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SharonTudi
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DIALOGUE_overfit_check

Text Classification
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Safetensors
distilbert
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End of training

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  1. README.md +49 -49
  2. model.safetensors +1 -1
  3. runs/Jan16_10-21-50_745092e66db7/events.out.tfevents.1705400515.745092e66db7.965.0 +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: 0.1563
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  - Accuracy: 0.9737
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  - Precision: 0.9762
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  - Recall: 0.9737
@@ -55,54 +55,54 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 1.0573 | 0.62 | 30 | 0.5369 | 0.9342 | 0.9421 | 0.9342 | 0.9339 |
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- | 0.3947 | 1.25 | 60 | 0.1511 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.1325 | 1.88 | 90 | 0.1414 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.026 | 2.5 | 120 | 0.0879 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0137 | 3.12 | 150 | 0.1188 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0074 | 3.75 | 180 | 0.1012 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.005 | 4.38 | 210 | 0.1139 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0038 | 5.0 | 240 | 0.1138 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0032 | 5.62 | 270 | 0.1188 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0024 | 6.25 | 300 | 0.1205 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0021 | 6.88 | 330 | 0.1262 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0019 | 7.5 | 360 | 0.1278 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0016 | 8.12 | 390 | 0.1286 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0014 | 8.75 | 420 | 0.1316 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0013 | 9.38 | 450 | 0.1338 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0012 | 10.0 | 480 | 0.1342 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0011 | 10.62 | 510 | 0.1368 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0009 | 11.25 | 540 | 0.1387 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.001 | 11.88 | 570 | 0.1401 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0008 | 12.5 | 600 | 0.1413 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0008 | 13.12 | 630 | 0.1418 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0008 | 13.75 | 660 | 0.1430 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0007 | 14.38 | 690 | 0.1434 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0007 | 15.0 | 720 | 0.1453 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0006 | 15.62 | 750 | 0.1451 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0006 | 16.25 | 780 | 0.1465 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0005 | 16.88 | 810 | 0.1473 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0005 | 17.5 | 840 | 0.1480 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0005 | 18.12 | 870 | 0.1505 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0005 | 18.75 | 900 | 0.1513 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0005 | 19.38 | 930 | 0.1510 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0005 | 20.0 | 960 | 0.1510 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0005 | 20.62 | 990 | 0.1515 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0004 | 21.25 | 1020 | 0.1526 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0004 | 21.88 | 1050 | 0.1527 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0004 | 22.5 | 1080 | 0.1523 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0004 | 23.12 | 1110 | 0.1527 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0004 | 23.75 | 1140 | 0.1535 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0004 | 24.38 | 1170 | 0.1539 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0004 | 25.0 | 1200 | 0.1546 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0004 | 25.62 | 1230 | 0.1551 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0003 | 26.25 | 1260 | 0.1556 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0004 | 26.88 | 1290 | 0.1558 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0003 | 27.5 | 1320 | 0.1560 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0003 | 28.12 | 1350 | 0.1562 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0004 | 28.75 | 1380 | 0.1563 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0003 | 29.38 | 1410 | 0.1563 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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- | 0.0003 | 30.0 | 1440 | 0.1563 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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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: 0.2184
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  - Accuracy: 0.9737
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  - Precision: 0.9762
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  - Recall: 0.9737
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 1.0414 | 0.62 | 30 | 0.5042 | 0.9211 | 0.9327 | 0.9211 | 0.9204 |
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+ | 0.3868 | 1.25 | 60 | 0.1559 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.1218 | 1.88 | 90 | 0.1743 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0363 | 2.5 | 120 | 0.1189 | 0.9474 | 0.9524 | 0.9474 | 0.9472 |
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+ | 0.0127 | 3.12 | 150 | 0.1455 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0077 | 3.75 | 180 | 0.1457 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.005 | 4.38 | 210 | 0.1587 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0039 | 5.0 | 240 | 0.1620 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0031 | 5.62 | 270 | 0.1667 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0026 | 6.25 | 300 | 0.1696 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0022 | 6.88 | 330 | 0.1768 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0019 | 7.5 | 360 | 0.1802 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0016 | 8.12 | 390 | 0.1811 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0015 | 8.75 | 420 | 0.1834 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0013 | 9.38 | 450 | 0.1872 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0012 | 10.0 | 480 | 0.1890 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0011 | 10.62 | 510 | 0.1924 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.001 | 11.25 | 540 | 0.1940 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0009 | 11.88 | 570 | 0.1967 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0008 | 12.5 | 600 | 0.1982 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0008 | 13.12 | 630 | 0.1995 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0008 | 13.75 | 660 | 0.2009 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0007 | 14.38 | 690 | 0.2023 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0007 | 15.0 | 720 | 0.2039 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0006 | 15.62 | 750 | 0.2049 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0006 | 16.25 | 780 | 0.2064 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0006 | 16.88 | 810 | 0.2075 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0005 | 17.5 | 840 | 0.2087 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0005 | 18.12 | 870 | 0.2101 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0005 | 18.75 | 900 | 0.2110 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0005 | 19.38 | 930 | 0.2116 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0005 | 20.0 | 960 | 0.2122 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0004 | 20.62 | 990 | 0.2130 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0004 | 21.25 | 1020 | 0.2138 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0004 | 21.88 | 1050 | 0.2143 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0004 | 22.5 | 1080 | 0.2146 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0004 | 23.12 | 1110 | 0.2152 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0004 | 23.75 | 1140 | 0.2158 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0004 | 24.38 | 1170 | 0.2162 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0004 | 25.0 | 1200 | 0.2167 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0004 | 25.62 | 1230 | 0.2170 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0004 | 26.25 | 1260 | 0.2174 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0003 | 26.88 | 1290 | 0.2177 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0003 | 27.5 | 1320 | 0.2179 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0003 | 28.12 | 1350 | 0.2181 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0003 | 28.75 | 1380 | 0.2183 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0003 | 29.38 | 1410 | 0.2183 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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+ | 0.0004 | 30.0 | 1440 | 0.2184 | 0.9737 | 0.9762 | 0.9737 | 0.9736 |
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  ### Framework versions
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