---
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: 0.91
---

<!-- 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: 0.3539
- Accuracy: 0.91

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- 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: 18

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2281        | 1.0   | 112  | 2.1128          | 0.26     |
| 1.7082        | 2.0   | 225  | 1.6252          | 0.52     |
| 1.267         | 3.0   | 337  | 1.3100          | 0.54     |
| 1.1791        | 4.0   | 450  | 1.0496          | 0.71     |
| 1.1765        | 5.0   | 562  | 0.8928          | 0.74     |
| 0.5714        | 6.0   | 675  | 0.8298          | 0.77     |
| 0.4869        | 7.0   | 787  | 0.7145          | 0.79     |
| 0.4967        | 8.0   | 900  | 0.6990          | 0.82     |
| 0.8314        | 9.0   | 1012 | 0.5657          | 0.83     |
| 0.4633        | 10.0  | 1125 | 0.4589          | 0.89     |
| 0.5547        | 11.0  | 1237 | 0.4919          | 0.86     |
| 0.4827        | 12.0  | 1350 | 0.4069          | 0.92     |
| 0.324         | 13.0  | 1462 | 0.4634          | 0.87     |
| 0.5224        | 14.0  | 1575 | 0.4419          | 0.86     |
| 0.1873        | 15.0  | 1687 | 0.3988          | 0.89     |
| 0.2852        | 16.0  | 1800 | 0.3788          | 0.9      |
| 0.3169        | 17.0  | 1912 | 0.3526          | 0.89     |
| 0.4491        | 17.92 | 2016 | 0.3539          | 0.91     |


### Framework versions

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