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--- |
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license: apache-2.0 |
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base_model: ntu-spml/distilhubert |
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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-30-epochs |
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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: marsyas/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: 0.81 |
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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-30-epochs |
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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: 1.1939 |
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- Accuracy: 0.81 |
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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: 30 |
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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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| 2.1804 | 1.0 | 113 | 2.1756 | 0.46 | |
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| 1.7271 | 2.0 | 226 | 1.6973 | 0.53 | |
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| 1.2703 | 3.0 | 339 | 1.2950 | 0.51 | |
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| 0.9446 | 4.0 | 452 | 0.9433 | 0.68 | |
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| 0.6192 | 5.0 | 565 | 0.7885 | 0.73 | |
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| 0.3628 | 6.0 | 678 | 0.8338 | 0.76 | |
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| 0.2871 | 7.0 | 791 | 0.8125 | 0.74 | |
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| 0.0587 | 8.0 | 904 | 0.7500 | 0.8 | |
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| 0.1316 | 9.0 | 1017 | 0.8711 | 0.79 | |
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| 0.0175 | 10.0 | 1130 | 0.7429 | 0.82 | |
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| 0.0818 | 11.0 | 1243 | 0.9848 | 0.81 | |
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| 0.0049 | 12.0 | 1356 | 1.0498 | 0.76 | |
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| 0.0034 | 13.0 | 1469 | 1.0422 | 0.84 | |
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| 0.0028 | 14.0 | 1582 | 1.0919 | 0.83 | |
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| 0.0023 | 15.0 | 1695 | 1.0565 | 0.82 | |
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| 0.0019 | 16.0 | 1808 | 1.0797 | 0.84 | |
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| 0.0769 | 17.0 | 1921 | 1.1430 | 0.82 | |
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| 0.104 | 18.0 | 2034 | 1.1482 | 0.8 | |
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| 0.0014 | 19.0 | 2147 | 1.0972 | 0.83 | |
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| 0.0012 | 20.0 | 2260 | 1.1867 | 0.82 | |
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| 0.0012 | 21.0 | 2373 | 1.1914 | 0.82 | |
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| 0.0012 | 22.0 | 2486 | 1.1461 | 0.84 | |
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| 0.0009 | 23.0 | 2599 | 1.1401 | 0.82 | |
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| 0.0009 | 24.0 | 2712 | 1.1686 | 0.84 | |
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| 0.0009 | 25.0 | 2825 | 1.1824 | 0.85 | |
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| 0.0009 | 26.0 | 2938 | 1.1815 | 0.81 | |
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| 0.0008 | 27.0 | 3051 | 1.1808 | 0.82 | |
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| 0.0008 | 28.0 | 3164 | 1.1904 | 0.81 | |
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| 0.0008 | 29.0 | 3277 | 1.1990 | 0.82 | |
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| 0.0008 | 30.0 | 3390 | 1.1939 | 0.81 | |
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### Framework versions |
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- Transformers 4.33.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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