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KanWasTaken
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UNetOscillatoryNeuron

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UNetOscillatoryNeuron
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  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: 0.0221
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- - Dice: 0.7035
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- - Iou: 0.5515
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- - Precision: 0.9921
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- - Recall: 0.5538
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  ## Model description
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@@ -40,7 +40,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.001
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
@@ -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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- | 1.0549 | 1.0 | 27 | 1.3770 | 0.4525 | 0.2943 | 0.3000 | 0.9615 |
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- | 0.4559 | 2.0 | 54 | 0.2323 | 0.2876 | 0.1730 | 0.9697 | 0.1739 |
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- | 0.1153 | 3.0 | 81 | 0.0996 | 0.7487 | 0.6070 | 0.9158 | 0.6461 |
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- | 0.0563 | 4.0 | 108 | 0.1447 | 0.7639 | 0.6262 | 0.8899 | 0.6814 |
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- | 0.0436 | 5.0 | 135 | 0.0435 | 0.7086 | 0.5582 | 0.9768 | 0.5663 |
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- | 0.0403 | 6.0 | 162 | 0.0817 | 0.7517 | 0.6101 | 0.9339 | 0.6371 |
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- | 0.035 | 7.0 | 189 | 0.0623 | 0.7451 | 0.6023 | 0.9483 | 0.6228 |
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- | 0.0315 | 8.0 | 216 | 0.0435 | 0.7371 | 0.5927 | 0.9679 | 0.6041 |
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- | 0.031 | 9.0 | 243 | 0.0361 | 0.7202 | 0.5728 | 0.9788 | 0.5807 |
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- | 0.0284 | 10.0 | 270 | 0.0285 | 0.7198 | 0.5714 | 0.9848 | 0.5763 |
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- | 0.0262 | 11.0 | 297 | 0.0322 | 0.7256 | 0.5785 | 0.9794 | 0.5855 |
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- | 0.0252 | 12.0 | 324 | 0.0286 | 0.7161 | 0.5666 | 0.9850 | 0.5713 |
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- | 0.0247 | 13.0 | 351 | 0.0600 | 0.7440 | 0.6010 | 0.9533 | 0.6196 |
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- | 0.0247 | 14.0 | 378 | 0.0412 | 0.7338 | 0.5883 | 0.9690 | 0.5996 |
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- | 0.0258 | 15.0 | 405 | 0.0287 | 0.7198 | 0.5714 | 0.9825 | 0.5774 |
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- | 0.0228 | 16.0 | 432 | 0.0278 | 0.7178 | 0.5686 | 0.9820 | 0.5749 |
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- | 0.0226 | 17.0 | 459 | 0.0250 | 0.7166 | 0.5673 | 0.9847 | 0.5725 |
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- | 0.0218 | 18.0 | 486 | 0.0436 | 0.7355 | 0.5905 | 0.9675 | 0.6027 |
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- | 0.0204 | 19.0 | 513 | 0.0631 | 0.7476 | 0.6054 | 0.9512 | 0.6244 |
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- | 0.0194 | 20.0 | 540 | 0.0268 | 0.7222 | 0.5736 | 0.9833 | 0.5788 |
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- | 0.0203 | 21.0 | 567 | 0.0323 | 0.7275 | 0.5806 | 0.9768 | 0.5885 |
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- | 0.0201 | 22.0 | 594 | 0.0337 | 0.7303 | 0.5839 | 0.9765 | 0.5920 |
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- | 0.0188 | 23.0 | 621 | 0.0314 | 0.7282 | 0.5811 | 0.9790 | 0.5879 |
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- | 0.0177 | 24.0 | 648 | 0.0263 | 0.7235 | 0.5756 | 0.9847 | 0.5802 |
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- | 0.018 | 25.0 | 675 | 0.0265 | 0.7225 | 0.5742 | 0.9845 | 0.5788 |
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- | 0.018 | 26.0 | 702 | 0.0238 | 0.7179 | 0.5690 | 0.9882 | 0.5726 |
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- | 0.0174 | 27.0 | 729 | 0.0243 | 0.7175 | 0.5679 | 0.9873 | 0.5716 |
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- | 0.0174 | 28.0 | 756 | 0.0214 | 0.7093 | 0.5590 | 0.9913 | 0.5617 |
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- | 0.017 | 29.0 | 783 | 0.0220 | 0.7074 | 0.5558 | 0.9918 | 0.5581 |
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- | 0.0162 | 30.0 | 810 | 0.0277 | 0.7237 | 0.5756 | 0.9833 | 0.5808 |
