---
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: 0.9399
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1679        | 1.0   | 113  | 2.0910          | 0.38     |
| 1.4665        | 2.0   | 226  | 1.4798          | 0.53     |
| 1.2128        | 3.0   | 339  | 1.1715          | 0.64     |
| 0.7499        | 4.0   | 452  | 0.9591          | 0.68     |
| 0.6869        | 5.0   | 565  | 0.8078          | 0.76     |
| 0.3399        | 6.0   | 678  | 0.7513          | 0.81     |
| 0.3071        | 7.0   | 791  | 0.6606          | 0.84     |
| 0.0791        | 8.0   | 904  | 0.6416          | 0.84     |
| 0.1047        | 9.0   | 1017 | 0.7613          | 0.82     |
| 0.0784        | 10.0  | 1130 | 0.8558          | 0.82     |
| 0.0097        | 11.0  | 1243 | 0.9087          | 0.82     |
| 0.0071        | 12.0  | 1356 | 0.9155          | 0.83     |
| 0.0052        | 13.0  | 1469 | 0.9210          | 0.85     |
| 0.0044        | 14.0  | 1582 | 0.9543          | 0.84     |
| 0.0035        | 15.0  | 1695 | 0.9726          | 0.85     |
| 0.0032        | 16.0  | 1808 | 0.9183          | 0.84     |
| 0.0029        | 17.0  | 1921 | 0.9181          | 0.83     |
| 0.0027        | 18.0  | 2034 | 0.9575          | 0.84     |
| 0.0027        | 19.0  | 2147 | 0.9427          | 0.83     |
| 0.0026        | 20.0  | 2260 | 0.9399          | 0.83     |


### Framework versions

- Transformers 4.39.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2