---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan4
  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: 0.78
---

<!-- 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-gtzan4

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: 1.0945
- Accuracy: 0.78

## 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: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 192
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.85  | 4    | 2.2991          | 0.06     |
| 2.2997        | 1.92  | 9    | 2.2668          | 0.28     |
| 2.2819        | 2.99  | 14   | 2.1877          | 0.33     |
| 2.2336        | 3.84  | 18   | 2.1023          | 0.47     |
| 2.1493        | 4.91  | 23   | 1.9895          | 0.52     |
| 2.0571        | 5.97  | 28   | 1.8745          | 0.51     |
| 1.9341        | 6.83  | 32   | 1.7838          | 0.57     |
| 1.8274        | 7.89  | 37   | 1.6784          | 0.64     |
| 1.724         | 8.96  | 42   | 1.5859          | 0.66     |
| 1.6407        | 9.81  | 46   | 1.5234          | 0.66     |
| 1.5593        | 10.88 | 51   | 1.4508          | 0.7      |
| 1.4735        | 11.95 | 56   | 1.3982          | 0.69     |
| 1.4185        | 12.8  | 60   | 1.3501          | 0.72     |
| 1.3613        | 13.87 | 65   | 1.3131          | 0.74     |
| 1.3099        | 14.93 | 70   | 1.2742          | 0.72     |
| 1.2762        | 16.0  | 75   | 1.2485          | 0.73     |
| 1.2762        | 16.85 | 79   | 1.2102          | 0.74     |
| 1.2379        | 17.92 | 84   | 1.1931          | 0.75     |
| 1.193         | 18.99 | 89   | 1.1647          | 0.75     |
| 1.1863        | 19.84 | 93   | 1.1488          | 0.77     |
| 1.1435        | 20.91 | 98   | 1.1349          | 0.78     |
| 1.1424        | 21.97 | 103  | 1.1166          | 0.79     |
| 1.0961        | 22.83 | 107  | 1.1025          | 0.78     |
| 1.0887        | 23.89 | 112  | 1.0993          | 0.78     |
| 1.0977        | 24.96 | 117  | 1.0952          | 0.78     |
| 1.0661        | 25.6  | 120  | 1.0945          | 0.78     |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3