---
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: 0.7692307692307693
---

<!-- 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: 1.2256
- Accuracy: 0.7692

## 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: 9e-05
- train_batch_size: 10
- eval_batch_size: 10
- 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: 18
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9893        | 1.0   | 70   | 1.9671          | 0.4615   |
| 1.1923        | 2.0   | 140  | 1.3634          | 0.5256   |
| 1.1937        | 3.0   | 210  | 1.0865          | 0.6154   |
| 0.5684        | 4.0   | 280  | 0.9352          | 0.6795   |
| 0.4571        | 5.0   | 350  | 0.7889          | 0.7564   |
| 0.1854        | 6.0   | 420  | 0.8209          | 0.7308   |
| 0.0688        | 7.0   | 490  | 0.9835          | 0.7692   |
| 0.087         | 8.0   | 560  | 1.1710          | 0.7179   |
| 0.0109        | 9.0   | 630  | 1.0900          | 0.7692   |
| 0.0049        | 10.0  | 700  | 1.2256          | 0.7692   |


### Framework versions

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