---
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: 0.83
---

<!-- 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: 0.9736
- Accuracy: 0.83

## 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: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7415        | 1.0   | 450  | 1.4915          | 0.61     |
| 1.2771        | 2.0   | 900  | 1.2322          | 0.64     |
| 0.3833        | 3.0   | 1350 | 0.7804          | 0.78     |
| 0.4718        | 4.0   | 1800 | 0.5409          | 0.82     |
| 0.0278        | 5.0   | 2250 | 0.7579          | 0.85     |
| 0.0044        | 6.0   | 2700 | 0.7676          | 0.82     |
| 0.002         | 7.0   | 3150 | 1.0458          | 0.81     |
| 0.0018        | 8.0   | 3600 | 0.5839          | 0.88     |
| 0.0013        | 9.0   | 4050 | 0.9576          | 0.83     |
| 0.0015        | 10.0  | 4500 | 0.9736          | 0.83     |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.14.7
- Tokenizers 0.15.0