---
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: 0.7345
- 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 12
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2637        | 1.0   | 75   | 2.2059          | 0.34     |
| 1.8944        | 2.0   | 150  | 1.8194          | 0.41     |
| 1.5462        | 3.0   | 225  | 1.4462          | 0.6      |
| 1.27          | 4.0   | 300  | 1.1931          | 0.66     |
| 1.0759        | 5.0   | 375  | 0.9130          | 0.76     |
| 0.6731        | 6.0   | 450  | 0.8307          | 0.75     |
| 0.5021        | 7.0   | 525  | 0.6785          | 0.82     |
| 0.351         | 8.0   | 600  | 0.6946          | 0.8      |
| 0.259         | 9.0   | 675  | 0.5913          | 0.82     |
| 0.1789        | 10.0  | 750  | 0.6499          | 0.83     |
| 0.0655        | 11.0  | 825  | 0.5624          | 0.88     |
| 0.1194        | 12.0  | 900  | 0.6549          | 0.83     |
| 0.0874        | 13.0  | 975  | 0.6412          | 0.86     |
| 0.0142        | 14.0  | 1050 | 0.7119          | 0.86     |
| 0.0119        | 15.0  | 1125 | 0.7415          | 0.85     |
| 0.0093        | 16.0  | 1200 | 0.6833          | 0.87     |
| 0.0089        | 17.0  | 1275 | 0.7802          | 0.85     |
| 0.0142        | 18.0  | 1350 | 0.7611          | 0.85     |
| 0.0072        | 19.0  | 1425 | 0.7262          | 0.86     |
| 0.057         | 20.0  | 1500 | 0.7345          | 0.87     |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3