	1800	0.9707	0.75
0.0234	5.0	2250	1.1124	0.75
0.0039	6.0	2700	0.9717	0.82
0.1781	7.0	3150	1.0491	0.82
0.0009	8.0	3600	1.1946	0.83
0.0007	9.0	4050	1.1116	0.84
0.0004	10.0	4500	1.0814	0.85
0.0004	11.0	4950	1.1160	0.85
0.0003	12.0	5400	1.1082	0.85
0.0003	13.0	5850	1.1311	0.86
0.0002	14.0	6300	1.1159	0.86
0.0003	15.0	6750	1.0924	0.87

Framework versions

Transformers 4.38.2
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.2