---
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: 0.9971
- 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: 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
- training_steps: 3000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2793        | 0.88  | 100  | 2.1792          | 0.41     |
| 1.992         | 1.77  | 200  | 1.6741          | 0.56     |
| 1.4928        | 2.65  | 300  | 1.2795          | 0.56     |
| 1.1156        | 3.54  | 400  | 0.9983          | 0.69     |
| 0.9162        | 4.42  | 500  | 0.8222          | 0.73     |
| 0.6785        | 5.31  | 600  | 0.8422          | 0.78     |
| 0.4695        | 6.19  | 700  | 0.7034          | 0.8      |
| 0.3362        | 7.08  | 800  | 0.9594          | 0.72     |
| 0.2051        | 7.96  | 900  | 0.6157          | 0.84     |
| 0.1242        | 8.85  | 1000 | 0.6059          | 0.86     |
| 0.0678        | 9.73  | 1100 | 0.7626          | 0.86     |
| 0.0479        | 10.62 | 1200 | 0.7886          | 0.84     |
| 0.0216        | 11.5  | 1300 | 0.8302          | 0.85     |
| 0.0202        | 12.39 | 1400 | 0.8921          | 0.86     |
| 0.0155        | 13.27 | 1500 | 0.9971          | 0.85     |


### Framework versions

- Transformers 4.32.0
- Pytorch 1.12.1+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3