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bert-sentiment-classifier

Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
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README.md CHANGED
@@ -1,7 +1,5 @@
1
  ---
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  library_name: transformers
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- license: mit
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- base_model: xlm-roberta-base
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  tags:
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  - generated_from_trainer
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  metrics:
@@ -19,16 +17,16 @@ should probably proofread and complete it, then remove this comment. -->
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  # bert-sentiment-classifier
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22
- This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
23
  It achieves the following results on the evaluation set:
24
- - Loss: 0.0820
25
- - Accuracy: 0.9736
26
- - F1: 0.9736
27
- - Precision: 0.9736
28
- - Recall: 0.9736
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- - F1 Negative: 0.9736
30
- - F1 Neutral: 0.9736
31
- - F1 Positive: 0.0
32
 
33
  ## Model description
34
 
@@ -47,67 +45,165 @@ More information needed
47
  ### Training hyperparameters
48
 
49
  The following hyperparameters were used during training:
50
- - learning_rate: 2e-05
51
- - train_batch_size: 64
52
- - eval_batch_size: 128
53
  - seed: 42
54
  - distributed_type: multi-GPU
55
  - num_devices: 2
56
  - gradient_accumulation_steps: 2
57
- - total_train_batch_size: 256
58
- - total_eval_batch_size: 256
59
  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
60
  - lr_scheduler_type: cosine
61
  - lr_scheduler_warmup_ratio: 0.1
62
- - num_epochs: 3
63
  - mixed_precision_training: Native AMP
64
 
