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CONTEXT_one

Text Classification
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Safetensors
distilbert
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  1. README.md +51 -51
  2. model.safetensors +1 -1
  3. runs/Jan29_14-51-37_32576c7247e4/events.out.tfevents.1706539898.32576c7247e4.511.0 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -20,10 +20,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.1031
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- - Precision: 0.8202
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  - Recall: 0.8158
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- - F1: 0.8134
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  - Accuracy: 0.8158
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  ## Model description
@@ -55,54 +55,54 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 1.3141 | 0.62 | 30 | 1.1728 | 0.4060 | 0.4868 | 0.4214 | 0.4868 |
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- | 0.8655 | 1.25 | 60 | 0.8567 | 0.7238 | 0.7237 | 0.7207 | 0.7237 |
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- | 0.6189 | 1.88 | 90 | 0.6433 | 0.7395 | 0.7368 | 0.7361 | 0.7368 |
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- | 0.4575 | 2.5 | 120 | 0.6314 | 0.7661 | 0.7632 | 0.7625 | 0.7632 |
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- | 0.3123 | 3.12 | 150 | 0.6091 | 0.7636 | 0.7632 | 0.7621 | 0.7632 |
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- | 0.215 | 3.75 | 180 | 0.6095 | 0.7769 | 0.7763 | 0.7758 | 0.7763 |
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- | 0.2901 | 4.38 | 210 | 0.6833 | 0.7409 | 0.7368 | 0.7367 | 0.7368 |
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- | 0.2169 | 5.0 | 240 | 0.6651 | 0.8354 | 0.8289 | 0.8285 | 0.8289 |
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- | 0.1721 | 5.62 | 270 | 0.6578 | 0.8530 | 0.8421 | 0.8416 | 0.8421 |
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- | 0.2103 | 6.25 | 300 | 0.7525 | 0.7506 | 0.75 | 0.7481 | 0.75 |
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- | 0.1021 | 6.88 | 330 | 0.6357 | 0.8725 | 0.8684 | 0.8681 | 0.8684 |
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- | 0.1115 | 7.5 | 360 | 1.0796 | 0.7510 | 0.75 | 0.7452 | 0.75 |
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- | 0.05 | 8.12 | 390 | 0.6933 | 0.8444 | 0.8289 | 0.8264 | 0.8289 |
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- | 0.0419 | 8.75 | 420 | 0.7248 | 0.8295 | 0.8158 | 0.8135 | 0.8158 |
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- | 0.0521 | 9.38 | 450 | 1.0193 | 0.7867 | 0.7895 | 0.7848 | 0.7895 |
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- | 0.0197 | 10.0 | 480 | 0.7878 | 0.7867 | 0.7895 | 0.7848 | 0.7895 |
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- | 0.0165 | 10.62 | 510 | 1.3815 | 0.7232 | 0.7105 | 0.6969 | 0.7105 |
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- | 0.0321 | 11.25 | 540 | 0.9198 | 0.7867 | 0.7895 | 0.7848 | 0.7895 |
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- | 0.0202 | 11.88 | 570 | 0.9919 | 0.8044 | 0.8026 | 0.7993 | 0.8026 |
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- | 0.0046 | 12.5 | 600 | 1.1230 | 0.7622 | 0.7632 | 0.7528 | 0.7632 |
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- | 0.0044 | 13.12 | 630 | 0.8484 | 0.8579 | 0.8553 | 0.8551 | 0.8553 |
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- | 0.0019 | 13.75 | 660 | 1.0979 | 0.7925 | 0.7895 | 0.7855 | 0.7895 |
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- | 0.0018 | 14.38 | 690 | 1.3561 | 0.7480 | 0.75 | 0.7438 | 0.75 |
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- | 0.0021 | 15.0 | 720 | 1.0228 | 0.8006 | 0.8026 | 0.7991 | 0.8026 |
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- | 0.0014 | 15.62 | 750 | 0.9298 | 0.8422 | 0.8421 | 0.8413 | 0.8421 |
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- | 0.0014 | 16.25 | 780 | 0.9537 | 0.8276 | 0.8289 | 0.8274 | 0.8289 |
