metadata
library_name: transformers
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned_gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: gtzan
type: gtzan
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.75
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: 0.8025
- Accuracy: 0.75
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: 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: 7
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.7782 | 1.0 | 113 | 1.7870 | 0.49 |
| 1.2942 | 2.0 | 226 | 1.3317 | 0.62 |
| 1.0869 | 3.0 | 339 | 1.0283 | 0.73 |
| 0.5358 | 4.0 | 452 | 0.9647 | 0.72 |
| 0.6048 | 5.0 | 565 | 0.7985 | 0.76 |
| 0.4135 | 6.0 | 678 | 0.8013 | 0.75 |
| 0.3721 | 7.0 | 791 | 0.8025 | 0.75 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1
- Datasets 2.19.1
- Tokenizers 0.21.0