metadata

library_name: transformers
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: 0.86

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:

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: 8e-05
train_batch_size: 12
eval_batch_size: 12
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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
2.1317	1.0	75	2.0386	0.33
1.36	2.0	150	1.4142	0.58
1.1456	3.0	225	1.1110	0.66
0.6417	4.0	300	1.0142	0.69
0.3324	5.0	375	0.5881	0.82
0.2208	6.0	450	0.5516	0.84
0.3346	7.0	525	0.5267	0.87
0.2309	8.0	600	0.7404	0.8
0.0267	9.0	675	0.6636	0.8
0.0309	10.0	750	0.6390	0.84
0.0076	11.0	825	0.6949	0.85
0.0053	12.0	900	0.6405	0.87
0.005	13.0	975	0.7065	0.84
0.004	14.0	1050	0.8570	0.84
0.0031	15.0	1125	0.6735	0.88
0.0028	16.0	1200	0.7023	0.85
0.0027	17.0	1275	0.6823	0.86
0.0369	18.0	1350	0.7320	0.85
0.0024	19.0	1425	0.6656	0.86
0.0023	20.0	1500	0.6628	0.86

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.20.3