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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: 0.4989 |
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- Accuracy: 0.91 |
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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: 4e-06 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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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: 50 |
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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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| 0.2359 | 1.0 | 112 | 0.4776 | 0.87 | |
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| 0.1235 | 2.0 | 225 | 0.4872 | 0.84 | |
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| 0.2083 | 3.0 | 337 | 0.4910 | 0.85 | |
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| 0.19 | 4.0 | 450 | 0.4953 | 0.87 | |
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| 0.1128 | 5.0 | 562 | 0.4801 | 0.87 | |
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| 0.1644 | 6.0 | 675 | 0.4703 | 0.87 | |
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| 0.0699 | 7.0 | 787 | 0.4692 | 0.85 | |
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| 0.1082 | 8.0 | 900 | 0.4708 | 0.87 | |
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| 0.0898 | 9.0 | 1012 | 0.4347 | 0.89 | |
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| 0.1071 | 10.0 | 1125 | 0.5310 | 0.85 | |
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| 0.0727 | 11.0 | 1237 | 0.4765 | 0.87 | |
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| 0.0338 | 12.0 | 1350 | 0.4859 | 0.87 | |
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| 0.0233 | 13.0 | 1462 | 0.4713 | 0.87 | |
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| 0.0248 | 14.0 | 1575 | 0.5068 | 0.88 | |
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| 0.0263 | 15.0 | 1687 | 0.4874 | 0.88 | |
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| 0.0185 | 16.0 | 1800 | 0.4925 | 0.88 | |
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| 0.0142 | 17.0 | 1912 | 0.4766 | 0.89 | |
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| 0.0178 | 18.0 | 2025 | 0.4850 | 0.89 | |
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| 0.0153 | 19.0 | 2137 | 0.4660 | 0.88 | |
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| 0.012 | 20.0 | 2250 | 0.4831 | 0.88 | |
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| 0.0113 | 21.0 | 2362 | 0.4965 | 0.89 | |
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| 0.0106 | 22.0 | 2475 | 0.5098 | 0.89 | |
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| 0.011 | 23.0 | 2587 | 0.5093 | 0.89 | |
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| 0.009 | 24.0 | 2700 | 0.4989 | 0.91 | |
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| 0.0094 | 25.0 | 2812 | 0.4999 | 0.89 | |
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| 0.0441 | 26.0 | 2925 | 0.5197 | 0.88 | |
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| 0.0079 | 27.0 | 3037 | 0.5115 | 0.89 | |
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| 0.0072 | 28.0 | 3150 | 0.5136 | 0.88 | |
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| 0.007 | 29.0 | 3262 | 0.5394 | 0.88 | |
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| 0.0068 | 30.0 | 3375 | 0.5374 | 0.88 | |
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| 0.0061 | 31.0 | 3487 | 0.5221 | 0.88 | |
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| 0.0533 | 32.0 | 3600 | 0.5775 | 0.87 | |
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| 0.0055 | 33.0 | 3712 | 0.5632 | 0.88 | |
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| 0.0059 | 34.0 | 3825 | 0.5584 | 0.87 | |
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| 0.0051 | 35.0 | 3937 | 0.5444 | 0.88 | |
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| 0.0051 | 36.0 | 4050 | 0.5373 | 0.88 | |
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| 0.0045 | 37.0 | 4162 | 0.5723 | 0.87 | |
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| 0.0058 | 38.0 | 4275 | 0.5773 | 0.87 | |
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| 0.0043 | 39.0 | 4387 | 0.5455 | 0.88 | |
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| 0.0044 | 40.0 | 4500 | 0.5686 | 0.88 | |
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| 0.004 | 41.0 | 4612 | 0.5622 | 0.87 | |
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| 0.004 | 42.0 | 4725 | 0.5797 | 0.88 | |
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| 0.0042 | 43.0 | 4837 | 0.5621 | 0.88 | |
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| 0.0037 | 44.0 | 4950 | 0.5734 | 0.87 | |
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| 0.0048 | 45.0 | 5062 | 0.5774 | 0.88 | |
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| 0.0039 | 46.0 | 5175 | 0.5901 | 0.87 | |
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| 0.0043 | 47.0 | 5287 | 0.5743 | 0.88 | |
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| 0.0043 | 48.0 | 5400 | 0.5757 | 0.87 | |
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| 0.0037 | 49.0 | 5512 | 0.5710 | 0.88 | |
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| 0.0036 | 49.78 | 5600 | 0.5759 | 0.87 | |
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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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