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roberta-large-ner-qlorafinetune-runs-colab

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README.md CHANGED
@@ -5,7 +5,7 @@ base_model: FacebookAI/xlm-roberta-large
5
  tags:
6
  - generated_from_trainer
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  datasets:
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- - conll2002
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  metrics:
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  - precision
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  - recall
@@ -21,13 +21,13 @@ should probably proofread and complete it, then remove this comment. -->
21
 
22
  # roberta-large-ner-qlorafinetune-runs-colab
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24
- This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the conll2002 dataset.
25
  It achieves the following results on the evaluation set:
26
- - Loss: 0.0861
27
- - Precision: 0.8792
28
- - Recall: 0.8860
29
- - F1: 0.8826
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- - Accuracy: 0.9817
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32
  ## Model description
33
 
@@ -50,106 +50,105 @@ The following hyperparameters were used during training:
50
  - train_batch_size: 32
51
  - eval_batch_size: 32
52
  - seed: 42
53
- - optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
54
  - lr_scheduler_type: linear
55
  - training_steps: 1820
56
- - mixed_precision_training: Native AMP
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58
  ### Training results
59
 
60
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
61
  |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
62
- | 1.1639 | 0.0766 | 20 | 0.4142 | 0.1415 | 0.0533 | 0.0774 | 0.8637 |
63
- | 0.2638 | 0.1533 | 40 | 0.2052 | 0.5873 | 0.6105 | 0.5987 | 0.9471 |
64
- | 0.1538 | 0.2299 | 60 | 0.1406 | 0.7147 | 0.7229 | 0.7188 | 0.9620 |
65
- | 0.1021 | 0.3065 | 80 | 0.1212 | 0.7429 | 0.7847 | 0.7632 | 0.9682 |
66
- | 0.1142 | 0.3831 | 100 | 0.1146 | 0.7883 | 0.7930 | 0.7906 | 0.9714 |
67
- | 0.0836 | 0.4598 | 120 | 0.1066 | 0.8086 | 0.8263 | 0.8174 | 0.9741 |
68
- | 0.0816 | 0.5364 | 140 | 0.0810 | 0.8441 | 0.8463 | 0.8452 | 0.9786 |
69
- | 0.0867 | 0.6130 | 160 | 0.0784 | 0.8462 | 0.8557 | 0.8509 | 0.9786 |
70
- | 0.0697 | 0.6897 | 180 | 0.0853 | 0.8248 | 0.8339 | 0.8293 | 0.9765 |
71
- | 0.0719 | 0.7663 | 200 | 0.0755 | 0.8433 | 0.8555 | 0.8493 | 0.9795 |
72
- | 0.0584 | 0.8429 | 220 | 0.0718 | 0.8492 | 0.8693 | 0.8591 | 0.9805 |
73
- | 0.0553 | 0.9195 | 240 | 0.0792 | 0.8399 | 0.8580 | 0.8488 | 0.9775 |
74
- | 0.049 | 0.9962 | 260 | 0.0748 | 0.8462 | 0.8725 | 0.8591 | 0.9798 |
75
- | 0.0458 | 1.0728 | 280 | 0.0820 | 0.8180 | 0.8532 | 0.8352 | 0.9777 |
76
- | 0.049 | 1.1494 | 300 | 0.0818 | 0.8490 | 0.8672 | 0.8580 | 0.9784 |
77
- | 0.0521 | 1.2261 | 320 | 0.0757 | 0.8626 | 0.8727 | 0.8676 | 0.9795 |
