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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: 0.9594
- Accuracy: 0.83
## 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: 8e-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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.874 | 1.0 | 113 | 1.8949 | 0.42 |
| 1.2872 | 2.0 | 226 | 1.3293 | 0.57 |
| 0.9764 | 3.0 | 339 | 0.9030 | 0.72 |
| 0.5805 | 4.0 | 452 | 0.6561 | 0.83 |
| 0.4618 | 5.0 | 565 | 0.5127 | 0.87 |
| 0.1487 | 6.0 | 678 | 0.7336 | 0.77 |
| 0.1542 | 7.0 | 791 | 0.5496 | 0.84 |
| 0.267 | 8.0 | 904 | 0.6534 | 0.85 |
| 0.037 | 9.0 | 1017 | 0.7327 | 0.85 |
| 0.0089 | 10.0 | 1130 | 1.1979 | 0.76 |
| 0.0436 | 11.0 | 1243 | 1.0857 | 0.82 |
| 0.003 | 12.0 | 1356 | 0.9266 | 0.84 |
| 0.0019 | 13.0 | 1469 | 0.9791 | 0.84 |
| 0.0017 | 14.0 | 1582 | 0.9259 | 0.84 |
| 0.0015 | 15.0 | 1695 | 0.9836 | 0.83 |
| 0.0014 | 16.0 | 1808 | 1.0018 | 0.83 |
| 0.0013 | 17.0 | 1921 | 0.9896 | 0.83 |
| 0.0012 | 18.0 | 2034 | 0.9836 | 0.84 |
| 0.0012 | 19.0 | 2147 | 0.9759 | 0.84 |
| 0.0011 | 20.0 | 2260 | 0.9594 | 0.83 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
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