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--- |
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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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- task: |
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name: Audio Classification |
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type: audio-classification |
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dataset: |
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name: gtzan |
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type: gtzan |
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config: all |
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split: train |
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args: all |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.88 |
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license: apache-2.0 |
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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 on the GTZAN dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.76 |
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- Accuracy: 0.88 |
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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: 4 |
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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: 15 |
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- .train_test_split(seed=2024, shuffle=True, test_size=0.1) |
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- |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | Validation Loss | |
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|:-------------:|:-----:|:----:|:--------:|:---------------:| |
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| 1.9415 | 1.0 | 113 | 0.55 | 1.8500 | |
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| 1.3078 | 2.0 | 226 | 0.58 | 1.3794 | |
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| 1.1238 | 3.0 | 339 | 0.65 | 1.0919 | |
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| 0.788 | 4.0 | 452 | 0.68 | 1.0212 | |
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| 0.5932 | 5.0 | 565 | 0.69 | 0.8691 | |
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| 0.4042 | 6.0 | 678 | 0.71 | 0.8527 | |
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| 0.3421 | 7.0 | 791 | 0.75 | 0.7737 | |
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| 0.223 | 8.0 | 904 | 0.75 | 0.8463 | |
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| 0.1162 | 9.0 | 1017 | 0.77 | 0.7808 | |
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| 0.0863 | 10.0 | 1130 | 0.75 | 0.7487 | |
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| 0.1357 | 11.0 | 1243 | 0.8839 | 0.76 | |
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| 0.0632 | 12.0 | 1356 | 0.7509 | 0.76 | |
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| 0.0342 | 13.0 | 1469 | 0.8219 | 0.77 | |
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| 0.0277 | 14.0 | 1582 | 0.7691 | 0.8 | |
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| 0.0307 | 15.0 | 1695 | 0.7854 | 0.77 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.16.1 |
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- Tokenizers 0.13.2 |
