metadata
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-VD
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: 0.8933256172839507
distilhubert-finetuned-VD
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: 0.7226
- Accuracy: 0.8933
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: 10
- eval_batch_size: 10
- 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: 25
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3302 | 1.0 | 195 | 0.3716 | 0.8800 |
| 0.6059 | 2.0 | 390 | 0.5195 | 0.8090 |
| 0.4938 | 3.0 | 585 | 1.0102 | 0.6260 |
| 0.836 | 4.0 | 780 | 1.1662 | 0.6742 |
| 0.2234 | 5.0 | 975 | 0.6792 | 0.8389 |
| 0.1444 | 6.0 | 1170 | 0.9137 | 0.8239 |
| 0.2986 | 7.0 | 1365 | 0.7987 | 0.8623 |
| 0.0004 | 8.0 | 1560 | 1.5075 | 0.7687 |
| 0.0005 | 9.0 | 1755 | 0.7226 | 0.8933 |
| 0.0002 | 10.0 | 1950 | 0.8246 | 0.8829 |
| 0.0002 | 11.0 | 2145 | 1.4227 | 0.8129 |
| 0.0001 | 12.0 | 2340 | 1.0478 | 0.8665 |
| 0.0001 | 13.0 | 2535 | 1.3328 | 0.8322 |
| 0.0001 | 14.0 | 2730 | 1.3480 | 0.8347 |
| 0.0001 | 15.0 | 2925 | 1.3559 | 0.8370 |
| 0.0 | 16.0 | 3120 | 1.3589 | 0.8407 |
| 0.0 | 17.0 | 3315 | 1.3706 | 0.8410 |
| 0.0 | 18.0 | 3510 | 1.3831 | 0.8410 |
| 0.0 | 19.0 | 3705 | 1.3954 | 0.8410 |
| 0.0 | 20.0 | 3900 | 1.4027 | 0.8412 |
| 0.0 | 21.0 | 4095 | 1.4132 | 0.8409 |
| 0.0 | 22.0 | 4290 | 1.4218 | 0.8407 |
| 0.0 | 23.0 | 4485 | 1.4272 | 0.8407 |
| 0.0 | 24.0 | 4680 | 1.4321 | 0.8399 |
| 0.0 | 25.0 | 4875 | 1.4337 | 0.8399 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2