---
license: apache-2.0
base_model: gnuevo/distilhubert-finetuned-gtzan
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-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: 0.84
---

<!-- 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-finetuned-gtzan

This model is a fine-tuned version of [gnuevo/distilhubert-finetuned-gtzan](https://huggingface.co/gnuevo/distilhubert-finetuned-gtzan) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9179
- Accuracy: 0.84

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.15
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.3025        | 1.0   | 57   | 2.2928          | 0.2      |
| 2.2609        | 2.0   | 114  | 2.2683          | 0.27     |
| 1.9479        | 3.0   | 171  | 1.9099          | 0.4      |
| 1.211         | 4.0   | 228  | 1.5258          | 0.39     |
| 0.9834        | 5.0   | 285  | 1.4254          | 0.52     |
| 0.6456        | 6.0   | 342  | 1.3216          | 0.57     |
| 0.5043        | 7.0   | 399  | 1.1890          | 0.69     |
| 0.4696        | 8.0   | 456  | 1.0764          | 0.8      |
| 0.3204        | 9.0   | 513  | 0.9564          | 0.82     |
| 0.3164        | 10.0  | 570  | 0.9101          | 0.83     |
| 0.2334        | 11.0  | 627  | 0.9021          | 0.84     |
| 0.217         | 12.0  | 684  | 0.9051          | 0.84     |
| 0.1781        | 13.0  | 741  | 0.9118          | 0.84     |
| 0.1203        | 14.0  | 798  | 0.9153          | 0.85     |
| 0.0639        | 15.0  | 855  | 0.9179          | 0.84     |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1