---
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: 0.81
---

<!-- 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: 0.6676
- Accuracy: 0.81

## 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: 3e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2592        | 1.0   | 75   | 2.2017          | 0.35     |
| 1.8413        | 2.0   | 150  | 1.8071          | 0.45     |
| 1.5432        | 3.0   | 225  | 1.4808          | 0.65     |
| 1.2137        | 4.0   | 300  | 1.2621          | 0.7      |
| 1.1546        | 5.0   | 375  | 1.0581          | 0.77     |
| 0.9996        | 6.0   | 450  | 0.9858          | 0.75     |
| 0.7508        | 7.0   | 525  | 0.9087          | 0.78     |
| 0.6669        | 8.0   | 600  | 0.7710          | 0.81     |
| 0.6834        | 9.0   | 675  | 0.7663          | 0.8      |
| 0.4495        | 10.0  | 750  | 0.7184          | 0.79     |
| 0.3677        | 11.0  | 825  | 0.6589          | 0.81     |
| 0.3092        | 12.0  | 900  | 0.7223          | 0.8      |
| 0.1846        | 13.0  | 975  | 0.6665          | 0.82     |
| 0.1797        | 14.0  | 1050 | 0.6500          | 0.8      |
| 0.1695        | 15.0  | 1125 | 0.6549          | 0.81     |
| 0.1104        | 16.0  | 1200 | 0.6636          | 0.81     |
| 0.1192        | 17.0  | 1275 | 0.6722          | 0.81     |
| 0.1226        | 18.0  | 1350 | 0.6540          | 0.82     |
| 0.1218        | 19.0  | 1425 | 0.6646          | 0.79     |
| 0.067         | 20.0  | 1500 | 0.6676          | 0.81     |


### Framework versions

- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3