---
license: apache-2.0
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: 0.84
---

<!-- 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: 0.9253
- Accuracy: 0.84

## 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: 2
- eval_batch_size: 2
- 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3972        | 1.0   | 450  | 1.4662          | 0.65     |
| 0.7118        | 2.0   | 900  | 0.9103          | 0.69     |
| 0.4653        | 3.0   | 1350 | 0.8097          | 0.73     |
| 0.934         | 4.0   | 1800 | 0.7674          | 0.83     |
| 0.3231        | 5.0   | 2250 | 1.2025          | 0.73     |
| 0.0038        | 6.0   | 2700 | 1.1013          | 0.8      |
| 0.002         | 7.0   | 3150 | 0.8540          | 0.86     |
| 0.0022        | 8.0   | 3600 | 0.8067          | 0.85     |
| 0.0013        | 9.0   | 4050 | 0.8682          | 0.86     |
| 0.0016        | 10.0  | 4500 | 0.9253          | 0.84     |


### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3