---
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-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: 0.88
---

<!-- 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: 0.5321
- Accuracy: 0.88

## 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: 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: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1271        | 1.0   | 113  | 2.0529          | 0.47     |
| 1.4245        | 2.0   | 226  | 1.4173          | 0.6      |
| 1.1783        | 3.0   | 339  | 1.0567          | 0.71     |
| 0.7597        | 4.0   | 452  | 0.8387          | 0.75     |
| 0.6043        | 5.0   | 565  | 0.6876          | 0.81     |
| 0.4758        | 6.0   | 678  | 0.6897          | 0.79     |
| 0.4882        | 7.0   | 791  | 0.6507          | 0.79     |
| 0.2361        | 8.0   | 904  | 0.6232          | 0.84     |
| 0.209         | 9.0   | 1017 | 0.5800          | 0.82     |
| 0.0859        | 10.0  | 1130 | 0.5414          | 0.85     |
| 0.0639        | 11.0  | 1243 | 0.5321          | 0.88     |
| 0.0405        | 12.0  | 1356 | 0.8187          | 0.82     |
| 0.0481        | 13.0  | 1469 | 0.7086          | 0.85     |
| 0.0127        | 14.0  | 1582 | 0.7394          | 0.84     |
| 0.0071        | 15.0  | 1695 | 0.6890          | 0.86     |
| 0.0073        | 16.0  | 1808 | 0.7361          | 0.86     |
| 0.0062        | 17.0  | 1921 | 0.9311          | 0.8      |
| 0.0028        | 18.0  | 2034 | 0.7819          | 0.84     |
| 0.0024        | 19.0  | 2147 | 0.8263          | 0.86     |
| 0.0023        | 20.0  | 2260 | 0.8049          | 0.86     |


### Framework versions

- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0