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- | 0.0012 | 16.88 | 810 | 0.9708 | 0.8276 | 0.8289 | 0.8274 | 0.8289 |
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- | 0.0013 | 17.5 | 840 | 1.0009 | 0.8276 | 0.8289 | 0.8274 | 0.8289 |
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- | 0.0011 | 18.12 | 870 | 0.9999 | 0.8037 | 0.8026 | 0.7997 | 0.8026 |
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- | 0.0011 | 18.75 | 900 | 0.9871 | 0.8037 | 0.8026 | 0.7997 | 0.8026 |
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- | 0.001 | 19.38 | 930 | 0.9885 | 0.8276 | 0.8289 | 0.8274 | 0.8289 |
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- | 0.001 | 20.0 | 960 | 1.0078 | 0.8276 | 0.8289 | 0.8274 | 0.8289 |
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- | 0.0009 | 20.62 | 990 | 1.0204 | 0.8037 | 0.8026 | 0.7997 | 0.8026 |
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- | 0.0008 | 21.25 | 1020 | 1.0312 | 0.8037 | 0.8026 | 0.7997 | 0.8026 |
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- | 0.0008 | 21.88 | 1050 | 1.0438 | 0.8037 | 0.8026 | 0.7997 | 0.8026 |
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- | 0.0008 | 22.5 | 1080 | 1.0647 | 0.8037 | 0.8026 | 0.7997 | 0.8026 |
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- | 0.0008 | 23.12 | 1110 | 1.0633 | 0.8202 | 0.8158 | 0.8134 | 0.8158 |
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- | 0.0007 | 23.75 | 1140 | 1.0661 | 0.8202 | 0.8158 | 0.8134 | 0.8158 |
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- | 0.0008 | 24.38 | 1170 | 1.0871 | 0.8202 | 0.8158 | 0.8134 | 0.8158 |
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- | 0.0007 | 25.0 | 1200 | 1.0965 | 0.8202 | 0.8158 | 0.8134 | 0.8158 |
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- | 0.0007 | 25.62 | 1230 | 1.0893 | 0.8202 | 0.8158 | 0.8134 | 0.8158 |
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- | 0.0007 | 26.25 | 1260 | 1.0935 | 0.8202 | 0.8158 | 0.8134 | 0.8158 |
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- | 0.0007 | 26.88 | 1290 | 1.0942 | 0.8202 | 0.8158 | 0.8134 | 0.8158 |
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- | 0.0007 | 27.5 | 1320 | 1.0949 | 0.8202 | 0.8158 | 0.8134 | 0.8158 |
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- | 0.0007 | 28.12 | 1350 | 1.0937 | 0.8202 | 0.8158 | 0.8134 | 0.8158 |
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- | 0.0007 | 28.75 | 1380 | 1.0986 | 0.8202 | 0.8158 | 0.8134 | 0.8158 |
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- | 0.0007 | 29.38 | 1410 | 1.1030 | 0.8202 | 0.8158 | 0.8134 | 0.8158 |
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- | 0.0007 | 30.0 | 1440 | 1.1031 | 0.8202 | 0.8158 | 0.8134 | 0.8158 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2483
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+ - Precision: 0.8152
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  - Recall: 0.8158
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+ - F1: 0.8141
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  - Accuracy: 0.8158
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  ## Model description
 
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 1.3127 | 0.62 | 30 | 1.1497 | 0.6818 | 0.5263 | 0.4785 | 0.5263 |
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+ | 0.8414 | 1.25 | 60 | 0.8096 | 0.7479 | 0.75 | 0.7472 | 0.75 |
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+ | 0.597 | 1.88 | 90 | 0.6579 | 0.7904 | 0.7895 | 0.7873 | 0.7895 |
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+ | 0.4417 | 2.5 | 120 | 0.5761 | 0.8072 | 0.8026 | 0.8026 | 0.8026 |
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+ | 0.3041 | 3.12 | 150 | 0.6691 | 0.7665 | 0.7632 | 0.7598 | 0.7632 |
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+ | 0.2384 | 3.75 | 180 | 0.6736 | 0.7717 | 0.7632 | 0.7645 | 0.7632 |
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+ | 0.28 | 4.38 | 210 | 0.7949 | 0.7602 | 0.7632 | 0.7574 | 0.7632 |
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+ | 0.22 | 5.0 | 240 | 0.8305 | 0.7917 | 0.7895 | 0.7879 | 0.7895 |
