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cxlrd/revix_classifier

sourceHugging Facebsd-3-clauseupdated 1y agoView on Hugging Face

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revix_classifier

This model is a fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 on the None dataset. It achieves the following results on the evaluation set:

—Loss: 0.4733
—Accuracy: 0.9292
—Precision: 0.9492
—Recall: 0.9106
—F1: 0.9295

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
—trainbatchsize: 8
—evalbatchsize: 8
—seed: 42
—optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
—lrschedulertype: linear
—num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.4055	1.0	120	0.4456	0.8	0.7698	0.8699	0.8168
0.4578	2.0	240	0.4217	0.875	0.8345	0.9431	0.8855
0.2235	3.0	360	0.4959	0.85	0.9780	0.7236	0.8318
0.092	4.0	480	0.3820	0.9083	0.9391	0.8780	0.9076
0.2152	5.0	600	0.5537	0.8792	0.8615	0.9106	0.8854
0.0612	6.0	720	0.4747	0.9292	0.9417	0.9187	0.9300
0.0382	7.0	840	0.4424	0.925	0.9412	0.9106	0.9256
0.0015	8.0	960	0.4647	0.925	0.9646	0.8862	0.9237
0.0005	9.0	1080	0.4684	0.9292	0.9492	0.9106	0.9295
0.0006	10.0	1200	0.4733	0.9292	0.9492	0.9106	0.9295

Framework versions

—Transformers 4.56.1
—Pytorch 2.8.0+cu126
—Datasets 4.0.0
—Tokenizers 0.22.0