---
license: apache-2.0
base_model: MariaK/distilhubert-finetuned-gtzan-v2
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-v2-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: 0.88
---

<!-- 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-v2-finetuned-gtzan

This model is a fine-tuned version of [MariaK/distilhubert-finetuned-gtzan-v2](https://huggingface.co/MariaK/distilhubert-finetuned-gtzan-v2) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5054
- Accuracy: 0.88

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.145         | 0.18  | 10   | 0.6024          | 0.85     |
| 0.117         | 0.35  | 20   | 0.4874          | 0.88     |
| 0.1236        | 0.53  | 30   | 0.6116          | 0.84     |
| 0.0977        | 0.71  | 40   | 0.5530          | 0.87     |
| 0.0664        | 0.88  | 50   | 0.5054          | 0.88     |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2