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: 0.88

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: 0.8080
Accuracy: 0.88

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
1.7263	1.0	113	1.6331	0.59
1.1167	2.0	226	1.2046	0.65
0.774	3.0	339	0.8365	0.8
0.6507	4.0	452	0.7015	0.81
0.5046	5.0	565	0.6722	0.81
0.2632	6.0	678	0.6743	0.82
0.203	7.0	791	0.7351	0.84
0.0902	8.0	904	0.5898	0.86
0.0215	9.0	1017	0.6213	0.87
0.0097	10.0	1130	0.6948	0.86
0.1171	11.0	1243	0.6228	0.87
0.0054	12.0	1356	0.7101	0.86
0.0035	13.0	1469	0.7626	0.87
0.0028	14.0	1582	0.7659	0.86
0.0027	15.0	1695	0.6993	0.87
0.0023	16.0	1808	0.7345	0.87
0.0023	17.0	1921	0.8363	0.86
0.0018	18.0	2034	0.7779	0.88
0.0018	19.0	2147	0.8275	0.87
0.0018	20.0	2260	0.8080	0.88

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.0
Tokenizers 0.15.0