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arabert_cross_vocabulary_task2_fold0

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@@ -3,20 +3,20 @@ base_model: aubmindlab/bert-base-arabertv02
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  tags:
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  - generated_from_trainer
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  model-index:
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- - name: arabert_cross_vocabulary_task1_fold6
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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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  should probably proofread and complete it, then remove this comment. -->
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- # arabert_cross_vocabulary_task1_fold6
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  This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4691
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- - Qwk: 0.6043
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- - Mse: 0.4695
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  ## Model description
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@@ -47,81 +47,81 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse |
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  |:-------------:|:------:|:----:|:---------------:|:------:|:------:|
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- | No log | 0.1333 | 2 | 3.5366 | 0.0172 | 3.5407 |
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- | No log | 0.2667 | 4 | 1.9631 | 0.0854 | 1.9633 |
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- | No log | 0.4 | 6 | 0.8549 | 0.3315 | 0.8566 |
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- | No log | 0.5333 | 8 | 0.8207 | 0.4747 | 0.8223 |
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- | No log | 0.6667 | 10 | 0.8985 | 0.3094 | 0.8995 |
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- | No log | 0.8 | 12 | 0.7171 | 0.3820 | 0.7187 |
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- | No log | 0.9333 | 14 | 0.4768 | 0.6161 | 0.4790 |
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- | No log | 1.0667 | 16 | 0.4681 | 0.5385 | 0.4699 |
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- | No log | 1.2 | 18 | 0.4222 | 0.6122 | 0.4237 |
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- | No log | 1.3333 | 20 | 0.4873 | 0.6466 | 0.4880 |
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- | No log | 1.4667 | 22 | 0.7664 | 0.5029 | 0.7657 |
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- | No log | 1.6 | 24 | 0.5655 | 0.6403 | 0.5656 |
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- | No log | 1.7333 | 26 | 0.4131 | 0.7256 | 0.4147 |
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- | No log | 1.8667 | 28 | 0.4061 | 0.6758 | 0.4080 |
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- | No log | 2.0 | 30 | 0.4877 | 0.5770 | 0.4895 |
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- | No log | 2.1333 | 32 | 0.4719 | 0.5718 | 0.4737 |
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- | No log | 2.2667 | 34 | 0.3880 | 0.6223 | 0.3901 |
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- | No log | 2.4 | 36 | 0.3630 | 0.7266 | 0.3652 |
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- | No log | 2.5333 | 38 | 0.3863 | 0.7041 | 0.3880 |
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- | No log | 2.6667 | 40 | 0.4796 | 0.6361 | 0.4807 |
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- | No log | 2.8 | 42 | 0.4544 | 0.6439 | 0.4556 |
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- | No log | 2.9333 | 44 | 0.3815 | 0.6948 | 0.3833 |
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- | No log | 3.0667 | 46 | 0.3666 | 0.7341 | 0.3685 |
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- | No log | 3.2 | 48 | 0.3991 | 0.6331 | 0.4003 |
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- | No log | 3.3333 | 50 | 0.5108 | 0.5581 | 0.5116 |
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- | No log | 3.4667 | 52 | 0.4518 | 0.5955 | 0.4529 |
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- | No log | 3.6 | 54 | 0.3551 | 0.7316 | 0.3569 |
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- | No log | 3.7333 | 56 | 0.3567 | 0.7709 | 0.3588 |
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- | No log | 3.8667 | 58 | 0.3551 | 0.7325 | 0.3569 |
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- | No log | 4.0 | 60 | 0.4528 | 0.6083 | 0.4540 |
