distilhubert-finetuned-roughness – AI Model by NiloofarMomeni | AlphaNeural AI

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NiloofarMomeni/
distilhubert-finetuned-roughness

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audio-classification

generated_from_trainer

ntu-spml/distilhubert

endpoints_compatible

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distilhubert-finetuned-roughness

This model is a fine-tuned version of ntu-spml/distilhubert on the audiofolder dataset. It achieves the following results on the evaluation set:

Loss: 0.8437
Accuracy: 0.5824

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: 16
eval_batch_size: 16
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6586	1.0	47	0.6817	0.5165
0.5605	2.0	94	0.6754	0.6154
0.5651	3.0	141	0.6738	0.6813
0.4983	4.0	188	0.6875	0.6484
0.3993	5.0	235	0.7058	0.6374
0.4897	6.0	282	0.7701	0.6264
0.3277	7.0	329	0.7521	0.6374
0.2516	8.0	376	0.8220	0.6264
0.1733	9.0	423	0.8176	0.6264
0.1744	10.0	470	0.8437	0.5824

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.0