metadata

license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
  - precision
  - recall
  - f1
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: 0.87
          - name: Precision
            type: precision
            value: 0.8802816627816629
          - name: Recall
            type: recall
            value: 0.87
          - name: F1
            type: f1
            value: 0.8627110595989314

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: 0.6501
Accuracy: 0.87
Precision: 0.8803
Recall: 0.87
F1: 0.8627

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	Precision	Recall	F1
2.1743	1.0	113	2.0604	0.38	0.5273	0.38	0.3101
1.6179	2.0	226	1.4299	0.62	0.6136	0.62	0.5877
1.0981	3.0	339	1.0223	0.79	0.8516	0.79	0.7669
0.9785	4.0	452	0.8722	0.71	0.7748	0.71	0.6733
0.8834	5.0	565	0.8363	0.76	0.7691	0.76	0.7449
0.4936	6.0	678	0.6241	0.82	0.8313	0.82	0.8193
0.2772	7.0	791	0.5648	0.85	0.8623	0.85	0.8459
0.1213	8.0	904	0.6919	0.81	0.8429	0.81	0.7997
0.0958	9.0	1017	0.5527	0.86	0.8682	0.86	0.8541
0.0194	10.0	1130	0.6840	0.85	0.8645	0.85	0.8420
0.0151	11.0	1243	0.6214	0.86	0.8642	0.86	0.8542
0.1239	12.0	1356	0.6501	0.87	0.8803	0.87	0.8627
0.0049	13.0	1469	0.6651	0.87	0.8803	0.87	0.8627
0.0043	14.0	1582	0.7188	0.87	0.8803	0.87	0.8627
0.0035	15.0	1695	0.6808	0.87	0.8803	0.87	0.8627

Framework versions

Transformers 4.42.3
Pytorch 2.1.2
Datasets 2.20.0
Tokenizers 0.19.1