|
--- |
|
license: apache-2.0 |
|
base_model: ntu-spml/distilhubert |
|
tags: |
|
- audio-classification |
|
- 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: 0.85 |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# distilhubert-finetuned-gtzan |
|
|
|
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.9665 |
|
- Accuracy: 0.85 |
|
|
|
## 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: 16 |
|
- eval_batch_size: 16 |
|
- 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: 20 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 2.1366 | 1.0 | 57 | 2.0376 | 0.46 | |
|
| 1.4386 | 2.0 | 114 | 1.3668 | 0.63 | |
|
| 1.0033 | 3.0 | 171 | 0.9527 | 0.7 | |
|
| 0.6843 | 4.0 | 228 | 0.8626 | 0.71 | |
|
| 0.48 | 5.0 | 285 | 0.5658 | 0.83 | |
|
| 0.2779 | 6.0 | 342 | 0.5189 | 0.86 | |
|
| 0.2089 | 7.0 | 399 | 0.6737 | 0.78 | |
|
| 0.0855 | 8.0 | 456 | 0.8386 | 0.75 | |
|
| 0.0262 | 9.0 | 513 | 0.7713 | 0.84 | |
|
| 0.0056 | 10.0 | 570 | 0.8664 | 0.82 | |
|
| 0.0103 | 11.0 | 627 | 1.0195 | 0.84 | |
|
| 0.0001 | 12.0 | 684 | 1.2668 | 0.84 | |
|
| 0.0 | 13.0 | 741 | 1.7110 | 0.83 | |
|
| 0.0 | 14.0 | 798 | 1.9586 | 0.84 | |
|
| 0.0 | 15.0 | 855 | 1.8981 | 0.85 | |
|
| 0.0 | 16.0 | 912 | 2.0259 | 0.84 | |
|
| 0.0 | 17.0 | 969 | 1.7196 | 0.85 | |
|
| 0.0284 | 18.0 | 1026 | 2.1840 | 0.85 | |
|
| 0.0 | 19.0 | 1083 | 1.9820 | 0.85 | |
|
| 0.0 | 20.0 | 1140 | 1.9665 | 0.85 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.0 |
|
- Pytorch 2.4.0 |
|
- Datasets 3.0.0 |
|
- Tokenizers 0.19.1 |
|
|
