---
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: 0.78
---

<!-- 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: 0.7136
- Accuracy: 0.78

## 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: 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1794        | 1.0   | 112  | 2.1111          | 0.29     |
| 1.8527        | 2.0   | 225  | 1.7586          | 0.33     |
| 1.4409        | 3.0   | 337  | 1.4728          | 0.48     |
| 1.4484        | 4.0   | 450  | 1.3092          | 0.59     |
| 1.409         | 5.0   | 562  | 1.1073          | 0.68     |
| 1.0274        | 6.0   | 675  | 1.1107          | 0.67     |
| 1.0141        | 7.0   | 787  | 0.9898          | 0.7      |
| 0.8946        | 8.0   | 900  | 0.9540          | 0.68     |
| 1.0337        | 9.0   | 1012 | 0.8722          | 0.72     |
| 0.8065        | 10.0  | 1125 | 0.8196          | 0.74     |
| 1.0335        | 11.0  | 1237 | 0.7891          | 0.75     |
| 0.7712        | 12.0  | 1350 | 0.7298          | 0.78     |
| 0.7041        | 13.0  | 1462 | 0.7240          | 0.8      |
| 0.7287        | 14.0  | 1575 | 0.7206          | 0.78     |
| 0.9821        | 14.93 | 1680 | 0.7136          | 0.78     |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3