---
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: 0.85
---

<!-- 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: 0.5960
- Accuracy: 0.85

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2592        | 0.99  | 28   | 2.2167          | 0.25     |
| 1.8769        | 1.98  | 56   | 1.8139          | 0.49     |
| 1.5783        | 2.97  | 84   | 1.5107          | 0.61     |
| 1.3068        | 4.0   | 113  | 1.2779          | 0.68     |
| 1.1062        | 4.99  | 141  | 1.0318          | 0.8      |
| 1.0125        | 5.98  | 169  | 0.9156          | 0.83     |
| 0.8787        | 6.97  | 197  | 0.8099          | 0.86     |
| 0.7658        | 8.0   | 226  | 0.7804          | 0.85     |
| 0.7811        | 8.99  | 254  | 0.7448          | 0.83     |
| 0.6369        | 9.98  | 282  | 0.6841          | 0.84     |
| 0.4859        | 10.97 | 310  | 0.6353          | 0.85     |
| 0.4705        | 12.0  | 339  | 0.6193          | 0.87     |
| 0.4571        | 12.99 | 367  | 0.6090          | 0.86     |
| 0.3999        | 13.98 | 395  | 0.5912          | 0.86     |
| 0.4007        | 14.87 | 420  | 0.5960          | 0.85     |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0