78
- | 0.0492 | 1.3027 | 340 | 0.0828 | 0.8284 | 0.8552 | 0.8416 | 0.9776 |
79
- | 0.0517 | 1.3793 | 360 | 0.0808 | 0.8409 | 0.8523 | 0.8465 | 0.9780 |
80
- | 0.0464 | 1.4559 | 380 | 0.0852 | 0.8385 | 0.8509 | 0.8447 | 0.9772 |
81
- | 0.0487 | 1.5326 | 400 | 0.0761 | 0.8573 | 0.8656 | 0.8614 | 0.9782 |
82
- | 0.0517 | 1.6092 | 420 | 0.0714 | 0.8403 | 0.8624 | 0.8512 | 0.9796 |
83
- | 0.0587 | 1.6858 | 440 | 0.0700 | 0.8398 | 0.8603 | 0.8499 | 0.9806 |
84
- | 0.0541 | 1.7625 | 460 | 0.0665 | 0.8679 | 0.8844 | 0.8761 | 0.9814 |
85
- | 0.0459 | 1.8391 | 480 | 0.0677 | 0.8653 | 0.8736 | 0.8694 | 0.9807 |
86
- | 0.0494 | 1.9157 | 500 | 0.0633 | 0.8707 | 0.8819 | 0.8763 | 0.9817 |
87
- | 0.0412 | 1.9923 | 520 | 0.0717 | 0.8552 | 0.8660 | 0.8606 | 0.9801 |
88
- | 0.048 | 2.0690 | 540 | 0.0735 | 0.8642 | 0.8757 | 0.8699 | 0.9797 |
89
- | 0.0343 | 2.1456 | 560 | 0.0721 | 0.8574 | 0.8690 | 0.8632 | 0.9799 |
90
- | 0.0276 | 2.2222 | 580 | 0.0784 | 0.8438 | 0.8612 | 0.8524 | 0.9800 |
91
- | 0.0376 | 2.2989 | 600 | 0.0758 | 0.8459 | 0.8631 | 0.8544 | 0.9787 |
92
- | 0.0334 | 2.3755 | 620 | 0.0736 | 0.8474 | 0.8578 | 0.8526 | 0.9791 |
93
- | 0.0342 | 2.4521 | 640 | 0.0681 | 0.8523 | 0.8789 | 0.8654 | 0.9813 |
94
- | 0.0325 | 2.5287 | 660 | 0.0708 | 0.8771 | 0.8837 | 0.8804 | 0.9814 |
95
- | 0.0319 | 2.6054 | 680 | 0.0822 | 0.8703 | 0.8729 | 0.8716 | 0.9793 |
96
- | 0.0376 | 2.6820 | 700 | 0.0733 | 0.8622 | 0.8727 | 0.8674 | 0.9793 |
97
- | 0.034 | 2.7586 | 720 | 0.0686 | 0.8808 | 0.8867 | 0.8838 | 0.9825 |
98
- | 0.0342 | 2.8352 | 740 | 0.0693 | 0.8654 | 0.8819 | 0.8736 | 0.9813 |
99
- | 0.0334 | 2.9119 | 760 | 0.0718 | 0.8686 | 0.8867 | 0.8775 | 0.9813 |
100
- | 0.0366 | 2.9885 | 780 | 0.0661 | 0.8775 | 0.8888 | 0.8831 | 0.9823 |
101
- | 0.0433 | 3.0651 | 800 | 0.0785 | 0.8434 | 0.8787 | 0.8607 | 0.9805 |
102
- | 0.0257 | 3.1418 | 820 | 0.0773 | 0.8705 | 0.8759 | 0.8732 | 0.9805 |
103
- | 0.0264 | 3.2184 | 840 | 0.0791 | 0.8736 | 0.8812 | 0.8774 | 0.9801 |
104
- | 0.0256 | 3.2950 | 860 | 0.0756 | 0.8591 | 0.8716 | 0.8653 | 0.9801 |
105
- | 0.0229 | 3.3716 | 880 | 0.0751 | 0.8656 | 0.8748 | 0.8702 | 0.9801 |
106
- | 0.0276 | 3.4483 | 900 | 0.0808 | 0.8440 | 0.8665 | 0.8551 | 0.9775 |
107
- | 0.0374 | 3.5249 | 920 | 0.0685 | 0.8824 | 0.8899 | 0.8862 | 0.9820 |
108
- | 0.0243 | 3.6015 | 940 | 0.0738 | 0.8776 | 0.8863 | 0.8819 | 0.9817 |
109
- | 0.027 | 3.6782 | 960 | 0.0741 | 0.8761 | 0.8835 | 0.8798 | 0.9813 |
110
- | 0.0225 | 3.7548 | 980 | 0.0674 | 0.8895 | 0.9012 | 0.8953 | 0.9830 |
111
- | 0.0285 | 3.8314 | 1000 | 0.0702 | 0.8801 | 0.8886 | 0.8843 | 0.9827 |
112
- | 0.0251 | 3.9080 | 1020 | 0.0745 | 0.8767 | 0.8842 | 0.8804 | 0.9810 |
113
- | 0.0297 | 3.9847 | 1040 | 0.0713 | 0.8782 | 0.8867 | 0.8825 | 0.9820 |
114
- | 0.0177 | 4.0613 | 1060 | 0.0805 | 0.8784 | 0.8851 | 0.8818 | 0.9814 |
115
- | 0.0211 | 4.1379 | 1080 | 0.0842 | 0.8802 | 0.8812 | 0.8807 | 0.9812 |
