---
library_name: transformers
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: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.86
---

<!-- 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: 1.1749
- Accuracy: 0.86

## 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: 4
- eval_batch_size: 4
- 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: 25
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0517        | 1.0   | 225  | 2.0004          | 0.47     |
| 1.3283        | 2.0   | 450  | 1.3458          | 0.57     |
| 0.729         | 3.0   | 675  | 0.8563          | 0.76     |
| 0.4007        | 4.0   | 900  | 0.6748          | 0.8      |
| 0.3923        | 5.0   | 1125 | 0.7340          | 0.78     |
| 0.2193        | 6.0   | 1350 | 0.8712          | 0.76     |
| 0.2383        | 7.0   | 1575 | 0.7414          | 0.79     |
| 0.3           | 8.0   | 1800 | 0.7387          | 0.86     |
| 0.006         | 9.0   | 2025 | 0.9203          | 0.85     |
| 0.002         | 10.0  | 2250 | 0.8956          | 0.85     |
| 0.0014        | 11.0  | 2475 | 0.9831          | 0.86     |
| 0.001         | 12.0  | 2700 | 0.9406          | 0.86     |
| 0.0009        | 13.0  | 2925 | 1.0288          | 0.86     |
| 0.0007        | 14.0  | 3150 | 1.0172          | 0.86     |
| 0.0007        | 15.0  | 3375 | 0.9912          | 0.89     |
| 0.0005        | 16.0  | 3600 | 1.0282          | 0.86     |
| 0.0006        | 17.0  | 3825 | 1.3495          | 0.83     |
| 0.2453        | 18.0  | 4050 | 1.0340          | 0.87     |
| 0.0004        | 19.0  | 4275 | 1.1048          | 0.86     |
| 0.0004        | 20.0  | 4500 | 1.3051          | 0.85     |
| 0.0003        | 21.0  | 4725 | 1.2280          | 0.85     |
| 0.0003        | 22.0  | 4950 | 1.2530          | 0.85     |
| 0.0003        | 23.0  | 5175 | 1.1992          | 0.85     |
| 0.0003        | 24.0  | 5400 | 1.1881          | 0.85     |
| 0.0003        | 25.0  | 5625 | 1.1749          | 0.86     |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.19.1