---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-v2
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: gtzan
      type: gtzan
      config: all
      split: train
      args: all
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.83
---

<!-- 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-v2

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: 0.6766
- Accuracy: 0.83

## 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: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0361        | 1.0   | 113  | 1.8915          | 0.41     |
| 1.3728        | 2.0   | 226  | 1.2725          | 0.64     |
| 1.0442        | 3.0   | 339  | 0.9188          | 0.78     |
| 0.9614        | 4.0   | 452  | 0.8790          | 0.7      |
| 0.6945        | 5.0   | 565  | 0.6933          | 0.79     |
| 0.3976        | 6.0   | 678  | 0.6891          | 0.79     |
| 0.345         | 7.0   | 791  | 0.6091          | 0.81     |
| 0.1068        | 8.0   | 904  | 0.5905          | 0.81     |
| 0.1646        | 9.0   | 1017 | 0.5809          | 0.82     |
| 0.1079        | 10.0  | 1130 | 0.6527          | 0.81     |
| 0.0311        | 11.0  | 1243 | 0.6393          | 0.86     |
| 0.0491        | 12.0  | 1356 | 0.6766          | 0.83     |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.13.2