---
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: 0.81
---

<!-- 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: 1.1842
- Accuracy: 0.81

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1069        | 1.0   | 29   | 2.0003          | 0.46     |
| 1.8026        | 2.0   | 58   | 1.6073          | 0.59     |
| 1.3938        | 3.0   | 87   | 1.2140          | 0.72     |
| 1.0295        | 4.0   | 116  | 1.0740          | 0.64     |
| 0.8339        | 5.0   | 145  | 0.9243          | 0.71     |
| 0.6347        | 6.0   | 174  | 0.8837          | 0.72     |
| 0.4137        | 7.0   | 203  | 0.8274          | 0.78     |
| 0.3162        | 8.0   | 232  | 0.7596          | 0.82     |
| 0.2055        | 9.0   | 261  | 0.8541          | 0.77     |
| 0.2237        | 10.0  | 290  | 0.7220          | 0.78     |
| 0.0601        | 11.0  | 319  | 0.7765          | 0.81     |
| 0.0817        | 12.0  | 348  | 0.7603          | 0.86     |
| 0.0196        | 13.0  | 377  | 0.8611          | 0.8      |
| 0.0641        | 14.0  | 406  | 0.9281          | 0.8      |
| 0.0253        | 15.0  | 435  | 1.2051          | 0.77     |
| 0.0079        | 16.0  | 464  | 1.1073          | 0.81     |
| 0.0055        | 17.0  | 493  | 1.0920          | 0.81     |
| 0.012         | 18.0  | 522  | 1.1882          | 0.82     |
| 0.0051        | 19.0  | 551  | 1.0023          | 0.81     |
| 0.0047        | 20.0  | 580  | 1.2339          | 0.79     |
| 0.0036        | 21.0  | 609  | 1.1471          | 0.79     |
| 0.0033        | 22.0  | 638  | 1.1924          | 0.8      |
| 0.0032        | 23.0  | 667  | 1.1064          | 0.81     |
| 0.0028        | 24.0  | 696  | 1.1140          | 0.8      |
| 0.0026        | 25.0  | 725  | 1.1344          | 0.81     |
| 0.0163        | 26.0  | 754  | 1.1551          | 0.8      |
| 0.0027        | 27.0  | 783  | 1.1843          | 0.81     |
| 0.0025        | 28.0  | 812  | 1.1824          | 0.81     |
| 0.0104        | 29.0  | 841  | 1.1636          | 0.8      |
| 0.0047        | 30.0  | 870  | 1.1842          | 0.81     |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3