---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-VD
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: GTZAN
      type: marsyas/gtzan
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8933256172839507
---

<!-- 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-VD

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: 0.7226
- Accuracy: 0.8933

## 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: 25
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3302        | 1.0   | 195  | 0.3716          | 0.8800   |
| 0.6059        | 2.0   | 390  | 0.5195          | 0.8090   |
| 0.4938        | 3.0   | 585  | 1.0102          | 0.6260   |
| 0.836         | 4.0   | 780  | 1.1662          | 0.6742   |
| 0.2234        | 5.0   | 975  | 0.6792          | 0.8389   |
| 0.1444        | 6.0   | 1170 | 0.9137          | 0.8239   |
| 0.2986        | 7.0   | 1365 | 0.7987          | 0.8623   |
| 0.0004        | 8.0   | 1560 | 1.5075          | 0.7687   |
| 0.0005        | 9.0   | 1755 | 0.7226          | 0.8933   |
| 0.0002        | 10.0  | 1950 | 0.8246          | 0.8829   |
| 0.0002        | 11.0  | 2145 | 1.4227          | 0.8129   |
| 0.0001        | 12.0  | 2340 | 1.0478          | 0.8665   |
| 0.0001        | 13.0  | 2535 | 1.3328          | 0.8322   |
| 0.0001        | 14.0  | 2730 | 1.3480          | 0.8347   |
| 0.0001        | 15.0  | 2925 | 1.3559          | 0.8370   |
| 0.0           | 16.0  | 3120 | 1.3589          | 0.8407   |
| 0.0           | 17.0  | 3315 | 1.3706          | 0.8410   |
| 0.0           | 18.0  | 3510 | 1.3831          | 0.8410   |
| 0.0           | 19.0  | 3705 | 1.3954          | 0.8410   |
| 0.0           | 20.0  | 3900 | 1.4027          | 0.8412   |
| 0.0           | 21.0  | 4095 | 1.4132          | 0.8409   |
| 0.0           | 22.0  | 4290 | 1.4218          | 0.8407   |
| 0.0           | 23.0  | 4485 | 1.4272          | 0.8407   |
| 0.0           | 24.0  | 4680 | 1.4321          | 0.8399   |
| 0.0           | 25.0  | 4875 | 1.4337          | 0.8399   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2