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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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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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--- |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5086 |
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- Accuracy: 0.89 |
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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: 4e-05 |
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- train_batch_size: 6 |
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- eval_batch_size: 6 |
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- seed: 42 |
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- gradient_accumulation_steps: 7 |
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- total_train_batch_size: 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: 25 |
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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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| 2.2912 | 0.98 | 21 | 2.2667 | 0.19 | |
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| 2.2263 | 1.96 | 42 | 2.1460 | 0.48 | |
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| 1.9552 | 2.99 | 64 | 1.8067 | 0.44 | |
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| 1.5982 | 3.97 | 85 | 1.5912 | 0.54 | |
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| 1.5182 | 4.99 | 107 | 1.4077 | 0.61 | |
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| 1.2855 | 5.97 | 128 | 1.2654 | 0.69 | |
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| 1.1649 | 7.0 | 150 | 1.1915 | 0.69 | |
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| 1.0742 | 7.98 | 171 | 1.0769 | 0.75 | |
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| 1.0495 | 8.96 | 192 | 1.0011 | 0.77 | |
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| 0.8827 | 9.99 | 214 | 0.9062 | 0.79 | |
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| 0.7886 | 10.97 | 235 | 0.8333 | 0.83 | |
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| 0.7019 | 11.99 | 257 | 0.7801 | 0.83 | |
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| 0.6642 | 12.97 | 278 | 0.7691 | 0.79 | |
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| 0.5982 | 14.0 | 300 | 0.6984 | 0.82 | |
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| 0.5002 | 14.98 | 321 | 0.6526 | 0.84 | |
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| 0.4789 | 15.96 | 342 | 0.5980 | 0.88 | |
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| 0.3908 | 16.99 | 364 | 0.5874 | 0.86 | |
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| 0.3892 | 17.97 | 385 | 0.5570 | 0.86 | |
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| 0.3675 | 18.99 | 407 | 0.5634 | 0.87 | |
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| 0.303 | 19.97 | 428 | 0.5387 | 0.87 | |
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| 0.3017 | 21.0 | 450 | 0.5086 | 0.89 | |
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| 0.2469 | 21.98 | 471 | 0.4969 | 0.89 | |
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| 0.2542 | 22.96 | 492 | 0.4972 | 0.88 | |
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| 0.2651 | 23.99 | 514 | 0.4947 | 0.89 | |
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| 0.2591 | 24.5 | 525 | 0.4929 | 0.89 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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