---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan-2
  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-2

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: 0.9149
- Accuracy: 0.83

## 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
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0823        | 1.0   | 113  | 2.0903          | 0.46     |
| 1.5111        | 2.0   | 226  | 1.5342          | 0.6      |
| 1.2342        | 3.0   | 339  | 1.1036          | 0.68     |
| 0.8352        | 4.0   | 452  | 0.9137          | 0.78     |
| 0.5727        | 5.0   | 565  | 0.6258          | 0.81     |
| 0.3957        | 6.0   | 678  | 0.5984          | 0.83     |
| 0.1851        | 7.0   | 791  | 0.6269          | 0.82     |
| 0.1607        | 8.0   | 904  | 0.6945          | 0.79     |
| 0.1426        | 9.0   | 1017 | 0.6103          | 0.86     |
| 0.0519        | 10.0  | 1130 | 0.7502          | 0.81     |
| 0.0097        | 11.0  | 1243 | 0.7101          | 0.85     |
| 0.006         | 12.0  | 1356 | 0.8174          | 0.82     |
| 0.0039        | 13.0  | 1469 | 0.8008          | 0.84     |
| 0.0032        | 14.0  | 1582 | 0.8438          | 0.81     |
| 0.0027        | 15.0  | 1695 | 0.8206          | 0.82     |
| 0.0024        | 16.0  | 1808 | 0.8563          | 0.82     |
| 0.002         | 17.0  | 1921 | 0.8884          | 0.82     |
| 0.0018        | 18.0  | 2034 | 0.9148          | 0.82     |
| 0.0018        | 19.0  | 2147 | 0.9017          | 0.83     |
| 0.0016        | 20.0  | 2260 | 0.9178          | 0.83     |
| 0.0015        | 21.0  | 2373 | 0.9070          | 0.83     |
| 0.0014        | 22.0  | 2486 | 0.9033          | 0.83     |
| 0.0014        | 23.0  | 2599 | 0.8975          | 0.84     |
| 0.0013        | 24.0  | 2712 | 0.9160          | 0.83     |
| 0.0013        | 25.0  | 2825 | 0.9149          | 0.83     |


### Framework versions

- Transformers 4.29.0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2