---
license: apache-2.0
base_model: yuval6967/distilhubert-finetuned-gtzan
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-music-genre-classification
  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: 0.935
---

<!-- 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-music-genre-classification

This model is a fine-tuned version of [yuval6967/distilhubert-finetuned-gtzan](https://huggingface.co/yuval6967/distilhubert-finetuned-gtzan) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4478
- Accuracy: 0.935

## 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: 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 100  | 0.3000          | 0.935    |
| No log        | 2.0   | 200  | 0.4770          | 0.905    |
| No log        | 3.0   | 300  | 0.5666          | 0.93     |
| No log        | 4.0   | 400  | 0.4572          | 0.92     |
| 0.0298        | 5.0   | 500  | 0.6038          | 0.9      |
| 0.0298        | 6.0   | 600  | 0.4111          | 0.925    |
| 0.0298        | 7.0   | 700  | 0.4528          | 0.93     |
| 0.0298        | 8.0   | 800  | 0.4400          | 0.94     |
| 0.0298        | 9.0   | 900  | 0.4638          | 0.935    |
| 0.0081        | 10.0  | 1000 | 0.4478          | 0.935    |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2