---
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: 0.85
---

<!-- 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: 0.6540
- Accuracy: 0.85

## 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: 7
- eval_batch_size: 7
- 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: 13

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.036         | 1.0   | 129  | 1.8621          | 0.54     |
| 1.272         | 2.0   | 258  | 1.2237          | 0.67     |
| 1.1092        | 3.0   | 387  | 0.9957          | 0.67     |
| 0.5955        | 4.0   | 516  | 0.8160          | 0.72     |
| 0.3345        | 5.0   | 645  | 0.6607          | 0.79     |
| 0.3451        | 6.0   | 774  | 0.7320          | 0.75     |
| 0.2405        | 7.0   | 903  | 0.4956          | 0.85     |
| 0.2242        | 8.0   | 1032 | 0.6112          | 0.81     |
| 0.0447        | 9.0   | 1161 | 0.6542          | 0.82     |
| 0.0194        | 10.0  | 1290 | 0.7455          | 0.84     |
| 0.0122        | 11.0  | 1419 | 0.6341          | 0.85     |
| 0.0119        | 12.0  | 1548 | 0.6671          | 0.84     |
| 0.0107        | 13.0  | 1677 | 0.6540          | 0.85     |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3