---
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: 0.7127
- 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: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 16

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2072        | 0.99  | 56   | 2.1364          | 0.37     |
| 1.6502        | 2.0   | 113  | 1.5282          | 0.63     |
| 1.2965        | 2.99  | 169  | 1.1371          | 0.69     |
| 1.0407        | 4.0   | 226  | 0.9643          | 0.74     |
| 0.6558        | 4.99  | 282  | 0.7303          | 0.76     |
| 0.3615        | 6.0   | 339  | 0.7688          | 0.78     |
| 0.3705        | 6.99  | 395  | 0.5905          | 0.85     |
| 0.2165        | 8.0   | 452  | 0.6988          | 0.81     |
| 0.1098        | 8.99  | 508  | 0.4604          | 0.9      |
| 0.0647        | 10.0  | 565  | 0.6756          | 0.87     |
| 0.0179        | 10.99 | 621  | 0.8108          | 0.83     |
| 0.0278        | 12.0  | 678  | 0.6674          | 0.87     |
| 0.0075        | 12.99 | 734  | 0.8230          | 0.83     |
| 0.0061        | 14.0  | 791  | 0.8155          | 0.85     |
| 0.0056        | 14.99 | 847  | 0.7233          | 0.87     |
| 0.0055        | 15.86 | 896  | 0.7127          | 0.87     |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3