distilhubert-finetuned-gtzan – AI Model by DavidFM43 | AlphaNeural AI

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DavidFM43/
distilhubert-finetuned-gtzan

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audio-classification

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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: 0.6925
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: 0.0001115511981046745
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 9

Training results

Training Loss	Epoch	Step	Accuracy	Validation Loss
1.278	1.0	112	0.57	1.3298
0.8315	2.0	225	0.73	0.9432
0.7709	3.0	337	0.72	0.9310
0.5427	4.0	450	0.72	0.8738
0.2645	4.98	560	0.79	0.6648
0.245	6.0	672	0.83	0.6147
0.1331	6.99	784	0.83	0.6305
0.1863	8.0	896	0.6356	0.84
0.0843	8.99	1008	0.6925	0.83

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3