---
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: 0.35
---

<!-- 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: 2.1821
- Accuracy: 0.35

## 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: 1e-06
- 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: 11

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2909        | 1.0   | 113  | 2.2939          | 0.16     |
| 2.2793        | 2.0   | 226  | 2.2789          | 0.2      |
| 2.2729        | 3.0   | 339  | 2.2617          | 0.19     |
| 2.2645        | 4.0   | 452  | 2.2446          | 0.28     |
| 2.2211        | 5.0   | 565  | 2.2290          | 0.29     |
| 2.2382        | 6.0   | 678  | 2.2162          | 0.31     |
| 2.2631        | 7.0   | 791  | 2.2039          | 0.31     |
| 2.247         | 8.0   | 904  | 2.1946          | 0.32     |
| 2.2255        | 9.0   | 1017 | 2.1877          | 0.33     |
| 2.1932        | 10.0  | 1130 | 2.1834          | 0.35     |
| 2.2213        | 11.0  | 1243 | 2.1821          | 0.35     |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3