---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
base_model: ntu-spml/distilhubert
model-index:
- name: distilhubert-finetuned-gtzan
  results: []
---

<!-- 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: 0.4989
- Accuracy: 0.91

## 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: 4e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2359        | 1.0   | 112  | 0.4776          | 0.87     |
| 0.1235        | 2.0   | 225  | 0.4872          | 0.84     |
| 0.2083        | 3.0   | 337  | 0.4910          | 0.85     |
| 0.19          | 4.0   | 450  | 0.4953          | 0.87     |
| 0.1128        | 5.0   | 562  | 0.4801          | 0.87     |
| 0.1644        | 6.0   | 675  | 0.4703          | 0.87     |
| 0.0699        | 7.0   | 787  | 0.4692          | 0.85     |
| 0.1082        | 8.0   | 900  | 0.4708          | 0.87     |
| 0.0898        | 9.0   | 1012 | 0.4347          | 0.89     |
| 0.1071        | 10.0  | 1125 | 0.5310          | 0.85     |
| 0.0727        | 11.0  | 1237 | 0.4765          | 0.87     |
| 0.0338        | 12.0  | 1350 | 0.4859          | 0.87     |
| 0.0233        | 13.0  | 1462 | 0.4713          | 0.87     |
| 0.0248        | 14.0  | 1575 | 0.5068          | 0.88     |
| 0.0263        | 15.0  | 1687 | 0.4874          | 0.88     |
| 0.0185        | 16.0  | 1800 | 0.4925          | 0.88     |
| 0.0142        | 17.0  | 1912 | 0.4766          | 0.89     |
| 0.0178        | 18.0  | 2025 | 0.4850          | 0.89     |
| 0.0153        | 19.0  | 2137 | 0.4660          | 0.88     |
| 0.012         | 20.0  | 2250 | 0.4831          | 0.88     |
| 0.0113        | 21.0  | 2362 | 0.4965          | 0.89     |
| 0.0106        | 22.0  | 2475 | 0.5098          | 0.89     |
| 0.011         | 23.0  | 2587 | 0.5093          | 0.89     |
| 0.009         | 24.0  | 2700 | 0.4989          | 0.91     |
| 0.0094        | 25.0  | 2812 | 0.4999          | 0.89     |
| 0.0441        | 26.0  | 2925 | 0.5197          | 0.88     |
| 0.0079        | 27.0  | 3037 | 0.5115          | 0.89     |
| 0.0072        | 28.0  | 3150 | 0.5136          | 0.88     |
| 0.007         | 29.0  | 3262 | 0.5394          | 0.88     |
| 0.0068        | 30.0  | 3375 | 0.5374          | 0.88     |
| 0.0061        | 31.0  | 3487 | 0.5221          | 0.88     |
| 0.0533        | 32.0  | 3600 | 0.5775          | 0.87     |
| 0.0055        | 33.0  | 3712 | 0.5632          | 0.88     |
| 0.0059        | 34.0  | 3825 | 0.5584          | 0.87     |
| 0.0051        | 35.0  | 3937 | 0.5444          | 0.88     |
| 0.0051        | 36.0  | 4050 | 0.5373          | 0.88     |
| 0.0045        | 37.0  | 4162 | 0.5723          | 0.87     |
| 0.0058        | 38.0  | 4275 | 0.5773          | 0.87     |
| 0.0043        | 39.0  | 4387 | 0.5455          | 0.88     |
| 0.0044        | 40.0  | 4500 | 0.5686          | 0.88     |
| 0.004         | 41.0  | 4612 | 0.5622          | 0.87     |
| 0.004         | 42.0  | 4725 | 0.5797          | 0.88     |
| 0.0042        | 43.0  | 4837 | 0.5621          | 0.88     |
| 0.0037        | 44.0  | 4950 | 0.5734          | 0.87     |
| 0.0048        | 45.0  | 5062 | 0.5774          | 0.88     |
| 0.0039        | 46.0  | 5175 | 0.5901          | 0.87     |
| 0.0043        | 47.0  | 5287 | 0.5743          | 0.88     |
| 0.0043        | 48.0  | 5400 | 0.5757          | 0.87     |
| 0.0037        | 49.0  | 5512 | 0.5710          | 0.88     |
| 0.0036        | 49.78 | 5600 | 0.5759          | 0.87     |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3