---
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: 0.8
---

<!-- 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: 0.8555
- Accuracy: 0.8

## 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: 14
- eval_batch_size: 14
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 28
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.238         | 0.9846 | 32   | 2.1541          | 0.41     |
| 1.7746        | 2.0    | 65   | 1.7033          | 0.6      |
| 1.4861        | 2.9846 | 97   | 1.4707          | 0.59     |
| 1.2082        | 4.0    | 130  | 1.2747          | 0.69     |
| 1.1266        | 4.9846 | 162  | 1.0837          | 0.76     |
| 0.9531        | 6.0    | 195  | 1.0155          | 0.72     |
| 0.9083        | 6.9846 | 227  | 0.9587          | 0.76     |
| 0.8255        | 8.0    | 260  | 0.8855          | 0.8      |
| 0.7696        | 8.9846 | 292  | 0.8766          | 0.79     |
| 0.7646        | 9.8462 | 320  | 0.8555          | 0.8      |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1