---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-bs-4
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: GTZAN
      type: marsyas/gtzan
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.86
---

<!-- 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-bs-4

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: 0.6851
- Accuracy: 0.86

## 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: 4
- eval_batch_size: 4
- 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8665        | 1.0   | 225  | 1.7962          | 0.45     |
| 1.1445        | 2.0   | 450  | 1.1084          | 0.68     |
| 0.9474        | 3.0   | 675  | 0.8338          | 0.73     |
| 0.8286        | 4.0   | 900  | 0.7530          | 0.76     |
| 0.2336        | 5.0   | 1125 | 0.5369          | 0.84     |
| 0.2092        | 6.0   | 1350 | 0.5608          | 0.86     |
| 0.2092        | 7.0   | 1575 | 0.5390          | 0.88     |
| 0.04          | 8.0   | 1800 | 0.5567          | 0.88     |
| 0.0046        | 9.0   | 2025 | 0.5736          | 0.86     |
| 0.0029        | 10.0  | 2250 | 0.6236          | 0.86     |
| 0.0035        | 11.0  | 2475 | 0.8139          | 0.85     |
| 0.0018        | 12.0  | 2700 | 0.5752          | 0.9      |
| 0.0016        | 13.0  | 2925 | 0.6745          | 0.85     |
| 0.0016        | 14.0  | 3150 | 0.6959          | 0.85     |
| 0.0014        | 15.0  | 3375 | 0.6851          | 0.86     |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3