---
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: 0.88
---

<!-- 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:
- Accuracy: 0.88
- Loss: 0.5101

## 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: 19
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 2.1142        | 1.0   | 57   | 0.5      | 1.9842          |
| 1.5086        | 2.0   | 114  | 0.63     | 1.4646          |
| 1.1112        | 3.0   | 171  | 0.76     | 1.1176          |
| 1.0085        | 4.0   | 228  | 0.74     | 0.9412          |
| 0.7851        | 5.0   | 285  | 0.8      | 0.7978          |
| 0.6372        | 6.0   | 342  | 0.78     | 0.7533          |
| 0.5404        | 7.0   | 399  | 0.75     | 0.7206          |
| 0.4701        | 8.0   | 456  | 0.8      | 0.6551          |
| 0.4362        | 9.0   | 513  | 0.77     | 0.6712          |
| 0.3737        | 10.0  | 570  | 0.81     | 0.6202          |
| 0.321         | 11.0  | 627  | 0.78     | 0.6756          |
| 0.2533        | 12.0  | 684  | 0.84     | 0.5602          |
| 0.326         | 13.0  | 741  | 0.84     | 0.5706          |
| 0.1789        | 14.0  | 798  | 0.83     | 0.5736          |
| 0.1841        | 15.0  | 855  | 0.85     | 0.5379          |
| 0.2496        | 16.0  | 912  | 0.87     | 0.5518          |
| 0.2002        | 17.0  | 969  | 0.86     | 0.5220          |
| 0.1164        | 18.0  | 1026 | 0.86     | 0.5213          |
| 0.096         | 19.0  | 1083 | 0.88     | 0.5101          |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.19.0
- Tokenizers 0.15.1