---
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-dropout0.25-split3-speed
  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: 0.8233333333333334
---

<!-- 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-dropout0.25-split3-speed

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: 1.0262
- Accuracy: 0.8233

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1701        | 1.0   | 169  | 1.2158          | 0.6633   |
| 1.0623        | 2.0   | 338  | 0.9563          | 0.7033   |
| 0.6686        | 3.0   | 507  | 0.8979          | 0.7067   |
| 0.4958        | 4.0   | 676  | 0.8167          | 0.79     |
| 0.3174        | 5.0   | 845  | 0.8568          | 0.8033   |
| 0.1967        | 6.0   | 1014 | 0.8837          | 0.8067   |
| 0.1126        | 7.0   | 1183 | 0.9364          | 0.8267   |
| 0.0536        | 8.0   | 1352 | 1.0097          | 0.8233   |
| 0.039         | 9.0   | 1521 | 1.0470          | 0.82     |
| 0.0227        | 10.0  | 1690 | 1.0262          | 0.8233   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0
- Datasets 3.3.2
- Tokenizers 0.21.0