metadata

license: apache-2.0
base_model: Sandiago21/distilhubert-finetuned-gtzan
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: 0.88

distilhubert-finetuned-gtzan

This model is a fine-tuned version of Sandiago21/distilhubert-finetuned-gtzan on the GTZAN dataset. It achieves the following results on the evaluation set:

Loss: 0.9951
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: 0.0001
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: 40

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0951	1.0	57	0.5566	0.87
0.0629	2.0	114	0.6819	0.83
0.0231	3.0	171	0.6118	0.86
0.0159	4.0	228	0.9208	0.83
0.0374	5.0	285	0.8746	0.85
0.1714	6.0	342	0.6671	0.87
0.2148	7.0	399	1.1850	0.79
0.0147	8.0	456	1.0551	0.79
0.0788	9.0	513	1.5179	0.79
0.0015	10.0	570	1.3290	0.8
0.0049	11.0	627	1.0943	0.85
0.0012	12.0	684	1.0667	0.85
0.0043	13.0	741	1.1816	0.82
0.0015	14.0	798	0.9108	0.88
0.0011	15.0	855	1.0289	0.87
0.001	16.0	912	0.7696	0.87
0.0006	17.0	969	0.8539	0.87
0.1001	18.0	1026	1.1917	0.78
0.0017	19.0	1083	1.0016	0.83
0.0525	20.0	1140	0.9513	0.88
0.0004	21.0	1197	0.9268	0.86
0.0003	22.0	1254	1.1209	0.82
0.0003	23.0	1311	0.9270	0.87
0.0003	24.0	1368	1.1148	0.84
0.0003	25.0	1425	1.0507	0.85
0.0002	26.0	1482	1.0156	0.86
0.0002	27.0	1539	1.0062	0.87
0.0002	28.0	1596	1.0124	0.87
0.0002	29.0	1653	1.0154	0.87
0.0002	30.0	1710	1.0092	0.88
0.0002	31.0	1767	1.0123	0.88
0.0175	32.0	1824	0.9928	0.88
0.0002	33.0	1881	1.0014	0.88
0.0115	34.0	1938	0.9989	0.88
0.0001	35.0	1995	0.9871	0.88
0.0001	36.0	2052	0.9920	0.88
0.0002	37.0	2109	0.9974	0.88
0.0002	38.0	2166	0.9950	0.88
0.0001	39.0	2223	0.9997	0.88
0.0001	40.0	2280	0.9951	0.88

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3