---
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: 0.7537
- Accuracy: 0.88

## 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: 16
- eval_batch_size: 16
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9647        | 1.0   | 113  | 1.8614          | 0.52     |
| 1.3987        | 2.0   | 226  | 1.3098          | 0.61     |
| 0.8809        | 3.0   | 339  | 0.8631          | 0.76     |
| 0.7643        | 4.0   | 452  | 0.8114          | 0.77     |
| 0.5958        | 5.0   | 565  | 0.7013          | 0.81     |
| 0.4405        | 6.0   | 678  | 0.5860          | 0.84     |
| 0.2183        | 7.0   | 791  | 0.6114          | 0.82     |
| 0.1587        | 8.0   | 904  | 0.5141          | 0.85     |
| 0.0899        | 9.0   | 1017 | 0.4760          | 0.87     |
| 0.0575        | 10.0  | 1130 | 0.5759          | 0.86     |
| 0.0647        | 11.0  | 1243 | 0.6467          | 0.86     |
| 0.0061        | 12.0  | 1356 | 0.6372          | 0.88     |
| 0.0029        | 13.0  | 1469 | 0.6721          | 0.88     |
| 0.0018        | 14.0  | 1582 | 0.7565          | 0.89     |
| 0.0013        | 15.0  | 1695 | 0.7537          | 0.88     |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.0
- Tokenizers 0.13.3