---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- gtzan
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: music-genre-detector-finetuned-gtzan_dset
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: GTZAN
      type: gtzan
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9298245614035088
    - name: Precision
      type: precision
      value: 0.9292447472185437
    - name: Recall
      type: recall
      value: 0.9298245614035088
    - name: F1
      type: f1
      value: 0.9293437948869628
---

<!-- 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. -->

# music-genre-detector-finetuned-gtzan_dset

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: 0.2288
- Accuracy: 0.9298
- Precision: 0.9292
- Recall: 0.9298
- F1: 0.9293

## 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: 9e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.2522        | 0.98  | 49   | 1.6370          | 0.6090   | 0.6189    | 0.6090 | 0.5764 |
| 1.2901        | 1.98  | 99   | 0.9974          | 0.7556   | 0.7655    | 0.7556 | 0.7426 |
| 1.0046        | 2.99  | 149  | 0.6645          | 0.8195   | 0.8226    | 0.8195 | 0.8162 |
| 0.5952        | 3.99  | 199  | 0.5054          | 0.8459   | 0.8561    | 0.8459 | 0.8460 |
| 0.3596        | 4.99  | 249  | 0.3729          | 0.9023   | 0.9117    | 0.9023 | 0.9041 |
| 0.2534        | 5.99  | 299  | 0.2953          | 0.9073   | 0.9088    | 0.9073 | 0.9075 |
| 0.1413        | 7.0   | 349  | 0.2545          | 0.9223   | 0.9229    | 0.9223 | 0.9216 |
| 0.0759        | 8.0   | 399  | 0.2593          | 0.9198   | 0.9209    | 0.9198 | 0.9190 |
| 0.0491        | 8.98  | 448  | 0.2288          | 0.9298   | 0.9292    | 0.9298 | 0.9293 |
| 0.0355        | 9.82  | 490  | 0.2392          | 0.9223   | 0.9231    | 0.9223 | 0.9221 |


### Framework versions

- Transformers 4.33.1
- Pytorch 1.10.2+cu111
- Datasets 2.14.5
- Tokenizers 0.13.3