---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-30-epochs
  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: 0.81
---

<!-- 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-30-epochs

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: 1.1939
- Accuracy: 0.81

## 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1804        | 1.0   | 113  | 2.1756          | 0.46     |
| 1.7271        | 2.0   | 226  | 1.6973          | 0.53     |
| 1.2703        | 3.0   | 339  | 1.2950          | 0.51     |
| 0.9446        | 4.0   | 452  | 0.9433          | 0.68     |
| 0.6192        | 5.0   | 565  | 0.7885          | 0.73     |
| 0.3628        | 6.0   | 678  | 0.8338          | 0.76     |
| 0.2871        | 7.0   | 791  | 0.8125          | 0.74     |
| 0.0587        | 8.0   | 904  | 0.7500          | 0.8      |
| 0.1316        | 9.0   | 1017 | 0.8711          | 0.79     |
| 0.0175        | 10.0  | 1130 | 0.7429          | 0.82     |
| 0.0818        | 11.0  | 1243 | 0.9848          | 0.81     |
| 0.0049        | 12.0  | 1356 | 1.0498          | 0.76     |
| 0.0034        | 13.0  | 1469 | 1.0422          | 0.84     |
| 0.0028        | 14.0  | 1582 | 1.0919          | 0.83     |
| 0.0023        | 15.0  | 1695 | 1.0565          | 0.82     |
| 0.0019        | 16.0  | 1808 | 1.0797          | 0.84     |
| 0.0769        | 17.0  | 1921 | 1.1430          | 0.82     |
| 0.104         | 18.0  | 2034 | 1.1482          | 0.8      |
| 0.0014        | 19.0  | 2147 | 1.0972          | 0.83     |
| 0.0012        | 20.0  | 2260 | 1.1867          | 0.82     |
| 0.0012        | 21.0  | 2373 | 1.1914          | 0.82     |
| 0.0012        | 22.0  | 2486 | 1.1461          | 0.84     |
| 0.0009        | 23.0  | 2599 | 1.1401          | 0.82     |
| 0.0009        | 24.0  | 2712 | 1.1686          | 0.84     |
| 0.0009        | 25.0  | 2825 | 1.1824          | 0.85     |
| 0.0009        | 26.0  | 2938 | 1.1815          | 0.81     |
| 0.0008        | 27.0  | 3051 | 1.1808          | 0.82     |
| 0.0008        | 28.0  | 3164 | 1.1904          | 0.81     |
| 0.0008        | 29.0  | 3277 | 1.1990          | 0.82     |
| 0.0008        | 30.0  | 3390 | 1.1939          | 0.81     |


### Framework versions

- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3