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factual-consistency-classification-ja

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distilbert
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factual-consistency-classification-ja

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  1. README.md +105 -35
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [line-corporation/line-distilbert-base-japanese](https://huggingface.co/line-corporation/line-distilbert-base-japanese) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8034
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- - Accuracy: 0.6230
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  ## Model description
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@@ -44,42 +44,112 @@ The following hyperparameters were used during training:
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  - distributed_type: tpu
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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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- - num_epochs: 30
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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 | 1.0 | 306 | 1.0539 | 0.2891 |
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- | 1.0502 | 2.0 | 612 | 1.0074 | 0.3203 |
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- | 1.0502 | 3.0 | 918 | 0.9738 | 0.3711 |
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- | 0.9895 | 4.0 | 1224 | 0.9452 | 0.4453 |
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- | 0.9483 | 5.0 | 1530 | 0.9245 | 0.4766 |
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- | 0.9483 | 6.0 | 1836 | 0.9041 | 0.5566 |
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- | 0.918 | 7.0 | 2142 | 0.8945 | 0.5117 |
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- | 0.918 | 8.0 | 2448 | 0.8853 | 0.5 |
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- | 0.9002 | 9.0 | 2754 | 0.8786 | 0.4922 |
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- | 0.884 | 10.0 | 3060 | 0.8658 | 0.5352 |
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- | 0.884 | 11.0 | 3366 | 0.8614 | 0.5176 |
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- | 0.8697 | 12.0 | 3672 | 0.8467 | 0.5938 |
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- | 0.8697 | 13.0 | 3978 | 0.8429 | 0.5801 |
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- | 0.8648 | 14.0 | 4284 | 0.8386 | 0.5703 |
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- | 0.8571 | 15.0 | 4590 | 0.8311 | 0.5996 |
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- | 0.8571 | 16.0 | 4896 | 0.8289 | 0.5879 |
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- | 0.8478 | 17.0 | 5202 | 0.8285 | 0.5762 |
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- | 0.8468 | 18.0 | 5508 | 0.8193 | 0.6152 |
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- | 0.8468 | 19.0 | 5814 | 0.8192 | 0.5957 |
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- | 0.8439 | 20.0 | 6120 | 0.8165 | 0.5996 |
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- | 0.8439 | 21.0 | 6426 | 0.8157 | 0.5918 |
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- | 0.8396 | 22.0 | 6732 | 0.8120 | 0.6055 |
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- | 0.8354 | 23.0 | 7038 | 0.8103 | 0.6055 |
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- | 0.8354 | 24.0 | 7344 | 0.8091 | 0.6035 |
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- | 0.8362 | 25.0 | 7650 | 0.8055 | 0.6152 |
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- | 0.8362 | 26.0 | 7956 | 0.8055 | 0.6074 |
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- | 0.8334 | 27.0 | 8262 | 0.8045 | 0.6211 |
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- | 0.8325 | 28.0 | 8568 | 0.8037 | 0.6191 |
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- | 0.8325 | 29.0 | 8874 | 0.8034 | 0.6230 |
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- | 0.833 | 30.0 | 9180 | 0.8034 | 0.6230 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [line-corporation/line-distilbert-base-japanese](https://huggingface.co/line-corporation/line-distilbert-base-japanese) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6901
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+ - Accuracy: 0.6855
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  ## Model description
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  - distributed_type: tpu
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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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+ - num_epochs: 100
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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 | 1.0 | 306 | 1.0484 | 0.2773 |
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+ | 1.044 | 2.0 | 612 | 1.0016 | 0.3320 |
