---
library_name: transformers
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: 0.87
---

<!-- 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-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: 0.7000
- 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: 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0826        | 1.0   | 113  | 0.5332          | 0.86     |
| 0.028         | 2.0   | 226  | 0.9901          | 0.77     |
| 0.0066        | 3.0   | 339  | 0.5829          | 0.87     |
| 0.0045        | 4.0   | 452  | 0.5893          | 0.87     |
| 0.0034        | 5.0   | 565  | 0.7000          | 0.87     |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1