---
license: apache-2.0
tags:
- music
- genre
- classification
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan_accuracy_93
  results: []
language:
- en
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilhubert-finetuned-gtzan_accuracy_93

### This model is a fine-tuned version of [yuval6967/distilhubert-finetuned-gtzan](https://huggingface.co/yuval6967/distilhubert-finetuned-gtzan) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5121
- __Accuracy: 0.93__

## Model description

- Fine-tuned model to demonstrate > 87% accuracy for the [Huggingface Audio course](https://huggingface.co/learn/audio-course/chapter0/introduction) 

## Intended uses & limitations

- Model is built to identify the genre of music based on a ~30 sec clip

## Training and evaluation data

More information needed

## Training procedure
- test_size = 0.20 was used for the split 

### 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0316        | 1.0   | 100  | 0.4338          | 0.895    |
| 0.0031        | 2.0   | 200  | 0.7039          | 0.86     |
| 0.0069        | 3.0   | 300  | 0.4526          | 0.925    |
| 0.1799        | 4.0   | 400  | 0.7071          | 0.88     |
| 0.1783        | 5.0   | 500  | 0.5923          | 0.92     |
| 0.0011        | 6.0   | 600  | 0.5498          | 0.92     |
| 0.0005        | 7.0   | 700  | 0.4927          | 0.925    |
| 0.0005        | 8.0   | 800  | 0.6172          | 0.915    |
| 0.0004        | 9.0   | 900  | 0.4988          | 0.925    |
| 0.0004        | 10.0  | 1000 | 0.5121          | 0.93     |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3