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-bs-8-fp16-false
    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: 0.84

distilhubert-finetuned-gtzan-bs-8-fp16-false

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: 0.7162
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: 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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.0985	1.0	113	1.9610	0.47
1.4194	2.0	226	1.3265	0.69
1.0868	3.0	339	0.9856	0.72
0.9134	4.0	452	0.8709	0.74
0.6349	5.0	565	0.7497	0.79
0.3755	6.0	678	0.7343	0.78
0.4007	7.0	791	0.5346	0.84
0.1607	8.0	904	0.5604	0.86
0.1802	9.0	1017	0.5005	0.89
0.0319	10.0	1130	0.6562	0.84
0.0158	11.0	1243	0.6639	0.84
0.1126	12.0	1356	0.6965	0.85
0.0095	13.0	1469	0.6919	0.84
0.0083	14.0	1582	0.7089	0.85
0.0088	15.0	1695	0.7162	0.84

Framework versions

Transformers 4.32.0.dev0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3