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: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.86

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: 1.1749
Accuracy: 0.86

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: 4
eval_batch_size: 4
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: 25
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.0517	1.0	225	2.0004	0.47
1.3283	2.0	450	1.3458	0.57
0.729	3.0	675	0.8563	0.76
0.4007	4.0	900	0.6748	0.8
0.3923	5.0	1125	0.7340	0.78
0.2193	6.0	1350	0.8712	0.76
0.2383	7.0	1575	0.7414	0.79
0.3	8.0	1800	0.7387	0.86
0.006	9.0	2025	0.9203	0.85
0.002	10.0	2250	0.8956	0.85
0.0014	11.0	2475	0.9831	0.86
0.001	12.0	2700	0.9406	0.86
0.0009	13.0	2925	1.0288	0.86
0.0007	14.0	3150	1.0172	0.86
0.0007	15.0	3375	0.9912	0.89
0.0005	16.0	3600	1.0282	0.86
0.0006	17.0	3825	1.3495	0.83
0.2453	18.0	4050	1.0340	0.87
0.0004	19.0	4275	1.1048	0.86
0.0004	20.0	4500	1.3051	0.85
0.0003	21.0	4725	1.2280	0.85
0.0003	22.0	4950	1.2530	0.85
0.0003	23.0	5175	1.1992	0.85
0.0003	24.0	5400	1.1881	0.85
0.0003	25.0	5625	1.1749	0.86

Framework versions

Transformers 4.44.2
Pytorch 2.3.1
Datasets 2.21.0
Tokenizers 0.19.1