---
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: 0.83
---

<!-- 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: 0.6340
- Accuracy: 0.83

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9747        | 1.0   | 112  | 1.7879          | 0.56     |
| 1.322         | 1.99  | 224  | 1.2554          | 0.67     |
| 1.0047        | 3.0   | 337  | 0.9381          | 0.73     |
| 0.8037        | 4.0   | 449  | 0.8347          | 0.77     |
| 0.5617        | 4.99  | 561  | 0.7889          | 0.76     |
| 0.4773        | 6.0   | 674  | 0.6480          | 0.84     |
| 0.2749        | 6.99  | 786  | 0.6533          | 0.79     |
| 0.1649        | 8.0   | 899  | 0.6974          | 0.79     |
| 0.1132        | 9.0   | 1011 | 0.6771          | 0.81     |
| 0.1243        | 9.97  | 1120 | 0.6340          | 0.83     |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 2.13.1
- Tokenizers 0.13.3