---
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: 0.83
---

<!-- 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

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: 0.9405
- 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1782        | 1.0   | 113  | 2.0403          | 0.46     |
| 1.487         | 2.0   | 226  | 1.3902          | 0.64     |
| 1.1634        | 3.0   | 339  | 1.0882          | 0.71     |
| 0.9874        | 4.0   | 452  | 0.9260          | 0.68     |
| 0.7754        | 5.0   | 565  | 0.7265          | 0.8      |
| 0.4512        | 6.0   | 678  | 0.6141          | 0.84     |
| 0.4947        | 7.0   | 791  | 0.8277          | 0.78     |
| 0.1896        | 8.0   | 904  | 0.7220          | 0.81     |
| 0.2142        | 9.0   | 1017 | 0.6393          | 0.85     |
| 0.0413        | 10.0  | 1130 | 0.8113          | 0.82     |
| 0.0105        | 11.0  | 1243 | 0.7368          | 0.82     |
| 0.1392        | 12.0  | 1356 | 0.8139          | 0.85     |
| 0.0051        | 13.0  | 1469 | 0.7893          | 0.86     |
| 0.0041        | 14.0  | 1582 | 0.8515          | 0.83     |
| 0.0041        | 15.0  | 1695 | 0.7707          | 0.85     |
| 0.0033        | 16.0  | 1808 | 0.8931          | 0.84     |
| 0.0772        | 17.0  | 1921 | 0.8411          | 0.86     |
| 0.0028        | 18.0  | 2034 | 0.8884          | 0.83     |
| 0.0025        | 19.0  | 2147 | 0.9094          | 0.84     |
| 0.0027        | 20.0  | 2260 | 0.9405          | 0.83     |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0