---
tags:
- generated_from_trainer
datasets:
- gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: gtzan
      type: gtzan
      config: all
      split: train
      args: all
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.75
---

<!-- 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 was trained from scratch on the gtzan dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.75
- Loss: 0.7487

## 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: 8
- 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: 2

### Training results

| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 1.9415        | 1.0   | 113  | 0.55     | 1.8500          |
| 1.3078        | 2.0   | 226  | 0.58     | 1.3794          |
| 1.1238        | 3.0   | 339  | 0.65     | 1.0919          |
| 0.788         | 4.0   | 452  | 0.68     | 1.0212          |
| 0.5932        | 5.0   | 565  | 0.69     | 0.8691          |
| 0.4042        | 6.0   | 678  | 0.71     | 0.8527          |
| 0.3421        | 7.0   | 791  | 0.75     | 0.7737          |
| 0.223         | 8.0   | 904  | 0.75     | 0.8463          |
| 0.1162        | 9.0   | 1017 | 0.77     | 0.7808          |
| 0.0863        | 10.0  | 1130 | 0.75     | 0.7487          |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.13.2