---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
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 GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8042
- 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: 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0168        | 1.0   | 113  | 2.0642          | 0.45     |
| 1.4374        | 2.0   | 226  | 1.4358          | 0.64     |
| 1.1551        | 3.0   | 339  | 0.9743          | 0.74     |
| 0.7756        | 4.0   | 452  | 0.7805          | 0.81     |
| 0.4436        | 5.0   | 565  | 0.6117          | 0.81     |
| 0.3047        | 6.0   | 678  | 0.7366          | 0.79     |
| 0.2288        | 7.0   | 791  | 0.5297          | 0.86     |
| 0.2728        | 8.0   | 904  | 0.5677          | 0.87     |
| 0.1072        | 9.0   | 1017 | 0.6887          | 0.86     |
| 0.137         | 10.0  | 1130 | 0.9238          | 0.8      |
| 0.021         | 11.0  | 1243 | 0.7738          | 0.84     |
| 0.007         | 12.0  | 1356 | 0.7002          | 0.86     |
| 0.0047        | 13.0  | 1469 | 0.7805          | 0.86     |
| 0.0039        | 14.0  | 1582 | 0.7624          | 0.85     |
| 0.0034        | 15.0  | 1695 | 0.7892          | 0.85     |
| 0.0031        | 16.0  | 1808 | 0.7806          | 0.85     |
| 0.0029        | 17.0  | 1921 | 0.8005          | 0.85     |
| 0.0028        | 18.0  | 2034 | 0.7942          | 0.85     |
| 0.0025        | 19.0  | 2147 | 0.8138          | 0.86     |
| 0.0025        | 20.0  | 2260 | 0.8042          | 0.86     |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3