---
license: apache-2.0
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
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.65
---

<!-- 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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4484
- Accuracy: 0.65

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2604        | 1.0   | 113  | 2.2157          | 0.38     |
| 1.972         | 2.0   | 226  | 1.9740          | 0.45     |
| 1.8073        | 3.0   | 339  | 1.7988          | 0.54     |
| 1.6946        | 4.0   | 452  | 1.6666          | 0.64     |
| 1.582         | 5.0   | 565  | 1.5732          | 0.61     |
| 1.6468        | 6.0   | 678  | 1.4905          | 0.65     |
| 1.4905        | 7.0   | 791  | 1.4661          | 0.65     |
| 1.4801        | 8.0   | 904  | 1.4484          | 0.65     |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0