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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: 0.86 |
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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: 0.5678 |
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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: 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: 15 |
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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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| 2.2153 | 1.0 | 57 | 2.1306 | 0.37 | |
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| 1.5998 | 2.0 | 114 | 1.5355 | 0.59 | |
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| 1.2268 | 3.0 | 171 | 1.1801 | 0.7 | |
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| 0.8839 | 4.0 | 228 | 1.0267 | 0.68 | |
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| 0.8058 | 5.0 | 285 | 0.8348 | 0.77 | |
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| 0.6722 | 6.0 | 342 | 0.7497 | 0.78 | |
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| 0.6603 | 7.0 | 399 | 0.6921 | 0.78 | |
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| 0.4026 | 8.0 | 456 | 0.6814 | 0.8 | |
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| 0.3244 | 9.0 | 513 | 0.6138 | 0.81 | |
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| 0.2639 | 10.0 | 570 | 0.5887 | 0.85 | |
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| 0.1619 | 11.0 | 627 | 0.6005 | 0.83 | |
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| 0.1652 | 12.0 | 684 | 0.5589 | 0.83 | |
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| 0.1354 | 13.0 | 741 | 0.6157 | 0.8 | |
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| 0.0821 | 14.0 | 798 | 0.6221 | 0.83 | |
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| 0.1009 | 15.0 | 855 | 0.5678 | 0.86 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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