distilhubert-finetuned-gtzan – AI Model by d-karpone | AlphaNeural AI

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distilhubert-finetuned-gtzan

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audio-classification

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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:

Accuracy: 0.87
Loss: 0.6300

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
gradient_accumulation_steps: 3
total_train_batch_size: 6
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Accuracy	Validation Loss
1.8192	1.0	150	0.41	1.7706
1.1745	2.0	300	0.64	1.2586
0.7885	3.0	450	0.76	0.8419
0.661	4.0	600	0.81	0.7019
0.3334	5.0	750	0.84	0.5766
0.3265	6.0	900	0.83	0.5862
0.0928	7.0	1050	0.88	0.5613
0.0698	8.0	1200	0.86	0.6432
0.0562	9.0	1350	0.87	0.6141
0.0268	10.0	1500	0.87	0.6300

Framework versions

Transformers 4.29.2
Pytorch 2.0.1
Datasets 2.13.1
Tokenizers 0.13.3