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: 0.91

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: 0.3539
Accuracy: 0.91

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 18

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.2281	1.0	112	2.1128	0.26
1.7082	2.0	225	1.6252	0.52
1.267	3.0	337	1.3100	0.54
1.1791	4.0	450	1.0496	0.71
1.1765	5.0	562	0.8928	0.74
0.5714	6.0	675	0.8298	0.77
0.4869	7.0	787	0.7145	0.79
0.4967	8.0	900	0.6990	0.82
0.8314	9.0	1012	0.5657	0.83
0.4633	10.0	1125	0.4589	0.89
0.5547	11.0	1237	0.4919	0.86
0.4827	12.0	1350	0.4069	0.92
0.324	13.0	1462	0.4634	0.87
0.5224	14.0	1575	0.4419	0.86
0.1873	15.0	1687	0.3988	0.89
0.2852	16.0	1800	0.3788	0.9
0.3169	17.0	1912	0.3526	0.89
0.4491	17.92	2016	0.3539	0.91

Framework versions

Transformers 4.32.0.dev0
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3