---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-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. -->

# distilhubert-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: 0.5146
- 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: 6
- eval_batch_size: 6
- 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: 12
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9404        | 1.0   | 150  | 1.8960          | 0.45     |
| 1.1667        | 2.0   | 300  | 1.2573          | 0.64     |
| 0.9325        | 3.0   | 450  | 0.9343          | 0.71     |
| 0.7688        | 4.0   | 600  | 0.9460          | 0.73     |
| 0.5211        | 5.0   | 750  | 0.6388          | 0.78     |
| 0.2001        | 6.0   | 900  | 0.5689          | 0.8      |
| 0.4134        | 7.0   | 1050 | 0.5351          | 0.82     |
| 0.2026        | 8.0   | 1200 | 0.6032          | 0.82     |
| 0.036         | 9.0   | 1350 | 0.5002          | 0.82     |
| 0.1023        | 10.0  | 1500 | 0.5171          | 0.82     |
| 0.0773        | 11.0  | 1650 | 0.5088          | 0.86     |
| 0.0147        | 12.0  | 1800 | 0.5146          | 0.85     |


### Framework versions

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