---
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: 0.79
---

<!-- 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: 0.8742
- Accuracy: 0.79

## 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: 3e-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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2265        | 1.0   | 57   | 2.1424          | 0.41     |
| 1.7837        | 2.0   | 114  | 1.6990          | 0.54     |
| 1.4838        | 3.0   | 171  | 1.4898          | 0.63     |
| 1.3231        | 4.0   | 228  | 1.2616          | 0.7      |
| 1.1623        | 5.0   | 285  | 1.1048          | 0.75     |
| 1.043         | 6.0   | 342  | 1.0032          | 0.77     |
| 0.9029        | 7.0   | 399  | 0.9896          | 0.76     |
| 0.8869        | 8.0   | 456  | 0.8895          | 0.81     |
| 0.8797        | 9.0   | 513  | 0.8821          | 0.8      |
| 0.8542        | 10.0  | 570  | 0.8742          | 0.79     |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0