---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: music-genre-classifer-20-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: 0.85
---

<!-- 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-classifer-20-finetuned-gtzan

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: 0.9000
- Accuracy: 0.85

## 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1605        | 1.0   | 113  | 2.0687          | 0.43     |
| 1.4882        | 2.0   | 226  | 1.4799          | 0.57     |
| 1.153         | 3.0   | 339  | 1.0688          | 0.72     |
| 0.7342        | 4.0   | 452  | 0.8125          | 0.77     |
| 0.6006        | 5.0   | 565  | 0.6751          | 0.81     |
| 0.3723        | 6.0   | 678  | 0.6401          | 0.8      |
| 0.2502        | 7.0   | 791  | 0.6562          | 0.8      |
| 0.1122        | 8.0   | 904  | 0.7022          | 0.81     |
| 0.0708        | 9.0   | 1017 | 0.6970          | 0.85     |
| 0.0181        | 10.0  | 1130 | 0.8265          | 0.84     |
| 0.0129        | 11.0  | 1243 | 0.7396          | 0.85     |
| 0.0061        | 12.0  | 1356 | 0.7550          | 0.87     |
| 0.0047        | 13.0  | 1469 | 0.8028          | 0.85     |
| 0.004         | 14.0  | 1582 | 0.8657          | 0.84     |
| 0.0039        | 15.0  | 1695 | 0.8750          | 0.85     |
| 0.0031        | 16.0  | 1808 | 0.8898          | 0.86     |
| 0.0028        | 17.0  | 1921 | 0.8835          | 0.85     |
| 0.0025        | 18.0  | 2034 | 0.9102          | 0.85     |
| 0.0025        | 19.0  | 2147 | 0.9007          | 0.86     |
| 0.0024        | 20.0  | 2260 | 0.9000          | 0.85     |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2