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+ | 1.044 | 3.0 | 918 | 0.9675 | 0.3633 |
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+ | 0.9847 | 4.0 | 1224 | 0.9380 | 0.4434 |
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+ | 0.9411 | 5.0 | 1530 | 0.9163 | 0.4863 |
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+ | 0.9411 | 6.0 | 1836 | 0.8943 | 0.5742 |
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+ | 0.9091 | 7.0 | 2142 | 0.8852 | 0.5137 |
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+ | 0.9091 | 8.0 | 2448 | 0.8750 | 0.5039 |
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+ | 0.8895 | 9.0 | 2754 | 0.8674 | 0.4941 |
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+ | 0.8736 | 10.0 | 3060 | 0.8529 | 0.5254 |
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+ | 0.8736 | 11.0 | 3366 | 0.8477 | 0.5195 |
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+ | 0.8569 | 12.0 | 3672 | 0.8290 | 0.6133 |
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+ | 0.8569 | 13.0 | 3978 | 0.8231 | 0.6055 |
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+ | 0.8512 | 14.0 | 4284 | 0.8181 | 0.5879 |
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+ | 0.8414 | 15.0 | 4590 | 0.8084 | 0.6211 |
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+ | 0.8414 | 16.0 | 4896 | 0.8050 | 0.6152 |
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+ | 0.8323 | 17.0 | 5202 | 0.8015 | 0.6016 |
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+ | 0.8276 | 18.0 | 5508 | 0.7893 | 0.6426 |
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+ | 0.8276 | 19.0 | 5814 | 0.7889 | 0.625 |
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+ | 0.8248 | 20.0 | 6120 | 0.7829 | 0.6289 |
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+ | 0.8248 | 21.0 | 6426 | 0.7796 | 0.6211 |
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+ | 0.8166 | 22.0 | 6732 | 0.7759 | 0.6211 |
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+ | 0.8107 | 23.0 | 7038 | 0.7720 | 0.6230 |
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+ | 0.8107 | 24.0 | 7344 | 0.7691 | 0.625 |
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+ | 0.8094 | 25.0 | 7650 | 0.7620 | 0.6426 |
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+ | 0.8094 | 26.0 | 7956 | 0.7646 | 0.6230 |
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+ | 0.8055 | 27.0 | 8262 | 0.7533 | 0.6582 |
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+ | 0.8006 | 28.0 | 8568 | 0.7546 | 0.6348 |
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+ | 0.8006 | 29.0 | 8874 | 0.7525 | 0.6348 |
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+ | 0.7987 | 30.0 | 9180 | 0.7493 | 0.6465 |
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+ | 0.7987 | 31.0 | 9486 | 0.7469 | 0.6484 |
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+ | 0.7945 | 32.0 | 9792 | 0.7417 | 0.6562 |
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+ | 0.7949 | 33.0 | 10098 | 0.7412 | 0.6465 |
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+ | 0.7949 | 34.0 | 10404 | 0.7440 | 0.6367 |
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+ | 0.7883 | 35.0 | 10710 | 0.7392 | 0.6504 |
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+ | 0.7874 | 36.0 | 11016 | 0.7316 | 0.6660 |
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+ | 0.7874 | 37.0 | 11322 | 0.7319 | 0.6543 |
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+ | 0.7855 | 38.0 | 11628 | 0.7339 | 0.6504 |
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+ | 0.7855 | 39.0 | 11934 | 0.7299 | 0.6562 |
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+ | 0.7856 | 40.0 | 12240 | 0.7299 | 0.6504 |
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+ | 0.7816 | 41.0 | 12546 | 0.7227 | 0.6738 |
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+ | 0.7816 | 42.0 | 12852 | 0.7275 | 0.6504 |
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+ | 0.7805 | 43.0 | 13158 | 0.7269 | 0.6543 |
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+ | 0.7805 | 44.0 | 13464 | 0.7206 | 0.6641 |
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+ | 0.7756 | 45.0 | 13770 | 0.7175 | 0.6777 |
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+ | 0.7779 | 46.0 | 14076 | 0.7172 | 0.6660 |
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+ | 0.7779 | 47.0 | 14382 | 0.7191 | 0.6582 |
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+ | 0.7778 | 48.0 | 14688 | 0.7145 | 0.6680 |
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+ | 0.7778 | 49.0 | 14994 | 0.7154 | 0.6602 |
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+ | 0.7701 | 50.0 | 15300 | 0.7121 | 0.6719 |
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+ | 0.774 | 51.0 | 15606 | 0.7142 | 0.6641 |
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+ | 0.774 | 52.0 | 15912 | 0.7132 | 0.6719 |
