---
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert_finetuned-finetuned-gtzan
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilhubert_finetuned-finetuned-gtzan

This model is a fine-tuned version of [JanLilan/distilhubert_finetuned-distilhubert](https://huggingface.co/JanLilan/distilhubert_finetuned-distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6325
- Accuracy: 0.9

## 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: 0.0005
- 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: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8777        | 0.99  | 33   | 0.4485          | 0.8333   |
| 0.6913        | 2.0   | 67   | 1.0592          | 0.7      |
| 0.5494        | 2.99  | 100  | 0.6168          | 0.7667   |
| 0.3589        | 4.0   | 134  | 0.7820          | 0.7833   |
| 0.2049        | 4.99  | 167  | 0.9303          | 0.7833   |
| 0.1663        | 6.0   | 201  | 0.3570          | 0.9      |
| 0.0446        | 6.99  | 234  | 0.5636          | 0.8667   |
| 0.0313        | 8.0   | 268  | 0.6592          | 0.85     |
| 0.0007        | 8.99  | 301  | 0.4721          | 0.8833   |
| 0.0004        | 9.85  | 330  | 0.6325          | 0.9      |

Check it out ![colab](https://colab.research.google.com/drive/1hDLWdDKAaULLIkiMNhuz_z5SVGfW-78_?usp=sharing)

### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3