---
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: 0.82
---

<!-- 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: 1.2523
- Accuracy: 0.82

## 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: 0.0002
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7948        | 1.0   | 113  | 1.6788          | 0.46     |
| 1.164         | 2.0   | 226  | 1.1871          | 0.54     |
| 0.8531        | 3.0   | 339  | 1.0579          | 0.66     |
| 0.8304        | 4.0   | 452  | 0.8808          | 0.73     |
| 0.2531        | 5.0   | 565  | 0.9542          | 0.74     |
| 0.3144        | 6.0   | 678  | 1.0149          | 0.78     |
| 0.2775        | 7.0   | 791  | 0.8875          | 0.77     |
| 0.0521        | 8.0   | 904  | 1.2458          | 0.78     |
| 0.0106        | 9.0   | 1017 | 0.9013          | 0.81     |
| 0.0088        | 10.0  | 1130 | 0.9802          | 0.84     |
| 0.0023        | 11.0  | 1243 | 1.1693          | 0.82     |
| 0.1901        | 12.0  | 1356 | 1.2588          | 0.82     |
| 0.0006        | 13.0  | 1469 | 1.2267          | 0.8      |
| 0.0005        | 14.0  | 1582 | 1.3400          | 0.81     |
| 0.0005        | 15.0  | 1695 | 1.1049          | 0.83     |
| 0.0004        | 16.0  | 1808 | 1.3025          | 0.8      |
| 0.1313        | 17.0  | 1921 | 1.2627          | 0.81     |
| 0.0003        | 18.0  | 2034 | 1.1620          | 0.84     |
| 0.0003        | 19.0  | 2147 | 1.2217          | 0.82     |
| 0.0003        | 20.0  | 2260 | 1.2523          | 0.82     |


### Framework versions

- Transformers 4.32.1
- Pytorch 1.13.1
- Datasets 2.14.4
- Tokenizers 0.13.3