---
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: 0.88
---

<!-- 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: 0.4795
- Accuracy: 0.88

## 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: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0422        | 1.0   | 225  | 2.0126          | 0.27     |
| 1.331         | 2.0   | 450  | 1.3795          | 0.54     |
| 1.2571        | 3.0   | 675  | 1.0070          | 0.72     |
| 1.2968        | 4.0   | 900  | 0.8590          | 0.77     |
| 0.7658        | 5.0   | 1125 | 0.7889          | 0.77     |
| 0.5499        | 6.0   | 1350 | 0.5743          | 0.82     |
| 0.8344        | 7.0   | 1575 | 0.6065          | 0.81     |
| 0.3919        | 8.0   | 1800 | 0.5650          | 0.87     |
| 0.2808        | 9.0   | 2025 | 0.4605          | 0.87     |
| 0.4463        | 10.0  | 2250 | 0.5161          | 0.86     |
| 0.5678        | 11.0  | 2475 | 0.5359          | 0.87     |
| 0.3032        | 12.0  | 2700 | 0.4795          | 0.88     |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.0
- Datasets 2.14.3
- Tokenizers 0.13.3