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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/Jan29_15-07-05_8d2717b59c88/events.out.tfevents.1706540827.8d2717b59c88.584.0 +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-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: 0.1637
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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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- | 1.1634 | 0.62 | 30 | 0.7096 | 0.8900 | 0.8421 | 0.8322 | 0.8421 |
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- | 0.5104 | 1.25 | 60 | 0.2078 | 0.9524 | 0.9474 | 0.9472 | 0.9474 |
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- | 0.1827 | 1.88 | 90 | 0.1302 | 0.9637 | 0.9605 | 0.9604 | 0.9605 |
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- | 0.0426 | 2.5 | 120 | 0.0556 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0178 | 3.12 | 150 | 0.1489 | 0.9421 | 0.9342 | 0.9339 | 0.9342 |
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- | 0.0091 | 3.75 | 180 | 0.1377 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0054 | 4.38 | 210 | 0.1268 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0041 | 5.0 | 240 | 0.1340 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0033 | 5.62 | 270 | 0.1405 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0029 | 6.25 | 300 | 0.1328 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0024 | 6.88 | 330 | 0.1372 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0021 | 7.5 | 360 | 0.1467 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0019 | 8.12 | 390 | 0.1397 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0016 | 8.75 | 420 | 0.1445 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0015 | 9.38 | 450 | 0.1510 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0013 | 10.0 | 480 | 0.1413 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0012 | 10.62 | 510 | 0.1453 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0011 | 11.25 | 540 | 0.1494 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.001 | 11.88 | 570 | 0.1480 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.001 | 12.5 | 600 | 0.1514 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 13.12 | 630 | 0.1513 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 13.75 | 660 | 0.1514 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 14.38 | 690 | 0.1521 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 15.0 | 720 | 0.1527 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0007 | 15.62 | 750 | 0.1528 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0007 | 16.25 | 780 | 0.1563 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0006 | 16.88 | 810 | 0.1570 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0006 | 17.5 | 840 | 0.1554 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0006 | 18.12 | 870 | 0.1560 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0006 | 18.75 | 900 | 0.1565 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0005 | 19.38 | 930 | 0.1571 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0005 | 20.0 | 960 | 0.1576 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0005 | 20.62 | 990 | 0.1591 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0005 | 21.25 | 1020 | 0.1605 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0005 | 21.88 | 1050 | 0.1608 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0005 | 22.5 | 1080 | 0.1613 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0005 | 23.12 | 1110 | 0.1609 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 23.75 | 1140 | 0.1611 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 24.38 | 1170 | 0.1617 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 25.0 | 1200 | 0.1617 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 25.62 | 1230 | 0.1625 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 26.25 | 1260 | 0.1628 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 26.88 | 1290 | 0.1630 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 27.5 | 1320 | 0.1635 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 28.12 | 1350 | 0.1636 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 28.75 | 1380 | 0.1637 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 29.38 | 1410 | 0.1637 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 30.0 | 1440 | 0.1637 | 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-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: 0.2052
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  - Precision: 0.9762
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  - 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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+ | 1.2045 | 0.62 | 30 | 0.7843 | 0.9565 | 0.9474 | 0.9468 | 0.9474 |
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+ | 0.5845 | 1.25 | 60 | 0.2507 | 0.9524 | 0.9474 | 0.9472 | 0.9474 |
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+ | 0.23 | 1.88 | 90 | 0.1376 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0722 | 2.5 | 120 | 0.0647 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0515 | 3.12 | 150 | 0.1376 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0197 | 3.75 | 180 | 0.1505 | 0.9637 | 0.9605 | 0.9604 | 0.9605 |
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+ | 0.0065 | 4.38 | 210 | 0.1456 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0046 | 5.0 | 240 | 0.1376 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0037 | 5.62 | 270 | 0.1569 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0028 | 6.25 | 300 | 0.1551 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0024 | 6.88 | 330 | 0.1594 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0022 | 7.5 | 360 | 0.1624 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0018 | 8.12 | 390 | 0.1687 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0016 | 8.75 | 420 | 0.1698 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0014 | 9.38 | 450 | 0.1732 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0013 | 10.0 | 480 | 0.1741 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0012 | 10.62 | 510 | 0.1772 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0011 | 11.25 | 540 | 0.1791 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.001 | 11.88 | 570 | 0.1814 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.001 | 12.5 | 600 | 0.1840 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 13.12 | 630 | 0.1858 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0009 | 13.75 | 660 | 0.1877 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 14.38 | 690 | 0.1893 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 15.0 | 720 | 0.1902 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 15.62 | 750 | 0.1908 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 16.25 | 780 | 0.1931 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0006 | 16.88 | 810 | 0.1936 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0006 | 17.5 | 840 | 0.1946 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0006 | 18.12 | 870 | 0.1961 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0006 | 18.75 | 900 | 0.1966 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 19.38 | 930 | 0.1965 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 20.0 | 960 | 0.1968 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 20.62 | 990 | 0.1974 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 21.25 | 1020 | 0.1987 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 21.88 | 1050 | 0.1995 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 22.5 | 1080 | 0.2001 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0005 | 23.12 | 1110 | 0.2010 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0004 | 23.75 | 1140 | 0.2018 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0004 | 24.38 | 1170 | 0.2021 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0004 | 25.0 | 1200 | 0.2025 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0004 | 25.62 | 1230 | 0.2034 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0004 | 26.25 | 1260 | 0.2038 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0004 | 26.88 | 1290 | 0.2042 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0004 | 27.5 | 1320 | 0.2047 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0004 | 28.12 | 1350 | 0.2048 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0004 | 28.75 | 1380 | 0.2050 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0004 | 29.38 | 1410 | 0.2051 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0004 | 30.0 | 1440 | 0.2052 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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  ### Framework versions
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