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: 0.84
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: 0.5992
- 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.9155 | 1.0 | 112 | 1.9092 | 0.42 |
| 1.2216 | 2.0 | 225 | 1.3236 | 0.61 |
| 0.9667 | 3.0 | 337 | 0.9840 | 0.75 |
| 0.8565 | 4.0 | 450 | 0.8011 | 0.8 |
| 0.5423 | 5.0 | 562 | 0.7550 | 0.77 |
| 0.4098 | 6.0 | 675 | 0.7600 | 0.75 |
| 0.2576 | 7.0 | 787 | 0.6959 | 0.81 |
| 0.1524 | 8.0 | 900 | 0.5586 | 0.82 |
| 0.1526 | 9.0 | 1012 | 0.5674 | 0.84 |
| 0.1845 | 9.96 | 1120 | 0.5992 | 0.84 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0