---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan2
  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: 0.7125
---

<!-- 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-gtzan2

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: 1.5220
- Accuracy: 0.7125

## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- 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: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7489        | 1.0   | 29   | 1.4959          | 0.3875   |
| 1.328         | 2.0   | 58   | 2.0243          | 0.35     |
| 1.2168        | 3.0   | 87   | 1.1332          | 0.5875   |
| 1.0299        | 4.0   | 116  | 1.4826          | 0.5375   |
| 0.911         | 5.0   | 145  | 1.2510          | 0.625    |
| 1.0819        | 6.0   | 174  | 1.7365          | 0.55     |
| 0.9513        | 7.0   | 203  | 1.3000          | 0.6      |
| 0.5687        | 8.0   | 232  | 1.0503          | 0.7125   |
| 0.4684        | 9.0   | 261  | 1.1167          | 0.7125   |
| 0.2836        | 10.0  | 290  | 1.5990          | 0.65     |
| 0.138         | 11.0  | 319  | 1.2096          | 0.7375   |
| 0.0406        | 12.0  | 348  | 1.7311          | 0.6375   |
| 0.0341        | 13.0  | 377  | 1.7048          | 0.6375   |
| 0.0059        | 14.0  | 406  | 1.4933          | 0.7      |
| 0.0034        | 15.0  | 435  | 1.5220          | 0.7125   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2