---
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
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8
---

<!-- 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: 0.7861
- Accuracy: 0.8

## 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: 16
- eval_batch_size: 16
- 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: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9073        | 1.0   | 113  | 1.8699          | 0.4      |
| 1.3144        | 2.0   | 226  | 1.2309          | 0.625    |
| 0.8747        | 3.0   | 339  | 0.9318          | 0.74     |
| 0.6776        | 4.0   | 452  | 0.8368          | 0.735    |
| 0.383         | 5.0   | 565  | 0.6930          | 0.745    |
| 0.3383        | 6.0   | 678  | 0.8012          | 0.755    |
| 0.2922        | 7.0   | 791  | 0.6724          | 0.78     |
| 0.1086        | 8.0   | 904  | 0.7984          | 0.755    |
| 0.0409        | 9.0   | 1017 | 0.7385          | 0.805    |
| 0.0507        | 10.0  | 1130 | 0.6669          | 0.805    |
| 0.0424        | 11.0  | 1243 | 0.7698          | 0.815    |
| 0.0078        | 12.0  | 1356 | 0.7985          | 0.81     |
| 0.0068        | 13.0  | 1469 | 0.7679          | 0.81     |
| 0.0063        | 14.0  | 1582 | 0.8139          | 0.795    |
| 0.0065        | 15.0  | 1695 | 0.7861          | 0.8      |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2