---
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: 0.77
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/scott-poynts-nil/huggingface/runs/mvcwa6jm)
# 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: 0.7624
- Accuracy: 0.77

## 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: 16
- eval_batch_size: 16
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.169         | 1.0   | 57   | 2.0533          | 0.51     |
| 1.6117        | 2.0   | 114  | 1.5264          | 0.57     |
| 1.3112        | 3.0   | 171  | 1.2764          | 0.64     |
| 0.9584        | 4.0   | 228  | 1.0663          | 0.72     |
| 0.8809        | 5.0   | 285  | 0.9548          | 0.72     |
| 0.7652        | 6.0   | 342  | 0.9119          | 0.77     |
| 0.6498        | 7.0   | 399  | 0.8271          | 0.77     |
| 0.5007        | 8.0   | 456  | 0.7962          | 0.76     |
| 0.4747        | 9.0   | 513  | 0.7583          | 0.77     |
| 0.4418        | 10.0  | 570  | 0.7624          | 0.77     |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1