116
- | 0.0182 | 4.2146 | 1100 | 0.0771 | 0.8747 | 0.8874 | 0.8810 | 0.9814 |
117
- | 0.0187 | 4.2912 | 1120 | 0.0844 | 0.8737 | 0.8851 | 0.8794 | 0.9813 |
118
- | 0.017 | 4.3678 | 1140 | 0.0779 | 0.8834 | 0.8929 | 0.8881 | 0.9822 |
119
- | 0.0203 | 4.4444 | 1160 | 0.0806 | 0.8798 | 0.8913 | 0.8855 | 0.9822 |
120
- | 0.0176 | 4.5211 | 1180 | 0.0839 | 0.8740 | 0.8865 | 0.8802 | 0.9804 |
121
- | 0.0168 | 4.5977 | 1200 | 0.0862 | 0.8759 | 0.8835 | 0.8797 | 0.9809 |
122
- | 0.0199 | 4.6743 | 1220 | 0.0795 | 0.8747 | 0.8869 | 0.8808 | 0.9819 |
123
- | 0.0238 | 4.7510 | 1240 | 0.0785 | 0.8680 | 0.8782 | 0.8731 | 0.9807 |
124
- | 0.0204 | 4.8276 | 1260 | 0.0775 | 0.8638 | 0.8741 | 0.8689 | 0.9800 |
125
- | 0.0205 | 4.9042 | 1280 | 0.0770 | 0.8699 | 0.8805 | 0.8752 | 0.9813 |
126
- | 0.0161 | 4.9808 | 1300 | 0.0764 | 0.8744 | 0.8817 | 0.8780 | 0.9813 |
127
- | 0.0145 | 5.0575 | 1320 | 0.0794 | 0.8755 | 0.8874 | 0.8814 | 0.9817 |
128
- | 0.0124 | 5.1341 | 1340 | 0.0790 | 0.8851 | 0.8886 | 0.8868 | 0.9823 |
129
- | 0.016 | 5.2107 | 1360 | 0.0818 | 0.8823 | 0.8890 | 0.8857 | 0.9815 |
130
- | 0.0151 | 5.2874 | 1380 | 0.0806 | 0.8844 | 0.8879 | 0.8861 | 0.9823 |
131
- | 0.0112 | 5.3640 | 1400 | 0.0833 | 0.8784 | 0.8844 | 0.8814 | 0.9813 |
132
- | 0.0141 | 5.4406 | 1420 | 0.0841 | 0.8805 | 0.8840 | 0.8822 | 0.9810 |
133
- | 0.0137 | 5.5172 | 1440 | 0.0851 | 0.8850 | 0.8879 | 0.8864 | 0.9822 |
134
- | 0.0167 | 5.5939 | 1460 | 0.0830 | 0.8807 | 0.8853 | 0.8830 | 0.9816 |
135
- | 0.0154 | 5.6705 | 1480 | 0.0808 | 0.8721 | 0.8819 | 0.8770 | 0.9810 |
136
- | 0.0114 | 5.7471 | 1500 | 0.0816 | 0.8816 | 0.8897 | 0.8856 | 0.9820 |
137
- | 0.0123 | 5.8238 | 1520 | 0.0832 | 0.8769 | 0.8835 | 0.8802 | 0.9814 |
138
- | 0.0151 | 5.9004 | 1540 | 0.0863 | 0.8735 | 0.8778 | 0.8756 | 0.9807 |
139
- | 0.0113 | 5.9770 | 1560 | 0.0874 | 0.8719 | 0.8805 | 0.8762 | 0.9807 |
140
- | 0.0076 | 6.0536 | 1580 | 0.0896 | 0.8763 | 0.8826 | 0.8795 | 0.9814 |
141
- | 0.0098 | 6.1303 | 1600 | 0.0880 | 0.8838 | 0.8876 | 0.8857 | 0.9819 |
142
- | 0.0104 | 6.2069 | 1620 | 0.0887 | 0.8760 | 0.8812 | 0.8786 | 0.9807 |
143
- | 0.0108 | 6.2835 | 1640 | 0.0854 | 0.8773 | 0.8837 | 0.8805 | 0.9815 |
144
- | 0.0116 | 6.3602 | 1660 | 0.0830 | 0.8837 | 0.8888 | 0.8862 | 0.9820 |
145
- | 0.0105 | 6.4368 | 1680 | 0.0838 | 0.8827 | 0.8890 | 0.8859 | 0.9820 |
146
- | 0.0092 | 6.5134 | 1700 | 0.0847 | 0.8794 | 0.8879 | 0.8836 | 0.9817 |
147
- | 0.0105 | 6.5900 | 1720 | 0.0842 | 0.8778 | 0.8865 | 0.8821 | 0.9819 |
148
- | 0.0114 | 6.6667 | 1740 | 0.0847 | 0.8802 | 0.8863 | 0.8832 | 0.9818 |
149
- | 0.0089 | 6.7433 | 1760 | 0.0851 | 0.8794 | 0.8865 | 0.8829 | 0.9819 |
150
- | 0.0104 | 6.8199 | 1780 | 0.0861 | 0.8797 | 0.8856 | 0.8826 | 0.9816 |
151
- | 0.0099 | 6.8966 | 1800 | 0.0863 | 0.8804 | 0.8860 | 0.8832 | 0.9816 |
152
- | 0.0078 | 6.9732 | 1820 | 0.0861 | 0.8792 | 0.8860 | 0.8826 | 0.9817 |
153
 