65
  ### Training results
66
 
67
- | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | F1 Negative | F1 Neutral | F1 Positive |
68
- |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-----------:|:----------:|:-----------:|
69
- | 0.1503 | 0.0711 | 1000 | 0.1399 | 0.9526 | 0.9526 | 0.9527 | 0.9526 | 0.9529 | 0.9524 | 0.0 |
70
- | 0.123 | 0.1422 | 2000 | 0.1169 | 0.9613 | 0.9613 | 0.9613 | 0.9613 | 0.9612 | 0.9614 | 0.0 |
71
- | 0.1085 | 0.2133 | 3000 | 0.1103 | 0.9623 | 0.9623 | 0.9625 | 0.9623 | 0.9619 | 0.9627 | 0.0 |
72
- | 0.1071 | 0.2845 | 4000 | 0.0997 | 0.9658 | 0.9658 | 0.9658 | 0.9658 | 0.9656 | 0.9659 | 0.0 |
73
- | 0.1048 | 0.3556 | 5000 | 0.0973 | 0.9669 | 0.9669 | 0.9670 | 0.9669 | 0.9671 | 0.9668 | 0.0 |
74
- | 0.0952 | 0.4267 | 6000 | 0.1002 | 0.9674 | 0.9674 | 0.9674 | 0.9674 | 0.9676 | 0.9671 | 0.0 |
75
- | 0.098 | 0.4978 | 7000 | 0.0952 | 0.9689 | 0.9689 | 0.9689 | 0.9689 | 0.9689 | 0.9689 | 0.0 |
76
- | 0.0967 | 0.5689 | 8000 | 0.0930 | 0.9689 | 0.9689 | 0.9689 | 0.9689 | 0.9689 | 0.9690 | 0.0 |
77
- | 0.0936 | 0.6400 | 9000 | 0.0926 | 0.9693 | 0.9693 | 0.9694 | 0.9693 | 0.9691 | 0.9695 | 0.0 |
78
- | 0.0904 | 0.7111 | 10000 | 0.0946 | 0.9691 | 0.9691 | 0.9693 | 0.9691 | 0.9689 | 0.9694 | 0.0 |
79
- | 0.0943 | 0.7823 | 11000 | 0.0880 | 0.9700 | 0.9700 | 0.9700 | 0.9700 | 0.9698 | 0.9701 | 0.0 |
80
- | 0.0921 | 0.8534 | 12000 | 0.0867 | 0.9703 | 0.9703 | 0.9703 | 0.9703 | 0.9701 | 0.9704 | 0.0 |
81
- | 0.0867 | 0.9245 | 13000 | 0.0878 | 0.9704 | 0.9704 | 0.9704 | 0.9704 | 0.9702 | 0.9706 | 0.0 |
82
- | 0.0863 | 0.9956 | 14000 | 0.0871 | 0.9707 | 0.9707 | 0.9707 | 0.9707 | 0.9706 | 0.9708 | 0.0 |
83
- | 0.0798 | 1.0667 | 15000 | 0.0883 | 0.9709 | 0.9709 | 0.9710 | 0.9709 | 0.9710 | 0.9709 | 0.0 |
84
- | 0.0772 | 1.1378 | 16000 | 0.0871 | 0.9711 | 0.9711 | 0.9712 | 0.9711 | 0.9710 | 0.9712 | 0.0 |
85
- | 0.0759 | 1.2089 | 17000 | 0.0884 | 0.9719 | 0.9719 | 0.9719 | 0.9719 | 0.9719 | 0.9719 | 0.0 |
86
- | 0.0767 | 1.2800 | 18000 | 0.0857 | 0.9717 | 0.9717 | 0.9717 | 0.9717 | 0.9715 | 0.9718 | 0.0 |
87
- | 0.0791 | 1.3512 | 19000 | 0.0870 | 0.9718 | 0.9718 | 0.9718 | 0.9718 | 0.9717 | 0.9719 | 0.0 |
88
- | 0.0766 | 1.4223 | 20000 | 0.0827 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9721 | 0.0 |
89
- | 0.0808 | 1.4934 | 21000 | 0.0829 | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 0.0 |
90
- | 0.0784 | 1.5645 | 22000 | 0.0824 | 0.9726 | 0.9726 | 0.9726 | 0.9726 | 0.9726 | 0.9725 | 0.0 |
91
- | 0.0814 | 1.6356 | 23000 | 0.0811 | 0.9727 | 0.9727 | 0.9727 | 0.9727 | 0.9727 | 0.9727 | 0.0 |
92
- | 0.0789 | 1.7067 | 24000 | 0.0825 | 0.9727 | 0.9727 | 0.9727 | 0.9727 | 0.9727 | 0.9727 | 0.0 |
93
- | 0.0762 | 1.7778 | 25000 | 0.0806 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.0 |
94
- | 0.0766 | 1.8490 | 26000 | 0.0813 | 0.9732 | 0.9732 | 0.9732 | 0.9732 | 0.9731 | 0.9732 | 0.0 |
95
- | 0.0764 | 1.9201 | 27000 | 0.0825 | 0.9728 | 0.9728 | 0.9729 | 0.9728 | 0.9727 | 0.9730 | 0.0 |
96
- | 0.0737 | 1.9912 | 28000 | 0.0818 | 0.9732 | 0.9732 | 0.9732 | 0.9732 | 0.9733 | 0.9730 | 0.0 |
97
- | 0.0644 | 2.0623 | 29000 | 0.0835 | 0.9733 | 0.9733 | 0.9733 | 0.9733 | 0.9732 | 0.9733 | 0.0 |
98
- | 0.0678 | 2.1334 | 30000 | 0.0841 | 0.9732 | 0.9732 | 0.9732 | 0.9732 | 0.9732 | 0.9732 | 0.0 |
99
- | 0.0653 | 2.2045 | 31000 | 0.0842 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.0 |
100
- | 0.0639 | 2.2756 | 32000 | 0.0827 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.9735 | 0.0 |
101
- | 0.0617 | 2.3468 | 33000 | 0.0835 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.9733 | 0.9734 | 0.0 |
102
- | 0.0645 | 2.4179 | 34000 | 0.0824 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.9735 | 0.9734 | 0.0 |
103
- | 0.0623 | 2.4890 | 35000 | 0.0827 | 0.9733 | 0.9733 | 0.9734 | 0.9733 | 0.9733 | 0.9734 | 0.0 |
104
- | 0.0602 | 2.5601 | 36000 | 0.0833 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.0 |
105
- | 0.0625 | 2.6312 | 37000 | 0.0830 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.9733 | 0.9734 | 0.0 |
106
- | 0.0649 | 2.7023 | 38000 | 0.0825 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.9735 | 0.0 |
107
- | 0.0574 | 2.7734 | 39000 | 0.0828 | 0.9735 | 0.9735 | 0.9735 | 0.9735 | 0.9735 | 0.9735 | 0.0 |
108
- | 0.0632 | 2.8445 | 40000 | 0.0821 | 0.9736 | 0.9736 | 0.9736 | 0.9736 | 0.9736 | 0.9736 | 0.0 |
109
- | 0.0643 | 2.9157 | 41000 | 0.0820 | 0.9736 | 0.9736 | 0.9736 | 0.9736 | 0.9736 | 0.9736 | 0.0 |
110
- | 0.0605 | 2.9868 | 42000 | 0.0820 | 0.9736 | 0.9736 | 0.9736 | 0.9736 | 0.9736 | 0.9736 | 0.0 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
1
  ---
2
  library_name: transformers
 
 
3
  tags:
4
  - generated_from_trainer
5
  metrics:
 
