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KanWasTaken
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UNetOscillatoryNeuron

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UNetOscillatoryNeuron
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  1. README.md +56 -56
  2. model.safetensors +1 -1
README.md CHANGED
@@ -17,11 +17,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0222
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- - Dice: 0.6912
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- - Iou: 0.5379
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- - Precision: 0.9949
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- - Recall: 0.5395
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  ## Model description
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@@ -45,7 +45,7 @@ The following hyperparameters were used during training:
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  - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.05
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  - num_epochs: 50
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@@ -53,56 +53,56 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Dice | Iou | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|:------:|
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- | 0.9974 | 1.0 | 27 | 6.4241 | 0.4013 | 0.2537 | 0.2590 | 0.9319 |
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- | 0.4597 | 2.0 | 54 | 0.5612 | 0.6393 | 0.4714 | 0.5705 | 0.7437 |
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- | 0.1191 | 3.0 | 81 | 0.1381 | 0.7612 | 0.6225 | 0.8853 | 0.6797 |
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- | 0.064 | 4.0 | 108 | 0.1400 | 0.7652 | 0.6276 | 0.8892 | 0.6825 |
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- | 0.0473 | 5.0 | 135 | 0.0575 | 0.7339 | 0.5885 | 0.9563 | 0.6051 |
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- | 0.0438 | 6.0 | 162 | 0.0864 | 0.7435 | 0.5995 | 0.9429 | 0.6212 |
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- | 0.0366 | 7.0 | 189 | 0.0374 | 0.6901 | 0.5353 | 0.9899 | 0.5384 |
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- | 0.0345 | 8.0 | 216 | 0.0530 | 0.7390 | 0.5943 | 0.9591 | 0.6092 |
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- | 0.0326 | 9.0 | 243 | 0.0351 | 0.7177 | 0.5690 | 0.9799 | 0.5761 |
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- | 0.0311 | 10.0 | 270 | 0.0345 | 0.7204 | 0.5714 | 0.9780 | 0.5787 |
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- | 0.028 | 11.0 | 297 | 0.0582 | 0.7436 | 0.6005 | 0.9527 | 0.6192 |
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- | 0.0272 | 12.0 | 324 | 0.0304 | 0.7126 | 0.5623 | 0.9838 | 0.5675 |
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- | 0.0269 | 13.0 | 351 | 0.0291 | 0.7126 | 0.5626 | 0.9832 | 0.5682 |
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- | 0.0269 | 14.0 | 378 | 0.0273 | 0.7053 | 0.5537 | 0.9886 | 0.5574 |
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- | 0.0264 | 15.0 | 405 | 0.0290 | 0.7202 | 0.5717 | 0.9837 | 0.5772 |
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- | 0.0238 | 16.0 | 432 | 0.0359 | 0.7309 | 0.5844 | 0.9743 | 0.5931 |
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- | 0.0247 | 17.0 | 459 | 0.0272 | 0.7204 | 0.5718 | 0.9844 | 0.5770 |
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- | 0.024 | 18.0 | 486 | 0.0419 | 0.7354 | 0.5902 | 0.9665 | 0.6029 |
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- | 0.021 | 19.0 | 513 | 0.0457 | 0.7404 | 0.5963 | 0.9627 | 0.6103 |
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- | 0.0217 | 20.0 | 540 | 0.0257 | 0.7176 | 0.5683 | 0.9855 | 0.5729 |
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- | 0.0228 | 21.0 | 567 | 0.0293 | 0.7195 | 0.5708 | 0.9801 | 0.5777 |
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- | 0.0218 | 22.0 | 594 | 0.0309 | 0.7271 | 0.5801 | 0.9782 | 0.5878 |
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- | 0.0207 | 23.0 | 621 | 0.0305 | 0.7267 | 0.5797 | 0.9783 | 0.5873 |
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- | 0.0192 | 24.0 | 648 | 0.0234 | 0.7141 | 0.5645 | 0.9881 | 0.5685 |
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- | 0.019 | 25.0 | 675 | 0.0247 | 0.7192 | 0.5706 | 0.9860 | 0.5752 |
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- | 0.0194 | 26.0 | 702 | 0.0251 | 0.7183 | 0.5695 | 0.9855 | 0.5744 |
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- | 0.0194 | 27.0 | 729 | 0.0220 | 0.7051 | 0.5535 | 0.9914 | 0.5563 |
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- | 0.0203 | 28.0 | 756 | 0.0237 | 0.7130 | 0.5635 | 0.9876 | 0.5678 |
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- | 0.019 | 29.0 | 783 | 0.0230 | 0.7157 | 0.5666 | 0.9881 | 0.5703 |
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- | 0.0176 | 30.0 | 810 | 0.0241 | 0.7198 | 0.5711 | 0.9865 | 0.5754 |
