---
license: apache-2.0
base_model: Sandiago21/distilhubert-finetuned-gtzan
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: 0.85
---

<!-- 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 [Sandiago21/distilhubert-finetuned-gtzan](https://huggingface.co./Sandiago21/distilhubert-finetuned-gtzan) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9021
- Accuracy: 0.85

## 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: 0.0001
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2617        | 1.0   | 57   | 0.8101          | 0.76     |
| 0.3005        | 2.0   | 114  | 0.8589          | 0.82     |
| 0.0123        | 3.0   | 171  | 1.0596          | 0.8      |
| 0.0141        | 4.0   | 228  | 1.0238          | 0.81     |
| 0.0047        | 5.0   | 285  | 0.8953          | 0.83     |
| 0.0889        | 6.0   | 342  | 0.8765          | 0.86     |
| 0.0482        | 7.0   | 399  | 1.1115          | 0.83     |
| 0.0013        | 8.0   | 456  | 1.0884          | 0.84     |
| 0.0009        | 9.0   | 513  | 1.0055          | 0.85     |
| 0.0008        | 10.0  | 570  | 0.9021          | 0.85     |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3