---
library_name: transformers
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned_gtzan
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: gtzan
      type: gtzan
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.74
---

<!-- 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: 0.8663
- Accuracy: 0.74

## 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 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: 7
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7423        | 1.0   | 113  | 1.8224          | 0.43     |
| 1.2488        | 2.0   | 226  | 1.3386          | 0.59     |
| 1.0844        | 3.0   | 339  | 1.0498          | 0.7      |
| 0.49          | 4.0   | 452  | 0.9295          | 0.72     |
| 0.5017        | 5.0   | 565  | 0.8418          | 0.75     |
| 0.3612        | 6.0   | 678  | 0.8414          | 0.75     |
| 0.3045        | 7.0   | 791  | 0.8663          | 0.74     |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1
- Datasets 2.19.1
- Tokenizers 0.21.0