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: 0.7392
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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
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

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.3055	0.97	7	1.2863	0.73
1.2903	1.93	14	1.2504	0.7
1.2118	2.9	21	1.1450	0.77
1.1443	4.0	29	1.1224	0.74
1.006	4.97	36	1.0376	0.79
1.0174	5.93	43	0.9681	0.8
0.9155	6.9	50	0.9322	0.81
0.8781	8.0	58	0.9266	0.78
0.819	8.97	65	0.8473	0.79
0.7984	9.93	72	0.8225	0.77
0.7254	10.9	79	0.8096	0.81
0.6752	12.0	87	0.7801	0.81
0.6132	12.97	94	0.7687	0.8
0.615	13.93	101	0.7603	0.79
0.6162	14.9	108	0.7599	0.82
0.5678	16.0	116	0.7414	0.81
0.548	16.97	123	0.7423	0.81
0.5495	17.93	130	0.7378	0.81
0.5185	18.9	137	0.7396	0.81
0.5544	19.31	140	0.7392	0.81

Framework versions

Transformers 4.32.0.dev0
Pytorch 2.0.1
Datasets 2.13.1
Tokenizers 0.13.3