---
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: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.782608695652174
---

<!-- 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: 1.2172
- Accuracy: 0.7826

## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1308        | 1.0   | 25   | 2.1309          | 0.0870   |
| 1.9034        | 2.0   | 50   | 1.9650          | 0.3478   |
| 1.6085        | 3.0   | 75   | 1.7543          | 0.5217   |
| 1.4083        | 4.0   | 100  | 1.6225          | 0.5217   |
| 1.4712        | 5.0   | 125  | 1.4741          | 0.6957   |
| 1.1667        | 6.0   | 150  | 1.3947          | 0.6087   |
| 1.0986        | 7.0   | 175  | 1.3320          | 0.7391   |
| 1.0781        | 8.0   | 200  | 1.2441          | 0.7391   |
| 0.96          | 9.0   | 225  | 1.2146          | 0.7826   |
| 0.9224        | 10.0  | 250  | 1.2172          | 0.7826   |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0