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-v3
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
metrics:
- name: Accuracy
type: accuracy
value: 0.87
pipeline_tag: audio-classification
distilhubert-finetuned-gtzan-v3
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: 1.0906
- Accuracy: 0.87
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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8287 | 1.0 | 899 | 0.8991 | 0.68 |
| 2.2164 | 2.0 | 1798 | 1.3184 | 0.71 |
| 0.0099 | 3.0 | 2697 | 0.9288 | 0.78 |
| 0.0679 | 4.0 | 3596 | 0.8131 | 0.84 |
| 0.0119 | 5.0 | 4495 | 1.1122 | 0.8 |
| 0.0051 | 6.0 | 5394 | 0.9594 | 0.86 |
| 0.0008 | 7.0 | 6293 | 0.9475 | 0.87 |
| 0.0002 | 8.0 | 7192 | 1.1026 | 0.86 |
| 0.0002 | 9.0 | 8091 | 1.0751 | 0.87 |
| 0.0002 | 10.0 | 8990 | 1.0906 | 0.87 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.1.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1