metadata

license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
model-index:
  - name: distilhubert-finetuned-gtzanVD
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: GTZAN
          type: marsyas/gtzan
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9839786381842457

distilhubert-finetuned-gtzanVD

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: 0.1334
Accuracy: 0.9840

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: 8
eval_batch_size: 8
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: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.3554	1.0	842	0.1898	0.9439
0.1136	2.0	1684	0.1657	0.9626
0.1571	3.0	2526	0.1132	0.9693
0.0004	4.0	3368	0.1235	0.9786
0.0011	5.0	4210	0.1555	0.9680
0.0001	6.0	5052	0.3138	0.9493
0.0001	7.0	5894	0.1825	0.9680
0.0001	8.0	6736	0.1982	0.9706
0.0001	9.0	7578	0.1690	0.9693
0.3166	10.0	8420	0.1487	0.9733
0.0	11.0	9262	0.2615	0.9680
0.0	12.0	10104	0.1536	0.9800
0.0001	13.0	10946	0.5478	0.9399
0.0	14.0	11788	0.1334	0.9840
0.0	15.0	12630	0.1270	0.9746
0.0	16.0	13472	0.1053	0.9840
0.0	17.0	14314	0.1181	0.9813
0.0	18.0	15156	0.1165	0.9826
0.0	19.0	15998	0.1191	0.9826
0.0	20.0	16840	0.1188	0.9826

Framework versions

Transformers 4.39.3
Pytorch 2.2.2+cu121
Datasets 2.18.0
Tokenizers 0.15.2