---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
  results: []
---

<!-- 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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5086
- Accuracy: 0.89

## 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: 4e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 7
- total_train_batch_size: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2912        | 0.98  | 21   | 2.2667          | 0.19     |
| 2.2263        | 1.96  | 42   | 2.1460          | 0.48     |
| 1.9552        | 2.99  | 64   | 1.8067          | 0.44     |
| 1.5982        | 3.97  | 85   | 1.5912          | 0.54     |
| 1.5182        | 4.99  | 107  | 1.4077          | 0.61     |
| 1.2855        | 5.97  | 128  | 1.2654          | 0.69     |
| 1.1649        | 7.0   | 150  | 1.1915          | 0.69     |
| 1.0742        | 7.98  | 171  | 1.0769          | 0.75     |
| 1.0495        | 8.96  | 192  | 1.0011          | 0.77     |
| 0.8827        | 9.99  | 214  | 0.9062          | 0.79     |
| 0.7886        | 10.97 | 235  | 0.8333          | 0.83     |
| 0.7019        | 11.99 | 257  | 0.7801          | 0.83     |
| 0.6642        | 12.97 | 278  | 0.7691          | 0.79     |
| 0.5982        | 14.0  | 300  | 0.6984          | 0.82     |
| 0.5002        | 14.98 | 321  | 0.6526          | 0.84     |
| 0.4789        | 15.96 | 342  | 0.5980          | 0.88     |
| 0.3908        | 16.99 | 364  | 0.5874          | 0.86     |
| 0.3892        | 17.97 | 385  | 0.5570          | 0.86     |
| 0.3675        | 18.99 | 407  | 0.5634          | 0.87     |
| 0.303         | 19.97 | 428  | 0.5387          | 0.87     |
| 0.3017        | 21.0  | 450  | 0.5086          | 0.89     |
| 0.2469        | 21.98 | 471  | 0.4969          | 0.89     |
| 0.2542        | 22.96 | 492  | 0.4972          | 0.88     |
| 0.2651        | 23.99 | 514  | 0.4947          | 0.89     |
| 0.2591        | 24.5  | 525  | 0.4929          | 0.89     |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1