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SharonTudi
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DIALOGUE_overfit_check

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distilbert
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End of training

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  1. README.md +49 -49
  2. model.safetensors +1 -1
  3. runs/Jan15_10-28-49_e72b419fbc15/events.out.tfevents.1705314534.e72b419fbc15.194.0 +3 -0
  4. runs/Jan15_10-57-51_e72b419fbc15/events.out.tfevents.1705316294.e72b419fbc15.194.1 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
@@ -17,7 +17,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2151
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  - Accuracy: 0.9737
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  ## Model description
@@ -49,54 +49,54 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 1.0679 | 0.62 | 30 | 0.5453 | 0.9342 |
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- | 0.3955 | 1.25 | 60 | 0.1425 | 0.9737 |
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- | 0.1248 | 1.88 | 90 | 0.1138 | 0.9737 |
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- | 0.03 | 2.5 | 120 | 0.0733 | 0.9737 |
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- | 0.0127 | 3.12 | 150 | 0.1634 | 0.9737 |
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- | 0.0184 | 3.75 | 180 | 0.1644 | 0.9737 |
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- | 0.0029 | 4.38 | 210 | 0.1650 | 0.9737 |
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- | 0.0023 | 5.0 | 240 | 0.1668 | 0.9737 |
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- | 0.0019 | 5.62 | 270 | 0.1700 | 0.9737 |
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- | 0.0016 | 6.25 | 300 | 0.1744 | 0.9737 |
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- | 0.0014 | 6.88 | 330 | 0.1786 | 0.9737 |
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- | 0.0012 | 7.5 | 360 | 0.1822 | 0.9737 |
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- | 0.0011 | 8.12 | 390 | 0.1836 | 0.9737 |
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- | 0.001 | 8.75 | 420 | 0.1853 | 0.9737 |
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- | 0.0009 | 9.38 | 450 | 0.1881 | 0.9737 |
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- | 0.0009 | 10.0 | 480 | 0.1896 | 0.9737 |
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- | 0.0008 | 10.62 | 510 | 0.1918 | 0.9737 |
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- | 0.0008 | 11.25 | 540 | 0.1933 | 0.9737 |
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- | 0.0007 | 11.88 | 570 | 0.1953 | 0.9737 |
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- | 0.0006 | 12.5 | 600 | 0.1964 | 0.9737 |
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- | 0.0006 | 13.12 | 630 | 0.1978 | 0.9737 |
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- | 0.0006 | 13.75 | 660 | 0.1996 | 0.9737 |
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- | 0.0006 | 14.38 | 690 | 0.2015 | 0.9737 |
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- | 0.0005 | 15.0 | 720 | 0.2022 | 0.9737 |
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- | 0.0005 | 15.62 | 750 | 0.2030 | 0.9737 |
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- | 0.0005 | 16.25 | 780 | 0.2045 | 0.9737 |
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- | 0.0005 | 16.88 | 810 | 0.2055 | 0.9737 |
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- | 0.0004 | 17.5 | 840 | 0.2063 | 0.9737 |
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- | 0.0004 | 18.12 | 870 | 0.2072 | 0.9737 |
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- | 0.0004 | 18.75 | 900 | 0.2078 | 0.9737 |
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- | 0.0004 | 19.38 | 930 | 0.2087 | 0.9737 |
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- | 0.0004 | 20.0 | 960 | 0.2093 | 0.9737 |
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- | 0.0004 | 20.62 | 990 | 0.2098 | 0.9737 |
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- | 0.0004 | 21.25 | 1020 | 0.2103 | 0.9737 |
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- | 0.0004 | 21.88 | 1050 | 0.2111 | 0.9737 |
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- | 0.0004 | 22.5 | 1080 | 0.2117 | 0.9737 |
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- | 0.0004 | 23.12 | 1110 | 0.2121 | 0.9737 |
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- | 0.0004 | 23.75 | 1140 | 0.2126 | 0.9737 |
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- | 0.0003 | 24.38 | 1170 | 0.2130 | 0.9737 |
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- | 0.0003 | 25.0 | 1200 | 0.2136 | 0.9737 |
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- | 0.0003 | 25.62 | 1230 | 0.2140 | 0.9737 |
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- | 0.0003 | 26.25 | 1260 | 0.2142 | 0.9737 |
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- | 0.0003 | 26.88 | 1290 | 0.2144 | 0.9737 |
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- | 0.0003 | 27.5 | 1320 | 0.2148 | 0.9737 |
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- | 0.0003 | 28.12 | 1350 | 0.2149 | 0.9737 |
