---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
- precision
- recall
- f1
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: 0.7733333333333333
    - name: Precision
      type: precision
      value: 0.775454513809777
    - name: Recall
      type: recall
      value: 0.7733333333333333
    - name: F1
      type: f1
      value: 0.7708532203254443
---

<!-- 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/raspuntinov_ai/huggingface/runs/xti2wn9w)
# 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: 0.7448
- Accuracy: 0.7733
- Precision: 0.7755
- Recall: 0.7733
- F1: 0.7709

## 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
- 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 | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.0182        | 1.0   | 88   | 2.0020          | 0.3333   | 0.3990    | 0.3333 | 0.2547 |
| 1.6019        | 2.0   | 176  | 1.4794          | 0.5333   | 0.6597    | 0.5333 | 0.4789 |
| 1.0733        | 3.0   | 264  | 1.2329          | 0.6133   | 0.6930    | 0.6133 | 0.5993 |
| 0.9451        | 4.0   | 352  | 1.1227          | 0.64     | 0.7214    | 0.64   | 0.6289 |
| 0.9232        | 5.0   | 440  | 0.9426          | 0.7133   | 0.7398    | 0.7133 | 0.7071 |
| 0.6552        | 6.0   | 528  | 0.8132          | 0.78     | 0.7795    | 0.78   | 0.7768 |
| 0.4019        | 7.0   | 616  | 0.8478          | 0.7333   | 0.7428    | 0.7333 | 0.7285 |
| 0.2836        | 8.0   | 704  | 0.7369          | 0.7933   | 0.8025    | 0.7933 | 0.7915 |
| 0.207         | 9.0   | 792  | 0.7440          | 0.7933   | 0.7926    | 0.7933 | 0.7879 |
| 0.3091        | 10.0  | 880  | 0.7448          | 0.7733   | 0.7755    | 0.7733 | 0.7709 |


### Framework versions

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