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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: 0.8042 |
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- Accuracy: 0.86 |
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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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| 2.0168 | 1.0 | 113 | 2.0642 | 0.45 | |
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| 1.4374 | 2.0 | 226 | 1.4358 | 0.64 | |
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| 1.1551 | 3.0 | 339 | 0.9743 | 0.74 | |
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| 0.7756 | 4.0 | 452 | 0.7805 | 0.81 | |
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| 0.4436 | 5.0 | 565 | 0.6117 | 0.81 | |
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| 0.3047 | 6.0 | 678 | 0.7366 | 0.79 | |
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| 0.2288 | 7.0 | 791 | 0.5297 | 0.86 | |
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| 0.2728 | 8.0 | 904 | 0.5677 | 0.87 | |
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| 0.1072 | 9.0 | 1017 | 0.6887 | 0.86 | |
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| 0.137 | 10.0 | 1130 | 0.9238 | 0.8 | |
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| 0.021 | 11.0 | 1243 | 0.7738 | 0.84 | |
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| 0.007 | 12.0 | 1356 | 0.7002 | 0.86 | |
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| 0.0047 | 13.0 | 1469 | 0.7805 | 0.86 | |
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| 0.0039 | 14.0 | 1582 | 0.7624 | 0.85 | |
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| 0.0034 | 15.0 | 1695 | 0.7892 | 0.85 | |
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| 0.0031 | 16.0 | 1808 | 0.7806 | 0.85 | |
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| 0.0029 | 17.0 | 1921 | 0.8005 | 0.85 | |
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| 0.0028 | 18.0 | 2034 | 0.7942 | 0.85 | |
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| 0.0025 | 19.0 | 2147 | 0.8138 | 0.86 | |
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| 0.0025 | 20.0 | 2260 | 0.8042 | 0.86 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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