---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-v3
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: GTZAN
      type: marsyas/gtzan
    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-v3

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: 1.0906
- 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8287        | 1.0   | 899  | 0.8991          | 0.68     |
| 2.2164        | 2.0   | 1798 | 1.3184          | 0.71     |
| 0.0099        | 3.0   | 2697 | 0.9288          | 0.78     |
| 0.0679        | 4.0   | 3596 | 0.8131          | 0.84     |
| 0.0119        | 5.0   | 4495 | 1.1122          | 0.8      |
| 0.0051        | 6.0   | 5394 | 0.9594          | 0.86     |
| 0.0008        | 7.0   | 6293 | 0.9475          | 0.87     |
| 0.0002        | 8.0   | 7192 | 1.1026          | 0.86     |
| 0.0002        | 9.0   | 8091 | 1.0751          | 0.87     |
| 0.0002        | 10.0  | 8990 | 1.0906          | 0.87     |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.1.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1