---
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: 0.87
---

<!-- 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: 0.5192  
- Accuracy: 0.87

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0055        | 0.99  | 56   | 1.7312          | 0.58     |
| 1.2943        | 2.0   | 113  | 1.1415          | 0.63     |
| 0.9066        | 2.99  | 169  | 0.8956          | 0.71     |
| 0.8174        | 4.0   | 226  | 0.8152          | 0.75     |
| 0.5274        | 4.99  | 282  | 0.6256          | 0.81     |
| 0.3878        | 6.0   | 339  | 0.7913          | 0.77     |
| 0.2518        | 6.99  | 395  | 0.5656          | 0.85     |
| 0.1841        | 8.0   | 452  | 0.5490          | 0.84     |
| 0.0929        | 8.99  | 508  | 0.5192          | 0.87     |
| 0.105         | 9.91  | 560  | 0.5287          | 0.85     |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3