---
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: 0.83
---

<!-- 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: 0.9268
- Accuracy: 0.83

## 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: 0.0001
- 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.956         | 0.99  | 56   | 1.8086          | 0.5      |
| 1.4192        | 2.0   | 113  | 1.3941          | 0.67     |
| 1.1689        | 2.99  | 169  | 1.0402          | 0.76     |
| 0.9796        | 4.0   | 226  | 0.9111          | 0.79     |
| 0.7421        | 4.99  | 282  | 0.9137          | 0.85     |
| 0.6455        | 6.0   | 339  | 0.9775          | 0.83     |
| 0.6572        | 6.99  | 395  | 0.9103          | 0.83     |
| 0.5707        | 8.0   | 452  | 0.9437          | 0.83     |
| 0.5765        | 8.99  | 508  | 0.9216          | 0.84     |
| 0.519         | 9.91  | 560  | 0.9268          | 0.83     |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3