---
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: 0.86
---

<!-- 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:
- Loss: 0.8060
- Accuracy: 0.86

## 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: 1e-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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4315        | 1.0   | 7    | 0.5652          | 0.94     |
| 1.8803        | 2.0   | 14   | 0.5438          | 0.9      |
| 1.236         | 3.0   | 21   | 0.5891          | 0.9      |
| 0.6882        | 4.0   | 28   | 0.6378          | 0.9      |
| 0.6126        | 5.0   | 35   | 0.6965          | 0.88     |
| 0.6847        | 6.0   | 42   | 0.7258          | 0.84     |
| 0.3871        | 7.0   | 49   | 0.7459          | 0.84     |
| 0.2981        | 8.0   | 56   | 0.8024          | 0.86     |
| 0.1321        | 9.0   | 63   | 0.8140          | 0.86     |
| 0.2443        | 10.0  | 70   | 0.8060          | 0.86     |


### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3