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---
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: 0.835
---
<!-- 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: 0.9299
- Accuracy: 0.835
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1474 | 1.0 | 100 | 2.1098 | 0.47 |
| 1.5063 | 2.0 | 200 | 1.5695 | 0.575 |
| 1.2171 | 3.0 | 300 | 1.1629 | 0.685 |
| 0.9388 | 4.0 | 400 | 0.9617 | 0.7 |
| 0.6208 | 5.0 | 500 | 0.9273 | 0.685 |
| 0.6771 | 6.0 | 600 | 0.7753 | 0.785 |
| 0.5799 | 7.0 | 700 | 0.8492 | 0.695 |
| 0.1527 | 8.0 | 800 | 0.6581 | 0.805 |
| 0.0586 | 9.0 | 900 | 0.6788 | 0.82 |
| 0.0355 | 10.0 | 1000 | 0.7627 | 0.81 |
| 0.0186 | 11.0 | 1100 | 0.7585 | 0.82 |
| 0.0102 | 12.0 | 1200 | 0.8328 | 0.825 |
| 0.0074 | 13.0 | 1300 | 0.8543 | 0.835 |
| 0.0063 | 14.0 | 1400 | 0.8574 | 0.83 |
| 0.0271 | 15.0 | 1500 | 0.8889 | 0.835 |
| 0.0043 | 16.0 | 1600 | 0.9197 | 0.83 |
| 0.0045 | 17.0 | 1700 | 0.9130 | 0.835 |
| 0.0036 | 18.0 | 1800 | 0.9242 | 0.835 |
| 0.0042 | 19.0 | 1900 | 0.9279 | 0.835 |
| 0.0034 | 20.0 | 2000 | 0.9299 | 0.835 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.14.7
- Tokenizers 0.15.0
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