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- | 0.0003 | 28.75 | 1380 | 0.2150 | 0.9737 |
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- | 0.0003 | 29.38 | 1410 | 0.2150 | 0.9737 |
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- | 0.0003 | 30.0 | 1440 | 0.2151 | 0.9737 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0774
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  - Accuracy: 0.9737
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  ## Model description
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.9838 | 0.62 | 30 | 0.4844 | 1.0 |
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+ | 0.3561 | 1.25 | 60 | 0.1123 | 0.9737 |
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+ | 0.1232 | 1.88 | 90 | 0.1118 | 0.9737 |
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+ | 0.0377 | 2.5 | 120 | 0.0134 | 1.0 |
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+ | 0.0088 | 3.12 | 150 | 0.1223 | 0.9737 |
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+ | 0.0039 | 3.75 | 180 | 0.0856 | 0.9737 |
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+ | 0.0027 | 4.38 | 210 | 0.0647 | 0.9737 |
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+ | 0.0022 | 5.0 | 240 | 0.0703 | 0.9737 |
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+ | 0.0018 | 5.62 | 270 | 0.0720 | 0.9737 |
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+ | 0.0015 | 6.25 | 300 | 0.0725 | 0.9737 |
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+ | 0.0013 | 6.88 | 330 | 0.0779 | 0.9737 |
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+ | 0.0012 | 7.5 | 360 | 0.0730 | 0.9737 |
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+ | 0.001 | 8.12 | 390 | 0.0739 | 0.9737 |
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+ | 0.0009 | 8.75 | 420 | 0.0716 | 0.9737 |
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+ | 0.0009 | 9.38 | 450 | 0.0735 | 0.9737 |
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+ | 0.0008 | 10.0 | 480 | 0.0725 | 0.9737 |
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+ | 0.0007 | 10.62 | 510 | 0.0710 | 0.9737 |
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+ | 0.0007 | 11.25 | 540 | 0.0715 | 0.9737 |
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+ | 0.0006 | 11.88 | 570 | 0.0718 | 0.9737 |
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+ | 0.0006 | 12.5 | 600 | 0.0719 | 0.9737 |
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+ | 0.0005 | 13.12 | 630 | 0.0728 | 0.9737 |
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+ | 0.0005 | 13.75 | 660 | 0.0746 | 0.9737 |
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+ | 0.0005 | 14.38 | 690 | 0.0753 | 0.9737 |
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+ | 0.0005 | 15.0 | 720 | 0.0747 | 0.9737 |
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+ | 0.0004 | 15.62 | 750 | 0.0742 | 0.9737 |
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+ | 0.0004 | 16.25 | 780 | 0.0762 | 0.9737 |
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+ | 0.0004 | 16.88 | 810 | 0.0762 | 0.9737 |
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+ | 0.0004 | 17.5 | 840 | 0.0769 | 0.9737 |
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+ | 0.0004 | 18.12 | 870 | 0.0776 | 0.9737 |
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+ | 0.0004 | 18.75 | 900 | 0.0765 | 0.9737 |
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+ | 0.0004 | 19.38 | 930 | 0.0770 | 0.9737 |
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+ | 0.0004 | 20.0 | 960 | 0.0762 | 0.9737 |
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+ | 0.0003 | 20.62 | 990 | 0.0762 | 0.9737 |
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+ | 0.0003 | 21.25 | 1020 | 0.0766 | 0.9737 |
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+ | 0.0003 | 21.88 | 1050 | 0.0765 | 0.9737 |
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+ | 0.0003 | 22.5 | 1080 | 0.0773 | 0.9737 |
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+ | 0.0003 | 23.12 | 1110 | 0.0776 | 0.9737 |
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+ | 0.0003 | 23.75 | 1140 | 0.0771 | 0.9737 |
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+ | 0.0003 | 24.38 | 1170 | 0.0770 | 0.9737 |
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+ | 0.0003 | 25.0 | 1200 | 0.0772 | 0.9737 |
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+ | 0.0003 | 25.62 | 1230 | 0.0777 | 0.9737 |
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+ | 0.0003 | 26.25 | 1260 | 0.0774 | 0.9737 |
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+ | 0.0003 | 26.88 | 1290 | 0.0774 | 0.9737 |
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+ | 0.0003 | 27.5 | 1320 | 0.0775 | 0.9737 |
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+ | 0.0003 | 28.12 | 1350 | 0.0775 | 0.9737 |
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+ | 0.0003 | 28.75 | 1380 | 0.0775 | 0.9737 |
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+ | 0.0003 | 29.38 | 1410 | 0.0774 | 0.9737 |
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+ | 0.0003 | 30.0 | 1440 | 0.0774 | 0.9737 |
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  ### Framework versions
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