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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- marsyas/gtzan
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metrics:
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- accuracy
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model-index:
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- name: distilhubert-finetuned-gtzan
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# distilhubert-finetuned-gtzan
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This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.0379
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- Accuracy: 0.81
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 20
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 2.0307 | 1.0 | 113 | 2.0561 | 0.41 |
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| 1.4208 | 2.0 | 226 | 1.4850 | 0.63 |
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| 1.1959 | 3.0 | 339 | 1.0617 | 0.66 |
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| 0.6929 | 4.0 | 452 | 0.8228 | 0.74 |
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| 0.5104 | 5.0 | 565 | 0.6969 | 0.77 |
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| 0.4735 | 6.0 | 678 | 0.7412 | 0.79 |
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| 0.2185 | 7.0 | 791 | 0.6586 | 0.76 |
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| 0.3087 | 8.0 | 904 | 0.8234 | 0.78 |
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| 0.1066 | 9.0 | 1017 | 0.8210 | 0.8 |
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| 0.0841 | 10.0 | 1130 | 1.0040 | 0.8 |
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| 0.0387 | 11.0 | 1243 | 0.9195 | 0.81 |
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| 0.0091 | 12.0 | 1356 | 0.9208 | 0.82 |
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| 0.006 | 13.0 | 1469 | 0.9190 | 0.81 |
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| 0.0051 | 14.0 | 1582 | 0.9796 | 0.8 |
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| 0.0038 | 15.0 | 1695 | 0.9823 | 0.8 |
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| 0.0035 | 16.0 | 1808 | 1.0252 | 0.8 |
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| 0.0032 | 17.0 | 1921 | 1.0172 | 0.8 |
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| 0.0032 | 18.0 | 2034 | 1.0433 | 0.81 |
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| 0.0029 | 19.0 | 2147 | 1.0577 | 0.81 |
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| 0.0029 | 20.0 | 2260 | 1.0379 | 0.81 |
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 2.0.1+cu117
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- Datasets 2.13.1
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- Tokenizers 0.13.3
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