metadata

license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
model-index:
  - name: distilhubert-finetuned-gtzan-2
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: GTZAN
          type: marsyas/gtzan
          config: all
          split: train
          args: all
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.86

distilhubert-finetuned-gtzan-2

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: 0.7203
Accuracy: 0.86

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: 10
eval_batch_size: 10
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.2
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.2521	1.0	90	2.2219	0.3
1.8502	2.0	180	1.8299	0.54
1.4155	3.0	270	1.4247	0.64
0.9885	4.0	360	1.0313	0.7
0.8111	5.0	450	0.8535	0.78
0.7023	6.0	540	0.7743	0.79
0.5663	7.0	630	0.6618	0.81
0.3577	8.0	720	0.6937	0.77
0.3003	9.0	810	0.6107	0.82
0.1321	10.0	900	0.5648	0.81
0.0488	11.0	990	0.5655	0.84
0.0323	12.0	1080	0.5612	0.86
0.0154	13.0	1170	0.6338	0.85
0.0108	14.0	1260	0.7292	0.84
0.0082	15.0	1350	0.7542	0.84
0.0065	16.0	1440	0.7123	0.86
0.0062	17.0	1530	0.6949	0.86
0.0848	18.0	1620	0.7332	0.85
0.0053	19.0	1710	0.7291	0.85
0.005	20.0	1800	0.7203	0.86

Framework versions

Transformers 4.38.1
Pytorch 2.2.1
Datasets 2.17.1
Tokenizers 0.15.2