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: 0.83
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: 0.6889
- Accuracy: 0.83
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: 20
- eval_batch_size: 20
- seed: 42
- optimizer: Use OptimizerNames.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
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.1788 | 1.0 | 45 | 2.0607 | 0.41 |
| 1.573 | 2.0 | 90 | 1.5523 | 0.49 |
| 1.2957 | 3.0 | 135 | 1.2926 | 0.6 |
| 1.0198 | 4.0 | 180 | 1.0833 | 0.74 |
| 0.9007 | 5.0 | 225 | 0.9275 | 0.79 |
| 0.7798 | 6.0 | 270 | 0.8880 | 0.76 |
| 0.744 | 7.0 | 315 | 0.7562 | 0.84 |
| 0.5967 | 8.0 | 360 | 0.7294 | 0.8 |
| 0.5833 | 9.0 | 405 | 0.7123 | 0.8 |
| 0.6378 | 10.0 | 450 | 0.6889 | 0.83 |
Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.1