154
 
155
  ### Framework versions
 
5
  tags:
6
  - generated_from_trainer
7
  datasets:
8
+ - biobert_json
9
  metrics:
10
  - precision
11
  - recall
 
21
 
22
  # roberta-large-ner-qlorafinetune-runs-colab
23
 
24
+ This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the biobert_json dataset.
25
  It achieves the following results on the evaluation set:
26
+ - Loss: 0.0732
27
+ - Precision: 0.9365
28
+ - Recall: 0.9562
29
+ - F1: 0.9462
30
+ - Accuracy: 0.9815
31
 
32
  ## Model description
33
 
 
50
  - train_batch_size: 32
51
  - eval_batch_size: 32
52
  - seed: 42
53
+ - optimizer: Use adamw_hf with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
54
  - lr_scheduler_type: linear
55
  - training_steps: 1820
 
56
 
57
  ### Training results
58
 
59
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
60
  |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
61
+ | 1.9454 | 0.0654 | 20 | 0.9974 | 0.3110 | 0.0618 | 0.1031 | 0.7469 |
62
+ | 0.7603 | 0.1307 | 40 | 0.4834 | 0.5661 | 0.7392 | 0.6412 | 0.8648 |
63
+ | 0.4087 | 0.1961 | 60 | 0.2348 | 0.8311 | 0.8242 | 0.8276 | 0.9379 |
64
+ | 0.2905 | 0.2614 | 80 | 0.1960 | 0.8021 | 0.8687 | 0.8341 | 0.9440 |
65
+ | 0.2262 | 0.3268 | 100 | 0.1468 | 0.8719 | 0.9001 | 0.8857 | 0.9597 |
66
+ | 0.2196 | 0.3922 | 120 | 0.1415 | 0.8444 | 0.9139 | 0.8778 | 0.9570 |
67
+ | 0.177 | 0.4575 | 140 | 0.1139 | 0.8889 | 0.9275 | 0.9077 | 0.9671 |
68
+ | 0.1525 | 0.5229 | 160 | 0.1190 | 0.8847 | 0.9352 | 0.9093 | 0.9665 |
69
+ | 0.1516 | 0.5882 | 180 | 0.1099 | 0.8887 | 0.9431 | 0.9151 | 0.9684 |
70
+ | 0.1281 | 0.6536 | 200 | 0.0891 | 0.9181 | 0.9417 | 0.9297 | 0.9745 |
71
+ | 0.1231 | 0.7190 | 220 | 0.0926 | 0.9200 | 0.9301 | 0.9250 | 0.9733 |
72
+ | 0.1239 | 0.7843 | 240 | 0.0956 | 0.9089 | 0.9509 | 0.9295 | 0.9733 |
73
+ | 0.1118 | 0.8497 | 260 | 0.0885 | 0.9135 | 0.9428 | 0.9279 | 0.9744 |
74
+ | 0.1119 | 0.9150 | 280 | 0.1002 | 0.9024 | 0.9430 | 0.9223 | 0.9711 |
75
+ | 0.1254 | 0.9804 | 300 | 0.0839 | 0.9209 | 0.9421 | 0.9314 | 0.9756 |
