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: 0.88
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: 0.5878
- Accuracy: 0.88
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.1351 | 1.0 | 113 | 1.9691 | 0.55 |
| 1.366 | 2.0 | 226 | 1.2824 | 0.71 |
| 1.1106 | 3.0 | 339 | 0.9803 | 0.72 |
| 0.9281 | 4.0 | 452 | 0.8342 | 0.73 |
| 0.625 | 5.0 | 565 | 0.6073 | 0.81 |
| 0.3546 | 6.0 | 678 | 0.6393 | 0.84 |
| 0.3526 | 7.0 | 791 | 0.5106 | 0.81 |
| 0.0914 | 8.0 | 904 | 0.3930 | 0.9 |
| 0.0563 | 9.0 | 1017 | 0.4089 | 0.88 |
| 0.0475 | 10.0 | 1130 | 0.5627 | 0.86 |
| 0.0144 | 11.0 | 1243 | 0.5824 | 0.86 |
| 0.0982 | 12.0 | 1356 | 0.5572 | 0.87 |
| 0.0082 | 13.0 | 1469 | 0.5770 | 0.88 |
| 0.0076 | 14.0 | 1582 | 0.5808 | 0.87 |
| 0.008 | 15.0 | 1695 | 0.5878 | 0.88 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3