17
 
18
  # bert-sentiment-classifier
19
 
20
+ This model was trained from scratch on the None dataset.
21
  It achieves the following results on the evaluation set:
22
+ - Loss: 0.1175
23
+ - Accuracy: 0.9717
24
+ - F1: 0.6478
25
+ - Precision: 0.6478
26
+ - Recall: 0.6478
27
+ - F1 Negative: 0.9717
28
+ - F1 Neutral: 0.0
29
+ - F1 Positive: 0.9718
30
 
31
  ## Model description
32
 
 
45
  ### Training hyperparameters
46
 
47
  The following hyperparameters were used during training:
48
+ - learning_rate: 2.0000000000000003e-06
49
+ - train_batch_size: 128
50
+ - eval_batch_size: 256
51
  - seed: 42
52
  - distributed_type: multi-GPU
53
  - num_devices: 2
54
  - gradient_accumulation_steps: 2
55
+ - total_train_batch_size: 512
56
+ - total_eval_batch_size: 512
57
  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
58
  - lr_scheduler_type: cosine
59
  - lr_scheduler_warmup_ratio: 0.1
60
+ - num_epochs: 10
61
  - mixed_precision_training: Native AMP
62
 
63
  ### Training results
64
 
65
+ | Training Loss | Epoch | Step | Accuracy | F1 | F1 Negative | F1 Neutral | F1 Positive | Validation Loss | Precision | Recall |
66
+ |:-------------:|:------:|:------:|:--------:|:------:|:-----------:|:----------:|:-----------:|:---------------:|:---------:|:------:|
67
+ | 0.1503 | 0.0711 | 1000 | 0.9526 | 0.9526 | 0.9529 | 0.9524 | 0.0 | 0.1399 | 0.9527 | 0.9526 |
68
+ | 0.123 | 0.1422 | 2000 | 0.9613 | 0.9613 | 0.9612 | 0.9614 | 0.0 | 0.1169 | 0.9613 | 0.9613 |
69
+ | 0.1085 | 0.2133 | 3000 | 0.9623 | 0.9623 | 0.9619 | 0.9627 | 0.0 | 0.1103 | 0.9625 | 0.9623 |
70
+ | 0.1071 | 0.2845 | 4000 | 0.9658 | 0.9658 | 0.9656 | 0.9659 | 0.0 | 0.0997 | 0.9658 | 0.9658 |
71
+ | 0.1048 | 0.3556 | 5000 | 0.9669 | 0.9669 | 0.9671 | 0.9668 | 0.0 | 0.0973 | 0.9670 | 0.9669 |
72
+ | 0.0952 | 0.4267 | 6000 | 0.9674 | 0.9674 | 0.9676 | 0.9671 | 0.0 | 0.1002 | 0.9674 | 0.9674 |
73
+ | 0.098 | 0.4978 | 7000 | 0.9689 | 0.9689 | 0.9689 | 0.9689 | 0.0 | 0.0952 | 0.9689 | 0.9689 |
74
+ | 0.0967 | 0.5689 | 8000 | 0.9689 | 0.9689 | 0.9689 | 0.9690 | 0.0 | 0.0930 | 0.9689 | 0.9689 |
75
+ | 0.0936 | 0.6400 | 9000 | 0.9693 | 0.9693 | 0.9691 | 0.9695 | 0.0 | 0.0926 | 0.9694 | 0.9693 |
76
+ | 0.0904 | 0.7111 | 10000 | 0.9691 | 0.9691 | 0.9689 | 0.9694 | 0.0 | 0.0946 | 0.9693 | 0.9691 |
77
+ | 0.0943 | 0.7823 | 11000 | 0.9700 | 0.9700 | 0.9698 | 0.9701 | 0.0 | 0.0880 | 0.9700 | 0.9700 |
78
+ | 0.0921 | 0.8534 | 12000 | 0.9703 | 0.9703 | 0.9701 | 0.9704 | 0.0 | 0.0867 | 0.9703 | 0.9703 |
79
+ | 0.0867 | 0.9245 | 13000 | 0.9704 | 0.9704 | 0.9702 | 0.9706 | 0.0 | 0.0878 | 0.9704 | 0.9704 |
80
+ | 0.0863 | 0.9956 | 14000 | 0.9707 | 0.9707 | 0.9706 | 0.9708 | 0.0 | 0.0871 | 0.9707 | 0.9707 |
