---
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: 0.8972431077694235
    - name: Precision
      type: precision
      value: 0.8989153352434833
    - name: Recall
      type: recall
      value: 0.8972431077694235
    - name: F1
      type: f1
      value: 0.8974179462177999
---

<!-- 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: 0.3892
- Accuracy: 0.8972
- Precision: 0.8989
- Recall: 0.8972
- F1: 0.8974

## 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: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.2319        | 0.98  | 49   | 1.5808          | 0.5263   | 0.5682    | 0.5263 | 0.4767 |
| 1.2682        | 1.98  | 99   | 0.9750          | 0.7556   | 0.7524    | 0.7556 | 0.7510 |
| 0.9462        | 2.99  | 149  | 0.7403          | 0.7945   | 0.7964    | 0.7945 | 0.7921 |
| 0.5946        | 3.99  | 199  | 0.5921          | 0.8233   | 0.8281    | 0.8233 | 0.8214 |
| 0.4095        | 4.99  | 249  | 0.4772          | 0.8634   | 0.8663    | 0.8634 | 0.8638 |
| 0.3349        | 5.99  | 299  | 0.4167          | 0.8835   | 0.8866    | 0.8835 | 0.8841 |
| 0.2427        | 6.88  | 343  | 0.3892          | 0.8972   | 0.8989    | 0.8972 | 0.8974 |


### Framework versions

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