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SharonTudi
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DIALOGUE_one

Text Classification
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Safetensors
distilbert
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End of training

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  1. README.md +53 -53
  2. model.safetensors +1 -1
  3. runs/Jan21_10-51-26_1a3a957c2842/events.out.tfevents.1705834295.1a3a957c2842.250.0 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -20,11 +20,11 @@ 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: 1.0430
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- - Precision: 0.7857
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- - Recall: 0.7368
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- - F1: 0.7178
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- - Accuracy: 0.7368
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  ## Model description
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@@ -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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- | 1.3081 | 0.62 | 30 | 1.1474 | 0.7026 | 0.5921 | 0.5799 | 0.5921 |
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- | 0.8403 | 1.25 | 60 | 0.8207 | 0.7988 | 0.75 | 0.7462 | 0.75 |
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- | 0.6042 | 1.88 | 90 | 0.5540 | 0.8344 | 0.8289 | 0.8298 | 0.8289 |
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- | 0.4338 | 2.5 | 120 | 0.5329 | 0.7908 | 0.7895 | 0.7892 | 0.7895 |
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- | 0.336 | 3.12 | 150 | 0.6554 | 0.7636 | 0.7632 | 0.7621 | 0.7632 |
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- | 0.217 | 3.75 | 180 | 0.7242 | 0.7581 | 0.75 | 0.7505 | 0.75 |
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- | 0.2956 | 4.38 | 210 | 0.7073 | 0.8273 | 0.8026 | 0.8014 | 0.8026 |
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- | 0.1924 | 5.0 | 240 | 0.8226 | 0.7176 | 0.7105 | 0.7002 | 0.7105 |
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- | 0.173 | 5.62 | 270 | 0.7027 | 0.8375 | 0.8289 | 0.8291 | 0.8289 |
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- | 0.1588 | 6.25 | 300 | 0.6656 | 0.8289 | 0.8289 | 0.8280 | 0.8289 |
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- | 0.1196 | 6.88 | 330 | 1.0001 | 0.8308 | 0.7895 | 0.7869 | 0.7895 |
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- | 0.0909 | 7.5 | 360 | 0.9005 | 0.7914 | 0.7895 | 0.7857 | 0.7895 |
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- | 0.071 | 8.12 | 390 | 0.7158 | 0.8262 | 0.8026 | 0.8028 | 0.8026 |
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- | 0.0873 | 8.75 | 420 | 0.7878 | 0.8309 | 0.8158 | 0.8136 | 0.8158 |
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- | 0.0488 | 9.38 | 450 | 0.8906 | 0.8159 | 0.7895 | 0.7858 | 0.7895 |
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- | 0.0444 | 10.0 | 480 | 0.9556 | 0.7607 | 0.7632 | 0.7589 | 0.7632 |
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- | 0.0073 | 10.62 | 510 | 1.5880 | 0.6047 | 0.6053 | 0.5725 | 0.6053 |
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- | 0.0638 | 11.25 | 540 | 0.7089 | 0.7891 | 0.7895 | 0.7874 | 0.7895 |
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- | 0.0045 | 11.88 | 570 | 1.4644 | 0.7340 | 0.6711 | 0.6488 | 0.6711 |
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- | 0.0021 | 12.5 | 600 | 1.0877 | 0.7919 | 0.7763 | 0.7725 | 0.7763 |
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- | 0.0325 | 13.12 | 630 | 0.8996 | 0.7747 | 0.7763 | 0.7747 | 0.7763 |
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- | 0.0094 | 13.75 | 660 | 1.1086 | 0.6707 | 0.6711 | 0.6487 | 0.6711 |
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- | 0.0222 | 14.38 | 690 | 1.5269 | 0.6340 | 0.6316 | 0.6062 | 0.6316 |
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- | 0.0173 | 15.0 | 720 | 1.0474 | 0.8010 | 0.7500 | 0.7233 | 0.75 |
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- | 0.0185 | 15.62 | 750 | 0.8577 | 0.8146 | 0.7763 | 0.7603 | 0.7763 |
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- | 0.0014 | 16.25 | 780 | 1.1964 | 0.7340 | 0.6711 | 0.6488 | 0.6711 |
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- | 0.003 | 16.88 | 810 | 0.8568 | 0.8265 | 0.8158 | 0.8152 | 0.8158 |
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- | 0.0014 | 17.5 | 840 | 0.9779 | 0.7907 | 0.75 | 0.7361 | 0.75 |
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- | 0.0012 | 18.12 | 870 | 1.1439 | 0.7771 | 0.7237 | 0.6983 | 0.7237 |
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- | 0.0011 | 18.75 | 900 | 1.0622 | 0.75 | 0.7237 | 0.6985 | 0.7237 |
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- | 0.001 | 19.38 | 930 | 1.0488 | 0.7640 | 0.7368 | 0.7179 | 0.7368 |
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- | 0.001 | 20.0 | 960 | 1.0189 | 0.75 | 0.7237 | 0.6985 | 0.7237 |
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- | 0.0009 | 20.62 | 990 | 1.0533 | 0.7771 | 0.7237 | 0.6983 | 0.7237 |
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- | 0.0008 | 21.25 | 1020 | 1.0633 | 0.7771 | 0.7237 | 0.6983 | 0.7237 |
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- | 0.0009 | 21.88 | 1050 | 1.0720 | 0.7771 | 0.7237 | 0.6983 | 0.7237 |
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- | 0.0008 | 22.5 | 1080 | 1.1308 | 0.7771 | 0.7237 | 0.6983 | 0.7237 |
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- | 0.0008 | 23.12 | 1110 | 1.2522 | 0.8155 | 0.7763 | 0.7708 | 0.7763 |
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- | 0.0007 | 23.75 | 1140 | 1.1484 | 0.7857 | 0.7368 | 0.7178 | 0.7368 |