81
+ | 0.0798 | 1.0667 | 15000 | 0.9709 | 0.9709 | 0.9710 | 0.9709 | 0.0 | 0.0883 | 0.9710 | 0.9709 |
82
+ | 0.0772 | 1.1378 | 16000 | 0.9711 | 0.9711 | 0.9710 | 0.9712 | 0.0 | 0.0871 | 0.9712 | 0.9711 |
83
+ | 0.0759 | 1.2089 | 17000 | 0.9719 | 0.9719 | 0.9719 | 0.9719 | 0.0 | 0.0884 | 0.9719 | 0.9719 |
84
+ | 0.0767 | 1.2800 | 18000 | 0.9717 | 0.9717 | 0.9715 | 0.9718 | 0.0 | 0.0857 | 0.9717 | 0.9717 |
85
+ | 0.0791 | 1.3512 | 19000 | 0.9718 | 0.9718 | 0.9717 | 0.9719 | 0.0 | 0.0870 | 0.9718 | 0.9718 |
86
+ | 0.0766 | 1.4223 | 20000 | 0.9722 | 0.9722 | 0.9722 | 0.9721 | 0.0 | 0.0827 | 0.9722 | 0.9722 |
87
+ | 0.0808 | 1.4934 | 21000 | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 0.0 | 0.0829 | 0.9725 | 0.9725 |
88
+ | 0.0784 | 1.5645 | 22000 | 0.9726 | 0.9726 | 0.9726 | 0.9725 | 0.0 | 0.0824 | 0.9726 | 0.9726 |
89
+ | 0.0814 | 1.6356 | 23000 | 0.9727 | 0.9727 | 0.9727 | 0.9727 | 0.0 | 0.0811 | 0.9727 | 0.9727 |
90
+ | 0.0789 | 1.7067 | 24000 | 0.9727 | 0.9727 | 0.9727 | 0.9727 | 0.0 | 0.0825 | 0.9727 | 0.9727 |
91
+ | 0.0762 | 1.7778 | 25000 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.0 | 0.0806 | 0.9734 | 0.9734 |
92
+ | 0.0766 | 1.8490 | 26000 | 0.9732 | 0.9732 | 0.9731 | 0.9732 | 0.0 | 0.0813 | 0.9732 | 0.9732 |
93
+ | 0.0764 | 1.9201 | 27000 | 0.9728 | 0.9728 | 0.9727 | 0.9730 | 0.0 | 0.0825 | 0.9729 | 0.9728 |
94
+ | 0.0737 | 1.9912 | 28000 | 0.9732 | 0.9732 | 0.9733 | 0.9730 | 0.0 | 0.0818 | 0.9732 | 0.9732 |
95
+ | 0.0644 | 2.0623 | 29000 | 0.9733 | 0.9733 | 0.9732 | 0.9733 | 0.0 | 0.0835 | 0.9733 | 0.9733 |
96
+ | 0.0678 | 2.1334 | 30000 | 0.9732 | 0.9732 | 0.9732 | 0.9732 | 0.0 | 0.0841 | 0.9732 | 0.9732 |
97
+ | 0.0653 | 2.2045 | 31000 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.0 | 0.0842 | 0.9734 | 0.9734 |
98
+ | 0.0639 | 2.2756 | 32000 | 0.9734 | 0.9734 | 0.9734 | 0.9735 | 0.0 | 0.0827 | 0.9734 | 0.9734 |
99
+ | 0.0617 | 2.3468 | 33000 | 0.9734 | 0.9734 | 0.9733 | 0.9734 | 0.0 | 0.0835 | 0.9734 | 0.9734 |
100
+ | 0.0645 | 2.4179 | 34000 | 0.9734 | 0.9734 | 0.9735 | 0.9734 | 0.0 | 0.0824 | 0.9734 | 0.9734 |
101
+ | 0.0623 | 2.4890 | 35000 | 0.9733 | 0.9733 | 0.9733 | 0.9734 | 0.0 | 0.0827 | 0.9734 | 0.9733 |
102
+ | 0.0602 | 2.5601 | 36000 | 0.9734 | 0.9734 | 0.9734 | 0.9734 | 0.0 | 0.0833 | 0.9734 | 0.9734 |
103
+ | 0.0625 | 2.6312 | 37000 | 0.9734 | 0.9734 | 0.9733 | 0.9734 | 0.0 | 0.0830 | 0.9734 | 0.9734 |
104
+ | 0.0649 | 2.7023 | 38000 | 0.9734 | 0.9734 | 0.9734 | 0.9735 | 0.0 | 0.0825 | 0.9734 | 0.9734 |
105
+ | 0.0574 | 2.7734 | 39000 | 0.9735 | 0.9735 | 0.9735 | 0.9735 | 0.0 | 0.0828 | 0.9735 | 0.9735 |
106
