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mhdiqbalpradipta
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emotion_classification

Image Classification
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mhdiqbalpradipta commited on
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End of training

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  1. README.md +63 -63
  2. model.safetensors +1 -1
  3. runs/Feb07_07-08-35_0ae7ab5927f1/events.out.tfevents.1707289716.0ae7ab5927f1.299.19 +2 -2
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.5875
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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
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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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: 1.1818
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- - Accuracy: 0.5875
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  ## Model description
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@@ -65,66 +65,66 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 2.0776 | 1.0 | 40 | 2.0526 | 0.1938 |
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- | 2.0359 | 2.0 | 80 | 2.0310 | 0.275 |
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- | 1.9827 | 3.0 | 120 | 1.9742 | 0.3625 |
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- | 1.9054 | 4.0 | 160 | 1.8990 | 0.3375 |
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- | 1.8156 | 5.0 | 200 | 1.7835 | 0.3563 |
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- | 1.7187 | 6.0 | 240 | 1.6893 | 0.4188 |
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- | 1.6383 | 7.0 | 280 | 1.6302 | 0.4188 |
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- | 1.569 | 8.0 | 320 | 1.5770 | 0.4437 |
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- | 1.5165 | 9.0 | 360 | 1.5336 | 0.5125 |
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- | 1.4685 | 10.0 | 400 | 1.4907 | 0.5375 |
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- | 1.4107 | 11.0 | 440 | 1.4671 | 0.5062 |
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- | 1.3648 | 12.0 | 480 | 1.4174 | 0.5188 |
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- | 1.3233 | 13.0 | 520 | 1.3953 | 0.5375 |
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- | 1.292 | 14.0 | 560 | 1.3926 | 0.5062 |
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- | 1.2375 | 15.0 | 600 | 1.3461 | 0.5437 |
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- | 1.1957 | 16.0 | 640 | 1.3432 | 0.55 |
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- | 1.1584 | 17.0 | 680 | 1.3313 | 0.5188 |
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- | 1.1175 | 18.0 | 720 | 1.2850 | 0.55 |
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- | 1.0764 | 19.0 | 760 | 1.2702 | 0.575 |
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- | 1.0425 | 20.0 | 800 | 1.2534 | 0.55 |
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- | 1.0203 | 21.0 | 840 | 1.2247 | 0.5687 |
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- | 1.0168 | 22.0 | 880 | 1.3001 | 0.5437 |
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- | 0.9792 | 23.0 | 920 | 1.2179 | 0.5563 |
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- | 0.9338 | 24.0 | 960 | 1.2689 | 0.5437 |
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- | 0.9186 | 25.0 | 1000 | 1.2245 | 0.5437 |
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- | 0.9016 | 26.0 | 1040 | 1.2936 | 0.475 |
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- | 0.8842 | 27.0 | 1080 | 1.2184 | 0.5375 |
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- | 0.8598 | 28.0 | 1120 | 1.2596 | 0.5 |
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- | 0.8717 | 29.0 | 1160 | 1.2129 | 0.55 |
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- | 0.8268 | 30.0 | 1200 | 1.2318 | 0.5563 |
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- | 0.8134 | 31.0 | 1240 | 1.1830 | 0.5687 |
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- | 0.7791 | 32.0 | 1280 | 1.2137 | 0.5687 |
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- | 0.7703 | 33.0 | 1320 | 1.1823 | 0.5625 |
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- | 0.7332 | 34.0 | 1360 | 1.2262 | 0.5687 |
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- | 0.7333 | 35.0 | 1400 | 1.2518 | 0.5062 |
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- | 0.7181 | 36.0 | 1440 | 1.2079 | 0.55 |
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- | 0.6848 | 37.0 | 1480 | 1.2215 | 0.5375 |
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- | 0.6558 | 38.0 | 1520 | 1.2051 | 0.5437 |
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- | 0.6736 | 39.0 | 1560 | 1.1729 | 0.575 |
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- | 0.647 | 40.0 | 1600 | 1.2747 | 0.5188 |
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- | 0.6834 | 41.0 | 1640 | 1.1812 | 0.5875 |
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- | 0.641 | 42.0 | 1680 | 1.2035 | 0.5312 |
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- | 0.6537 | 43.0 | 1720 | 1.2023 | 0.5625 |
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- | 0.6311 | 44.0 | 1760 | 1.1682 | 0.5813 |
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- | 0.6226 | 45.0 | 1800 | 1.2076 | 0.5687 |
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- | 0.5747 | 46.0 | 1840 | 1.1784 | 0.55 |
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- | 0.5964 | 47.0 | 1880 | 1.1840 | 0.55 |
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- | 0.5987 | 48.0 | 1920 | 1.1519 | 0.5813 |
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- | 0.5987 | 49.0 | 1960 | 1.2202 | 0.525 |
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- | 0.5833 | 50.0 | 2000 | 1.2534 | 0.5375 |
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- | 0.5764 | 51.0 | 2040 | 1.1237 | 0.5938 |
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- | 0.578 | 52.0 | 2080 | 1.2250 | 0.525 |
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- | 0.5588 | 53.0 | 2120 | 1.2510 | 0.525 |
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- | 0.552 | 54.0 | 2160 | 1.1878 | 0.5813 |
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- | 0.5677 | 55.0 | 2200 | 1.1924 | 0.5687 |
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- | 0.5726 | 56.0 | 2240 | 1.2164 | 0.5563 |
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- | 0.5337 | 57.0 | 2280 | 1.1853 | 0.5938 |
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- | 0.5542 | 58.0 | 2320 | 1.2727 | 0.5188 |
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- | 0.5682 | 59.0 | 2360 | 1.1833 | 0.5563 |
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- | 0.5544 | 60.0 | 2400 | 1.1818 | 0.5875 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.58125
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  ---
27
 
