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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 |
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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 |
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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: 1.1842 |
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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: 0.0001 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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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| 2.1069 | 1.0 | 29 | 2.0003 | 0.46 | |
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| 1.8026 | 2.0 | 58 | 1.6073 | 0.59 | |
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| 1.3938 | 3.0 | 87 | 1.2140 | 0.72 | |
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| 1.0295 | 4.0 | 116 | 1.0740 | 0.64 | |
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| 0.8339 | 5.0 | 145 | 0.9243 | 0.71 | |
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| 0.6347 | 6.0 | 174 | 0.8837 | 0.72 | |
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| 0.4137 | 7.0 | 203 | 0.8274 | 0.78 | |
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| 0.3162 | 8.0 | 232 | 0.7596 | 0.82 | |
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| 0.2055 | 9.0 | 261 | 0.8541 | 0.77 | |
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| 0.2237 | 10.0 | 290 | 0.7220 | 0.78 | |
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| 0.0601 | 11.0 | 319 | 0.7765 | 0.81 | |
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| 0.0817 | 12.0 | 348 | 0.7603 | 0.86 | |
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| 0.0196 | 13.0 | 377 | 0.8611 | 0.8 | |
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| 0.0641 | 14.0 | 406 | 0.9281 | 0.8 | |
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| 0.0253 | 15.0 | 435 | 1.2051 | 0.77 | |
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| 0.0079 | 16.0 | 464 | 1.1073 | 0.81 | |
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| 0.0055 | 17.0 | 493 | 1.0920 | 0.81 | |
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| 0.012 | 18.0 | 522 | 1.1882 | 0.82 | |
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| 0.0051 | 19.0 | 551 | 1.0023 | 0.81 | |
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| 0.0047 | 20.0 | 580 | 1.2339 | 0.79 | |
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| 0.0036 | 21.0 | 609 | 1.1471 | 0.79 | |
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| 0.0033 | 22.0 | 638 | 1.1924 | 0.8 | |
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| 0.0032 | 23.0 | 667 | 1.1064 | 0.81 | |
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| 0.0028 | 24.0 | 696 | 1.1140 | 0.8 | |
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| 0.0026 | 25.0 | 725 | 1.1344 | 0.81 | |
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| 0.0163 | 26.0 | 754 | 1.1551 | 0.8 | |
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| 0.0027 | 27.0 | 783 | 1.1843 | 0.81 | |
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| 0.0025 | 28.0 | 812 | 1.1824 | 0.81 | |
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| 0.0104 | 29.0 | 841 | 1.1636 | 0.8 | |
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| 0.0047 | 30.0 | 870 | 1.1842 | 0.81 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.0 |
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- Tokenizers 0.13.3 |
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