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

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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: 0.7127
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: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.2
num_epochs: 16

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.2072	0.99	56	2.1364	0.37
1.6502	2.0	113	1.5282	0.63
1.2965	2.99	169	1.1371	0.69
1.0407	4.0	226	0.9643	0.74
0.6558	4.99	282	0.7303	0.76
0.3615	6.0	339	0.7688	0.78
0.3705	6.99	395	0.5905	0.85
0.2165	8.0	452	0.6988	0.81
0.1098	8.99	508	0.4604	0.9
0.0647	10.0	565	0.6756	0.87
0.0179	10.99	621	0.8108	0.83
0.0278	12.0	678	0.6674	0.87
0.0075	12.99	734	0.8230	0.83
0.0061	14.0	791	0.8155	0.85
0.0056	14.99	847	0.7233	0.87
0.0055	15.86	896	0.7127	0.87

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.1
Tokenizers 0.13.3