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
    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: 0.83

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: 0.9399
Accuracy: 0.83

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: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.1679	1.0	113	2.0910	0.38
1.4665	2.0	226	1.4798	0.53
1.2128	3.0	339	1.1715	0.64
0.7499	4.0	452	0.9591	0.68
0.6869	5.0	565	0.8078	0.76
0.3399	6.0	678	0.7513	0.81
0.3071	7.0	791	0.6606	0.84
0.0791	8.0	904	0.6416	0.84
0.1047	9.0	1017	0.7613	0.82
0.0784	10.0	1130	0.8558	0.82
0.0097	11.0	1243	0.9087	0.82
0.0071	12.0	1356	0.9155	0.83
0.0052	13.0	1469	0.9210	0.85
0.0044	14.0	1582	0.9543	0.84
0.0035	15.0	1695	0.9726	0.85
0.0032	16.0	1808	0.9183	0.84
0.0029	17.0	1921	0.9181	0.83
0.0027	18.0	2034	0.9575	0.84
0.0027	19.0	2147	0.9427	0.83
0.0026	20.0	2260	0.9399	0.83

Framework versions

Transformers 4.39.2
Pytorch 2.2.2+cu121
Datasets 2.18.0
Tokenizers 0.15.2