---
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: 0.8349877949552482
---

<!-- 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: 0.4702
- Accuracy: 0.8350

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.555         | 1.0   | 167  | 0.4702          | 0.8350   |
| 0.3965        | 2.0   | 334  | 0.4398          | 0.7570   |
| 0.4106        | 3.0   | 501  | 0.7742          | 0.6713   |
| 0.4372        | 4.0   | 668  | 0.9340          | 0.6827   |
| 0.2087        | 5.0   | 835  | 1.0133          | 0.7574   |
| 0.124         | 6.0   | 1002 | 1.1049          | 0.7437   |
| 0.0509        | 7.0   | 1169 | 1.2264          | 0.7590   |
| 0.0016        | 8.0   | 1336 | 1.2315          | 0.7845   |
| 0.0064        | 9.0   | 1503 | 1.3620          | 0.7762   |
| 0.0006        | 10.0  | 1670 | 1.3149          | 0.8039   |
| 0.0007        | 11.0  | 1837 | 1.2818          | 0.8116   |
| 0.0003        | 12.0  | 2004 | 1.2635          | 0.8298   |
| 0.0003        | 13.0  | 2171 | 1.3287          | 0.8225   |
| 0.0002        | 14.0  | 2338 | 1.3200          | 0.8295   |
| 0.0001        | 15.0  | 2505 | 1.4146          | 0.8226   |
| 0.0001        | 16.0  | 2672 | 1.4359          | 0.8221   |
| 0.0001        | 17.0  | 2839 | 1.4443          | 0.8233   |
| 0.0001        | 18.0  | 3006 | 1.5031          | 0.8184   |
| 0.0001        | 19.0  | 3173 | 1.5111          | 0.8182   |
| 0.0001        | 20.0  | 3340 | 1.5145          | 0.8182   |


### Framework versions

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