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