---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-88
  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-88

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: 0.6139
- 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: 8e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0172        | 1.0   | 112  | 1.8314          | 0.37     |
| 1.5433        | 2.0   | 225  | 1.2575          | 0.5      |
| 1.1517        | 3.0   | 337  | 0.9577          | 0.7      |
| 0.904         | 4.0   | 450  | 0.7582          | 0.77     |
| 0.4788        | 5.0   | 562  | 0.7504          | 0.79     |
| 0.3843        | 6.0   | 675  | 0.6265          | 0.79     |
| 0.3683        | 7.0   | 787  | 0.6683          | 0.8      |
| 0.2278        | 8.0   | 900  | 0.8167          | 0.77     |
| 0.4534        | 9.0   | 1012 | 0.6023          | 0.83     |
| 0.2357        | 10.0  | 1125 | 0.6185          | 0.83     |
| 0.3674        | 11.0  | 1237 | 0.6079          | 0.86     |
| 0.148         | 11.95 | 1344 | 0.6139          | 0.87     |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3