---
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: 0.86
---

<!-- 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: 0.8540
- Accuracy: 0.86

## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2594        | 1.0   | 57   | 2.2216          | 0.37     |
| 1.941         | 2.0   | 114  | 1.8715          | 0.59     |
| 1.4613        | 3.0   | 171  | 1.4244          | 0.65     |
| 1.2449        | 4.0   | 228  | 1.1359          | 0.71     |
| 0.8682        | 5.0   | 285  | 0.9472          | 0.74     |
| 0.6808        | 6.0   | 342  | 0.7817          | 0.78     |
| 0.4759        | 7.0   | 399  | 0.7428          | 0.74     |
| 0.3316        | 8.0   | 456  | 0.6441          | 0.78     |
| 0.2228        | 9.0   | 513  | 0.5838          | 0.83     |
| 0.1367        | 10.0  | 570  | 0.5843          | 0.86     |
| 0.0921        | 11.0  | 627  | 0.5745          | 0.86     |
| 0.0462        | 12.0  | 684  | 0.7029          | 0.83     |
| 0.0513        | 13.0  | 741  | 0.7116          | 0.86     |
| 0.0151        | 14.0  | 798  | 0.7017          | 0.86     |
| 0.0113        | 15.0  | 855  | 0.7439          | 0.85     |
| 0.0572        | 16.0  | 912  | 0.7691          | 0.84     |
| 0.0073        | 17.0  | 969  | 0.7918          | 0.84     |
| 0.0076        | 18.0  | 1026 | 0.8202          | 0.84     |
| 0.0053        | 19.0  | 1083 | 0.8238          | 0.86     |
| 0.0547        | 20.0  | 1140 | 0.8147          | 0.86     |
| 0.0045        | 21.0  | 1197 | 0.8201          | 0.86     |
| 0.004         | 22.0  | 1254 | 0.8282          | 0.83     |
| 0.0038        | 23.0  | 1311 | 0.8387          | 0.86     |
| 0.0035        | 24.0  | 1368 | 0.8398          | 0.86     |
| 0.0033        | 25.0  | 1425 | 0.8403          | 0.86     |
| 0.0031        | 26.0  | 1482 | 0.8464          | 0.86     |
| 0.0032        | 27.0  | 1539 | 0.8456          | 0.86     |
| 0.0031        | 28.0  | 1596 | 0.8505          | 0.86     |
| 0.0031        | 29.0  | 1653 | 0.8517          | 0.86     |
| 0.003         | 30.0  | 1710 | 0.8540          | 0.86     |


### Framework versions

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