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+ | 0.1427 | 5.62 | 270 | 0.7339 | 0.8041 | 0.8026 | 0.8025 | 0.8026 |
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+ | 0.1875 | 6.25 | 300 | 0.7198 | 0.8031 | 0.7895 | 0.7909 | 0.7895 |
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+ | 0.1216 | 6.88 | 330 | 0.7462 | 0.8315 | 0.8289 | 0.8287 | 0.8289 |
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+ | 0.0895 | 7.5 | 360 | 0.8646 | 0.8070 | 0.8026 | 0.8006 | 0.8026 |
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+ | 0.0758 | 8.12 | 390 | 1.0129 | 0.7883 | 0.7632 | 0.7642 | 0.7632 |
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+ | 0.0636 | 8.75 | 420 | 0.9161 | 0.7893 | 0.7895 | 0.7866 | 0.7895 |
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+ | 0.0239 | 9.38 | 450 | 0.9354 | 0.7409 | 0.7368 | 0.7367 | 0.7368 |
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+ | 0.0449 | 10.0 | 480 | 1.0156 | 0.7994 | 0.8026 | 0.7980 | 0.8026 |
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+ | 0.0089 | 10.62 | 510 | 0.9735 | 0.8125 | 0.8158 | 0.8125 | 0.8158 |
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+ | 0.0348 | 11.25 | 540 | 1.0077 | 0.7867 | 0.7895 | 0.7848 | 0.7895 |
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+ | 0.0037 | 11.88 | 570 | 1.1631 | 0.7868 | 0.7895 | 0.7857 | 0.7895 |
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+ | 0.0022 | 12.5 | 600 | 1.1037 | 0.7998 | 0.8026 | 0.7993 | 0.8026 |
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+ | 0.026 | 13.12 | 630 | 1.0309 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0118 | 13.75 | 660 | 1.0360 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0125 | 14.38 | 690 | 1.2095 | 0.7867 | 0.7895 | 0.7848 | 0.7895 |
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+ | 0.0158 | 15.0 | 720 | 1.0658 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0072 | 15.62 | 750 | 1.1267 | 0.7708 | 0.7763 | 0.7688 | 0.7763 |
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+ | 0.0015 | 16.25 | 780 | 1.1247 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0018 | 16.88 | 810 | 1.1386 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0013 | 17.5 | 840 | 1.1468 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0011 | 18.12 | 870 | 1.1692 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0013 | 18.75 | 900 | 1.1734 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0011 | 19.38 | 930 | 1.1857 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.001 | 20.0 | 960 | 1.1890 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.001 | 20.62 | 990 | 1.1924 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0009 | 21.25 | 1020 | 1.2005 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0009 | 21.88 | 1050 | 1.2084 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0009 | 22.5 | 1080 | 1.2216 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0009 | 23.12 | 1110 | 1.2237 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0008 | 23.75 | 1140 | 1.2231 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0008 | 24.38 | 1170 | 1.2286 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0008 | 25.0 | 1200 | 1.2312 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0008 | 25.62 | 1230 | 1.2325 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0008 | 26.25 | 1260 | 1.2362 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0008 | 26.88 | 1290 | 1.2415 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0007 | 27.5 | 1320 | 1.2462 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0008 | 28.12 | 1350 | 1.2471 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0007 | 28.75 | 1380 | 1.2466 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0007 | 29.38 | 1410 | 1.2478 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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+ | 0.0007 | 30.0 | 1440 | 1.2483 | 0.8152 | 0.8158 | 0.8141 | 0.8158 |
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  ### Framework versions
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