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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: 0.8042
- 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
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