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- | No log | 4.1333 | 62 | 0.5123 | 0.5776 | 0.5133 |
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- | No log | 4.2667 | 64 | 0.4206 | 0.6489 | 0.4217 |
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- | No log | 4.4 | 66 | 0.3971 | 0.6953 | 0.3982 |
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- | No log | 4.5333 | 68 | 0.3984 | 0.6877 | 0.3995 |
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- | No log | 4.6667 | 70 | 0.4585 | 0.6205 | 0.4594 |
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- | No log | 4.8 | 72 | 0.4840 | 0.5808 | 0.4849 |
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- | No log | 4.9333 | 74 | 0.3955 | 0.6703 | 0.3964 |
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- | No log | 5.0667 | 76 | 0.3740 | 0.7160 | 0.3752 |
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- | No log | 5.2 | 78 | 0.3739 | 0.6999 | 0.3750 |
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- | No log | 5.3333 | 80 | 0.4368 | 0.6324 | 0.4377 |
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- | No log | 5.4667 | 82 | 0.5136 | 0.5929 | 0.5141 |
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- | No log | 5.6 | 84 | 0.4855 | 0.6019 | 0.4859 |
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- | No log | 5.7333 | 86 | 0.4397 | 0.6381 | 0.4401 |
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- | No log | 5.8667 | 88 | 0.4409 | 0.6371 | 0.4412 |
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- | No log | 6.0 | 90 | 0.4781 | 0.6117 | 0.4783 |
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- | No log | 6.1333 | 92 | 0.5415 | 0.5949 | 0.5416 |
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- | No log | 6.2667 | 94 | 0.5043 | 0.6038 | 0.5046 |
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- | No log | 6.4 | 96 | 0.4420 | 0.6502 | 0.4425 |
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- | No log | 6.5333 | 98 | 0.4236 | 0.6652 | 0.4242 |
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- | No log | 6.6667 | 100 | 0.4415 | 0.6391 | 0.4420 |
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- | No log | 6.8 | 102 | 0.5253 | 0.5827 | 0.5257 |
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- | No log | 6.9333 | 104 | 0.5412 | 0.5748 | 0.5416 |
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- | No log | 7.0667 | 106 | 0.4800 | 0.6034 | 0.4805 |
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- | No log | 7.2 | 108 | 0.4525 | 0.6318 | 0.4532 |
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- | No log | 7.3333 | 110 | 0.4582 | 0.6290 | 0.4589 |
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- | No log | 7.4667 | 112 | 0.4760 | 0.6084 | 0.4767 |
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- | No log | 7.6 | 114 | 0.4942 | 0.5915 | 0.4948 |
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- | No log | 7.7333 | 116 | 0.5342 | 0.5816 | 0.5347 |
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- | No log | 7.8667 | 118 | 0.5123 | 0.5954 | 0.5127 |
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- | No log | 8.0 | 120 | 0.4615 | 0.6280 | 0.4619 |
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- | No log | 8.1333 | 122 | 0.4445 | 0.6622 | 0.4449 |
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- | No log | 8.2667 | 124 | 0.4551 | 0.6529 | 0.4555 |
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- | No log | 8.4 | 126 | 0.4888 | 0.6100 | 0.4891 |
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- | No log | 8.5333 | 128 | 0.5436 | 0.5912 | 0.5439 |
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- | No log | 8.6667 | 130 | 0.5938 | 0.5773 | 0.5940 |
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- | No log | 8.8 | 132 | 0.5869 | 0.5781 | 0.5872 |
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- | No log | 8.9333 | 134 | 0.5432 | 0.5847 | 0.5436 |
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- | No log | 9.0667 | 136 | 0.4923 | 0.6054 | 0.4927 |
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- | No log | 9.2 | 138 | 0.4568 | 0.6321 | 0.4573 |
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- | No log | 9.3333 | 140 | 0.4453 | 0.6608 | 0.4458 |
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- | No log | 9.4667 | 142 | 0.4465 | 0.6608 | 0.4470 |
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- | No log | 9.6 | 144 | 0.4542 | 0.6387 | 0.4547 |
