---
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: 0.8403
- 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: 5e-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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0376        | 1.0   | 57   | 0.6132          | 0.88     |
| 0.052         | 2.0   | 114  | 0.8688          | 0.84     |
| 0.0047        | 3.0   | 171  | 0.7919          | 0.84     |
| 0.0029        | 4.0   | 228  | 0.8666          | 0.85     |
| 0.0021        | 5.0   | 285  | 0.8617          | 0.87     |
| 0.0813        | 6.0   | 342  | 0.9202          | 0.86     |
| 0.0461        | 7.0   | 399  | 0.8868          | 0.85     |
| 0.0014        | 8.0   | 456  | 0.8567          | 0.86     |
| 0.0012        | 9.0   | 513  | 0.8471          | 0.86     |
| 0.0013        | 10.0  | 570  | 0.8403          | 0.87     |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3