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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: 0.7667
- 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1524 | 1.0 | 225 | 2.0279 | 0.45 |
| 1.2284 | 2.0 | 450 | 1.3462 | 0.62 |
| 1.014 | 3.0 | 675 | 0.9385 | 0.71 |
| 1.2816 | 4.0 | 900 | 0.8428 | 0.75 |
| 0.3312 | 5.0 | 1125 | 0.5206 | 0.83 |
| 0.7004 | 6.0 | 1350 | 0.9608 | 0.76 |
| 0.0515 | 7.0 | 1575 | 0.6214 | 0.85 |
| 0.0114 | 8.0 | 1800 | 0.7193 | 0.83 |
| 0.0032 | 9.0 | 2025 | 0.7997 | 0.86 |
| 0.0021 | 10.0 | 2250 | 1.0831 | 0.81 |
| 0.0059 | 11.0 | 2475 | 0.9561 | 0.83 |
| 0.0011 | 12.0 | 2700 | 0.7667 | 0.88 |
| 0.0008 | 13.0 | 2925 | 0.8389 | 0.87 |
| 0.0007 | 14.0 | 3150 | 0.8570 | 0.87 |
| 0.0006 | 15.0 | 3375 | 0.8778 | 0.86 |
| 0.0005 | 16.0 | 3600 | 0.9170 | 0.87 |
| 0.0004 | 17.0 | 3825 | 0.9422 | 0.87 |
| 0.0003 | 18.0 | 4050 | 0.9408 | 0.87 |
| 0.0005 | 19.0 | 4275 | 0.8940 | 0.87 |
| 0.0003 | 20.0 | 4500 | 0.9724 | 0.86 |
| 0.0003 | 21.0 | 4725 | 0.8904 | 0.85 |
| 0.0002 | 22.0 | 4950 | 0.9573 | 0.86 |
| 0.0002 | 23.0 | 5175 | 0.9292 | 0.87 |
| 0.0002 | 24.0 | 5400 | 0.9209 | 0.86 |
| 0.0002 | 25.0 | 5625 | 0.9184 | 0.86 |
| 0.0002 | 26.0 | 5850 | 0.9005 | 0.85 |
| 0.0002 | 27.0 | 6075 | 0.9656 | 0.86 |
| 0.0002 | 28.0 | 6300 | 0.9685 | 0.86 |
| 0.0002 | 29.0 | 6525 | 0.9810 | 0.86 |
| 0.0002 | 30.0 | 6750 | 0.9860 | 0.86 |
### Framework versions
- Transformers 4.29.2
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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