---
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: 1.1893
- 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: 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9486        | 1.0   | 225  | 1.8744          | 0.54     |
| 1.0616        | 2.0   | 450  | 1.2196          | 0.66     |
| 1.0193        | 3.0   | 675  | 0.7841          | 0.78     |
| 0.81          | 4.0   | 900  | 0.7212          | 0.8      |
| 0.2171        | 5.0   | 1125 | 0.7194          | 0.77     |
| 0.0458        | 6.0   | 1350 | 0.8966          | 0.81     |
| 0.3485        | 7.0   | 1575 | 0.7960          | 0.81     |
| 0.09          | 8.0   | 1800 | 1.0860          | 0.82     |
| 0.0031        | 9.0   | 2025 | 0.7744          | 0.84     |
| 0.0026        | 10.0  | 2250 | 0.8249          | 0.87     |
| 0.0032        | 11.0  | 2475 | 1.0680          | 0.84     |
| 0.0012        | 12.0  | 2700 | 1.0724          | 0.83     |
| 0.0011        | 13.0  | 2925 | 1.1407          | 0.83     |
| 0.0009        | 14.0  | 3150 | 1.0395          | 0.85     |
| 0.0007        | 15.0  | 3375 | 1.2991          | 0.83     |
| 0.0006        | 16.0  | 3600 | 1.1403          | 0.83     |
| 0.0007        | 17.0  | 3825 | 1.0837          | 0.83     |
| 0.0005        | 18.0  | 4050 | 1.1463          | 0.83     |
| 0.0005        | 19.0  | 4275 | 1.1987          | 0.83     |
| 0.0005        | 20.0  | 4500 | 1.1893          | 0.83     |


### Framework versions

- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4.dev0
- Tokenizers 0.13.3