+ | 0.0632 | 2.8445 | 40000 | 0.9736 | 0.9736 | 0.9736 | 0.9736 | 0.0 | 0.0821 | 0.9736 | 0.9736 |
107
+ | 0.0643 | 2.9157 | 41000 | 0.9736 | 0.9736 | 0.9736 | 0.9736 | 0.0 | 0.0820 | 0.9736 | 0.9736 |
108
+ | 0.0605 | 2.9868 | 42000 | 0.9736 | 0.9736 | 0.9736 | 0.9736 | 0.0 | 0.0820 | 0.9736 | 0.9736 |
109
+ | 0.0648 | 3.0579 | 43000 | 0.0896 | 0.9723 | 0.9723 | 0.9723 | 0.9723 | 0.9724 | 0.9722 | 0.0 |
110
+ | 0.0748 | 3.1290 | 44000 | 0.0896 | 0.9716 | 0.9716 | 0.9717 | 0.9716 | 0.9714 | 0.9718 | 0.0 |
111
+ | 0.067 | 3.2001 | 45000 | 0.0862 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9723 | 0.9721 | 0.0 |
112
+ | 0.0708 | 3.2712 | 46000 | 0.0861 | 0.9726 | 0.9726 | 0.9726 | 0.9726 | 0.9726 | 0.9725 | 0.0 |
113
+ | 0.0678 | 3.3423 | 47000 | 0.0857 | 0.9724 | 0.9724 | 0.9724 | 0.9724 | 0.9723 | 0.9724 | 0.0 |
114
+ | 0.071 | 3.4135 | 48000 | 0.0877 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9721 | 0.0 |
115
+ | 0.0717 | 3.4846 | 49000 | 0.0846 | 0.9719 | 0.9719 | 0.9720 | 0.9719 | 0.9718 | 0.9721 | 0.0 |
116
+ | 0.0731 | 3.5557 | 50000 | 0.0836 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.0 |
117
+ | 0.073 | 3.6268 | 51000 | 0.0825 | 0.9726 | 0.9726 | 0.9726 | 0.9726 | 0.9727 | 0.9725 | 0.0 |
118
+ | 0.0726 | 3.6979 | 52000 | 0.0823 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9727 | 0.9728 | 0.0 |
119
+ | 0.0686 | 3.7690 | 53000 | 0.0826 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9729 | 0.0 |
120
+ | 0.068 | 3.8401 | 54000 | 0.0824 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.0 |
121
+ | 0.0713 | 3.9113 | 55000 | 0.0835 | 0.9728 | 0.9728 | 0.9729 | 0.9728 | 0.9729 | 0.9728 | 0.0 |
122
+ | 0.0706 | 3.9824 | 56000 | 0.0827 | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 0.9724 | 0.9726 | 0.0 |
123
+ | 0.0601 | 4.0535 | 57000 | 0.0844 | 0.9726 | 0.9726 | 0.9726 | 0.9726 | 0.9726 | 0.9725 | 0.0 |
124
+ | 0.0589 | 4.1246 | 58000 | 0.0879 | 0.9724 | 0.9724 | 0.9724 | 0.9724 | 0.9724 | 0.9724 | 0.0 |
125
+ | 0.0618 | 4.1957 | 59000 | 0.0868 | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 0.0 |
126
+ | 0.0609 | 4.2668 | 60000 | 0.0876 | 0.9724 | 0.9724 | 0.9725 | 0.9724 | 0.9725 | 0.9723 | 0.0 |
127
+ | 0.0605 | 4.3379 | 61000 | 0.0934 | 0.9716 | 0.9716 | 0.9717 | 0.9716 | 0.9714 | 0.9718 | 0.0 |
128
+ | 0.0588 | 4.4090 | 62000 | 0.0929 | 0.9728 | 0.9728 | 0.9728 | 0.9728 | 0.9727 | 0.9728 | 0.0 |
129
+ | 0.0613 | 4.4802 | 63000 | 0.0875 | 0.9723 | 0.9723 | 0.9723 | 0.9723 | 0.9722 | 0.9724 | 0.0 |
130
+ | 0.0591 | 4.5513 | 64000 | 0.0888 | 0.9726 | 0.9726 | 0.9726 | 0.9726 | 0.9726 | 0.9726 | 0.0 |
131
+ | 0.0618 | 4.6224 | 65000 | 0.0856 | 0.9724 | 0.9724 | 0.9725 | 0.9724 | 0.9725 | 0.9724 | 0.0 |