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- | 0.0154 | 31.0 | 837 | 0.0303 | 0.7278 | 0.5806 | 0.9809 | 0.5866 |
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- | 0.0152 | 32.0 | 864 | 0.0238 | 0.7160 | 0.5663 | 0.9875 | 0.5701 |
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- | 0.015 | 33.0 | 891 | 0.0242 | 0.7174 | 0.5677 | 0.9873 | 0.5714 |
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- | 0.0149 | 34.0 | 918 | 0.0253 | 0.7205 | 0.5718 | 0.9859 | 0.5761 |
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- | 0.0146 | 35.0 | 945 | 0.0235 | 0.7154 | 0.5655 | 0.9879 | 0.5691 |
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- | 0.0142 | 36.0 | 972 | 0.0225 | 0.7110 | 0.5601 | 0.9901 | 0.5629 |
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- | 0.014 | 37.0 | 999 | 0.0222 | 0.7087 | 0.5575 | 0.9907 | 0.5602 |
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- | 0.0137 | 38.0 | 1026 | 0.0232 | 0.7103 | 0.5594 | 0.9895 | 0.5626 |
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- | 0.0137 | 39.0 | 1053 | 0.0227 | 0.7117 | 0.5611 | 0.9893 | 0.5643 |
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- | 0.0134 | 40.0 | 1080 | 0.0220 | 0.7043 | 0.5524 | 0.9923 | 0.5546 |
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- | 0.0133 | 41.0 | 1107 | 0.0226 | 0.7085 | 0.5574 | 0.9904 | 0.5602 |
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- | 0.0132 | 42.0 | 1134 | 0.0229 | 0.7091 | 0.5582 | 0.9899 | 0.5612 |
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- | 0.0132 | 43.0 | 1161 | 0.0220 | 0.7039 | 0.5520 | 0.9921 | 0.5542 |
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- | 0.013 | 44.0 | 1188 | 0.0225 | 0.7046 | 0.5528 | 0.9916 | 0.5552 |
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- | 0.0129 | 45.0 | 1215 | 0.0222 | 0.7058 | 0.5542 | 0.9915 | 0.5566 |
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- | 0.0129 | 46.0 | 1242 | 0.0226 | 0.7036 | 0.5515 | 0.9913 | 0.5540 |
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- | 0.0128 | 47.0 | 1269 | 0.0219 | 0.7031 | 0.5511 | 0.9924 | 0.5533 |
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- | 0.0129 | 48.0 | 1296 | 0.0222 | 0.7035 | 0.5515 | 0.9922 | 0.5538 |
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- | 0.0127 | 49.0 | 1323 | 0.0224 | 0.7048 | 0.5531 | 0.9912 | 0.5557 |
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- | 0.0126 | 50.0 | 1350 | 0.0221 | 0.7035 | 0.5515 | 0.9921 | 0.5538 |
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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: 3.7960
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+ - Dice: 0.4819
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+ - Iou: 0.3220
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+ - Precision: 0.3221
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+ - Recall: 0.9992
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.01
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Dice | Iou | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:---------:|:------:|
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+ | 0.6629 | 1.0 | 27 | 1.0761 | 0.6220 | 0.4539 | 0.5274 | 0.7807 |
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+ | 0.0824 | 2.0 | 54 | 2.6095 | 0.4847 | 0.3248 | 0.3250 | 0.9989 |
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+ | 0.0599 | 3.0 | 81 | 1.1832 | 0.6502 | 0.4912 | 0.5257 | 0.9234 |
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+ | 0.0433 | 4.0 | 108 | 0.6347 | 0.7121 | 0.5568 | 0.6076 | 0.8862 |
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+ | 0.0377 | 5.0 | 135 | 0.4589 | 0.7358 | 0.5849 | 0.6738 | 0.8398 |
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+ | 0.0346 | 6.0 | 162 | 0.2619 | 0.7473 | 0.6010 | 0.7821 | 0.7346 |
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+ | 0.0405 | 7.0 | 189 | 0.6009 | 0.6874 | 0.5273 | 0.5677 | 0.9005 |
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+ | 0.0365 | 8.0 | 216 | 0.1796 | 0.7700 | 0.6334 | 0.8727 | 0.6996 |
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+ | 0.0292 | 9.0 | 243 | 0.4657 | 0.7180 | 0.5644 | 0.6381 | 0.8580 |
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+ | 0.0244 | 10.0 | 270 | 0.4219 | 0.7184 | 0.5652 | 0.6679 | 0.8029 |
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+ | 0.0341 | 11.0 | 297 | 0.1566 | 0.7624 | 0.6229 | 0.8840 | 0.6795 |
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+ | 0.063 | 12.0 | 324 | 1.2719 | 0.6887 | 0.5306 | 0.5455 | 0.9657 |
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+ | 0.0921 | 13.0 | 351 | 0.6939 | 0.7049 | 0.5477 | 0.5990 | 0.8952 |
