---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-2
  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-2

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: 0.6290
- 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: 10
- eval_batch_size: 10
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9857        | 1.0   | 90   | 1.8850          | 0.56     |
| 1.2735        | 2.0   | 180  | 1.3243          | 0.64     |
| 1.0297        | 3.0   | 270  | 1.0371          | 0.7      |
| 0.6856        | 4.0   | 360  | 0.9535          | 0.74     |
| 0.5659        | 5.0   | 450  | 0.7661          | 0.78     |
| 0.4125        | 6.0   | 540  | 0.6502          | 0.81     |
| 0.3883        | 7.0   | 630  | 0.6516          | 0.83     |
| 0.2705        | 8.0   | 720  | 0.6270          | 0.81     |
| 0.2147        | 9.0   | 810  | 0.6383          | 0.83     |
| 0.17          | 10.0  | 900  | 0.6290          | 0.83     |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1