---
library_name: transformers
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
metrics:
- accuracy
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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1988
- Accuracy: 0.9404

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7008        | 1.0   | 76   | 1.6010          | 0.5497   |
| 0.8918        | 2.0   | 152  | 0.9346          | 0.6954   |
| 0.6802        | 3.0   | 228  | 0.6734          | 0.7815   |
| 0.3291        | 4.0   | 304  | 0.4803          | 0.8543   |
| 0.2609        | 5.0   | 380  | 0.3473          | 0.8808   |
| 0.1061        | 6.0   | 456  | 0.2439          | 0.9272   |
| 0.1252        | 7.0   | 532  | 0.2127          | 0.9536   |
| 0.084         | 8.0   | 608  | 0.1980          | 0.9404   |
| 0.0374        | 9.0   | 684  | 0.2005          | 0.9404   |
| 0.0431        | 10.0  | 760  | 0.1988          | 0.9404   |


### Framework versions

- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0