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vit-base-patch16-224-in21k-finetuned-papsmear

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  2. model.safetensors +1 -1
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README.md CHANGED
@@ -4,26 +4,11 @@ license: apache-2.0
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  base_model: google/vit-base-patch16-224-in21k
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  tags:
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  - generated_from_trainer
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- datasets:
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- - imagefolder
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  metrics:
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  - accuracy
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  model-index:
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  - name: vit-base-patch16-224-in21k-finetuned-papsmear
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- results:
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- - task:
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- name: Image Classification
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- type: image-classification
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- dataset:
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- name: imagefolder
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- type: imagefolder
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- config: default
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- split: train
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- args: default
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- metrics:
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- - name: Accuracy
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- type: accuracy
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- value: 0.8897058823529411
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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
@@ -31,10 +16,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # vit-base-patch16-224-in21k-finetuned-papsmear
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- This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3853
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- - Accuracy: 0.8897
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  ## Model description
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@@ -66,55 +51,58 @@ The following hyperparameters were used during training:
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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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- | No log | 0.9231 | 9 | 1.7589 | 0.2426 |
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- | 1.7862 | 1.9487 | 19 | 1.5880 | 0.3824 |
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- | 1.6727 | 2.9744 | 29 | 1.4212 | 0.4265 |
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- | 1.5102 | 4.0 | 39 | 1.2241 | 0.5809 |
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- | 1.3247 | 4.9231 | 48 | 1.0906 | 0.6103 |
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- | 1.1047 | 5.9487 | 58 | 0.9747 | 0.6765 |
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- | 0.9405 | 6.9744 | 68 | 0.8745 | 0.7426 |
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- | 0.823 | 8.0 | 78 | 0.7833 | 0.7426 |
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- | 0.7244 | 8.9231 | 87 | 0.7160 | 0.7794 |
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- | 0.6367 | 9.9487 | 97 | 0.7328 | 0.7794 |
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- | 0.5537 | 10.9744 | 107 | 0.6573 | 0.7868 |
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- | 0.484 | 12.0 | 117 | 0.5988 | 0.8088 |
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- | 0.4642 | 12.9231 | 126 | 0.6268 | 0.7941 |
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- | 0.4166 | 13.9487 | 136 | 0.6549 | 0.7794 |
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- | 0.4106 | 14.9744 | 146 | 0.5330 | 0.8529 |
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- | 0.3947 | 16.0 | 156 | 0.5134 | 0.8382 |
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- | 0.3469 | 16.9231 | 165 | 0.5879 | 0.7794 |
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- | 0.3151 | 17.9487 | 175 | 0.5683 | 0.8382 |
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- | 0.2946 | 18.9744 | 185 | 0.5383 | 0.8162 |
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- | 0.2927 | 20.0 | 195 | 0.5682 | 0.8162 |
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- | 0.2879 | 20.9231 | 204 | 0.4722 | 0.8603 |
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- | 0.2512 | 21.9487 | 214 | 0.4806 | 0.8456 |
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- | 0.2633 | 22.9744 | 224 | 0.4713 | 0.8456 |
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- | 0.2286 | 24.0 | 234 | 0.5167 | 0.8382 |
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- | 0.2265 | 24.9231 | 243 | 0.3886 | 0.8824 |
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- | 0.2107 | 25.9487 | 253 | 0.4396 | 0.8676 |
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- | 0.2044 | 26.9744 | 263 | 0.4734 | 0.8456 |
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- | 0.1925 | 28.0 | 273 | 0.4606 | 0.8529 |
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- | 0.1866 | 28.9231 | 282 | 0.5061 | 0.8309 |
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- | 0.1928 | 29.9487 | 292 | 0.4202 | 0.8824 |
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- | 0.1907 | 30.9744 | 302 | 0.5120 | 0.8309 |
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- | 0.1631 | 32.0 | 312 | 0.4165 | 0.8676 |
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- | 0.1654 | 32.9231 | 321 | 0.4600 | 0.8676 |
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- | 0.154 | 33.9487 | 331 | 0.3834 | 0.8971 |
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- | 0.1459 | 34.9744 | 341 | 0.3686 | 0.8897 |
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- | 0.1452 | 36.0 | 351 | 0.4174 | 0.8676 |
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- | 0.1548 | 36.9231 | 360 | 0.3791 | 0.9044 |
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- | 0.1395 | 37.9487 | 370 | 0.4512 | 0.8529 |
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- | 0.1333 | 38.9744 | 380 | 0.3775 | 0.8897 |
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- | 0.1236 | 40.0 | 390 | 0.3666 | 0.8971 |
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- | 0.1236 | 40.9231 | 399 | 0.3892 | 0.8971 |
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- | 0.1314 | 41.9487 | 409 | 0.3832 | 0.8897 |
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- | 0.1322 | 42.9744 | 419 | 0.3919 | 0.8824 |
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- | 0.1156 | 44.0 | 429 | 0.3699 | 0.8971 |
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- | 0.1222 | 44.9231 | 438 | 0.3828 | 0.8971 |
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- | 0.1254 | 45.9487 | 448 | 0.3853 | 0.8897 |
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- | 0.1129 | 46.1538 | 450 | 0.3853 | 0.8897 |
 
