---
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: 0.86
---

<!-- 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

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: 1.0283
- 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0235        | 0.99  | 28   | 1.0778          | 0.83     |
| 0.0072        | 1.98  | 56   | 1.0815          | 0.83     |
| 0.0004        | 2.97  | 84   | 1.1249          | 0.82     |
| 0.0003        | 4.0   | 113  | 1.1113          | 0.81     |
| 0.0002        | 4.99  | 141  | 1.1442          | 0.79     |
| 0.0137        | 5.98  | 169  | 1.0623          | 0.84     |
| 0.0048        | 6.97  | 197  | 1.0193          | 0.86     |
| 0.0087        | 8.0   | 226  | 1.0578          | 0.84     |
| 0.0055        | 8.99  | 254  | 1.0279          | 0.86     |
| 0.005         | 9.91  | 280  | 1.0283          | 0.86     |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3