---
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: 0.87
---

<!-- 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: 0.5690
- Accuracy: 0.87

## 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: 2e-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: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2968        | 1.0   | 57   | 1.2136          | 0.7      |
| 1.0931        | 2.0   | 114  | 1.1346          | 0.7      |
| 0.9362        | 3.0   | 171  | 0.9992          | 0.76     |
| 0.948         | 4.0   | 228  | 0.9344          | 0.76     |
| 0.7033        | 5.0   | 285  | 0.7802          | 0.81     |
| 0.6625        | 6.0   | 342  | 0.7777          | 0.79     |
| 0.5627        | 7.0   | 399  | 0.7143          | 0.81     |
| 0.5081        | 8.0   | 456  | 0.6232          | 0.86     |
| 0.4635        | 9.0   | 513  | 0.6564          | 0.85     |
| 0.3347        | 10.0  | 570  | 0.6108          | 0.85     |
| 0.2895        | 11.0  | 627  | 0.7139          | 0.8      |
| 0.2493        | 12.0  | 684  | 0.5887          | 0.84     |
| 0.2673        | 13.0  | 741  | 0.5907          | 0.86     |
| 0.1949        | 14.0  | 798  | 0.5798          | 0.83     |
| 0.1541        | 15.0  | 855  | 0.5532          | 0.87     |
| 0.1913        | 16.0  | 912  | 0.5314          | 0.87     |
| 0.1339        | 17.0  | 969  | 0.5337          | 0.88     |
| 0.0876        | 18.0  | 1026 | 0.5815          | 0.87     |
| 0.0713        | 19.0  | 1083 | 0.5847          | 0.85     |
| 0.0869        | 20.0  | 1140 | 0.5456          | 0.86     |
| 0.0587        | 21.0  | 1197 | 0.5480          | 0.86     |
| 0.0524        | 22.0  | 1254 | 0.5534          | 0.87     |
| 0.0621        | 23.0  | 1311 | 0.5707          | 0.87     |
| 0.0452        | 24.0  | 1368 | 0.5748          | 0.87     |
| 0.0464        | 25.0  | 1425 | 0.5690          | 0.87     |


### Framework versions

- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3