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: 0.83
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: 0.9268
- 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: 0.0001
- 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- label_smoothing_factor: 0.1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.956 | 0.99 | 56 | 1.8086 | 0.5 |
| 1.4192 | 2.0 | 113 | 1.3941 | 0.67 |
| 1.1689 | 2.99 | 169 | 1.0402 | 0.76 |
| 0.9796 | 4.0 | 226 | 0.9111 | 0.79 |
| 0.7421 | 4.99 | 282 | 0.9137 | 0.85 |
| 0.6455 | 6.0 | 339 | 0.9775 | 0.83 |
| 0.6572 | 6.99 | 395 | 0.9103 | 0.83 |
| 0.5707 | 8.0 | 452 | 0.9437 | 0.83 |
| 0.5765 | 8.99 | 508 | 0.9216 | 0.84 |
| 0.519 | 9.91 | 560 | 0.9268 | 0.83 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3