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: 0.81

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: 1.0178
Accuracy: 0.81

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: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.2598	1.0	57	2.2140	0.34
1.8981	2.0	114	1.8262	0.56
1.4487	3.0	171	1.4402	0.64
1.1792	4.0	228	1.1520	0.69
0.9231	5.0	285	0.9415	0.75
0.7141	6.0	342	0.8904	0.73
0.5477	7.0	399	0.7395	0.78
0.3968	8.0	456	0.6359	0.81
0.4259	9.0	513	0.6345	0.8
0.2474	10.0	570	0.6333	0.8
0.1379	11.0	627	0.5374	0.83
0.0781	12.0	684	0.6484	0.84
0.0337	13.0	741	0.7072	0.84
0.0211	14.0	798	0.7023	0.83
0.0135	15.0	855	0.8199	0.83
0.0097	16.0	912	0.8009	0.83
0.065	17.0	969	0.8992	0.81
0.0067	18.0	1026	0.8628	0.82
0.0118	19.0	1083	0.6922	0.85
0.0052	20.0	1140	0.8001	0.84
0.077	21.0	1197	0.8324	0.82
0.0043	22.0	1254	0.9468	0.8
0.0039	23.0	1311	0.8866	0.8
0.0696	24.0	1368	0.9424	0.82
0.0037	25.0	1425	0.7855	0.81
0.0631	26.0	1482	0.7659	0.82
0.0592	27.0	1539	0.8605	0.83
0.0034	28.0	1596	0.9266	0.82
0.0032	29.0	1653	0.9831	0.82
0.0032	30.0	1710	1.0178	0.81

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.15.0
Tokenizers 0.15.0