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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: 0.6711 |
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- Accuracy: 0.82 |
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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: 2e-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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- distributed_type: multi-GPU |
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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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- mixed_precision_training: Native AMP |
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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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| 2.1962 | 1.0 | 113 | 2.2220 | 0.29 | |
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| 1.9431 | 2.0 | 226 | 1.8877 | 0.5 | |
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| 1.634 | 3.0 | 339 | 1.5106 | 0.63 | |
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| 1.3403 | 4.0 | 452 | 1.3191 | 0.66 | |
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| 1.1067 | 5.0 | 565 | 1.1082 | 0.68 | |
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| 1.0416 | 6.0 | 678 | 1.0664 | 0.72 | |
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| 0.7723 | 7.0 | 791 | 0.9729 | 0.77 | |
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| 0.8281 | 8.0 | 904 | 0.8799 | 0.78 | |
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| 0.6344 | 9.0 | 1017 | 0.8142 | 0.77 | |
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| 0.8819 | 10.0 | 1130 | 0.8719 | 0.73 | |
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| 0.4279 | 11.0 | 1243 | 0.8150 | 0.78 | |
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| 0.425 | 12.0 | 1356 | 0.7137 | 0.81 | |
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| 0.2749 | 13.0 | 1469 | 0.6987 | 0.8 | |
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| 0.2182 | 14.0 | 1582 | 0.6849 | 0.82 | |
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| 0.2128 | 15.0 | 1695 | 0.6918 | 0.82 | |
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| 0.1831 | 16.0 | 1808 | 0.6600 | 0.81 | |
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| 0.1517 | 17.0 | 1921 | 0.6571 | 0.82 | |
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| 0.2888 | 18.0 | 2034 | 0.6880 | 0.81 | |
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| 0.1605 | 19.0 | 2147 | 0.6874 | 0.82 | |
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| 0.1492 | 20.0 | 2260 | 0.6711 | 0.82 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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