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+ | 0.1498 | 14.0 | 378 | 0.6770 | 0.7466 | 0.5985 | 0.6553 | 0.8978 |
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+ | 0.1376 | 15.0 | 405 | 6.0601 | 0.4263 | 0.2745 | 0.2745 | 1.0000 |
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+ | 0.1153 | 16.0 | 432 | 7.4114 | 0.4068 | 0.2585 | 0.2585 | 1.0000 |
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+ | 0.1002 | 17.0 | 459 | 4.9600 | 0.4500 | 0.2945 | 0.2945 | 0.9998 |
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+ | 0.1005 | 18.0 | 486 | 3.8347 | 0.4785 | 0.3192 | 0.3193 | 0.9994 |
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+ | 0.1013 | 19.0 | 513 | 5.3824 | 0.4298 | 0.2774 | 0.2774 | 0.9998 |
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+ | 0.1137 | 20.0 | 540 | 4.5780 | 0.4536 | 0.2973 | 0.2974 | 0.9997 |
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+ | 0.1183 | 21.0 | 567 | 4.4835 | 0.4493 | 0.2937 | 0.2937 | 0.9998 |
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+ | 0.1327 | 22.0 | 594 | 3.5288 | 0.4896 | 0.3289 | 0.3291 | 0.9988 |
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+ | 0.1329 | 23.0 | 621 | 3.3211 | 0.5030 | 0.3411 | 0.3415 | 0.9982 |
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+ | 0.1344 | 24.0 | 648 | 3.8180 | 0.4797 | 0.3201 | 0.3203 | 0.9989 |
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+ | 0.1273 | 25.0 | 675 | 4.5982 | 0.4478 | 0.2924 | 0.2925 | 0.9998 |
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+ | 0.1275 | 26.0 | 702 | 4.1043 | 0.4658 | 0.3079 | 0.3080 | 0.9995 |
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+ | 0.1354 | 27.0 | 729 | 3.8208 | 0.4807 | 0.3210 | 0.3212 | 0.9989 |
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+ | 0.1361 | 28.0 | 756 | 3.6626 | 0.4884 | 0.3278 | 0.3281 | 0.9988 |
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+ | 0.1347 | 29.0 | 783 | 3.4135 | 0.4996 | 0.3380 | 0.3382 | 0.9986 |
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+ | 0.1309 | 30.0 | 810 | 3.8916 | 0.4758 | 0.3167 | 0.3169 | 0.9992 |
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+ | 0.1321 | 31.0 | 837 | 3.6416 | 0.4875 | 0.3271 | 0.3273 | 0.9989 |
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+ | 0.1284 | 32.0 | 864 | 4.1093 | 0.4674 | 0.3094 | 0.3095 | 0.9995 |
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+ | 0.1281 | 33.0 | 891 | 3.6306 | 0.4889 | 0.3282 | 0.3284 | 0.9990 |
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+ | 0.1243 | 34.0 | 918 | 3.9137 | 0.4763 | 0.3170 | 0.3171 | 0.9992 |
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+ | 0.1251 | 35.0 | 945 | 3.9373 | 0.4760 | 0.3169 | 0.3170 | 0.9993 |
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+ | 0.1268 | 36.0 | 972 | 4.0403 | 0.4696 | 0.3112 | 0.3113 | 0.9995 |
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+ | 0.1251 | 37.0 | 999 | 3.9820 | 0.4735 | 0.3147 | 0.3148 | 0.9994 |
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+ | 0.1254 | 38.0 | 1026 | 4.1664 | 0.4637 | 0.3061 | 0.3062 | 0.9996 |
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+ | 0.1256 | 39.0 | 1053 | 3.8444 | 0.4770 | 0.3177 | 0.3178 | 0.9993 |
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+ | 0.1247 | 40.0 | 1080 | 3.7488 | 0.4821 | 0.3222 | 0.3224 | 0.9991 |
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+ | 0.123 | 41.0 | 1107 | 3.9956 | 0.4723 | 0.3135 | 0.3136 | 0.9994 |
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+ | 0.121 | 42.0 | 1134 | 3.8087 | 0.4807 | 0.3209 | 0.3211 | 0.9991 |
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+ | 0.1202 | 43.0 | 1161 | 3.9857 | 0.4723 | 0.3136 | 0.3137 | 0.9994 |
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+ | 0.1202 | 44.0 | 1188 | 3.9039 | 0.4755 | 0.3164 | 0.3165 | 0.9993 |
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+ | 0.1213 | 45.0 | 1215 | 3.6906 | 0.4852 | 0.3250 | 0.3252 | 0.9991 |
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+ | 0.1198 | 46.0 | 1242 | 3.8138 | 0.4801 | 0.3205 | 0.3206 | 0.9992 |
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+ | 0.1203 | 47.0 | 1269 | 3.7747 | 0.4818 | 0.3220 | 0.3222 | 0.9992 |
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+ | 0.1201 | 48.0 | 1296 | 3.7842 | 0.4819 | 0.3220 | 0.3221 | 0.9991 |
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+ | 0.1191 | 49.0 | 1323 | 3.7903 | 0.4818 | 0.3220 | 0.3222 | 0.9992 |
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+ | 0.1198 | 50.0 | 1350 | 3.7960 | 0.4819 | 0.3220 | 0.3221 | 0.9992 |
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  ### Framework versions
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