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: 0.7435897435897436
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: 0.8173
- Accuracy: 0.7436
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: 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: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.1874 | 1.0 | 44 | 2.1429 | 0.3974 |
| 1.8257 | 2.0 | 88 | 1.7390 | 0.4872 |
| 1.4881 | 3.0 | 132 | 1.3711 | 0.6026 |
| 1.0373 | 4.0 | 176 | 1.1632 | 0.6667 |
| 0.7621 | 5.0 | 220 | 1.0026 | 0.7308 |
| 0.6114 | 6.0 | 264 | 0.8857 | 0.7436 |
| 0.5642 | 7.0 | 308 | 0.8796 | 0.7179 |
| 0.3386 | 8.0 | 352 | 1.0714 | 0.6923 |
| 0.3364 | 9.0 | 396 | 0.8363 | 0.7308 |
| 0.1678 | 10.0 | 440 | 0.7834 | 0.7436 |
| 0.1154 | 11.0 | 484 | 0.8173 | 0.7436 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0