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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: 0.835 |
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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: 0.9299 |
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- Accuracy: 0.835 |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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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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| 2.1474 | 1.0 | 100 | 2.1098 | 0.47 | |
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| 1.5063 | 2.0 | 200 | 1.5695 | 0.575 | |
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| 1.2171 | 3.0 | 300 | 1.1629 | 0.685 | |
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| 0.9388 | 4.0 | 400 | 0.9617 | 0.7 | |
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| 0.6208 | 5.0 | 500 | 0.9273 | 0.685 | |
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| 0.6771 | 6.0 | 600 | 0.7753 | 0.785 | |
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| 0.5799 | 7.0 | 700 | 0.8492 | 0.695 | |
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| 0.1527 | 8.0 | 800 | 0.6581 | 0.805 | |
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| 0.0586 | 9.0 | 900 | 0.6788 | 0.82 | |
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| 0.0355 | 10.0 | 1000 | 0.7627 | 0.81 | |
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| 0.0186 | 11.0 | 1100 | 0.7585 | 0.82 | |
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| 0.0102 | 12.0 | 1200 | 0.8328 | 0.825 | |
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| 0.0074 | 13.0 | 1300 | 0.8543 | 0.835 | |
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| 0.0063 | 14.0 | 1400 | 0.8574 | 0.83 | |
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| 0.0271 | 15.0 | 1500 | 0.8889 | 0.835 | |
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| 0.0043 | 16.0 | 1600 | 0.9197 | 0.83 | |
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| 0.0045 | 17.0 | 1700 | 0.9130 | 0.835 | |
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| 0.0036 | 18.0 | 1800 | 0.9242 | 0.835 | |
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| 0.0042 | 19.0 | 1900 | 0.9279 | 0.835 | |
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| 0.0034 | 20.0 | 2000 | 0.9299 | 0.835 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.14.7 |
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- Tokenizers 0.15.0 |
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