76
+ | 0.1053 | 1.0458 | 320 | 0.0827 | 0.9216 | 0.9458 | 0.9335 | 0.9761 |
77
+ | 0.0905 | 1.1111 | 340 | 0.1008 | 0.9057 | 0.9530 | 0.9287 | 0.9711 |
78
+ | 0.0955 | 1.1765 | 360 | 0.0784 | 0.9240 | 0.9477 | 0.9357 | 0.9771 |
79
+ | 0.0938 | 1.2418 | 380 | 0.0844 | 0.9288 | 0.9396 | 0.9341 | 0.9766 |
80
+ | 0.1074 | 1.3072 | 400 | 0.0818 | 0.9249 | 0.9425 | 0.9337 | 0.9771 |
81
+ | 0.1064 | 1.3725 | 420 | 0.0980 | 0.8976 | 0.9351 | 0.9160 | 0.9695 |
82
+ | 0.0913 | 1.4379 | 440 | 0.0815 | 0.9247 | 0.9366 | 0.9306 | 0.9768 |
83
+ | 0.089 | 1.5033 | 460 | 0.0789 | 0.9228 | 0.9463 | 0.9344 | 0.9757 |
84
+ | 0.1175 | 1.5686 | 480 | 0.0873 | 0.9210 | 0.9315 | 0.9262 | 0.9729 |
85
+ | 0.0906 | 1.6340 | 500 | 0.0926 | 0.9121 | 0.9423 | 0.9269 | 0.9736 |
86
+ | 0.0814 | 1.6993 | 520 | 0.0873 | 0.9153 | 0.9636 | 0.9388 | 0.9768 |
87
+ | 0.0806 | 1.7647 | 540 | 0.0757 | 0.9263 | 0.9495 | 0.9378 | 0.9789 |
88
+ | 0.0906 | 1.8301 | 560 | 0.0749 | 0.9244 | 0.9635 | 0.9436 | 0.9795 |
89
+ | 0.0858 | 1.8954 | 580 | 0.1098 | 0.9006 | 0.9561 | 0.9275 | 0.9691 |
90
+ | 0.092 | 1.9608 | 600 | 0.1023 | 0.9035 | 0.9561 | 0.9291 | 0.9710 |
91
+ | 0.0764 | 2.0261 | 620 | 0.0840 | 0.9195 | 0.9543 | 0.9366 | 0.9767 |
92
+ | 0.0655 | 2.0915 | 640 | 0.0762 | 0.9259 | 0.9542 | 0.9398 | 0.9777 |
93
+ | 0.0573 | 2.1569 | 660 | 0.0846 | 0.9112 | 0.9503 | 0.9303 | 0.9749 |
94
+ | 0.077 | 2.2222 | 680 | 0.0750 | 0.9300 | 0.9576 | 0.9436 | 0.9793 |
95
+ | 0.0712 | 2.2876 | 700 | 0.0830 | 0.9186 | 0.9575 | 0.9376 | 0.9776 |
96
+ | 0.0592 | 2.3529 | 720 | 0.0743 | 0.9338 | 0.9569 | 0.9452 | 0.9802 |
97
+ | 0.0638 | 2.4183 | 740 | 0.0725 | 0.9349 | 0.9469 | 0.9408 | 0.9789 |
98
+ | 0.0893 | 2.4837 | 760 | 0.0724 | 0.9295 | 0.9597 | 0.9443 | 0.9801 |
99
+ | 0.0672 | 2.5490 | 780 | 0.0729 | 0.9389 | 0.9616 | 0.9501 | 0.9818 |
100
+ | 0.0692 | 2.6144 | 800 | 0.0724 | 0.9427 | 0.9531 | 0.9479 | 0.9810 |
101
+ | 0.0667 | 2.6797 | 820 | 0.0757 | 0.9418 | 0.9531 | 0.9474 | 0.9802 |