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- | 0.0168 | 31.0 | 837 | 0.0261 | 0.7235 | 0.5759 | 0.9835 | 0.5815 |
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- | 0.0169 | 32.0 | 864 | 0.0264 | 0.7213 | 0.5732 | 0.9826 | 0.5794 |
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- | 0.0165 | 33.0 | 891 | 0.0243 | 0.7196 | 0.5710 | 0.9849 | 0.5760 |
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- | 0.0163 | 34.0 | 918 | 0.0213 | 0.7033 | 0.5514 | 0.9915 | 0.5541 |
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- | 0.0161 | 35.0 | 945 | 0.0214 | 0.7057 | 0.5544 | 0.9906 | 0.5574 |
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- | 0.0153 | 36.0 | 972 | 0.0208 | 0.7093 | 0.5587 | 0.9908 | 0.5616 |
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- | 0.0152 | 37.0 | 999 | 0.0218 | 0.7101 | 0.5595 | 0.9898 | 0.5627 |
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- | 0.0151 | 38.0 | 1026 | 0.0224 | 0.7160 | 0.5666 | 0.9875 | 0.5707 |
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- | 0.0145 | 39.0 | 1053 | 0.0204 | 0.7015 | 0.5497 | 0.9927 | 0.5521 |
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- | 0.0143 | 40.0 | 1080 | 0.0208 | 0.7035 | 0.5519 | 0.9921 | 0.5545 |
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- | 0.014 | 41.0 | 1107 | 0.0205 | 0.7015 | 0.5496 | 0.9932 | 0.5517 |
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- | 0.0142 | 42.0 | 1134 | 0.0207 | 0.7027 | 0.5512 | 0.9924 | 0.5536 |
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- | 0.014 | 43.0 | 1161 | 0.0214 | 0.7121 | 0.5623 | 0.9892 | 0.5658 |
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- | 0.0136 | 44.0 | 1188 | 0.0213 | 0.6950 | 0.5423 | 0.9944 | 0.5441 |
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- | 0.0134 | 45.0 | 1215 | 0.0212 | 0.6980 | 0.5456 | 0.9935 | 0.5477 |
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- | 0.0133 | 46.0 | 1242 | 0.0214 | 0.6946 | 0.5420 | 0.9943 | 0.5438 |
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- | 0.013 | 47.0 | 1269 | 0.0210 | 0.6959 | 0.5432 | 0.9945 | 0.5449 |
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- | 0.013 | 48.0 | 1296 | 0.0221 | 0.6919 | 0.5387 | 0.9949 | 0.5403 |
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- | 0.0129 | 49.0 | 1323 | 0.0219 | 0.6925 | 0.5393 | 0.9945 | 0.5410 |
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- | 0.0125 | 50.0 | 1350 | 0.0222 | 0.6912 | 0.5379 | 0.9949 | 0.5395 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
19
  It achieves the following results on the evaluation set:
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+ - Loss: 0.5575
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+ - Dice: 0.6904
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+ - Iou: 0.5364
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+ - Precision: 0.9958
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+ - Recall: 0.5374
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  ## Model description
27
 
 
45
  - eval_batch_size: 8
46
  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: cosine
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  - lr_scheduler_warmup_ratio: 0.05
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  - num_epochs: 50
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  | Training Loss | Epoch | Step | Validation Loss | Dice | Iou | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|:------:|
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+ | 0.5468 | 1.0 | 27 | 0.6598 | 0.4436 | 0.2873 | 0.2982 | 0.9041 |
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+ | 0.324 | 2.0 | 54 | 0.5230 | 0.5408 | 0.3726 | 0.6035 | 0.5030 |
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+ | 0.2251 | 3.0 | 81 | 0.6158 | 0.7171 | 0.5703 | 0.9217 | 0.6041 |
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+ | 0.4844 | 4.0 | 108 | 0.6834 | 0.7842 | 0.6496 | 0.8024 | 0.7784 |
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+ | 0.5407 | 5.0 | 135 | 0.5531 | 0.6705 | 0.5126 | 0.9877 | 0.5157 |
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+ | 0.5445 | 6.0 | 162 | 0.6066 | 0.7390 | 0.5945 | 0.9493 | 0.6140 |
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+ | 0.543 | 7.0 | 189 | 0.5387 | 0.6575 | 0.4977 | 0.9919 | 0.4999 |
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+ | 0.5402 | 8.0 | 216 | 0.5975 | 0.7322 | 0.5865 | 0.9599 | 0.6016 |
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+ | 0.5479 | 9.0 | 243 | 0.5710 | 0.7032 | 0.5515 | 0.9845 | 0.5563 |
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+ | 0.5428 | 10.0 | 270 | 0.5723 | 0.7053 | 0.5527 | 0.9849 | 0.5570 |
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+ | 0.5412 | 11.0 | 297 | 0.5991 | 0.7363 | 0.5911 | 0.9633 | 0.6044 |