 
 
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  ### Framework versions
 
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  base_model: google/vit-base-patch16-224-in21k
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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: vit-base-patch16-224-in21k-finetuned-papsmear
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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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  # vit-base-patch16-224-in21k-finetuned-papsmear
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+ This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1325
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+ - Accuracy: 0.9732
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  ## Model description
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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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+ | 1.1553 | 1.0 | 42 | 1.0950 | 0.5477 |
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+ | 0.7791 | 2.0 | 84 | 0.6486 | 0.8526 |
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+ | 0.433 | 3.0 | 126 | 0.3716 | 0.9129 |
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+ | 0.3495 | 4.0 | 168 | 0.2869 | 0.9347 |
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+ | 0.2556 | 5.0 | 210 | 0.2722 | 0.9280 |
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+ | 0.2791 | 6.0 | 252 | 0.2611 | 0.9330 |
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+ | 0.2343 | 7.0 | 294 | 0.2377 | 0.9380 |
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+ | 0.186 | 8.0 | 336 | 0.2158 | 0.9397 |
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+ | 0.1984 | 9.0 | 378 | 0.2222 | 0.9347 |
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+ | 0.1751 | 10.0 | 420 | 0.1993 | 0.9514 |
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+ | 0.1529 | 11.0 | 462 | 0.2101 | 0.9430 |
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+ | 0.1616 | 12.0 | 504 | 0.2543 | 0.9296 |
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+ | 0.1404 | 13.0 | 546 | 0.2029 | 0.9397 |
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+ | 0.1078 | 14.0 | 588 | 0.2087 | 0.9414 |
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+ | 0.1109 | 15.0 | 630 | 0.1381 | 0.9615 |
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+ | 0.1072 | 16.0 | 672 | 0.1895 | 0.9414 |
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+ | 0.0949 | 17.0 | 714 | 0.1981 | 0.9397 |
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+ | 0.0908 | 18.0 | 756 | 0.1608 | 0.9581 |
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+ | 0.0809 | 19.0 | 798 | 0.1764 | 0.9581 |
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+ | 0.0708 | 20.0 | 840 | 0.1512 | 0.9531 |
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+ | 0.0757 | 21.0 | 882 | 0.2027 | 0.9481 |
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+ | 0.0919 | 22.0 | 924 | 0.1487 | 0.9615 |
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+ | 0.07 | 23.0 | 966 | 0.1667 | 0.9615 |
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+ | 0.0629 | 24.0 | 1008 | 0.1904 | 0.9531 |
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+ | 0.0584 | 25.0 | 1050 | 0.1521 | 0.9631 |
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+ | 0.0666 | 26.0 | 1092 | 0.1326 | 0.9665 |
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+ | 0.062 | 27.0 | 1134 | 0.1772 | 0.9564 |
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+ | 0.0568 | 28.0 | 1176 | 0.1465 | 0.9564 |
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+ | 0.0453 | 29.0 | 1218 | 0.1347 | 0.9682 |
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+ | 0.0469 | 30.0 | 1260 | 0.1687 | 0.9631 |
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+ | 0.0541 | 31.0 | 1302 | 0.1390 | 0.9715 |
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+ | 0.0602 | 32.0 | 1344 | 0.1618 | 0.9615 |
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+ | 0.0497 | 33.0 | 1386 | 0.1415 | 0.9615 |
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+ | 0.0493 | 34.0 | 1428 | 0.1521 | 0.9631 |
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+ | 0.0606 | 35.0 | 1470 | 0.1429 | 0.9698 |
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+ | 0.0332 | 36.0 | 1512 | 0.1671 | 0.9648 |
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+ | 0.0432 | 37.0 | 1554 | 0.1441 | 0.9665 |
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+ | 0.0354 | 38.0 | 1596 | 0.1593 | 0.9682 |
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+ | 0.0432 | 39.0 | 1638 | 0.1395 | 0.9665 |
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+ | 0.0363 | 40.0 | 1680 | 0.1092 | 0.9732 |
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+ | 0.0288 | 41.0 | 1722 | 0.1550 | 0.9665 |
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+ | 0.0305 | 42.0 | 1764 | 0.1462 | 0.9682 |
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+ | 0.0326 | 43.0 | 1806 | 0.1343 | 0.9682 |
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+ | 0.027 | 44.0 | 1848 | 0.1109 | 0.9732 |
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+ | 0.0233 | 45.0 | 1890 | 0.1315 | 0.9732 |
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+ | 0.042 | 46.0 | 1932 | 0.1261 | 0.9732 |
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+ | 0.0251 | 47.0 | 1974 | 0.1320 | 0.9732 |
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+ | 0.041 | 48.0 | 2016 | 0.1282 | 0.9732 |
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+ | 0.0445 | 49.0 | 2058 | 0.1296 | 0.9732 |
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+ | 0.0308 | 50.0 | 2100 | 0.1325 | 0.9732 |
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  ### Framework versions
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