---
library_name: transformers
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: 0.7435897435897436
---

<!-- 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 [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8173
- Accuracy: 0.7436

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1874        | 1.0   | 44   | 2.1429          | 0.3974   |
| 1.8257        | 2.0   | 88   | 1.7390          | 0.4872   |
| 1.4881        | 3.0   | 132  | 1.3711          | 0.6026   |
| 1.0373        | 4.0   | 176  | 1.1632          | 0.6667   |
| 0.7621        | 5.0   | 220  | 1.0026          | 0.7308   |
| 0.6114        | 6.0   | 264  | 0.8857          | 0.7436   |
| 0.5642        | 7.0   | 308  | 0.8796          | 0.7179   |
| 0.3386        | 8.0   | 352  | 1.0714          | 0.6923   |
| 0.3364        | 9.0   | 396  | 0.8363          | 0.7308   |
| 0.1678        | 10.0  | 440  | 0.7834          | 0.7436   |
| 0.1154        | 11.0  | 484  | 0.8173          | 0.7436   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0