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: 0.85

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: 1.7615
Accuracy: 0.85

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: 16
eval_batch_size: 16
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: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.2914	1.0	57	2.2595	0.25
2.1225	2.0	114	2.0265	0.57
1.7631	3.0	171	1.6482	0.59
1.3445	4.0	228	1.3380	0.62
1.1548	5.0	285	1.0589	0.72
0.9289	6.0	342	0.8541	0.76
0.708	7.0	399	0.7628	0.79
0.4497	8.0	456	0.7088	0.82
0.4061	9.0	513	0.6118	0.85
0.286	10.0	570	0.6684	0.79
0.1739	11.0	627	0.5965	0.83
0.1103	12.0	684	0.8414	0.81
0.0922	13.0	741	0.5937	0.87
0.0166	14.0	798	0.5786	0.86
0.0075	15.0	855	0.7950	0.84
0.0014	16.0	912	0.8492	0.87
0.0006	17.0	969	1.2642	0.82
0.0815	18.0	1026	1.1173	0.87
0.0	19.0	1083	1.2181	0.86
0.0	20.0	1140	1.6673	0.85
0.0	21.0	1197	1.4749	0.86
0.0611	22.0	1254	2.2533	0.82
0.0978	23.0	1311	2.0092	0.86
0.0	24.0	1368	2.3586	0.83
0.0	25.0	1425	1.7617	0.86
0.0	26.0	1482	1.7425	0.86
0.0	27.0	1539	1.8418	0.85
0.0	28.0	1596	1.6987	0.87
0.0	29.0	1653	1.9399	0.85
0.0	30.0	1710	2.4230	0.81
0.0	31.0	1767	1.4312	0.88
0.1807	32.0	1824	1.5278	0.87
0.0	33.0	1881	1.3795	0.88
0.0	34.0	1938	1.5051	0.88
0.0	35.0	1995	1.6587	0.85
0.0	36.0	2052	1.6256	0.86
0.0	37.0	2109	1.7290	0.85
0.0	38.0	2166	1.8676	0.87
0.0	39.0	2223	1.8963	0.86
0.166	40.0	2280	1.7057	0.85
0.1293	41.0	2337	1.4235	0.87
0.1491	42.0	2394	1.7916	0.85
0.1416	43.0	2451	1.8634	0.85
0.0	44.0	2508	1.6286	0.86
0.0526	45.0	2565	1.6242	0.86
0.0	46.0	2622	1.7576	0.85
0.0	47.0	2679	1.7897	0.85
0.0	48.0	2736	1.7571	0.85
0.0018	49.0	2793	1.6993	0.85
0.0	50.0	2850	1.7615	0.85

Framework versions

Transformers 4.42.3
Pytorch 2.1.2
Datasets 2.20.0
Tokenizers 0.19.1