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- | No log | 9.7333 | 146 | 0.4614 | 0.6167 | 0.4619 |
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- | No log | 9.8667 | 148 | 0.4677 | 0.6043 | 0.4682 |
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- | No log | 10.0 | 150 | 0.4691 | 0.6043 | 0.4695 |
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  ### Framework versions
 
3
  tags:
4
  - generated_from_trainer
5
  model-index:
6
+ - name: arabert_cross_vocabulary_task2_fold0
7
  results: []
8
  ---
9
 
10
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
11
  should probably proofread and complete it, then remove this comment. -->
12
 
13
+ # arabert_cross_vocabulary_task2_fold0
14
 
15
  This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7744
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+ - Qwk: 0.5129
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+ - Mse: 0.7751
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse |
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  |:-------------:|:------:|:----:|:---------------:|:------:|:------:|
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+ | No log | 0.1333 | 2 | 6.0135 | 0.0063 | 6.0118 |
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+ | No log | 0.2667 | 4 | 3.5790 | 0.0426 | 3.5769 |
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+ | No log | 0.4 | 6 | 1.5189 | 0.1873 | 1.5182 |
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+ | No log | 0.5333 | 8 | 1.0415 | 0.1741 | 1.0415 |
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+ | No log | 0.6667 | 10 | 1.3450 | 0.2327 | 1.3453 |
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+ | No log | 0.8 | 12 | 2.7213 | 0.1379 | 2.7215 |
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+ | No log | 0.9333 | 14 | 1.9117 | 0.2036 | 1.9120 |
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+ | No log | 1.0667 | 16 | 1.0551 | 0.2973 | 1.0556 |
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+ | No log | 1.2 | 18 | 0.8085 | 0.4178 | 0.8090 |
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+ | No log | 1.3333 | 20 | 0.8599 | 0.4235 | 0.8605 |
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+ | No log | 1.4667 | 22 | 1.2293 | 0.3473 | 1.2300 |
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+ | No log | 1.6 | 24 | 1.4481 | 0.3228 | 1.4490 |
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+ | No log | 1.7333 | 26 | 1.0749 | 0.4124 | 1.0757 |
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+ | No log | 1.8667 | 28 | 0.8047 | 0.4812 | 0.8054 |
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+ | No log | 2.0 | 30 | 0.7416 | 0.5115 | 0.7424 |
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+ | No log | 2.1333 | 32 | 0.8219 | 0.4458 | 0.8228 |
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+ | No log | 2.2667 | 34 | 1.0031 | 0.4070 | 1.0041 |
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+ | No log | 2.4 | 36 | 1.1303 | 0.4041 | 1.1312 |
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+ | No log | 2.5333 | 38 | 1.1323 | 0.4064 | 1.1331 |
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+ | No log | 2.6667 | 40 | 1.0716 | 0.4240 | 1.0724 |
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+ | No log | 2.8 | 42 | 0.8847 | 0.4894 | 0.8855 |
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+ | No log | 2.9333 | 44 | 0.8007 | 0.5087 | 0.8014 |
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+ | No log | 3.0667 | 46 | 0.7579 | 0.5083 | 0.7586 |
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+ | No log | 3.2 | 48 | 0.9311 | 0.4512 | 0.9317 |
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+ | No log | 3.3333 | 50 | 1.0232 | 0.4313 | 1.0237 |
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+ | No log | 3.4667 | 52 | 0.8885 | 0.4592 | 0.8890 |
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+ | No log | 3.6 | 54 | 0.8515 | 0.4675 | 0.8520 |
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+ | No log | 3.7333 | 56 | 0.8352 | 0.4824 | 0.8355 |
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+ | No log | 3.8667 | 58 | 0.8884 | 0.4855 | 0.8887 |
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+ | No log | 4.0 | 60 | 1.0524 | 0.4439 | 1.0527 |
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+ | No log | 4.1333 | 62 | 1.1255 | 0.4180 | 1.1260 |
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+ | No log | 4.2667 | 64 | 0.9389 | 0.4769 | 0.9395 |
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+ | No log | 4.4 | 66 | 0.8175 | 0.5051 | 0.8181 |
