---
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: 0.5991
- 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1211        | 1.0   | 57   | 1.9967          | 0.4      |
| 1.6311        | 2.0   | 114  | 1.5599          | 0.58     |
| 1.2082        | 3.0   | 171  | 1.2194          | 0.72     |
| 1.1853        | 4.0   | 228  | 1.0276          | 0.75     |
| 0.7278        | 5.0   | 285  | 0.9232          | 0.78     |
| 0.6999        | 6.0   | 342  | 0.7392          | 0.82     |
| 0.4983        | 7.0   | 399  | 0.6779          | 0.84     |
| 0.5142        | 8.0   | 456  | 0.6483          | 0.83     |
| 0.417         | 9.0   | 513  | 0.6554          | 0.82     |
| 0.3725        | 10.0  | 570  | 0.5991          | 0.83     |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3