---
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: train
      split: train
      args: train
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.86
---

<!-- 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: 0.86
- Loss: 0.7644

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 2.1622        | 1.0   | 113  | 0.36     | 2.0289          |
| 1.6015        | 2.0   | 226  | 0.59     | 1.4290          |
| 1.1929        | 3.0   | 339  | 0.7      | 1.1003          |
| 0.9015        | 4.0   | 452  | 0.76     | 0.8761          |
| 0.7038        | 5.0   | 565  | 0.76     | 0.7516          |
| 0.3261        | 6.0   | 678  | 0.77     | 0.7753          |
| 0.5327        | 7.0   | 791  | 0.79     | 0.6131          |
| 0.1239        | 8.0   | 904  | 0.8      | 0.6283          |
| 0.1193        | 9.0   | 1017 | 0.85     | 0.5770          |
| 0.1405        | 10.0  | 1130 | 0.8      | 0.7979          |
| 0.0113        | 11.0  | 1243 | 0.81     | 0.7830          |
| 0.1392        | 12.0  | 1356 | 0.85     | 0.7350          |
| 0.0065        | 13.0  | 1469 | 0.82     | 0.7935          |
| 0.0049        | 14.0  | 1582 | 0.84     | 0.8323          |
| 0.0041        | 15.0  | 1695 | 0.86     | 0.7644          |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.0
- Tokenizers 0.13.3