---
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: 0.85
---

<!-- 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: 0.4678
- Accuracy: 0.85

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9524        | 1.0   | 113  | 1.8916          | 0.47     |
| 1.1655        | 2.0   | 226  | 1.2480          | 0.63     |
| 1.0333        | 3.0   | 339  | 0.9655          | 0.75     |
| 0.6364        | 4.0   | 452  | 0.7689          | 0.79     |
| 0.6355        | 5.0   | 565  | 0.6403          | 0.82     |
| 0.4071        | 6.0   | 678  | 0.4922          | 0.85     |
| 0.2838        | 7.0   | 791  | 0.5228          | 0.82     |
| 0.1085        | 8.0   | 904  | 0.5689          | 0.83     |
| 0.1358        | 9.0   | 1017 | 0.4693          | 0.86     |
| 0.0793        | 10.0  | 1130 | 0.4678          | 0.85     |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1