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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-v2 |
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results: [] |
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datasets: |
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- marsyas/gtzan |
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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-v2 |
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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: 0.5575 |
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- Accuracy: 0.87 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 64 |
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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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- 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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| 2.2975 | 1.0 | 14 | 2.2790 | 0.26 | |
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| 2.255 | 1.99 | 28 | 2.1863 | 0.39 | |
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| 2.0948 | 2.99 | 42 | 1.9637 | 0.43 | |
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| 1.847 | 3.98 | 56 | 1.7093 | 0.54 | |
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| 1.5798 | 4.98 | 70 | 1.5095 | 0.62 | |
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| 1.4674 | 5.97 | 84 | 1.3173 | 0.67 | |
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| 1.2969 | 6.97 | 98 | 1.1894 | 0.72 | |
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| 1.1472 | 7.96 | 112 | 1.0415 | 0.77 | |
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| 0.9815 | 8.96 | 126 | 1.0004 | 0.74 | |
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| 0.8838 | 9.96 | 140 | 0.8808 | 0.78 | |
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| 0.8294 | 10.95 | 154 | 0.8551 | 0.78 | |
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| 0.768 | 11.95 | 168 | 0.7939 | 0.79 | |
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| 0.6499 | 12.94 | 182 | 0.7467 | 0.81 | |
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| 0.6014 | 13.94 | 196 | 0.6995 | 0.82 | |
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| 0.5296 | 14.93 | 210 | 0.7152 | 0.79 | |
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| 0.4478 | 16.0 | 225 | 0.6561 | 0.83 | |
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| 0.4082 | 17.0 | 239 | 0.6399 | 0.84 | |
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| 0.374 | 17.99 | 253 | 0.6217 | 0.86 | |
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| 0.3282 | 18.99 | 267 | 0.5991 | 0.85 | |
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| 0.28 | 19.98 | 281 | 0.6043 | 0.84 | |
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| 0.2754 | 20.98 | 295 | 0.5831 | 0.87 | |
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| 0.2409 | 21.97 | 309 | 0.5680 | 0.85 | |
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| 0.2172 | 22.97 | 323 | 0.5729 | 0.85 | |
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| 0.1855 | 23.96 | 337 | 0.5645 | 0.86 | |
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| 0.1729 | 24.96 | 351 | 0.5576 | 0.86 | |
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| 0.161 | 25.96 | 365 | 0.5378 | 0.86 | |
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| 0.1586 | 26.95 | 379 | 0.5662 | 0.86 | |
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| 0.1452 | 27.95 | 393 | 0.5575 | 0.87 | |
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| 0.1444 | 28.94 | 407 | 0.5491 | 0.86 | |
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| 0.1343 | 29.87 | 420 | 0.5528 | 0.86 | |
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### Framework versions |
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- Transformers 4.39.2 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
