---
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: 0.66
---

<!-- 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: 1.6170
- Accuracy: 0.66

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2999        | 0.97  | 7    | 2.2700          | 0.28     |
| 2.2713        | 1.93  | 14   | 2.1859          | 0.36     |
| 2.1478        | 2.9   | 21   | 2.0656          | 0.47     |
| 2.0863        | 4.0   | 29   | 1.9387          | 0.53     |
| 1.9229        | 4.97  | 36   | 1.8303          | 0.62     |
| 1.8399        | 5.93  | 43   | 1.7453          | 0.59     |
| 1.7467        | 6.9   | 50   | 1.6898          | 0.58     |
| 1.7223        | 8.0   | 58   | 1.6360          | 0.6      |
| 1.6716        | 8.97  | 65   | 1.6243          | 0.65     |
| 1.6509        | 9.66  | 70   | 1.6170          | 0.66     |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.0+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3