---
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: 0.88
---

<!-- 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: 0.5534
- Accuracy: 0.88

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0235        | 1.0   | 112  | 1.8164          | 0.52     |
| 1.3943        | 2.0   | 225  | 1.2865          | 0.65     |
| 0.9238        | 3.0   | 337  | 0.9596          | 0.76     |
| 0.7587        | 4.0   | 450  | 0.8548          | 0.79     |
| 0.5283        | 5.0   | 562  | 0.7655          | 0.82     |
| 0.2717        | 6.0   | 675  | 0.6910          | 0.79     |
| 0.2399        | 7.0   | 787  | 0.6660          | 0.83     |
| 0.2417        | 8.0   | 900  | 0.5973          | 0.84     |
| 0.3339        | 9.0   | 1012 | 0.5669          | 0.84     |
| 0.1585        | 9.96  | 1120 | 0.5534          | 0.88     |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3