---
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: 0.84
---

<!-- 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: 0.5833
- 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
- gradient_accumulation_steps: 5
- total_train_batch_size: 10
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9977        | 1.0   | 90   | 1.8501          | 0.47     |
| 1.2442        | 2.0   | 180  | 1.2525          | 0.65     |
| 1.1725        | 3.0   | 270  | 1.1111          | 0.68     |
| 0.955         | 4.0   | 360  | 0.8526          | 0.74     |
| 0.7524        | 5.0   | 450  | 0.7258          | 0.77     |
| 0.5618        | 6.0   | 540  | 0.7356          | 0.75     |
| 0.3265        | 7.0   | 630  | 0.6126          | 0.78     |
| 0.3194        | 8.0   | 720  | 0.5614          | 0.84     |
| 0.3098        | 9.0   | 810  | 0.5797          | 0.81     |
| 0.3189        | 10.0  | 900  | 0.5833          | 0.84     |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1
- Datasets 2.4.0
- Tokenizers 0.13.3