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---
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: 0.9570
- 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1586 | 1.0 | 112 | 2.0855 | 0.45 |
| 1.4771 | 2.0 | 225 | 1.3396 | 0.72 |
| 1.181 | 3.0 | 337 | 0.9735 | 0.76 |
| 0.8133 | 4.0 | 450 | 0.8692 | 0.76 |
| 0.5397 | 5.0 | 562 | 0.7118 | 0.81 |
| 0.3424 | 6.0 | 675 | 0.6237 | 0.81 |
| 0.2717 | 7.0 | 787 | 0.6551 | 0.83 |
| 0.2653 | 8.0 | 900 | 0.6707 | 0.83 |
| 0.0503 | 9.0 | 1012 | 0.7025 | 0.84 |
| 0.0168 | 10.0 | 1125 | 0.7643 | 0.87 |
| 0.1125 | 11.0 | 1237 | 0.8550 | 0.86 |
| 0.155 | 12.0 | 1350 | 0.9796 | 0.82 |
| 0.005 | 13.0 | 1462 | 0.9539 | 0.86 |
| 0.0038 | 14.0 | 1575 | 0.9206 | 0.86 |
| 0.0035 | 15.0 | 1687 | 0.8725 | 0.88 |
| 0.051 | 16.0 | 1800 | 0.9980 | 0.86 |
| 0.003 | 17.0 | 1912 | 0.9579 | 0.86 |
| 0.0025 | 18.0 | 2025 | 0.9735 | 0.86 |
| 0.0023 | 19.0 | 2137 | 0.9589 | 0.86 |
| 0.0022 | 19.91 | 2240 | 0.9570 | 0.86 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0
- Datasets 2.13.1
- Tokenizers 0.13.3
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