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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 +48 -48
  2. model.safetensors +1 -1
  3. runs/Jan18_15-04-55_12c048e2721e/events.out.tfevents.1705590297.12c048e2721e.167.76 +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: 0.1806
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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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- | 1.1511 | 0.62 | 30 | 0.7534 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.5652 | 1.25 | 60 | 0.2659 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.2111 | 1.88 | 90 | 0.1361 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0781 | 2.5 | 120 | 0.1057 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0357 | 3.12 | 150 | 0.1143 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0194 | 3.75 | 180 | 0.1174 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0106 | 4.38 | 210 | 0.1445 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0077 | 5.0 | 240 | 0.1394 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0061 | 5.62 | 270 | 0.1390 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0053 | 6.25 | 300 | 0.1400 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0044 | 6.88 | 330 | 0.1436 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0038 | 7.5 | 360 | 0.1446 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0031 | 8.12 | 390 | 0.1473 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0028 | 8.75 | 420 | 0.1502 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0025 | 9.38 | 450 | 0.1530 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0023 | 10.0 | 480 | 0.1542 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0021 | 10.62 | 510 | 0.1572 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.002 | 11.25 | 540 | 0.1604 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0018 | 11.88 | 570 | 0.1614 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0017 | 12.5 | 600 | 0.1623 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0015 | 13.12 | 630 | 0.1630 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0015 | 13.75 | 660 | 0.1629 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0014 | 14.38 | 690 | 0.1650 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0013 | 15.0 | 720 | 0.1673 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0012 | 15.62 | 750 | 0.1675 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0011 | 16.25 | 780 | 0.1693 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0011 | 16.88 | 810 | 0.1705 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.001 | 17.5 | 840 | 0.1719 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.001 | 18.12 | 870 | 0.1733 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.001 | 18.75 | 900 | 0.1741 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0009 | 19.38 | 930 | 0.1743 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0009 | 20.0 | 960 | 0.1744 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0009 | 20.62 | 990 | 0.1755 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0009 | 21.25 | 1020 | 0.1765 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 21.88 | 1050 | 0.1770 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 22.5 | 1080 | 0.1769 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 23.12 | 1110 | 0.1776 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 23.75 | 1140 | 0.1786 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0007 | 24.38 | 1170 | 0.1789 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0007 | 25.0 | 1200 | 0.1791 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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  | 0.0008 | 25.62 | 1230 | 0.1799 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0007 | 26.25 | 1260 | 0.1801 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0007 | 26.88 | 1290 | 0.1800 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0007 | 27.5 | 1320 | 0.1802 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0007 | 28.12 | 1350 | 0.1804 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0007 | 28.75 | 1380 | 0.1805 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0007 | 29.38 | 1410 | 0.1806 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0007 | 30.0 | 1440 | 0.1806 | 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: 0.1809
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  - Precision: 0.9762
25
  - Recall: 0.9737
26
  - 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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+ | 1.1724 | 0.62 | 30 | 0.8000 | 0.9224 | 0.9079 | 0.9071 | 0.9079 |
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+ | 0.6097 | 1.25 | 60 | 0.2953 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.2608 | 1.88 | 90 | 0.1342 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0958 | 2.5 | 120 | 0.0711 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0424 | 3.12 | 150 | 0.1116 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0205 | 3.75 | 180 | 0.1195 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0119 | 4.38 | 210 | 0.0987 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0087 | 5.0 | 240 | 0.1092 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0068 | 5.62 | 270 | 0.1103 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0057 | 6.25 | 300 | 0.1116 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0048 | 6.88 | 330 | 0.1350 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0041 | 7.5 | 360 | 0.1359 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0037 | 8.12 | 390 | 0.1339 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0031 | 8.75 | 420 | 0.1655 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0028 | 9.38 | 450 | 0.1608 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0026 | 10.0 | 480 | 0.1545 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0024 | 10.62 | 510 | 0.1554 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0022 | 11.25 | 540 | 0.1602 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.002 | 11.88 | 570 | 0.1619 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0019 | 12.5 | 600 | 0.1632 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0017 | 13.12 | 630 | 0.1643 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0016 | 13.75 | 660 | 0.1647 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0016 | 14.38 | 690 | 0.1667 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0015 | 15.0 | 720 | 0.1683 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0013 | 15.62 | 750 | 0.1688 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0013 | 16.25 | 780 | 0.1702 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0012 | 16.88 | 810 | 0.1708 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0011 | 17.5 | 840 | 0.1715 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0011 | 18.12 | 870 | 0.1742 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.001 | 18.75 | 900 | 0.1754 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.001 | 19.38 | 930 | 0.1755 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.001 | 20.0 | 960 | 0.1759 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0009 | 20.62 | 990 | 0.1765 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0009 | 21.25 | 1020 | 0.1776 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0009 | 21.88 | 1050 | 0.1779 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 22.5 | 1080 | 0.1776 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0009 | 23.12 | 1110 | 0.1782 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 23.75 | 1140 | 0.1789 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 24.38 | 1170 | 0.1792 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 25.0 | 1200 | 0.1796 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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  | 0.0008 | 25.62 | 1230 | 0.1799 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 26.25 | 1260 | 0.1803 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 26.88 | 1290 | 0.1804 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 27.5 | 1320 | 0.1808 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 28.12 | 1350 | 0.1809 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 28.75 | 1380 | 0.1810 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 29.38 | 1410 | 0.1809 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 30.0 | 1440 | 0.1809 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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  ### Framework versions
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