---
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: 0.82
---

<!-- 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: 1.0646
- Accuracy: 0.82

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 18

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0114        | 1.0   | 225  | 1.8491          | 0.5      |
| 1.1983        | 2.0   | 450  | 1.1911          | 0.68     |
| 1.1408        | 3.0   | 675  | 0.9290          | 0.72     |
| 0.7166        | 4.0   | 900  | 0.7200          | 0.78     |
| 0.5334        | 5.0   | 1125 | 0.7233          | 0.79     |
| 0.3294        | 6.0   | 1350 | 0.4989          | 0.83     |
| 0.2949        | 7.0   | 1575 | 0.5294          | 0.85     |
| 0.0067        | 8.0   | 1800 | 0.7724          | 0.83     |
| 0.0041        | 9.0   | 2025 | 0.8986          | 0.8      |
| 0.0049        | 10.0  | 2250 | 0.9146          | 0.83     |
| 0.0016        | 11.0  | 2475 | 0.8999          | 0.85     |
| 0.0013        | 12.0  | 2700 | 0.8947          | 0.86     |
| 0.0015        | 13.0  | 2925 | 0.9257          | 0.85     |
| 0.0009        | 14.0  | 3150 | 1.0211          | 0.82     |
| 0.0009        | 15.0  | 3375 | 0.9288          | 0.84     |
| 0.0008        | 16.0  | 3600 | 0.9672          | 0.82     |
| 0.0009        | 17.0  | 3825 | 1.0717          | 0.82     |
| 0.0756        | 18.0  | 4050 | 1.0646          | 0.82     |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3