---
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: 0.9861
- 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: 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1171        | 1.0   | 70   | 2.1232          | 0.2308   |
| 1.534         | 2.0   | 140  | 1.6014          | 0.5128   |
| 1.4328        | 3.0   | 210  | 1.2896          | 0.5641   |
| 0.8631        | 4.0   | 280  | 1.1275          | 0.5897   |
| 0.6448        | 5.0   | 350  | 1.0679          | 0.6667   |
| 0.482         | 6.0   | 420  | 0.8798          | 0.7051   |
| 0.2458        | 7.0   | 490  | 0.8290          | 0.7564   |
| 0.2264        | 8.0   | 560  | 0.8350          | 0.7564   |
| 0.1661        | 9.0   | 630  | 0.8284          | 0.7179   |
| 0.0286        | 10.0  | 700  | 0.9681          | 0.7179   |
| 0.0155        | 11.0  | 770  | 0.9861          | 0.7436   |


### Framework versions

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