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- | 0.0008 | 24.38 | 1170 | 1.1589 | 0.7771 | 0.7237 | 0.6983 | 0.7237 |
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- | 0.0007 | 25.0 | 1200 | 1.1886 | 0.7771 | 0.7237 | 0.6983 | 0.7237 |
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- | 0.0007 | 25.62 | 1230 | 1.1599 | 0.7771 | 0.7237 | 0.6983 | 0.7237 |
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- | 0.0007 | 26.25 | 1260 | 1.1569 | 0.7771 | 0.7237 | 0.6983 | 0.7237 |
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- | 0.0007 | 26.88 | 1290 | 1.0849 | 0.7771 | 0.7237 | 0.6983 | 0.7237 |
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- | 0.0006 | 27.5 | 1320 | 1.0791 | 0.7771 | 0.7237 | 0.6983 | 0.7237 |
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- | 0.0007 | 28.12 | 1350 | 1.0786 | 0.7771 | 0.7237 | 0.6983 | 0.7237 |
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- | 0.0007 | 28.75 | 1380 | 1.0750 | 0.7857 | 0.7368 | 0.7178 | 0.7368 |
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- | 0.0009 | 29.38 | 1410 | 1.0517 | 0.7857 | 0.7368 | 0.7178 | 0.7368 |
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- | 0.0007 | 30.0 | 1440 | 1.0430 | 0.7857 | 0.7368 | 0.7178 | 0.7368 |
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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: 0.1059
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+ - Precision: 0.9762
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+ - Recall: 0.9737
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+ - F1: 0.9736
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+ - Accuracy: 0.9737
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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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+ | 1.1272 | 0.62 | 30 | 0.6482 | 0.9327 | 0.9079 | 0.9047 | 0.9079 |
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+ | 0.5355 | 1.25 | 60 | 0.2417 | 0.9637 | 0.9605 | 0.9604 | 0.9605 |
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+ | 0.2089 | 1.88 | 90 | 0.1224 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0526 | 2.5 | 120 | 0.0476 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0215 | 3.12 | 150 | 0.0580 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0096 | 3.75 | 180 | 0.0548 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0067 | 4.38 | 210 | 0.1091 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0049 | 5.0 | 240 | 0.0871 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0039 | 5.62 | 270 | 0.0828 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0033 | 6.25 | 300 | 0.0825 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0027 | 6.88 | 330 | 0.0886 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0025 | 7.5 | 360 | 0.0903 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0022 | 8.12 | 390 | 0.0922 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0019 | 8.75 | 420 | 0.0902 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0017 | 9.38 | 450 | 0.0911 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0016 | 10.0 | 480 | 0.0895 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0014 | 10.62 | 510 | 0.0908 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0014 | 11.25 | 540 | 0.0917 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0012 | 11.88 | 570 | 0.0925 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0011 | 12.5 | 600 | 0.0924 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.001 | 13.12 | 630 | 0.0921 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.001 | 13.75 | 660 | 0.0941 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0009 | 14.38 | 690 | 0.0956 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0009 | 15.0 | 720 | 0.0938 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 15.62 | 750 | 0.0932 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 16.25 | 780 | 0.0940 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 16.88 | 810 | 0.0976 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 17.5 | 840 | 0.0962 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 18.12 | 870 | 0.0974 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 18.75 | 900 | 0.1010 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0006 | 19.38 | 930 | 0.1006 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0006 | 20.0 | 960 | 0.1008 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0006 | 20.62 | 990 | 0.1006 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0006 | 21.25 | 1020 | 0.1016 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0006 | 21.88 | 1050 | 0.1013 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 22.5 | 1080 | 0.1000 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0006 | 23.12 | 1110 | 0.1019 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 23.75 | 1140 | 0.1079 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 24.38 | 1170 | 0.1081 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 25.0 | 1200 | 0.1068 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 25.62 | 1230 | 0.1072 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 26.25 | 1260 | 0.1066 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 26.88 | 1290 | 0.1060 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 27.5 | 1320 | 0.1059 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 28.12 | 1350 | 0.1058 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 28.75 | 1380 | 0.1057 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 29.38 | 1410 | 0.1058 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 30.0 | 1440 | 0.1059 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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  ### Framework versions
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