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End of training

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  1. README.md +154 -19
  2. pytorch_model.bin +1 -1
  3. tokenizer.json +2 -16
  4. tokenizer_config.json +7 -0
README.md CHANGED
@@ -1,5 +1,5 @@
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  ---
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- base_model: microsoft/layoutlm-base-uncased
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  tags:
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  - generated_from_trainer
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  model-index:
@@ -12,9 +12,9 @@ should probably proofread and complete it, then remove this comment. -->
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  # layoutlm-funsd
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- This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6861
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  - Section Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
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  - Overall Precision: 0.0
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  - Overall Recall: 0.0
@@ -44,27 +44,162 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 15
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  ### Training results
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51
  | Training Loss | Epoch | Step | Validation Loss | Section Header | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
52
  |:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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- | 1.6076 | 1.0 | 1 | 1.4695 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.5385 |
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- | 1.3484 | 2.0 | 2 | 1.3198 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.5385 |
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- | 1.1705 | 3.0 | 3 | 1.2039 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.4615 |
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- | 1.0283 | 4.0 | 4 | 1.1135 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.3846 |
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- | 0.9069 | 5.0 | 5 | 1.0386 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.5385 |
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- | 0.8178 | 6.0 | 6 | 0.9659 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.5385 |
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- | 0.757 | 7.0 | 7 | 0.8979 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6154 |
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- | 0.6973 | 8.0 | 8 | 0.8384 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6154 |
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- | 0.6574 | 9.0 | 9 | 0.7922 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6154 |
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- | 0.6102 | 10.0 | 10 | 0.7577 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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- | 0.5741 | 11.0 | 11 | 0.7315 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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- | 0.5334 | 12.0 | 12 | 0.7121 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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- | 0.5261 | 13.0 | 13 | 0.6988 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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- | 0.5058 | 14.0 | 14 | 0.6902 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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- | 0.4897 | 15.0 | 15 | 0.6861 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
1
  ---
2
+ base_model: venneladondapati/layoutLM
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  tags:
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  - generated_from_trainer
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  model-index:
 
12
 
13
  # layoutlm-funsd
14
 
15
+ This model is a fine-tuned version of [venneladondapati/layoutLM](https://huggingface.co/venneladondapati/layoutLM) on the None dataset.
16
  It achieves the following results on the evaluation set:
17
+ - Loss: 0.9143
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  - Section Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
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  - Overall Precision: 0.0
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  - Overall Recall: 0.0
 
44
  - seed: 42
45
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
46
  - lr_scheduler_type: linear
47
+ - num_epochs: 150
48
 
