---
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: 0.82
---

<!-- 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: 1.0242
- Accuracy: 0.82

## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.496         | 0.99  | 56   | 1.8467          | 0.27     |
| 1.1313        | 2.0   | 113  | 1.1757          | 0.61     |
| 1.2432        | 2.99  | 169  | 1.3774          | 0.58     |
| 0.7301        | 4.0   | 226  | 0.9738          | 0.66     |
| 0.5192        | 4.99  | 282  | 0.9078          | 0.73     |
| 0.4163        | 6.0   | 339  | 0.9996          | 0.71     |
| 0.2178        | 6.99  | 395  | 0.7683          | 0.79     |
| 0.0814        | 8.0   | 452  | 0.9985          | 0.78     |
| 0.0075        | 8.99  | 508  | 1.1056          | 0.78     |
| 0.003         | 9.91  | 560  | 1.0242          | 0.82     |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.1