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: 0.87
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: 0.5653
- Accuracy: 0.87
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: 3e-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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.0686 | 1.0 | 112 | 1.1059 | 0.65 |
| 0.7149 | 2.0 | 225 | 0.8356 | 0.74 |
| 0.7336 | 3.0 | 337 | 0.7207 | 0.82 |
| 0.5571 | 4.0 | 450 | 0.7318 | 0.8 |
| 0.3585 | 5.0 | 562 | 0.6034 | 0.83 |
| 0.2655 | 6.0 | 675 | 0.6394 | 0.81 |
| 0.1647 | 7.0 | 787 | 0.6114 | 0.81 |
| 0.1216 | 8.0 | 900 | 0.5841 | 0.85 |
| 0.1731 | 9.0 | 1012 | 0.5620 | 0.87 |
| 0.1316 | 9.96 | 1120 | 0.5653 | 0.87 |
Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3