49
  ### Training results
50
 
51
  | Training Loss | Epoch | Step | Validation Loss | Section Header | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
52
  |:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
53
+ | 2.5715 | 1.0 | 1 | 1.2902 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.3846 |
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+ | 1.0647 | 2.0 | 2 | 0.9327 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.5385 |
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+ | 0.7982 | 3.0 | 3 | 1.0898 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.3846 |
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+ | 0.6123 | 4.0 | 4 | 1.0862 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.3846 |
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+ | 0.4924 | 5.0 | 5 | 0.8896 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.3846 |
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+ | 0.3507 | 6.0 | 6 | 0.8219 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.3846 |
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+ | 0.2262 | 7.0 | 7 | 0.8697 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.3846 |
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+ | 0.1376 | 8.0 | 8 | 0.8038 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.5385 |
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+ | 0.083 | 9.0 | 9 | 0.7854 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.5385 |
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+ | 0.0412 | 10.0 | 10 | 0.7917 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.5385 |
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+ | 0.0238 | 11.0 | 11 | 0.7740 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6154 |
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+ | 0.0123 | 12.0 | 12 | 0.7669 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.5385 |
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+ | 0.0078 | 13.0 | 13 | 0.7576 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.5385 |
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+ | 0.0066 | 14.0 | 14 | 0.7570 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6154 |
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+ | 0.0033 | 15.0 | 15 | 0.7443 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0026 | 16.0 | 16 | 0.7203 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.7692 |
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+ | 0.0015 | 17.0 | 17 | 0.6999 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0012 | 18.0 | 18 | 0.6884 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0008 | 19.0 | 19 | 0.6817 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0007 | 20.0 | 20 | 0.6793 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0005 | 21.0 | 21 | 0.6784 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0005 | 22.0 | 22 | 0.6807 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0004 | 23.0 | 23 | 0.6831 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0004 | 24.0 | 24 | 0.6875 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0004 | 25.0 | 25 | 0.6923 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0004 | 26.0 | 26 | 0.6984 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0003 | 27.0 | 27 | 0.7060 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0003 | 28.0 | 28 | 0.7140 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0003 | 29.0 | 29 | 0.7210 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0003 | 30.0 | 30 | 0.7286 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0002 | 31.0 | 31 | 0.7355 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0003 | 32.0 | 32 | 0.7453 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0003 | 33.0 | 33 | 0.7562 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0002 | 34.0 | 34 | 0.7660 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0002 | 35.0 | 35 | 0.7767 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0002 | 36.0 | 36 | 0.7874 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0002 | 37.0 | 37 | 0.7985 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0002 | 38.0 | 38 | 0.8103 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0002 | 39.0 | 39 | 0.8219 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0002 | 40.0 | 40 | 0.8328 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0002 | 41.0 | 41 | 0.8426 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0002 | 42.0 | 42 | 0.8528 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0002 | 43.0 | 43 | 0.8629 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 44.0 | 44 | 0.8728 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0002 | 45.0 | 45 | 0.8823 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 46.0 | 46 | 0.8916 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 47.0 | 47 | 0.9011 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 48.0 | 48 | 0.9096 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 49.0 | 49 | 0.9170 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 50.0 | 50 | 0.9244 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 51.0 | 51 | 0.9308 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 52.0 | 52 | 0.9379 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 53.0 | 53 | 0.9437 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 54.0 | 54 | 0.9490 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 55.0 | 55 | 0.9529 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 56.0 | 56 | 0.9564 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 57.0 | 57 | 0.9598 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 58.0 | 58 | 0.9622 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 59.0 | 59 | 0.9649 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
112
+ | 0.0001 | 60.0 | 60 | 0.9676 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 61.0 | 61 | 0.9695 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 62.0 | 62 | 0.9707 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 63.0 | 63 | 0.9719 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
116
+ | 0.0001 | 64.0 | 64 | 0.9730 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
117
+ | 0.0001 | 65.0 | 65 | 0.9734 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
118
+ | 0.0001 | 66.0 | 66 | 0.9735 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
119
+ | 0.0001 | 67.0 | 67 | 0.9744 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 68.0 | 68 | 0.9736 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
121
+ | 0.0001 | 69.0 | 69 | 0.9740 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
122
+ | 0.0001 | 70.0 | 70 | 0.9742 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
123
+ | 0.0001 | 71.0 | 71 | 0.9740 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
124
+ | 0.0001 | 72.0 | 72 | 0.9741 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
125
+ | 0.0001 | 73.0 | 73 | 0.9738 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
126
+ | 0.0001 | 74.0 | 74 | 0.9736 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
127