132
+ | 0.0564 | 4.6935 | 66000 | 0.0884 | 0.9727 | 0.9727 | 0.9727 | 0.9727 | 0.9727 | 0.9727 | 0.0 |
133
+ | 0.0629 | 4.7646 | 67000 | 0.0854 | 0.9729 | 0.9729 | 0.9729 | 0.9729 | 0.9729 | 0.9729 | 0.0 |
134
+ | 0.0601 | 4.8357 | 68000 | 0.0881 | 0.9729 | 0.9729 | 0.9729 | 0.9729 | 0.9729 | 0.9729 | 0.0 |
135
+ | 0.0619 | 4.9068 | 69000 | 0.0857 | 0.9726 | 0.9726 | 0.9726 | 0.9726 | 0.9726 | 0.9726 | 0.0 |
136
+ | 0.0591 | 4.9780 | 70000 | 0.0857 | 0.9726 | 0.9726 | 0.9726 | 0.9726 | 0.9725 | 0.9727 | 0.0 |
137
+ | 0.0499 | 5.0491 | 71000 | 0.0895 | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 0.9726 | 0.0 |
138
+ | 0.0526 | 5.1202 | 72000 | 0.0912 | 0.9724 | 0.9724 | 0.9724 | 0.9724 | 0.9723 | 0.9724 | 0.0 |
139
+ | 0.0543 | 5.1913 | 73000 | 0.0943 | 0.9727 | 0.9727 | 0.9727 | 0.9727 | 0.9726 | 0.9727 | 0.0 |
140
+ | 0.0526 | 5.2624 | 74000 | 0.0920 | 0.9723 | 0.9723 | 0.9723 | 0.9723 | 0.9723 | 0.9724 | 0.0 |
141
+ | 0.0576 | 5.3335 | 75000 | 0.0901 | 0.9721 | 0.9721 | 0.9721 | 0.9721 | 0.9720 | 0.9722 | 0.0 |
142
+ | 0.0518 | 5.4046 | 76000 | 0.0951 | 0.9718 | 0.9718 | 0.9718 | 0.9718 | 0.9717 | 0.9719 | 0.0 |
143
+ | 0.0533 | 5.4758 | 77000 | 0.0898 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.0 |
144
+ | 0.0485 | 5.5469 | 78000 | 0.0941 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9721 | 0.9722 | 0.0 |
145
+ | 0.052 | 5.6180 | 79000 | 0.0909 | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 0.9724 | 0.9725 | 0.0 |
146
+ | 0.0505 | 5.6891 | 80000 | 0.0957 | 0.9721 | 0.9721 | 0.9722 | 0.9721 | 0.9720 | 0.9722 | 0.0 |
147
+ | 0.0525 | 5.7602 | 81000 | 0.0934 | 0.9723 | 0.9723 | 0.9723 | 0.9723 | 0.9723 | 0.9723 | 0.0 |
148
+ | 0.0491 | 5.8313 | 82000 | 0.0921 | 0.9724 | 0.9724 | 0.9724 | 0.9724 | 0.9723 | 0.9724 | 0.0 |
149
+ | 0.0538 | 5.9024 | 83000 | 0.0920 | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 0.9724 | 0.9725 | 0.0 |
150
+ | 0.0516 | 5.9735 | 84000 | 0.0917 | 0.9725 | 0.9725 | 0.9725 | 0.9725 | 0.9724 | 0.9725 | 0.0 |
151
+ | 0.041 | 6.0447 | 85000 | 0.1008 | 0.9721 | 0.9721 | 0.9721 | 0.9721 | 0.9719 | 0.9722 | 0.0 |
152
+ | 0.0424 | 6.1158 | 86000 | 0.1065 | 0.9719 | 0.9719 | 0.9719 | 0.9719 | 0.9718 | 0.9720 | 0.0 |
153
+ | 0.0442 | 6.1869 | 87000 | 0.0982 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9723 | 0.0 |
154
+ | 0.0435 | 6.2580 | 88000 | 0.1031 | 0.9721 | 0.9721 | 0.9721 | 0.9721 | 0.9720 | 0.9722 | 0.0 |
155
+ | 0.0421 | 6.3291 | 89000 | 0.1003 | 0.9719 | 0.9719 | 0.9719 | 0.9719 | 0.9719 | 0.9719 | 0.0 |
156
+ | 0.0408 | 6.4002 | 90000 | 0.1028 | 0.9720 | 0.9720 | 0.9720 | 0.9720 | 0.9719 | 0.9720 | 0.0 |
