---
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: 0.6237
- 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1543        | 1.0   | 57   | 2.0380          | 0.53     |
| 1.5511        | 2.0   | 114  | 1.4662          | 0.64     |
| 1.2385        | 3.0   | 171  | 1.1992          | 0.67     |
| 0.8789        | 4.0   | 228  | 1.0423          | 0.68     |
| 0.818         | 5.0   | 285  | 0.7940          | 0.8      |
| 0.6487        | 6.0   | 342  | 0.7579          | 0.74     |
| 0.635         | 7.0   | 399  | 0.7074          | 0.79     |
| 0.4116        | 8.0   | 456  | 0.6650          | 0.8      |
| 0.3642        | 9.0   | 513  | 0.6494          | 0.82     |
| 0.396         | 10.0  | 570  | 0.6237          | 0.83     |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1