metadata
library_name: transformers
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: 0.84
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: 0.6434
- 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.8264 | 1.0 | 150 | 1.6957 | 0.57 |
| 1.0773 | 2.0 | 300 | 1.1566 | 0.61 |
| 0.8647 | 3.0 | 450 | 0.9786 | 0.71 |
| 0.6303 | 4.0 | 600 | 0.8904 | 0.71 |
| 0.4542 | 5.0 | 750 | 0.6887 | 0.8 |
| 0.1867 | 6.0 | 900 | 0.5686 | 0.83 |
| 0.1188 | 7.0 | 1050 | 0.6222 | 0.83 |
| 0.1415 | 8.0 | 1200 | 0.7066 | 0.83 |
| 0.0406 | 9.0 | 1350 | 0.6119 | 0.87 |
| 0.0331 | 10.0 | 1500 | 0.6434 | 0.84 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0