---
license: apache-2.0
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: 0.87
---

<!-- 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

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: 0.5409
- 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: 4e-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 | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8732        | 1.0   | 113  | 1.9457          | 0.37     |
| 1.3925        | 2.0   | 226  | 1.4068          | 0.62     |
| 1.2338        | 3.0   | 339  | 1.0258          | 0.75     |
| 0.7905        | 4.0   | 452  | 0.8239          | 0.79     |
| 0.623         | 5.0   | 565  | 0.7121          | 0.78     |
| 0.4855        | 6.0   | 678  | 0.6421          | 0.83     |
| 0.3692        | 7.0   | 791  | 0.6564          | 0.79     |
| 0.4578        | 8.0   | 904  | 0.5604          | 0.87     |
| 0.3329        | 9.0   | 1017 | 0.5426          | 0.88     |
| 0.5075        | 10.0  | 1130 | 0.5409          | 0.87     |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3