metadata
library_name: peft
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-gtzan-loraAL-dropout0.5-split3
results:
- task:
type: audio-classification
name: Audio Classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: None
args: all
metrics:
- type: accuracy
value: 0.84
name: Accuracy
distilhubert-gtzan-loraAL-dropout0.5-split3
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: 1.7145
- 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: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.2071 | 1.0 | 169 | 1.4833 | 0.46 |
| 1.1886 | 2.0 | 338 | 1.1455 | 0.6367 |
| 0.8256 | 3.0 | 507 | 0.9532 | 0.7367 |
| 0.6972 | 4.0 | 676 | 1.0910 | 0.7 |
| 0.6284 | 5.0 | 845 | 0.7128 | 0.7667 |
| 0.5584 | 6.0 | 1014 | 0.7270 | 0.8 |
| 0.4614 | 7.0 | 1183 | 0.7947 | 0.8067 |
| 0.3895 | 8.0 | 1352 | 0.8538 | 0.7967 |
| 0.3605 | 9.0 | 1521 | 1.1667 | 0.7633 |
| 0.2569 | 10.0 | 1690 | 1.0894 | 0.8133 |
| 0.2078 | 11.0 | 1859 | 1.2071 | 0.8033 |
| 0.159 | 12.0 | 2028 | 1.4872 | 0.78 |
| 0.1177 | 13.0 | 2197 | 1.2216 | 0.8467 |
| 0.0692 | 14.0 | 2366 | 1.4827 | 0.82 |
| 0.0624 | 15.0 | 2535 | 1.5108 | 0.8433 |
| 0.0406 | 16.0 | 2704 | 1.6933 | 0.8333 |
| 0.0136 | 17.0 | 2873 | 1.6956 | 0.8333 |
| 0.0148 | 18.0 | 3042 | 1.7178 | 0.8367 |
| 0.0065 | 19.0 | 3211 | 1.7237 | 0.8333 |
| 0.0078 | 20.0 | 3380 | 1.7145 | 0.84 |
Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0
- Datasets 3.3.2
- Tokenizers 0.21.0