---
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:
- Accuracy: 0.85
- Loss: 0.7531

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 2.2849        | 1.0   | 14   | 0.17     | 2.2588          |
| 2.1931        | 1.99  | 28   | 0.47     | 2.0874          |
| 1.9194        | 2.99  | 42   | 0.58     | 1.8044          |
| 1.6351        | 3.98  | 56   | 0.61     | 1.5806          |
| 1.4473        | 4.98  | 70   | 0.71     | 1.3886          |
| 1.3131        | 5.97  | 84   | 0.7      | 1.2738          |
| 1.2141        | 6.97  | 98   | 0.72     | 1.1616          |
| 1.0657        | 7.96  | 112  | 0.74     | 1.1272          |
| 0.96          | 8.96  | 126  | 0.75     | 1.0251          |
| 0.8387        | 9.96  | 140  | 0.8      | 0.9364          |
| 0.8653        | 10.95 | 154  | 0.79     | 0.8858          |
| 0.7653        | 11.95 | 168  | 0.8      | 0.8233          |
| 0.7329        | 12.94 | 182  | 0.83     | 0.7982          |
| 0.675         | 13.94 | 196  | 0.81     | 0.8189          |
| 0.6174        | 14.93 | 210  | 0.82     | 0.8236          |
| 0.5714        | 16.0  | 225  | 0.82     | 0.7755          |
| 0.598         | 17.0  | 239  | 0.81     | 0.7511          |
| 0.5794        | 17.99 | 253  | 0.84     | 0.7553          |
| 0.589         | 18.99 | 267  | 0.85     | 0.7533          |
| 0.5717        | 19.91 | 280  | 0.85     | 0.7531          |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0