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
32
 
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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: 1.1749
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+ - Accuracy: 0.5813
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 2.073 | 1.0 | 40 | 2.0553 | 0.2188 |
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+ | 2.0238 | 2.0 | 80 | 2.0162 | 0.275 |
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+ | 1.9598 | 3.0 | 120 | 1.9458 | 0.4 |
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+ | 1.8585 | 4.0 | 160 | 1.8555 | 0.3937 |
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+ | 1.7579 | 5.0 | 200 | 1.7204 | 0.475 |
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+ | 1.6636 | 6.0 | 240 | 1.6270 | 0.4688 |
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+ | 1.5809 | 7.0 | 280 | 1.5691 | 0.525 |
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+ | 1.4996 | 8.0 | 320 | 1.5250 | 0.5062 |
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+ | 1.4555 | 9.0 | 360 | 1.4532 | 0.525 |
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+ | 1.4088 | 10.0 | 400 | 1.4374 | 0.5188 |
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+ | 1.3475 | 11.0 | 440 | 1.4162 | 0.5375 |
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+ | 1.3107 | 12.0 | 480 | 1.3727 | 0.525 |
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+ | 1.2669 | 13.0 | 520 | 1.3535 | 0.5375 |
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+ | 1.2375 | 14.0 | 560 | 1.3533 | 0.525 |
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+ | 1.1865 | 15.0 | 600 | 1.3284 | 0.5375 |
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+ | 1.156 | 16.0 | 640 | 1.3288 | 0.5312 |
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+ | 1.1148 | 17.0 | 680 | 1.2972 | 0.55 |
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+ | 1.0744 | 18.0 | 720 | 1.2742 | 0.5563 |
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+ | 1.0481 | 19.0 | 760 | 1.2473 | 0.575 |
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+ | 1.0008 | 20.0 | 800 | 1.2330 | 0.5875 |
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+ | 0.9788 | 21.0 | 840 | 1.2163 | 0.575 |
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+ | 0.9766 | 22.0 | 880 | 1.2781 | 0.5687 |
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+ | 0.933 | 23.0 | 920 | 1.2021 | 0.575 |
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+ | 0.8953 | 24.0 | 960 | 1.2426 | 0.5625 |
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+ | 0.8701 | 25.0 | 1000 | 1.1871 | 0.5813 |
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+ | 0.8647 | 26.0 | 1040 | 1.2654 | 0.5125 |
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+ | 0.8432 | 27.0 | 1080 | 1.2051 | 0.5437 |
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+ | 0.8128 | 28.0 | 1120 | 1.2658 | 0.5437 |
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+ | 0.8382 | 29.0 | 1160 | 1.2093 | 0.55 |
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+ | 0.7872 | 30.0 | 1200 | 1.2364 | 0.55 |
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+ | 0.7662 | 31.0 | 1240 | 1.1820 | 0.5875 |
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+ | 0.7354 | 32.0 | 1280 | 1.2133 | 0.5813 |
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+ | 0.7251 | 33.0 | 1320 | 1.1519 | 0.5938 |
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+ | 0.7003 | 34.0 | 1360 | 1.2387 | 0.5813 |
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+ | 0.6787 | 35.0 | 1400 | 1.2615 | 0.525 |
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+ | 0.6776 | 36.0 | 1440 | 1.2160 | 0.5813 |
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+ | 0.6486 | 37.0 | 1480 | 1.2137 | 0.5687 |
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+ | 0.6107 | 38.0 | 1520 | 1.2362 | 0.55 |
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+ | 0.6231 | 39.0 | 1560 | 1.1770 | 0.5625 |
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+ | 0.5947 | 40.0 | 1600 | 1.2345 | 0.5437 |
108
+ | 0.6302 | 41.0 | 1640 | 1.1654 | 0.6125 |
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+ | 0.5881 | 42.0 | 1680 | 1.2213 | 0.5625 |
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+ | 0.6075 | 43.0 | 1720 | 1.2112 | 0.55 |
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+ | 0.581 | 44.0 | 1760 | 1.1680 | 0.6 |
112
+ | 0.5646 | 45.0 | 1800 | 1.1939 | 0.6 |
113
+ | 0.5306 | 46.0 | 1840 | 1.1687 | 0.6188 |
114
+ | 0.5545 | 47.0 | 1880 | 1.1530 | 0.5687 |
115
+ | 0.5585 | 48.0 | 1920 | 1.1791 | 0.5813 |
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+ | 0.5484 | 49.0 | 1960 | 1.2595 | 0.55 |
117
+ | 0.5492 | 50.0 | 2000 | 1.2213 | 0.5437 |
118
+ | 0.5276 | 51.0 | 2040 | 1.1498 | 0.5938 |
119
+ | 0.5298 | 52.0 | 2080 | 1.1860 | 0.5875 |
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+ | 0.5006 | 53.0 | 2120 | 1.2137 | 0.5563 |
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+ | 0.522 | 54.0 | 2160 | 1.2012 | 0.5687 |
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+ | 0.5287 | 55.0 | 2200 | 1.1927 | 0.5312 |
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+ | 0.516 | 56.0 | 2240 | 1.1973 | 0.5375 |
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+ | 0.5 | 57.0 | 2280 | 1.1854 | 0.5687 |
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+ | 0.4906 | 58.0 | 2320 | 1.2936 | 0.5 |
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+ | 0.5329 | 59.0 | 2360 | 1.2269 | 0.5437 |
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+ | 0.5122 | 60.0 | 2400 | 1.1749 | 0.5813 |
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  ### Framework versions
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