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+ | No log | 4.5333 | 68 | 0.8469 | 0.5060 | 0.8476 |
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+ | No log | 4.6667 | 70 | 0.9671 | 0.4843 | 0.9678 |
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+ | No log | 4.8 | 72 | 0.9258 | 0.4895 | 0.9264 |
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+ | No log | 4.9333 | 74 | 0.8740 | 0.4900 | 0.8746 |
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+ | No log | 5.0667 | 76 | 0.8529 | 0.4874 | 0.8535 |
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+ | No log | 5.2 | 78 | 0.9148 | 0.4757 | 0.9154 |
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+ | No log | 5.3333 | 80 | 0.9168 | 0.4791 | 0.9175 |
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+ | No log | 5.4667 | 82 | 0.7903 | 0.4954 | 0.7910 |
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+ | No log | 5.6 | 84 | 0.7057 | 0.5514 | 0.7065 |
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+ | No log | 5.7333 | 86 | 0.7449 | 0.5282 | 0.7458 |
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+ | No log | 5.8667 | 88 | 0.8859 | 0.4980 | 0.8867 |
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+ | No log | 6.0 | 90 | 1.0776 | 0.4447 | 1.0784 |
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+ | No log | 6.1333 | 92 | 1.0638 | 0.4471 | 1.0646 |
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+ | No log | 6.2667 | 94 | 0.8896 | 0.4741 | 0.8904 |
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+ | No log | 6.4 | 96 | 0.7857 | 0.5028 | 0.7865 |
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+ | No log | 6.5333 | 98 | 0.7723 | 0.5134 | 0.7731 |
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+ | No log | 6.6667 | 100 | 0.8110 | 0.5025 | 0.8118 |
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+ | No log | 6.8 | 102 | 0.8781 | 0.4840 | 0.8789 |
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+ | No log | 6.9333 | 104 | 0.9719 | 0.4805 | 0.9727 |
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+ | No log | 7.0667 | 106 | 1.0214 | 0.4585 | 1.0221 |
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+ | No log | 7.2 | 108 | 1.0030 | 0.4702 | 1.0037 |
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+ | No log | 7.3333 | 110 | 0.8786 | 0.5008 | 0.8793 |
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+ | No log | 7.4667 | 112 | 0.8115 | 0.5083 | 0.8122 |
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+ | No log | 7.6 | 114 | 0.8162 | 0.5036 | 0.8169 |
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+ | No log | 7.7333 | 116 | 0.8235 | 0.5011 | 0.8242 |
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+ | No log | 7.8667 | 118 | 0.8121 | 0.5015 | 0.8128 |
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+ | No log | 8.0 | 120 | 0.7687 | 0.5116 | 0.7694 |
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+ | No log | 8.1333 | 122 | 0.7624 | 0.5101 | 0.7631 |
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+ | No log | 8.2667 | 124 | 0.8069 | 0.5054 | 0.8075 |
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+ | No log | 8.4 | 126 | 0.8526 | 0.5050 | 0.8532 |
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+ | No log | 8.5333 | 128 | 0.8600 | 0.5050 | 0.8606 |
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+ | No log | 8.6667 | 130 | 0.8586 | 0.5055 | 0.8593 |
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+ | No log | 8.8 | 132 | 0.8700 | 0.5019 | 0.8707 |
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+ | No log | 8.9333 | 134 | 0.8744 | 0.5019 | 0.8750 |
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+ | No log | 9.0667 | 136 | 0.8880 | 0.4899 | 0.8886 |
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+ | No log | 9.2 | 138 | 0.8928 | 0.4840 | 0.8934 |
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+ | No log | 9.3333 | 140 | 0.8662 | 0.4960 | 0.8668 |
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+ | No log | 9.4667 | 142 | 0.8312 | 0.5059 | 0.8318 |
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+ | No log | 9.6 | 144 | 0.8041 | 0.5063 | 0.8048 |
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+ | No log | 9.7333 | 146 | 0.7847 | 0.5136 | 0.7854 |
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+ | No log | 9.8667 | 148 | 0.7768 | 0.5125 | 0.7775 |
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+ | No log | 10.0 | 150 | 0.7744 | 0.5129 | 0.7751 |
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  ### Framework versions