---
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: None
      args: all
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.84
---

<!-- 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: 0.7755
- Accuracy: 0.84

## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2322        | 1.0   | 57   | 2.1521          | 0.37     |
| 1.7413        | 2.0   | 114  | 1.6606          | 0.47     |
| 1.3543        | 3.0   | 171  | 1.2698          | 0.69     |
| 0.9436        | 4.0   | 228  | 1.0440          | 0.71     |
| 0.7976        | 5.0   | 285  | 0.8338          | 0.79     |
| 0.6615        | 6.0   | 342  | 0.6933          | 0.84     |
| 0.5743        | 7.0   | 399  | 0.6180          | 0.84     |
| 0.4349        | 8.0   | 456  | 0.5931          | 0.84     |
| 0.2949        | 9.0   | 513  | 0.5794          | 0.85     |
| 0.2274        | 10.0  | 570  | 0.5901          | 0.84     |
| 0.1067        | 11.0  | 627  | 0.6496          | 0.81     |
| 0.104         | 12.0  | 684  | 0.6921          | 0.82     |
| 0.0781        | 13.0  | 741  | 0.6653          | 0.83     |
| 0.0245        | 14.0  | 798  | 0.6621          | 0.84     |
| 0.0144        | 15.0  | 855  | 0.7015          | 0.82     |
| 0.0104        | 16.0  | 912  | 0.7109          | 0.85     |
| 0.007         | 17.0  | 969  | 0.7472          | 0.84     |
| 0.0163        | 18.0  | 1026 | 0.7603          | 0.86     |
| 0.0039        | 19.0  | 1083 | 0.7710          | 0.85     |
| 0.0035        | 20.0  | 1140 | 0.7755          | 0.84     |


### Framework versions

- Transformers 4.43.0.dev0
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1