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SharonTudi
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DIALOGUE_one

Text Classification
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TensorBoard
Safetensors
distilbert
Generated from Trainer
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End of training

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  1. README.md +49 -49
  2. model.safetensors +1 -1
  3. runs/Jan19_10-18-33_eab14bd28cfb/events.out.tfevents.1705659514.eab14bd28cfb.651.0 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -20,7 +20,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1858
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  - Precision: 0.9762
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  - Recall: 0.9737
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  - F1: 0.9736
@@ -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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- | 1.1337 | 0.62 | 30 | 0.7680 | 0.9196 | 0.8816 | 0.8745 | 0.8816 |
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- | 0.6026 | 1.25 | 60 | 0.2921 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.2622 | 1.88 | 90 | 0.1333 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0839 | 2.5 | 120 | 0.0827 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0477 | 3.12 | 150 | 0.1079 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.031 | 3.75 | 180 | 0.1360 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0119 | 4.38 | 210 | 0.1309 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0087 | 5.0 | 240 | 0.1303 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0067 | 5.62 | 270 | 0.1373 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0055 | 6.25 | 300 | 0.1401 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0049 | 6.88 | 330 | 0.1459 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0043 | 7.5 | 360 | 0.1443 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0034 | 8.12 | 390 | 0.1448 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0033 | 8.75 | 420 | 0.1477 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.003 | 9.38 | 450 | 0.1531 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0026 | 10.0 | 480 | 0.1543 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0024 | 10.62 | 510 | 0.1591 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0022 | 11.25 | 540 | 0.1612 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0021 | 11.88 | 570 | 0.1672 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0018 | 12.5 | 600 | 0.1672 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0017 | 13.12 | 630 | 0.1677 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0017 | 13.75 | 660 | 0.1677 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0015 | 14.38 | 690 | 0.1698 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0014 | 15.0 | 720 | 0.1714 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0013 | 15.62 | 750 | 0.1721 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0013 | 16.25 | 780 | 0.1733 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0012 | 16.88 | 810 | 0.1752 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0012 | 17.5 | 840 | 0.1769 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0011 | 18.12 | 870 | 0.1778 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0011 | 18.75 | 900 | 0.1788 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.001 | 19.38 | 930 | 0.1786 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.001 | 20.0 | 960 | 0.1794 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.001 | 20.62 | 990 | 0.1802 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.001 | 21.25 | 1020 | 0.1811 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0009 | 21.88 | 1050 | 0.1819 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0009 | 22.5 | 1080 | 0.1823 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0009 | 23.12 | 1110 | 0.1833 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0009 | 23.75 | 1140 | 0.1843 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 24.38 | 1170 | 0.1842 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 25.0 | 1200 | 0.1842 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 25.62 | 1230 | 0.1846 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 26.25 | 1260 | 0.1850 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 26.88 | 1290 | 0.1851 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 27.5 | 1320 | 0.1853 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0007 | 28.12 | 1350 | 0.1855 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 28.75 | 1380 | 0.1856 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 29.38 | 1410 | 0.1857 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 30.0 | 1440 | 0.1858 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1947
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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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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 1.1919 | 0.62 | 30 | 0.8161 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.6182 | 1.25 | 60 | 0.2981 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.2564 | 1.88 | 90 | 0.1427 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0833 | 2.5 | 120 | 0.0918 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0436 | 3.12 | 150 | 0.1185 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0215 | 3.75 | 180 | 0.1243 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0109 | 4.38 | 210 | 0.1179 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0075 | 5.0 | 240 | 0.1240 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0062 | 5.62 | 270 | 0.1362 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0049 | 6.25 | 300 | 0.1385 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0042 | 6.88 | 330 | 0.1572 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0037 | 7.5 | 360 | 0.1569 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0031 | 8.12 | 390 | 0.1501 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0029 | 8.75 | 420 | 0.1563 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0024 | 9.38 | 450 | 0.1617 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0023 | 10.0 | 480 | 0.1625 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0021 | 10.62 | 510 | 0.1658 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.002 | 11.25 | 540 | 0.1699 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0017 | 11.88 | 570 | 0.1727 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0017 | 12.5 | 600 | 0.1731 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0015 | 13.12 | 630 | 0.1756 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0015 | 13.75 | 660 | 0.1764 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0014 | 14.38 | 690 | 0.1797 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0013 | 15.0 | 720 | 0.1817 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0012 | 15.62 | 750 | 0.1822 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0011 | 16.25 | 780 | 0.1833 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0011 | 16.88 | 810 | 0.1843 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.001 | 17.5 | 840 | 0.1857 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.001 | 18.12 | 870 | 0.1872 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0009 | 18.75 | 900 | 0.1884 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0009 | 19.38 | 930 | 0.1879 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0009 | 20.0 | 960 | 0.1882 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 20.62 | 990 | 0.1888 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 21.25 | 1020 | 0.1895 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 21.88 | 1050 | 0.1902 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 22.5 | 1080 | 0.1904 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 23.12 | 1110 | 0.1911 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 23.75 | 1140 | 0.1919 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 24.38 | 1170 | 0.1923 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 25.0 | 1200 | 0.1928 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 25.62 | 1230 | 0.1933 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 26.25 | 1260 | 0.1938 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 26.88 | 1290 | 0.1939 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 27.5 | 1320 | 0.1943 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0006 | 28.12 | 1350 | 0.1945 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 28.75 | 1380 | 0.1946 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 29.38 | 1410 | 0.1947 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 30.0 | 1440 | 0.1947 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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  ### Framework versions
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