102
+ | 0.071 | 2.7451 | 840 | 0.0777 | 0.9249 | 0.9577 | 0.9410 | 0.9791 |
103
+ | 0.0686 | 2.8105 | 860 | 0.0721 | 0.9393 | 0.9606 | 0.9498 | 0.9819 |
104
+ | 0.0668 | 2.8758 | 880 | 0.0767 | 0.9360 | 0.9558 | 0.9458 | 0.9788 |
105
+ | 0.0573 | 2.9412 | 900 | 0.0762 | 0.9283 | 0.9605 | 0.9441 | 0.9793 |
106
+ | 0.0593 | 3.0065 | 920 | 0.0681 | 0.9414 | 0.9595 | 0.9504 | 0.9823 |
107
+ | 0.0463 | 3.0719 | 940 | 0.0751 | 0.9319 | 0.9595 | 0.9455 | 0.9805 |
108
+ | 0.0501 | 3.1373 | 960 | 0.0904 | 0.9169 | 0.9524 | 0.9343 | 0.9758 |
109
+ | 0.0483 | 3.2026 | 980 | 0.0736 | 0.9366 | 0.9526 | 0.9445 | 0.9799 |
110
+ | 0.0535 | 3.2680 | 1000 | 0.0785 | 0.9285 | 0.9542 | 0.9411 | 0.9785 |
111
+ | 0.0526 | 3.3333 | 1020 | 0.0747 | 0.9365 | 0.9581 | 0.9472 | 0.9806 |
112
+ | 0.0534 | 3.3987 | 1040 | 0.0788 | 0.9255 | 0.9631 | 0.9439 | 0.9795 |
113
+ | 0.0615 | 3.4641 | 1060 | 0.0719 | 0.9304 | 0.9589 | 0.9445 | 0.9799 |
114
+ | 0.0485 | 3.5294 | 1080 | 0.0712 | 0.9327 | 0.9525 | 0.9425 | 0.9797 |
115
+ | 0.0484 | 3.5948 | 1100 | 0.0749 | 0.9329 | 0.9625 | 0.9475 | 0.9804 |
116
+ | 0.0452 | 3.6601 | 1120 | 0.0701 | 0.9378 | 0.9580 | 0.9478 | 0.9819 |
117
+ | 0.0622 | 3.7255 | 1140 | 0.0706 | 0.9412 | 0.9580 | 0.9495 | 0.9815 |
118
+ | 0.0491 | 3.7908 | 1160 | 0.0718 | 0.9363 | 0.9588 | 0.9474 | 0.9814 |
119
+ | 0.0601 | 3.8562 | 1180 | 0.0804 | 0.9331 | 0.9617 | 0.9472 | 0.9798 |
120
+ | 0.0592 | 3.9216 | 1200 | 0.0803 | 0.9353 | 0.9569 | 0.9460 | 0.9789 |
121
+ | 0.0596 | 3.9869 | 1220 | 0.0711 | 0.9344 | 0.9600 | 0.9470 | 0.9815 |
122
+ | 0.0416 | 4.0523 | 1240 | 0.0726 | 0.9362 | 0.9594 | 0.9477 | 0.9811 |
123
+ | 0.0357 | 4.1176 | 1260 | 0.0682 | 0.9381 | 0.9621 | 0.9499 | 0.9820 |
124
+ | 0.0416 | 4.1830 | 1280 | 0.0678 | 0.9381 | 0.9611 | 0.9495 | 0.9823 |
125
+ | 0.0444 | 4.2484 | 1300 | 0.0738 | 0.9340 | 0.9554 | 0.9446 | 0.9802 |
126
+ | 0.0414 | 4.3137 | 1320 | 0.0702 | 0.9430 | 0.9520 | 0.9475 | 0.9818 |
127
+ | 0.047 | 4.3791 | 1340 | 0.0715 | 0.9330 | 0.9570 | 0.9449 | 0.9811 |
