---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: 25-distilhubert-finetuned-gtzan
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: GTZAN
      type: marsyas/gtzan
      config: default
      split: train[:25%]
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.96
---

<!-- 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. -->

# 25-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: 0.1955
- Accuracy: 0.96

## 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
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3123        | 1.0   | 57   | 1.3463          | 0.56     |
| 0.6113        | 2.0   | 114  | 0.5379          | 0.96     |
| 0.1295        | 3.0   | 171  | 0.2719          | 0.96     |
| 0.2878        | 4.0   | 228  | 0.0979          | 0.96     |
| 0.0168        | 5.0   | 285  | 0.1527          | 0.96     |
| 0.0104        | 6.0   | 342  | 0.2320          | 0.96     |
| 0.0067        | 7.0   | 399  | 0.1798          | 0.96     |
| 0.0051        | 8.0   | 456  | 0.1827          | 0.96     |
| 0.0041        | 9.0   | 513  | 0.1918          | 0.96     |
| 0.0055        | 10.0  | 570  | 0.1955          | 0.96     |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2