---
license: apache-2.0
base_model: ntu-spml/distilhubert
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: 0.78
---

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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/scott-poynts-nil/huggingface/runs/mvcwa6jm)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/scott-poynts-nil/huggingface/runs/mvcwa6jm)
# 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: 0.9511
- Accuracy: 0.78

## 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
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6478        | 0.9912 | 56   | 0.7848          | 0.77     |
| 0.4009        | 2.0    | 113  | 0.8213          | 0.73     |
| 0.2155        | 2.9912 | 169  | 0.7877          | 0.76     |
| 0.1813        | 4.0    | 226  | 0.8529          | 0.75     |
| 0.0851        | 4.9912 | 282  | 0.8632          | 0.73     |
| 0.063         | 6.0    | 339  | 0.9026          | 0.78     |
| 0.0372        | 6.9912 | 395  | 0.8418          | 0.8      |
| 0.021         | 8.0    | 452  | 0.8672          | 0.79     |
| 0.0113        | 8.9912 | 508  | 0.9186          | 0.79     |
| 0.0098        | 9.9115 | 560  | 0.9511          | 0.78     |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1