---
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: None
      args: all
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.82
---

<!-- 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: 2.2594
- Accuracy: 0.82

## 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2669        | 1.0   | 57   | 2.2222          | 0.29     |
| 1.9365        | 2.0   | 114  | 1.8485          | 0.53     |
| 1.5115        | 3.0   | 171  | 1.4544          | 0.64     |
| 1.1314        | 4.0   | 228  | 1.1404          | 0.7      |
| 0.9473        | 5.0   | 285  | 0.9750          | 0.7      |
| 0.8026        | 6.0   | 342  | 0.8381          | 0.76     |
| 0.669         | 7.0   | 399  | 0.7231          | 0.81     |
| 0.5026        | 8.0   | 456  | 0.7019          | 0.8      |
| 0.3179        | 9.0   | 513  | 0.6318          | 0.81     |
| 0.2934        | 10.0  | 570  | 0.6551          | 0.81     |
| 0.1709        | 11.0  | 627  | 0.6041          | 0.81     |
| 0.1502        | 12.0  | 684  | 0.7066          | 0.84     |
| 0.0626        | 13.0  | 741  | 0.6859          | 0.84     |
| 0.0184        | 14.0  | 798  | 0.7444          | 0.8      |
| 0.0345        | 15.0  | 855  | 0.9701          | 0.8      |
| 0.0034        | 16.0  | 912  | 1.0236          | 0.83     |
| 0.0014        | 17.0  | 969  | 1.1226          | 0.81     |
| 0.0811        | 18.0  | 1026 | 1.2570          | 0.81     |
| 0.0002        | 19.0  | 1083 | 1.3850          | 0.81     |
| 0.0           | 20.0  | 1140 | 1.6715          | 0.82     |
| 0.0           | 21.0  | 1197 | 1.8665          | 0.8      |
| 0.1033        | 22.0  | 1254 | 1.8919          | 0.79     |
| 0.047         | 23.0  | 1311 | 1.9730          | 0.82     |
| 0.0           | 24.0  | 1368 | 2.1126          | 0.81     |
| 0.0           | 25.0  | 1425 | 2.1545          | 0.79     |
| 0.0           | 26.0  | 1482 | 2.2609          | 0.79     |
| 0.0           | 27.0  | 1539 | 2.2284          | 0.81     |
| 0.0           | 28.0  | 1596 | 2.2374          | 0.81     |
| 0.0           | 29.0  | 1653 | 2.2590          | 0.82     |
| 0.0           | 30.0  | 1710 | 2.2594          | 0.82     |


### Framework versions

- Transformers 4.43.0.dev0
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1