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: 0.84

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: 0.5583
Accuracy: 0.84

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: 11
eval_batch_size: 11
seed: 42
optimizer: Use OptimizerNames.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: 16
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.1691	1.0	82	2.0890	0.44
1.6372	2.0	164	1.5252	0.57
1.348	3.0	246	1.1441	0.72
0.8319	4.0	328	0.9642	0.74
0.5931	5.0	410	0.6850	0.82
0.5049	6.0	492	0.5984	0.83
0.3853	7.0	574	0.5737	0.81
0.217	8.0	656	0.6157	0.82
0.2158	9.0	738	0.4505	0.89
0.0759	10.0	820	0.5020	0.87
0.0412	11.0	902	0.5438	0.83
0.0325	12.0	984	0.5729	0.83
0.0221	13.0	1066	0.5432	0.83
0.0154	14.0	1148	0.5868	0.83
0.015	15.0	1230	0.5330	0.86
0.0138	16.0	1312	0.5583	0.84

Framework versions

Transformers 4.53.0.dev0
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1