157
+ | 0.0441 | 6.4713 | 91000 | 0.0981 | 0.9718 | 0.9718 | 0.9718 | 0.9718 | 0.9717 | 0.9719 | 0.0 |
158
+ | 0.0447 | 6.5425 | 92000 | 0.0952 | 0.9723 | 0.9723 | 0.9723 | 0.9723 | 0.9723 | 0.9723 | 0.0 |
159
+ | 0.0461 | 6.6136 | 93000 | 0.0949 | 0.9720 | 0.9720 | 0.9720 | 0.9720 | 0.9720 | 0.9720 | 0.0 |
160
+ | 0.0439 | 6.6847 | 94000 | 0.0988 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9723 | 0.0 |
161
+ | 0.0443 | 6.7558 | 95000 | 0.0988 | 0.9720 | 0.9720 | 0.9720 | 0.9720 | 0.9719 | 0.9720 | 0.0 |
162
+ | 0.0395 | 6.8269 | 96000 | 0.1013 | 0.9723 | 0.9723 | 0.9723 | 0.9723 | 0.9724 | 0.9722 | 0.0 |
163
+ | 0.0414 | 6.8980 | 97000 | 0.1010 | 0.9724 | 0.9724 | 0.9724 | 0.9724 | 0.9724 | 0.9724 | 0.0 |
164
+ | 0.0469 | 6.9691 | 98000 | 0.0998 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9723 | 0.0 |
165
+ | 0.0329 | 7.0403 | 99000 | 0.1126 | 0.9721 | 0.9721 | 0.9721 | 0.9721 | 0.9721 | 0.9721 | 0.0 |
166
+ | 0.038 | 7.1114 | 100000 | 0.1076 | 0.9714 | 0.9714 | 0.9714 | 0.9714 | 0.9713 | 0.9715 | 0.0 |
167
+ | 0.0374 | 7.1825 | 101000 | 0.1045 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.0 |
168
+ | 0.0377 | 7.2536 | 102000 | 0.1080 | 0.9718 | 0.6479 | 0.6479 | 0.6479 | 0.9717 | 0.0 | 0.9719 |
169
+ | 0.0393 | 7.3247 | 103000 | 0.1036 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.0 |
170
+ | 0.0393 | 7.3958 | 104000 | 0.1045 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.0 |
171
+ | 0.0382 | 7.4669 | 105000 | 0.1081 | 0.9718 | 0.9718 | 0.9718 | 0.9718 | 0.9717 | 0.9719 | 0.0 |
172
+ | 0.0374 | 7.5380 | 106000 | 0.1010 | 0.9719 | 0.9719 | 0.9719 | 0.9719 | 0.9719 | 0.9719 | 0.0 |
173
+ | 0.0355 | 7.6092 | 107000 | 0.1092 | 0.9717 | 0.9717 | 0.9718 | 0.9717 | 0.9716 | 0.9719 | 0.0 |
174
+ | 0.035 | 7.6803 | 108000 | 0.1101 | 0.9721 | 0.9721 | 0.9721 | 0.9721 | 0.9720 | 0.9721 | 0.0 |
175
+ | 0.0344 | 7.7514 | 109000 | 0.1089 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9721 | 0.9722 | 0.0 |
176
+ | 0.0348 | 7.8225 | 110000 | 0.1095 | 0.9721 | 0.9721 | 0.9721 | 0.9721 | 0.9721 | 0.9721 | 0.0 |
177
+ | 0.0387 | 7.8936 | 111000 | 0.1062 | 0.9717 | 0.9717 | 0.9717 | 0.9717 | 0.9716 | 0.9718 | 0.0 |
178
+ | 0.0352 | 7.9647 | 112000 | 0.1076 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | 0.0 |
179
+ | 0.0331 | 8.0358 | 113000 | 0.1149 | 0.9719 | 0.9719 | 0.9719 | 0.9719 | 0.9718 | 0.9719 | 0.0 |
180
+ | 0.0362 | 8.1070 | 114000 | 0.1122 | 0.9718 | 0.9718 | 0.9718 | 0.9718 | 0.9718 | 0.9718 | 0.0 |
181
+ | 0.0357 | 8.1781 | 115000 | 0.1107 | 0.9714 | 0.9714 | 0.9714 | 0.9714 | 0.9713 | 0.9715 | 0.0 |
182
