---
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: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.79
---

<!-- 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: 0.6405
- Accuracy: 0.79

## 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: 6
- eval_batch_size: 6
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9208        | 1.0   | 150  | 1.7528          | 0.52     |
| 1.0745        | 2.0   | 300  | 1.2385          | 0.6      |
| 0.8249        | 3.0   | 450  | 0.8622          | 0.79     |
| 0.6652        | 4.0   | 600  | 0.9211          | 0.72     |
| 0.4782        | 5.0   | 750  | 0.6200          | 0.8      |
| 0.2865        | 6.0   | 900  | 0.6526          | 0.76     |
| 0.1781        | 7.0   | 1050 | 0.5741          | 0.82     |
| 0.1675        | 8.0   | 1200 | 0.5487          | 0.82     |
| 0.0497        | 9.0   | 1350 | 0.6100          | 0.8      |
| 0.0813        | 10.0  | 1500 | 0.6405          | 0.79     |


### Framework versions

- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3