---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-v3
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: GTZAN
      type: marsyas/gtzan
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.86
---

<!-- 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-v3

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: 1.1337
- Accuracy: 0.86

## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7102        | 1.0   | 899  | 0.9058          | 0.69     |
| 1.9987        | 2.0   | 1798 | 1.3215          | 0.71     |
| 0.0086        | 3.0   | 2697 | 0.9701          | 0.75     |
| 0.0284        | 4.0   | 3596 | 0.8695          | 0.82     |
| 0.1668        | 5.0   | 4495 | 1.1303          | 0.81     |
| 0.0013        | 6.0   | 5394 | 1.0664          | 0.84     |
| 0.0026        | 7.0   | 6293 | 1.0936          | 0.87     |
| 0.0003        | 8.0   | 7192 | 1.1882          | 0.86     |
| 0.0004        | 9.0   | 8091 | 1.2463          | 0.85     |
| 0.0002        | 10.0  | 8990 | 1.1337          | 0.86     |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.1.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1