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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: 0.85
---
<!-- 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: 0.8389
- 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: 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2212 | 1.0 | 113 | 2.1403 | 0.33 |
| 1.7338 | 2.0 | 226 | 1.7175 | 0.38 |
| 1.6091 | 3.0 | 339 | 1.2717 | 0.63 |
| 1.486 | 4.0 | 452 | 1.1239 | 0.66 |
| 0.9635 | 5.0 | 565 | 0.8998 | 0.75 |
| 0.7295 | 6.0 | 678 | 0.7689 | 0.77 |
| 0.6776 | 7.0 | 791 | 0.5876 | 0.81 |
| 0.4629 | 8.0 | 904 | 0.5705 | 0.82 |
| 0.5087 | 9.0 | 1017 | 0.5388 | 0.83 |
| 0.2993 | 10.0 | 1130 | 0.6192 | 0.83 |
| 0.0446 | 11.0 | 1243 | 0.6592 | 0.81 |
| 0.1498 | 12.0 | 1356 | 0.7227 | 0.82 |
| 0.0139 | 13.0 | 1469 | 0.6565 | 0.83 |
| 0.1203 | 14.0 | 1582 | 0.8235 | 0.83 |
| 0.3866 | 15.0 | 1695 | 0.6868 | 0.84 |
| 0.2157 | 16.0 | 1808 | 0.8739 | 0.83 |
| 0.1954 | 17.0 | 1921 | 0.8418 | 0.84 |
| 0.0051 | 18.0 | 2034 | 0.8042 | 0.84 |
| 0.0777 | 19.0 | 2147 | 0.8573 | 0.84 |
| 0.2714 | 20.0 | 2260 | 0.8389 | 0.85 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
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