+ | 0.0328 | 8.2492 | 116000 | 0.1136 | 0.9719 | 0.9719 | 0.9719 | 0.9719 | 0.9719 | 0.9719 | 0.0 |
183
+ | 0.0327 | 8.3203 | 117000 | 0.1178 | 0.9717 | 0.9717 | 0.9717 | 0.9717 | 0.9717 | 0.9718 | 0.0 |
184
+ | 0.0315 | 8.3914 | 118000 | 0.1154 | 0.9715 | 0.6477 | 0.6477 | 0.6477 | 0.9714 | 0.0 | 0.9716 |
185
+ | 0.031 | 8.4625 | 119000 | 0.1137 | 0.9717 | 0.9717 | 0.9717 | 0.9717 | 0.9717 | 0.9718 | 0.0 |
186
+ | 0.0314 | 8.5336 | 120000 | 0.1128 | 0.9718 | 0.9718 | 0.9718 | 0.9718 | 0.9718 | 0.9718 | 0.0 |
187
+ | 0.0352 | 8.6048 | 121000 | 0.1111 | 0.9717 | 0.9717 | 0.9717 | 0.9717 | 0.9716 | 0.9717 | 0.0 |
188
+ | 0.0356 | 8.6759 | 122000 | 0.1112 | 0.9718 | 0.9718 | 0.9718 | 0.9718 | 0.9718 | 0.9719 | 0.0 |
189
+ | 0.0345 | 8.7470 | 123000 | 0.1135 | 0.9718 | 0.9718 | 0.9718 | 0.9718 | 0.9718 | 0.9719 | 0.0 |
190
+ | 0.0351 | 8.8181 | 124000 | 0.1144 | 0.9718 | 0.9718 | 0.9718 | 0.9718 | 0.9717 | 0.9718 | 0.0 |
191
+ | 0.0311 | 8.8892 | 125000 | 0.1149 | 0.9719 | 0.9719 | 0.9719 | 0.9719 | 0.9719 | 0.9720 | 0.0 |
192
+ | 0.0353 | 8.9603 | 126000 | 0.1118 | 0.9718 | 0.9718 | 0.9718 | 0.9718 | 0.9718 | 0.9718 | 0.0 |
193
+ | 0.0331 | 9.0314 | 127000 | 0.1162 | 0.9717 | 0.9717 | 0.9717 | 0.9717 | 0.9717 | 0.9718 | 0.0 |
194
+ | 0.0282 | 9.1025 | 128000 | 0.1181 | 0.9716 | 0.6478 | 0.6478 | 0.6478 | 0.9716 | 0.0 | 0.9717 |
195
+ | 0.0307 | 9.1737 | 129000 | 0.1176 | 0.9717 | 0.6478 | 0.6478 | 0.6478 | 0.9716 | 0.0 | 0.9717 |
196
+ | 0.0297 | 9.2448 | 130000 | 0.1192 | 0.9716 | 0.6477 | 0.6478 | 0.6477 | 0.9716 | 0.0 | 0.9717 |
197
+ | 0.0334 | 9.3159 | 131000 | 0.1174 | 0.9717 | 0.6478 | 0.6478 | 0.6478 | 0.9716 | 0.0 | 0.9717 |
198
+ | 0.0295 | 9.3870 | 132000 | 0.1178 | 0.9717 | 0.6478 | 0.6478 | 0.6478 | 0.9717 | 0.0 | 0.9718 |
199
+ | 0.0321 | 9.4581 | 133000 | 0.1168 | 0.9717 | 0.6478 | 0.6478 | 0.6478 | 0.9717 | 0.0 | 0.9718 |
200
+ | 0.0323 | 9.5292 | 134000 | 0.1169 | 0.9717 | 0.6478 | 0.6478 | 0.6478 | 0.9716 | 0.0 | 0.9717 |
201
+ | 0.0295 | 9.6003 | 135000 | 0.1174 | 0.9717 | 0.6478 | 0.6478 | 0.6478 | 0.9716 | 0.0 | 0.9718 |
202
+ | 0.0327 | 9.6715 | 136000 | 0.1179 | 0.9717 | 0.6478 | 0.6478 | 0.6478 | 0.9717 | 0.0 | 0.9718 |
203
+ | 0.0323 | 9.7426 | 137000 | 0.1176 | 0.9717 | 0.6478 | 0.6478 | 0.6478 | 0.9717 | 0.0 | 0.9718 |
204
+ | 0.0275 | 9.8137 | 138000 | 0.1177 | 0.9717 | 0.6478 | 0.6478 | 0.6478 | 0.9717 | 0.0 | 0.9718 |
205
+ | 0.0339 | 9.8848 | 139000 | 0.1176 | 0.9717 | 0.6478 | 0.6478 | 0.6478 | 0.9717 | 0.0 | 0.9718 |
206
+ | 0.0325 | 9.9559 | 140000 | 0.1175 | 0.9717 | 0.6478 | 0.6478 | 0.6478 | 0.9717 | 0.0 | 0.9718 |
207
 
208
 
209
  ### Framework versions
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