---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzanVD
  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: 0.9839786381842457
---

<!-- 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-gtzanVD

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: 0.1334
- Accuracy: 0.9840

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3554        | 1.0   | 842   | 0.1898          | 0.9439   |
| 0.1136        | 2.0   | 1684  | 0.1657          | 0.9626   |
| 0.1571        | 3.0   | 2526  | 0.1132          | 0.9693   |
| 0.0004        | 4.0   | 3368  | 0.1235          | 0.9786   |
| 0.0011        | 5.0   | 4210  | 0.1555          | 0.9680   |
| 0.0001        | 6.0   | 5052  | 0.3138          | 0.9493   |
| 0.0001        | 7.0   | 5894  | 0.1825          | 0.9680   |
| 0.0001        | 8.0   | 6736  | 0.1982          | 0.9706   |
| 0.0001        | 9.0   | 7578  | 0.1690          | 0.9693   |
| 0.3166        | 10.0  | 8420  | 0.1487          | 0.9733   |
| 0.0           | 11.0  | 9262  | 0.2615          | 0.9680   |
| 0.0           | 12.0  | 10104 | 0.1536          | 0.9800   |
| 0.0001        | 13.0  | 10946 | 0.5478          | 0.9399   |
| 0.0           | 14.0  | 11788 | 0.1334          | 0.9840   |
| 0.0           | 15.0  | 12630 | 0.1270          | 0.9746   |
| 0.0           | 16.0  | 13472 | 0.1053          | 0.9840   |
| 0.0           | 17.0  | 14314 | 0.1181          | 0.9813   |
| 0.0           | 18.0  | 15156 | 0.1165          | 0.9826   |
| 0.0           | 19.0  | 15998 | 0.1191          | 0.9826   |
| 0.0           | 20.0  | 16840 | 0.1188          | 0.9826   |


### Framework versions

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