---
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: 0.7186
- 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-05
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 12
- label_smoothing_factor: 0.05

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7045        | 1.0   | 113  | 1.7952          | 0.44     |
| 1.1808        | 2.0   | 226  | 1.1510          | 0.66     |
| 1.0978        | 3.0   | 339  | 0.9947          | 0.74     |
| 0.837         | 4.0   | 452  | 0.8767          | 0.81     |
| 0.5078        | 5.0   | 565  | 0.7830          | 0.86     |
| 0.3832        | 6.0   | 678  | 0.7838          | 0.84     |
| 0.3902        | 7.0   | 791  | 0.8064          | 0.83     |
| 0.3322        | 8.0   | 904  | 0.7964          | 0.82     |
| 0.3455        | 9.0   | 1017 | 0.7507          | 0.87     |
| 0.2924        | 10.0  | 1130 | 0.8073          | 0.86     |
| 0.2925        | 11.0  | 1243 | 0.7269          | 0.86     |
| 0.2853        | 12.0  | 1356 | 0.7186          | 0.86     |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3