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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: 0.9006
- Accuracy: 0.8
## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1981 | 1.0 | 57 | 2.1804 | 0.37 |
| 1.7932 | 2.0 | 114 | 1.7160 | 0.62 |
| 1.3257 | 3.0 | 171 | 1.2539 | 0.67 |
| 1.1239 | 4.0 | 228 | 1.1187 | 0.68 |
| 0.7457 | 5.0 | 285 | 0.9367 | 0.73 |
| 0.6922 | 6.0 | 342 | 0.7564 | 0.81 |
| 0.5718 | 7.0 | 399 | 0.8179 | 0.78 |
| 0.3729 | 8.0 | 456 | 0.7299 | 0.79 |
| 0.2667 | 9.0 | 513 | 0.6415 | 0.82 |
| 0.4672 | 10.0 | 570 | 0.8068 | 0.78 |
| 0.1392 | 11.0 | 627 | 0.7228 | 0.81 |
| 0.1069 | 12.0 | 684 | 0.7787 | 0.79 |
| 0.0659 | 13.0 | 741 | 0.7720 | 0.8 |
| 0.0291 | 14.0 | 798 | 0.7609 | 0.79 |
| 0.0263 | 15.0 | 855 | 0.8363 | 0.8 |
| 0.0177 | 16.0 | 912 | 0.8796 | 0.78 |
| 0.0166 | 17.0 | 969 | 0.8844 | 0.79 |
| 0.0139 | 18.0 | 1026 | 0.8909 | 0.8 |
| 0.0132 | 19.0 | 1083 | 0.9017 | 0.8 |
| 0.0131 | 20.0 | 1140 | 0.9006 | 0.8 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
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