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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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- audio-classification |
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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: 0.84 |
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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: 1.2162 |
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- Accuracy: 0.84 |
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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: 6e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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| 2.2216 | 1.0 | 57 | 2.1520 | 0.45 | |
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| 1.704 | 2.0 | 114 | 1.6254 | 0.59 | |
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| 1.1885 | 3.0 | 171 | 1.1741 | 0.7 | |
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| 0.8575 | 4.0 | 228 | 0.9653 | 0.7 | |
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| 0.6822 | 5.0 | 285 | 0.8219 | 0.81 | |
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| 0.5103 | 6.0 | 342 | 0.7254 | 0.8 | |
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| 0.4386 | 7.0 | 399 | 0.6772 | 0.84 | |
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| 0.2862 | 8.0 | 456 | 0.7047 | 0.8 | |
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| 0.1639 | 9.0 | 513 | 0.7126 | 0.8 | |
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| 0.0998 | 10.0 | 570 | 0.8339 | 0.77 | |
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| 0.0585 | 11.0 | 627 | 0.7380 | 0.8 | |
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| 0.0256 | 12.0 | 684 | 0.7606 | 0.84 | |
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| 0.0201 | 13.0 | 741 | 0.8292 | 0.83 | |
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| 0.0058 | 14.0 | 798 | 0.9495 | 0.83 | |
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| 0.0029 | 15.0 | 855 | 1.1009 | 0.82 | |
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| 0.0016 | 16.0 | 912 | 1.1451 | 0.82 | |
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| 0.001 | 17.0 | 969 | 1.1886 | 0.84 | |
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| 0.0071 | 18.0 | 1026 | 1.1731 | 0.84 | |
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| 0.0004 | 19.0 | 1083 | 1.2255 | 0.84 | |
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| 0.0003 | 20.0 | 1140 | 1.2162 | 0.84 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.19.1 |
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