---
library_name: transformers
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- gtzan
metrics:
- accuracy
model-index:
- name: ft-hubert-on-gtzan
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: gtzan
      type: gtzan
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.825
---

<!-- 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. -->

# ft-hubert-on-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: 0.6574
- Accuracy: 0.825

## 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: 4
- total_train_batch_size: 8
- optimizer: Use 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 100  | 1.5408          | 0.58     |
| No log        | 2.0   | 200  | 1.1600          | 0.615    |
| No log        | 3.0   | 300  | 0.9942          | 0.705    |
| No log        | 4.0   | 400  | 0.8390          | 0.77     |
| 1.0814        | 5.0   | 500  | 0.8495          | 0.745    |
| 1.0814        | 6.0   | 600  | 0.6807          | 0.79     |
| 1.0814        | 7.0   | 700  | 0.7361          | 0.78     |
| 1.0814        | 8.0   | 800  | 0.6250          | 0.815    |
| 1.0814        | 9.0   | 900  | 0.6308          | 0.83     |
| 0.2344        | 10.0  | 1000 | 0.6574          | 0.825    |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0