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+ | 0.7732 | 53.0 | 16218 | 0.7078 | 0.6836 |
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+ | 0.768 | 54.0 | 16524 | 0.7123 | 0.6641 |
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+ | 0.768 | 55.0 | 16830 | 0.7048 | 0.6855 |
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+ | 0.7681 | 56.0 | 17136 | 0.7091 | 0.6641 |
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+ | 0.7681 | 57.0 | 17442 | 0.7055 | 0.6797 |
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+ | 0.7685 | 58.0 | 17748 | 0.7047 | 0.6816 |
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+ | 0.7684 | 59.0 | 18054 | 0.7036 | 0.6836 |
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+ | 0.7684 | 60.0 | 18360 | 0.7025 | 0.6836 |
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+ | 0.7633 | 61.0 | 18666 | 0.7042 | 0.6699 |
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+ | 0.7633 | 62.0 | 18972 | 0.7040 | 0.6699 |
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+ | 0.7659 | 63.0 | 19278 | 0.7017 | 0.6777 |
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+ | 0.7647 | 64.0 | 19584 | 0.7003 | 0.6836 |
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+ | 0.7647 | 65.0 | 19890 | 0.7015 | 0.6738 |
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+ | 0.7676 | 66.0 | 20196 | 0.6987 | 0.6816 |
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+ | 0.7607 | 67.0 | 20502 | 0.6972 | 0.6875 |
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+ | 0.7607 | 68.0 | 20808 | 0.6988 | 0.6777 |
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+ | 0.7637 | 69.0 | 21114 | 0.6968 | 0.6875 |
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+ | 0.7637 | 70.0 | 21420 | 0.6968 | 0.6816 |
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+ | 0.7556 | 71.0 | 21726 | 0.6980 | 0.6738 |
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+ | 0.7608 | 72.0 | 22032 | 0.6983 | 0.6758 |
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+ | 0.7608 | 73.0 | 22338 | 0.6967 | 0.6758 |
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+ | 0.7532 | 74.0 | 22644 | 0.6950 | 0.6816 |
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+ | 0.7532 | 75.0 | 22950 | 0.6961 | 0.6738 |
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+ | 0.7592 | 76.0 | 23256 | 0.6949 | 0.6797 |
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+ | 0.7553 | 77.0 | 23562 | 0.6936 | 0.6836 |
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+ | 0.7553 | 78.0 | 23868 | 0.6939 | 0.6855 |
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+ | 0.7581 | 79.0 | 24174 | 0.6937 | 0.6816 |
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+ | 0.7581 | 80.0 | 24480 | 0.6922 | 0.6836 |
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+ | 0.7558 | 81.0 | 24786 | 0.6934 | 0.6758 |
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+ | 0.7581 | 82.0 | 25092 | 0.6922 | 0.6855 |
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+ | 0.7581 | 83.0 | 25398 | 0.6939 | 0.6738 |
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+ | 0.7561 | 84.0 | 25704 | 0.6931 | 0.6797 |
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+ | 0.7581 | 85.0 | 26010 | 0.6914 | 0.6836 |
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+ | 0.7581 | 86.0 | 26316 | 0.6923 | 0.6797 |
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+ | 0.7553 | 87.0 | 26622 | 0.6921 | 0.6816 |
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+ | 0.7553 | 88.0 | 26928 | 0.6923 | 0.6797 |
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+ | 0.7553 | 89.0 | 27234 | 0.6913 | 0.6816 |
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+ | 0.7551 | 90.0 | 27540 | 0.6911 | 0.6816 |
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+ | 0.7551 | 91.0 | 27846 | 0.6920 | 0.6777 |
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+ | 0.7515 | 92.0 | 28152 | 0.6907 | 0.6797 |
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+ | 0.7515 | 93.0 | 28458 | 0.6914 | 0.6797 |
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+ | 0.7574 | 94.0 | 28764 | 0.6912 | 0.6797 |
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+ | 0.7525 | 95.0 | 29070 | 0.6906 | 0.6836 |
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+ | 0.7525 | 96.0 | 29376 | 0.6905 | 0.6836 |
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+ | 0.7539 | 97.0 | 29682 | 0.6899 | 0.6855 |
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+ | 0.7539 | 98.0 | 29988 | 0.6899 | 0.6855 |
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+ | 0.754 | 99.0 | 30294 | 0.6901 | 0.6855 |
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+ | 0.7573 | 100.0 | 30600 | 0.6901 | 0.6855 |
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  ### Framework versions
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