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SharonTudi
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DIALOGUE_one

Text Classification
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Safetensors
distilbert
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End of training

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  1. README.md +50 -50
  2. model.safetensors +1 -1
  3. runs/Jan18_11-20-46_3c59cc46f7a9/events.out.tfevents.1705576868.3c59cc46f7a9.1662.4 +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: 0.2117
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  - Precision: 0.9762
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  - Recall: 0.9737
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  - F1: 0.9736
@@ -43,7 +43,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 3e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
@@ -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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- | 0.9949 | 0.62 | 30 | 0.4697 | 0.9659 | 0.9605 | 0.9603 | 0.9605 |
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- | 0.3831 | 1.25 | 60 | 0.1338 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.1135 | 1.88 | 90 | 0.1407 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0256 | 2.5 | 120 | 0.1359 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0126 | 3.12 | 150 | 0.1449 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0227 | 3.75 | 180 | 0.1552 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0051 | 4.38 | 210 | 0.1573 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0037 | 5.0 | 240 | 0.1594 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.003 | 5.62 | 270 | 0.1626 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0024 | 6.25 | 300 | 0.1645 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0021 | 6.88 | 330 | 0.1737 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0018 | 7.5 | 360 | 0.1759 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0015 | 8.12 | 390 | 0.1774 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0014 | 8.75 | 420 | 0.1801 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0013 | 9.38 | 450 | 0.1837 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0011 | 10.0 | 480 | 0.1852 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.001 | 10.62 | 510 | 0.1878 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0009 | 11.25 | 540 | 0.1892 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0009 | 11.88 | 570 | 0.1939 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 12.5 | 600 | 0.1948 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0008 | 13.12 | 630 | 0.1961 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0007 | 13.75 | 660 | 0.1965 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0007 | 14.38 | 690 | 0.1972 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0006 | 15.0 | 720 | 0.1988 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0006 | 15.62 | 750 | 0.1993 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0005 | 16.25 | 780 | 0.2006 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0005 | 16.88 | 810 | 0.2020 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0005 | 17.5 | 840 | 0.2031 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0005 | 18.12 | 870 | 0.2045 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0005 | 18.75 | 900 | 0.2054 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 19.38 | 930 | 0.2051 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 20.0 | 960 | 0.2053 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 20.62 | 990 | 0.2058 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 21.25 | 1020 | 0.2069 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 21.88 | 1050 | 0.2076 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 22.5 | 1080 | 0.2079 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 23.12 | 1110 | 0.2084 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 23.75 | 1140 | 0.2092 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 24.38 | 1170 | 0.2095 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 25.0 | 1200 | 0.2100 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 25.62 | 1230 | 0.2104 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0003 | 26.25 | 1260 | 0.2109 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0003 | 26.88 | 1290 | 0.2111 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0003 | 27.5 | 1320 | 0.2113 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0003 | 28.12 | 1350 | 0.2115 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0003 | 28.75 | 1380 | 0.2116 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0003 | 29.38 | 1410 | 0.2117 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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- | 0.0004 | 30.0 | 1440 | 0.2117 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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  ### Framework versions
 
20
 
21
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
22
  It achieves the following results on the evaluation set:
23
+ - Loss: 0.2058
24
  - Precision: 0.9762
25
  - Recall: 0.9737
26
  - F1: 0.9736
 
43
  ### Training hyperparameters
44
 
45
  The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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  - train_batch_size: 8
48
  - eval_batch_size: 8
49
  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 1.2014 | 0.62 | 30 | 0.8233 | 0.9565 | 0.9474 | 0.9468 | 0.9474 |
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+ | 0.6189 | 1.25 | 60 | 0.3016 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.2599 | 1.88 | 90 | 0.1623 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0907 | 2.5 | 120 | 0.1075 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0423 | 3.12 | 150 | 0.1191 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0278 | 3.75 | 180 | 0.1517 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0115 | 4.38 | 210 | 0.1488 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0087 | 5.0 | 240 | 0.1489 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.007 | 5.62 | 270 | 0.1541 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0057 | 6.25 | 300 | 0.1573 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0051 | 6.88 | 330 | 0.1702 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0041 | 7.5 | 360 | 0.1715 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0034 | 8.12 | 390 | 0.1725 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0031 | 8.75 | 420 | 0.1746 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0028 | 9.38 | 450 | 0.1769 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0026 | 10.0 | 480 | 0.1781 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0024 | 10.62 | 510 | 0.1808 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0022 | 11.25 | 540 | 0.1827 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.002 | 11.88 | 570 | 0.1845 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0018 | 12.5 | 600 | 0.1861 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0017 | 13.12 | 630 | 0.1877 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0016 | 13.75 | 660 | 0.1886 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0014 | 14.38 | 690 | 0.1897 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0014 | 15.0 | 720 | 0.1915 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0014 | 15.62 | 750 | 0.1925 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0012 | 16.25 | 780 | 0.1937 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0012 | 16.88 | 810 | 0.1949 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0011 | 17.5 | 840 | 0.1962 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0011 | 18.12 | 870 | 0.1970 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.001 | 18.75 | 900 | 0.1977 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.001 | 19.38 | 930 | 0.1982 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.001 | 20.0 | 960 | 0.1990 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0009 | 20.62 | 990 | 0.1999 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0009 | 21.25 | 1020 | 0.2008 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0009 | 21.88 | 1050 | 0.2016 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0009 | 22.5 | 1080 | 0.2021 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0009 | 23.12 | 1110 | 0.2027 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 23.75 | 1140 | 0.2033 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 24.38 | 1170 | 0.2036 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 25.0 | 1200 | 0.2041 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 25.62 | 1230 | 0.2044 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 26.25 | 1260 | 0.2049 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 26.88 | 1290 | 0.2052 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 27.5 | 1320 | 0.2054 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 28.12 | 1350 | 0.2056 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0007 | 28.75 | 1380 | 0.2057 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 29.38 | 1410 | 0.2058 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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+ | 0.0008 | 30.0 | 1440 | 0.2058 | 0.9762 | 0.9737 | 0.9736 | 0.9737 |
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  ### Framework versions
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