---
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: 0.87
---

<!-- 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:
- Loss: 0.6333
- Accuracy: 0.87

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2417        | 1.0   | 57   | 2.1896          | 0.42     |
| 1.8003        | 2.0   | 114  | 1.6369          | 0.52     |
| 1.3938        | 3.0   | 171  | 1.2560          | 0.72     |
| 1.2724        | 4.0   | 228  | 1.1942          | 0.68     |
| 0.9682        | 5.0   | 285  | 0.8864          | 0.8      |
| 0.7111        | 6.0   | 342  | 0.7542          | 0.82     |
| 0.6339        | 7.0   | 399  | 0.7712          | 0.81     |
| 0.4599        | 8.0   | 456  | 0.6080          | 0.84     |
| 0.3261        | 9.0   | 513  | 0.5998          | 0.84     |
| 0.2991        | 10.0  | 570  | 0.6767          | 0.79     |
| 0.1615        | 11.0  | 627  | 0.5817          | 0.87     |
| 0.0854        | 12.0  | 684  | 0.5859          | 0.83     |
| 0.0752        | 13.0  | 741  | 0.5681          | 0.85     |
| 0.0341        | 14.0  | 798  | 0.5916          | 0.88     |
| 0.0331        | 15.0  | 855  | 0.6028          | 0.87     |
| 0.02          | 16.0  | 912  | 0.6283          | 0.85     |
| 0.0175        | 17.0  | 969  | 0.6103          | 0.88     |
| 0.0151        | 18.0  | 1026 | 0.6244          | 0.88     |
| 0.014         | 19.0  | 1083 | 0.6293          | 0.86     |
| 0.0181        | 20.0  | 1140 | 0.6333          | 0.87     |


### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3