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

<!-- 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-hyperparam-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: 1.2045
- 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7469        | 1.0   | 113  | 1.3737          | 0.57     |
| 0.7973        | 2.0   | 226  | 1.5247          | 0.57     |
| 0.6831        | 3.0   | 339  | 0.8961          | 0.74     |
| 0.4573        | 4.0   | 452  | 0.8638          | 0.76     |
| 0.1874        | 5.0   | 565  | 0.7839          | 0.81     |
| 0.0829        | 6.0   | 678  | 1.0174          | 0.79     |
| 0.0306        | 7.0   | 791  | 0.9393          | 0.81     |
| 0.004         | 8.0   | 904  | 0.9737          | 0.85     |
| 0.1209        | 9.0   | 1017 | 1.0625          | 0.8      |
| 0.0237        | 10.0  | 1130 | 1.3653          | 0.8      |
| 0.0164        | 11.0  | 1243 | 1.3065          | 0.81     |
| 0.0007        | 12.0  | 1356 | 1.1272          | 0.83     |
| 0.0004        | 13.0  | 1469 | 1.3226          | 0.83     |
| 0.0001        | 14.0  | 1582 | 1.6092          | 0.82     |
| 0.0001        | 15.0  | 1695 | 1.2045          | 0.86     |
| 0.0002        | 16.0  | 1808 | 1.1312          | 0.85     |
| 0.0003        | 17.0  | 1921 | 1.0911          | 0.86     |
| 0.0           | 18.0  | 2034 | 1.1983          | 0.84     |
| 0.0001        | 19.0  | 2147 | 1.1363          | 0.85     |
| 0.0           | 20.0  | 2260 | 1.2547          | 0.85     |
| 0.0002        | 21.0  | 2373 | 1.2394          | 0.84     |
| 0.0001        | 22.0  | 2486 | 1.5508          | 0.85     |
| 0.0           | 23.0  | 2599 | 1.2689          | 0.83     |
| 0.0           | 24.0  | 2712 | 1.2343          | 0.83     |
| 0.0003        | 25.0  | 2825 | 1.2313          | 0.81     |
| 0.0           | 26.0  | 2938 | 1.2217          | 0.83     |
| 0.0           | 27.0  | 3051 | 1.1596          | 0.84     |
| 0.0           | 28.0  | 3164 | 1.1081          | 0.85     |
| 0.0001        | 29.0  | 3277 | 1.1394          | 0.85     |
| 0.0           | 30.0  | 3390 | 1.1215          | 0.85     |


### Framework versions

- Transformers 4.48.0.dev0
- Pytorch 2.6.0+cu126
- Datasets 3.2.0
- Tokenizers 0.21.0