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+ | 0.5448 | 12.0 | 324 | 0.5792 | 0.7143 | 0.5638 | 0.9817 | 0.5694 |
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+ | 0.5439 | 13.0 | 351 | 0.5896 | 0.7273 | 0.5801 | 0.9764 | 0.5877 |
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+ | 0.5422 | 14.0 | 378 | 0.5863 | 0.7235 | 0.5758 | 0.9789 | 0.5827 |
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+ | 0.5433 | 15.0 | 405 | 0.5615 | 0.6933 | 0.5389 | 0.9925 | 0.5408 |
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+ | 0.5429 | 16.0 | 432 | 0.5913 | 0.7298 | 0.5835 | 0.9752 | 0.5917 |
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+ | 0.5426 | 17.0 | 459 | 0.5643 | 0.6976 | 0.5446 | 0.9920 | 0.5469 |
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+ | 0.541 | 18.0 | 486 | 0.5971 | 0.7359 | 0.5911 | 0.9689 | 0.6022 |
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+ | 0.543 | 19.0 | 513 | 0.6000 | 0.7390 | 0.5947 | 0.9665 | 0.6064 |
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+ | 0.5414 | 20.0 | 540 | 0.5751 | 0.7117 | 0.5614 | 0.9892 | 0.5646 |
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+ | 0.5443 | 21.0 | 567 | 0.5708 | 0.7066 | 0.5558 | 0.9908 | 0.5585 |
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+ | 0.5418 | 22.0 | 594 | 0.5826 | 0.7203 | 0.5714 | 0.9820 | 0.5770 |
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+ | 0.5455 | 23.0 | 621 | 0.5835 | 0.7219 | 0.5738 | 0.9832 | 0.5790 |
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+ | 0.5414 | 24.0 | 648 | 0.5673 | 0.7025 | 0.5508 | 0.9932 | 0.5528 |
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+ | 0.5457 | 25.0 | 675 | 0.5911 | 0.7307 | 0.5844 | 0.9779 | 0.5917 |
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+ | 0.5422 | 26.0 | 702 | 0.5705 | 0.7066 | 0.5554 | 0.9921 | 0.5576 |
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+ | 0.5427 | 27.0 | 729 | 0.5716 | 0.7079 | 0.5566 | 0.9911 | 0.5591 |
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+ | 0.5421 | 28.0 | 756 | 0.5687 | 0.7041 | 0.5528 | 0.9904 | 0.5557 |
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+ | 0.5451 | 29.0 | 783 | 0.5626 | 0.6968 | 0.5438 | 0.9942 | 0.5453 |
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+ | 0.5424 | 30.0 | 810 | 0.5704 | 0.7068 | 0.5554 | 0.9914 | 0.5578 |
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+ | 0.5438 | 31.0 | 837 | 0.5693 | 0.7054 | 0.5541 | 0.9923 | 0.5563 |
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+ | 0.5432 | 32.0 | 864 | 0.5668 | 0.7022 | 0.5497 | 0.9930 | 0.5516 |
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+ | 0.5431 | 33.0 | 891 | 0.5708 | 0.7075 | 0.5561 | 0.9917 | 0.5584 |
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+ | 0.5408 | 34.0 | 918 | 0.5758 | 0.7137 | 0.5640 | 0.9898 | 0.5671 |
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+ | 0.5423 | 35.0 | 945 | 0.5779 | 0.7161 | 0.5664 | 0.9886 | 0.5698 |
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+ | 0.5466 | 36.0 | 972 | 0.5599 | 0.6936 | 0.5399 | 0.9957 | 0.5410 |
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+ | 0.5434 | 37.0 | 999 | 0.5766 | 0.7149 | 0.5651 | 0.9898 | 0.5681 |
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+ | 0.5437 | 38.0 | 1026 | 0.5602 | 0.6940 | 0.5405 | 0.9951 | 0.5418 |
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+ | 0.5432 | 39.0 | 1053 | 0.5690 | 0.7055 | 0.5539 | 0.9923 | 0.5560 |
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+ | 0.5446 | 40.0 | 1080 | 0.5607 | 0.6944 | 0.5410 | 0.9948 | 0.5423 |
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+ | 0.5437 | 41.0 | 1107 | 0.5650 | 0.7002 | 0.5475 | 0.9939 | 0.5491 |
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+ | 0.545 | 42.0 | 1134 | 0.5626 | 0.6972 | 0.5443 | 0.9946 | 0.5457 |
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+ | 0.5457 | 43.0 | 1161 | 0.5565 | 0.6892 | 0.5348 | 0.9961 | 0.5357 |
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+ | 0.5444 | 44.0 | 1188 | 0.5600 | 0.6938 | 0.5403 | 0.9952 | 0.5415 |
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+ | 0.5419 | 45.0 | 1215 | 0.5621 | 0.6966 | 0.5435 | 0.9948 | 0.5449 |
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+ | 0.543 | 46.0 | 1242 | 0.5596 | 0.6932 | 0.5394 | 0.9950 | 0.5407 |
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+ | 0.5436 | 47.0 | 1269 | 0.5587 | 0.6919 | 0.5380 | 0.9959 | 0.5390 |
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+ | 0.5444 | 48.0 | 1296 | 0.5585 | 0.6917 | 0.5379 | 0.9957 | 0.5390 |
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+ | 0.5441 | 49.0 | 1323 | 0.5602 | 0.6941 | 0.5406 | 0.9951 | 0.5419 |
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+ | 0.5425 | 50.0 | 1350 | 0.5575 | 0.6904 | 0.5364 | 0.9958 | 0.5374 |
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  ### Framework versions
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