---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-2
  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: 0.86
---

<!-- 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-2

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: 0.7203
- 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2521        | 1.0   | 90   | 2.2219          | 0.3      |
| 1.8502        | 2.0   | 180  | 1.8299          | 0.54     |
| 1.4155        | 3.0   | 270  | 1.4247          | 0.64     |
| 0.9885        | 4.0   | 360  | 1.0313          | 0.7      |
| 0.8111        | 5.0   | 450  | 0.8535          | 0.78     |
| 0.7023        | 6.0   | 540  | 0.7743          | 0.79     |
| 0.5663        | 7.0   | 630  | 0.6618          | 0.81     |
| 0.3577        | 8.0   | 720  | 0.6937          | 0.77     |
| 0.3003        | 9.0   | 810  | 0.6107          | 0.82     |
| 0.1321        | 10.0  | 900  | 0.5648          | 0.81     |
| 0.0488        | 11.0  | 990  | 0.5655          | 0.84     |
| 0.0323        | 12.0  | 1080 | 0.5612          | 0.86     |
| 0.0154        | 13.0  | 1170 | 0.6338          | 0.85     |
| 0.0108        | 14.0  | 1260 | 0.7292          | 0.84     |
| 0.0082        | 15.0  | 1350 | 0.7542          | 0.84     |
| 0.0065        | 16.0  | 1440 | 0.7123          | 0.86     |
| 0.0062        | 17.0  | 1530 | 0.6949          | 0.86     |
| 0.0848        | 18.0  | 1620 | 0.7332          | 0.85     |
| 0.0053        | 19.0  | 1710 | 0.7291          | 0.85     |
| 0.005         | 20.0  | 1800 | 0.7203          | 0.86     |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.2.1
- Datasets 2.17.1
- Tokenizers 0.15.2