metadata

license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
  - precision
  - recall
  - f1
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: 0.87
          - name: Precision
            type: precision
            value: 0.8753213453213452
          - name: Recall
            type: recall
            value: 0.87
          - name: F1
            type: f1
            value: 0.8641214483158217
pipeline_tag: audio-classification

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: 0.5488
Accuracy: 0.87
Precision: 0.8753
Recall: 0.87
F1: 0.8641

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	Precision	Recall	F1
2.1729	1.0	113	2.0581	0.63	0.6670	0.63	0.5957
1.6552	2.0	226	1.3957	0.7	0.6894	0.7	0.6857
1.0753	3.0	339	0.9783	0.75	0.8154	0.75	0.7277
0.8519	4.0	452	0.8087	0.75	0.8120	0.75	0.7380
0.8623	5.0	565	0.7393	0.75	0.7622	0.75	0.7373
0.506	6.0	678	0.6861	0.81	0.8449	0.81	0.7997
0.2052	7.0	791	0.6505	0.81	0.8254	0.81	0.8024
0.1583	8.0	904	0.5365	0.86	0.8770	0.86	0.8545
0.0699	9.0	1017	0.5488	0.87	0.8753	0.87	0.8641
0.0177	10.0	1130	0.6330	0.83	0.8312	0.83	0.8245
0.0071	11.0	1243	0.6268	0.84	0.8410	0.84	0.8348
0.0746	12.0	1356	0.6051	0.87	0.8732	0.87	0.8675

Framework versions

Transformers 4.42.3
Pytorch 2.1.2
Datasets 2.20.0
Tokenizers 0.19.1