---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
base_model: ntu-spml/distilhubert
model-index:
- name: distilhubert-finetuned-gtzan
  results:
  - task:
      type: audio-classification
      name: Audio Classification
    dataset:
      name: GTZAN
      type: marsyas/gtzan
      split: None
    metrics:
    - type: accuracy
      value: 0.9319319319319319
      name: Accuracy
---

<!-- 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: 0.2387
- Accuracy: 0.9319

## 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: 0.001
- train_batch_size: 6
- eval_batch_size: 6
- 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: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7644        | 1.0   | 167  | 1.7832          | 0.3554   |
| 1.2856        | 2.0   | 334  | 1.4226          | 0.4745   |
| 1.2123        | 3.0   | 501  | 1.0047          | 0.6737   |
| 0.6613        | 4.0   | 668  | 0.8091          | 0.6987   |
| 0.6442        | 5.0   | 835  | 0.6713          | 0.7858   |
| 0.7172        | 6.0   | 1002 | 0.5749          | 0.8238   |
| 0.5394        | 7.0   | 1169 | 0.5079          | 0.8408   |
| 0.3853        | 8.0   | 1336 | 0.4574          | 0.8539   |
| 0.5441        | 9.0   | 1503 | 0.3729          | 0.8869   |
| 0.5062        | 10.0  | 1670 | 0.3319          | 0.9009   |
| 0.3955        | 11.0  | 1837 | 0.3745          | 0.8849   |
| 0.3112        | 12.0  | 2004 | 0.2752          | 0.9289   |
| 0.2887        | 13.0  | 2171 | 0.2544          | 0.9289   |
| 0.2038        | 14.0  | 2338 | 0.2344          | 0.9329   |
| 0.2374        | 15.0  | 2505 | 0.2387          | 0.9319   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
## Training procedure


### Framework versions


- PEFT 0.6.2