128
+ | 0.0409 | 4.4444 | 1360 | 0.0723 | 0.9314 | 0.9555 | 0.9433 | 0.9807 |
129
+ | 0.0318 | 4.5098 | 1380 | 0.0736 | 0.9347 | 0.9598 | 0.9471 | 0.9817 |
130
+ | 0.0459 | 4.5752 | 1400 | 0.0723 | 0.9393 | 0.9583 | 0.9488 | 0.9820 |
131
+ | 0.0435 | 4.6405 | 1420 | 0.0729 | 0.9332 | 0.9604 | 0.9466 | 0.9812 |
132
+ | 0.0354 | 4.7059 | 1440 | 0.0745 | 0.9326 | 0.9611 | 0.9467 | 0.9809 |
133
+ | 0.046 | 4.7712 | 1460 | 0.0747 | 0.9345 | 0.9600 | 0.9471 | 0.9812 |
134
+ | 0.0418 | 4.8366 | 1480 | 0.0712 | 0.9421 | 0.9631 | 0.9525 | 0.9827 |
135
+ | 0.0353 | 4.9020 | 1500 | 0.0741 | 0.9337 | 0.9623 | 0.9478 | 0.9814 |
136
+ | 0.0501 | 4.9673 | 1520 | 0.0727 | 0.9348 | 0.9564 | 0.9455 | 0.9813 |
137
+ | 0.0354 | 5.0327 | 1540 | 0.0756 | 0.9314 | 0.9588 | 0.9449 | 0.9806 |
138
+ | 0.0323 | 5.0980 | 1560 | 0.0722 | 0.9382 | 0.9587 | 0.9483 | 0.9820 |
139
+ | 0.0376 | 5.1634 | 1580 | 0.0732 | 0.9354 | 0.9589 | 0.9470 | 0.9813 |
140
+ | 0.0323 | 5.2288 | 1600 | 0.0730 | 0.9336 | 0.9564 | 0.9449 | 0.9809 |
141
+ | 0.0315 | 5.2941 | 1620 | 0.0740 | 0.9342 | 0.9572 | 0.9456 | 0.9808 |
142
+ | 0.0288 | 5.3595 | 1640 | 0.0728 | 0.9376 | 0.9567 | 0.9470 | 0.9815 |
143
+ | 0.0353 | 5.4248 | 1660 | 0.0711 | 0.9369 | 0.9563 | 0.9465 | 0.9815 |
144
+ | 0.0378 | 5.4902 | 1680 | 0.0725 | 0.9379 | 0.9576 | 0.9476 | 0.9820 |
145
+ | 0.0326 | 5.5556 | 1700 | 0.0710 | 0.9411 | 0.9583 | 0.9497 | 0.9824 |
146
+ | 0.0349 | 5.6209 | 1720 | 0.0731 | 0.9346 | 0.9562 | 0.9453 | 0.9812 |
147
+ | 0.0341 | 5.6863 | 1740 | 0.0728 | 0.9361 | 0.9557 | 0.9458 | 0.9813 |
148
+ | 0.0323 | 5.7516 | 1760 | 0.0729 | 0.9367 | 0.9552 | 0.9459 | 0.9815 |
149
+ | 0.0293 | 5.8170 | 1780 | 0.0736 | 0.9340 | 0.9556 | 0.9447 | 0.9809 |
150
+ | 0.0325 | 5.8824 | 1800 | 0.0738 | 0.9348 | 0.9562 | 0.9454 | 0.9812 |
151
+ | 0.0287 | 5.9477 | 1820 | 0.0732 | 0.9365 | 0.9562 | 0.9462 | 0.9815 |
152
 
153
 
154
  ### Framework versions
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