metadata

license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
  - generated_from_trainer
datasets:
  - aesdd
metrics:
  - accuracy
model-index:
  - name: distilhubert-finetuned-AESDD
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: aesdd
          type: aesdd
          config: AESDD
          split: train
          args: AESDD
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9016393442622951

distilhubert-finetuned-AESDD

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

Loss: 0.4389
Accuracy: 0.9016

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: 8
eval_batch_size: 8
seed: 42
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

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.1249	1.0	68	1.1905	0.5082
0.7441	2.0	136	0.8850	0.6721
0.5941	3.0	204	0.6579	0.8361
0.4349	4.0	272	0.9638	0.6721
0.2612	5.0	340	0.5081	0.8689
0.1883	6.0	408	0.6223	0.8197
0.0978	7.0	476	0.4671	0.8689
0.0425	8.0	544	0.4338	0.8852
0.0264	9.0	612	0.4488	0.8525
0.0219	10.0	680	0.4389	0.9016

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.2
Tokenizers 0.13.3