---
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: 0.7392
- 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3055        | 0.97  | 7    | 1.2863          | 0.73     |
| 1.2903        | 1.93  | 14   | 1.2504          | 0.7      |
| 1.2118        | 2.9   | 21   | 1.1450          | 0.77     |
| 1.1443        | 4.0   | 29   | 1.1224          | 0.74     |
| 1.006         | 4.97  | 36   | 1.0376          | 0.79     |
| 1.0174        | 5.93  | 43   | 0.9681          | 0.8      |
| 0.9155        | 6.9   | 50   | 0.9322          | 0.81     |
| 0.8781        | 8.0   | 58   | 0.9266          | 0.78     |
| 0.819         | 8.97  | 65   | 0.8473          | 0.79     |
| 0.7984        | 9.93  | 72   | 0.8225          | 0.77     |
| 0.7254        | 10.9  | 79   | 0.8096          | 0.81     |
| 0.6752        | 12.0  | 87   | 0.7801          | 0.81     |
| 0.6132        | 12.97 | 94   | 0.7687          | 0.8      |
| 0.615         | 13.93 | 101  | 0.7603          | 0.79     |
| 0.6162        | 14.9  | 108  | 0.7599          | 0.82     |
| 0.5678        | 16.0  | 116  | 0.7414          | 0.81     |
| 0.548         | 16.97 | 123  | 0.7423          | 0.81     |
| 0.5495        | 17.93 | 130  | 0.7378          | 0.81     |
| 0.5185        | 18.9  | 137  | 0.7396          | 0.81     |
| 0.5544        | 19.31 | 140  | 0.7392          | 0.81     |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3