+ | 0.0001 | 75.0 | 75 | 0.9732 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
128
+ | 0.0001 | 76.0 | 76 | 0.9728 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
129
+ | 0.0001 | 77.0 | 77 | 0.9717 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
130
+ | 0.0001 | 78.0 | 78 | 0.9702 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
131
+ | 0.0001 | 79.0 | 79 | 0.9692 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
132
+ | 0.0001 | 80.0 | 80 | 0.9679 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
133
+ | 0.0001 | 81.0 | 81 | 0.9662 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
134
+ | 0.0001 | 82.0 | 82 | 0.9652 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
135
+ | 0.0001 | 83.0 | 83 | 0.9634 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
136
+ | 0.0001 | 84.0 | 84 | 0.9615 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
137
+ | 0.0001 | 85.0 | 85 | 0.9603 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
138
+ | 0.0001 | 86.0 | 86 | 0.9587 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
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+ | 0.0001 | 87.0 | 87 | 0.9575 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
140
+ | 0.0001 | 88.0 | 88 | 0.9559 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
141
+ | 0.0001 | 89.0 | 89 | 0.9542 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
142
+ | 0.0001 | 90.0 | 90 | 0.9520 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
143
+ | 0.0001 | 91.0 | 91 | 0.9504 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
144
+ | 0.0001 | 92.0 | 92 | 0.9490 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
145
+ | 0.0001 | 93.0 | 93 | 0.9471 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
146
+ | 0.0001 | 94.0 | 94 | 0.9458 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
147
+ | 0.0001 | 95.0 | 95 | 0.9440 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
148
+ | 0.0001 | 96.0 | 96 | 0.9426 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
149
+ | 0.0001 | 97.0 | 97 | 0.9412 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
150
+ | 0.0001 | 98.0 | 98 | 0.9403 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
151
+ | 0.0001 | 99.0 | 99 | 0.9395 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
152
+ | 0.0001 | 100.0 | 100 | 0.9388 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
153
+ | 0.0001 | 101.0 | 101 | 0.9377 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
154
+ | 0.0001 | 102.0 | 102 | 0.9367 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
155
+ | 0.0001 | 103.0 | 103 | 0.9361 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
156
+ | 0.0001 | 104.0 | 104 | 0.9351 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
157
+ | 0.0001 | 105.0 | 105 | 0.9341 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
158
+ | 0.0001 | 106.0 | 106 | 0.9334 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
159
+ | 0.0001 | 107.0 | 107 | 0.9327 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
160
+ | 0.0001 | 108.0 | 108 | 0.9321 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
161
+ | 0.0001 | 109.0 | 109 | 0.9315 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
162
+ | 0.0001 | 110.0 | 110 | 0.9304 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
163
+ | 0.0001 | 111.0 | 111 | 0.9306 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
164
+ | 0.0001 | 112.0 | 112 | 0.9295 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
165
+ | 0.0001 | 113.0 | 113 | 0.9288 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
166
+ | 0.0001 | 114.0 | 114 | 0.9283 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
167
+ | 0.0001 | 115.0 | 115 | 0.9278 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
168
+ | 0.0001 | 116.0 | 116 | 0.9269 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
169
+ | 0.0001 | 117.0 | 117 | 0.9264 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
170
+ | 0.0001 | 118.0 | 118 | 0.9256 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
171
+ | 0.0001 | 119.0 | 119 | 0.9252 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
172
+ | 0.0001 | 120.0 | 120 | 0.9244 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
173
+ | 0.0001 | 121.0 | 121 | 0.9242 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
174
+ | 0.0001 | 122.0 | 122 | 0.9233 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
175
+ | 0.0001 | 123.0 | 123 | 0.9226 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
176
+ | 0.0001 | 124.0 | 124 | 0.9222 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
177
+ | 0.0001 | 125.0 | 125 | 0.9210 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
178
+ | 0.0001 | 126.0 | 126 | 0.9203 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
179
+ | 0.0001 | 127.0 | 127 | 0.9192 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
180
+ | 0.0001 | 128.0 | 128 | 0.9184 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
181
+ | 0.0001 | 129.0 | 129 | 0.9185 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
182
+ | 0.0001 | 130.0 | 130 | 0.9180 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
183
+ | 0.0001 | 131.0 | 131 | 0.9178 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
184
+ | 0.0001 | 132.0 | 132 | 0.9173 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
185
+ | 0.0001 | 133.0 | 133 | 0.9170 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
186
+ | 0.0001 | 134.0 | 134 | 0.9164 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
187
+ | 0.0001 | 135.0 | 135 | 0.9161 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
188
+ | 0.0001 | 136.0 | 136 | 0.9157 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
189
+ | 0.0001 | 137.0 | 137 | 0.9154 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
190
+ | 0.0001 | 138.0 | 138 | 0.9159 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
191
+ | 0.0001 | 139.0 | 139 | 0.9156 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
192
+ | 0.0001 | 140.0 | 140 | 0.9156 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
193
+ | 0.0001 | 141.0 | 141 | 0.9154 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
194
+ | 0.0001 | 142.0 | 142 | 0.9150 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
195
+ | 0.0001 | 143.0 | 143 | 0.9148 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
196
+ | 0.0001 | 144.0 | 144 | 0.9148 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
197
+ | 0.0001 | 145.0 | 145 | 0.9149 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
198
+ | 0.0001 | 146.0 | 146 | 0.9144 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
199
+ | 0.0001 | 147.0 | 147 | 0.9153 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
200
+ | 0.0001 | 148.0 | 148 | 0.9148 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
201
+ | 0.0001 | 149.0 | 149 | 0.9148 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
202
+ | 0.0001 | 150.0 | 150 | 0.9143 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | 0.0 | 0.0 | 0.0 | 0.6923 |
203
 
204
 
205
  ### Framework versions
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