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: 0.8

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: 0.8614
Accuracy: 0.8

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
gradient_accumulation_steps: 2
total_train_batch_size: 16
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
2.2268	0.99	56	2.1858	0.48
1.7472	2.0	113	1.6259	0.58
1.3293	2.99	169	1.1815	0.72
1.0368	4.0	226	1.0176	0.69
0.8106	4.99	282	0.8129	0.76
0.5371	6.0	339	0.8296	0.72
0.6545	6.99	395	0.7186	0.77
0.4676	8.0	452	0.6627	0.76
0.2729	8.99	508	0.5993	0.84
0.2113	10.0	565	0.6360	0.8
0.1475	10.99	621	0.6244	0.78
0.0616	12.0	678	0.6762	0.83
0.0429	12.99	734	0.7241	0.82
0.0259	14.0	791	0.7547	0.82
0.0207	14.99	847	0.7636	0.82
0.0179	16.0	904	0.7817	0.82
0.0304	16.99	960	0.7976	0.81
0.0146	18.0	1017	0.8193	0.81
0.0135	18.99	1073	0.8402	0.8
0.0136	19.82	1120	0.8614	0.8

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0