metadata
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-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: 0.81
distilhubert-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: 1.4633
- 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: 1
- eval_batch_size: 1
- 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3417 | 1.0 | 899 | 1.2462 | 0.6 |
| 1.2614 | 2.0 | 1798 | 1.7089 | 0.62 |
| 0.0142 | 3.0 | 2697 | 1.1245 | 0.74 |
| 0.0175 | 4.0 | 3596 | 1.0612 | 0.81 |
| 0.0012 | 5.0 | 4495 | 1.2188 | 0.8 |
| 0.0005 | 6.0 | 5394 | 0.9683 | 0.83 |
| 0.0003 | 7.0 | 6293 | 1.4129 | 0.81 |
| 0.0001 | 8.0 | 7192 | 1.4452 | 0.8 |
| 0.0001 | 9.0 | 8091 | 1.5118 | 0.82 |
| 0.0001 | 10.0 | 8990 | 1.4633 | 0.81 |
Framework versions
- Transformers 4.41.0
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.19.1