metadata
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: 0.82
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: 1.0242
- Accuracy: 0.82
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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 |
|---|---|---|---|---|
| 1.496 | 0.99 | 56 | 1.8467 | 0.27 |
| 1.1313 | 2.0 | 113 | 1.1757 | 0.61 |
| 1.2432 | 2.99 | 169 | 1.3774 | 0.58 |
| 0.7301 | 4.0 | 226 | 0.9738 | 0.66 |
| 0.5192 | 4.99 | 282 | 0.9078 | 0.73 |
| 0.4163 | 6.0 | 339 | 0.9996 | 0.71 |
| 0.2178 | 6.99 | 395 | 0.7683 | 0.79 |
| 0.0814 | 8.0 | 452 | 0.9985 | 0.78 |
| 0.0075 | 8.99 | 508 | 1.1056 | 0.78 |
| 0.003 | 9.91 | 560 | 1.0242 | 0.82 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.1