---
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: 0.8818
- Accuracy: 0.85

## 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: 17

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5851        | 1.0   | 113  | 1.7243          | 0.5      |
| 1.2937        | 2.0   | 226  | 1.2310          | 0.68     |
| 0.9718        | 3.0   | 339  | 0.8918          | 0.76     |
| 0.6613        | 4.0   | 452  | 0.6837          | 0.81     |
| 0.3693        | 5.0   | 565  | 0.6250          | 0.82     |
| 0.2991        | 6.0   | 678  | 0.5740          | 0.82     |
| 0.1381        | 7.0   | 791  | 0.5874          | 0.83     |
| 0.2047        | 8.0   | 904  | 0.5824          | 0.86     |
| 0.1192        | 9.0   | 1017 | 0.7106          | 0.83     |
| 0.0652        | 10.0  | 1130 | 0.6576          | 0.87     |
| 0.0105        | 11.0  | 1243 | 0.8236          | 0.84     |
| 0.0074        | 12.0  | 1356 | 0.7874          | 0.85     |
| 0.0064        | 13.0  | 1469 | 0.9066          | 0.84     |
| 0.0041        | 14.0  | 1582 | 0.8426          | 0.85     |
| 0.0038        | 15.0  | 1695 | 0.8676          | 0.84     |
| 0.0039        | 16.0  | 1808 | 0.8820          | 0.85     |
| 0.0036        | 17.0  | 1921 | 0.8818          | 0.85     |


### Framework versions

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