---
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: train
      split: train
      args: train
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.87
---

<!-- 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:
- Accuracy: 0.87
- Loss: 0.9175

## 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: 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: 17

### Training results

| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 2.2295        | 1.0   | 113  | 0.4      | 2.1501          |
| 1.7373        | 2.0   | 226  | 0.6      | 1.6194          |
| 1.3497        | 3.0   | 339  | 0.72     | 1.1717          |
| 1.0135        | 4.0   | 452  | 0.71     | 1.0361          |
| 0.6951        | 5.0   | 565  | 0.77     | 0.7724          |
| 0.4279        | 6.0   | 678  | 0.76     | 0.7731          |
| 0.5178        | 7.0   | 791  | 0.82     | 0.6048          |
| 0.141         | 8.0   | 904  | 0.79     | 0.7486          |
| 0.2459        | 9.0   | 1017 | 0.85     | 0.6326          |
| 0.0331        | 10.0  | 1130 | 0.82     | 0.8706          |
| 0.0214        | 11.0  | 1243 | 0.81     | 1.0099          |
| 0.0744        | 12.0  | 1356 | 0.8      | 1.0210          |
| 0.0043        | 13.0  | 1469 | 0.82     | 0.9894          |
| 0.0032        | 14.0  | 1582 | 0.82     | 0.9803          |
| 0.0025        | 15.0  | 1695 | 0.83     | 1.0476          |
| 0.0021        | 16.0  | 1808 | 0.82     | 1.0483          |
| 0.0183        | 17.0  | 1921 | 0.87     | 0.9175          |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.0
- Tokenizers 0.13.3