---
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: 0.8
---

<!-- 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: 0.8209
- Accuracy: 0.8

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8561        | 1.0   | 225  | 1.6555          | 0.56     |
| 1.109         | 2.0   | 450  | 1.2396          | 0.58     |
| 0.6901        | 3.0   | 675  | 0.8904          | 0.71     |
| 0.2618        | 4.0   | 900  | 0.6728          | 0.8      |
| 0.296         | 5.0   | 1125 | 0.6022          | 0.8      |
| 0.1734        | 6.0   | 1350 | 0.6310          | 0.83     |
| 0.1562        | 7.0   | 1575 | 0.6711          | 0.8      |
| 0.1927        | 8.0   | 1800 | 0.7798          | 0.8      |
| 0.0102        | 9.0   | 2025 | 0.8040          | 0.78     |
| 0.0102        | 10.0  | 2250 | 0.8209          | 0.8      |


### Framework versions

- Transformers 4.41.0.dev0
- Pytorch 2.3.0+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1