readme: ---
  license: mit
  inference: False
  ---
  
  # PS: 效果不怎么好，体验一下就行了。。。。。。wwm-MLM最终准确率55.5左右。
  # cluner NER实验(globalpointer的结果差不多，softmax结果差好多- -)
  ```python
  # flash base  + globalpointer
  04/08/2022 10:53:34 - INFO - __main__ - ADDRESS = Score(f1=0.607703, precision=0.64939, recall=0.571046, tp=213, pred=328, gold=373)
  04/08/2022 10:53:34 - INFO - __main__ - BOOK = Score(f1=0.8125, precision=0.873134, recall=0.75974, tp=117, pred=134, gold=154)
  04/08/2022 10:53:34 - INFO - __main__ - COMPANY = Score(f1=0.818304, precision=0.832877, recall=0.804233, tp=304, pred=365, gold=378)
  04/08/2022 10:53:34 - INFO - __main__ - GAME = Score(f1=0.854305, precision=0.834951, recall=0.874576, tp=258, pred=309, gold=295)
  04/08/2022 10:53:34 - INFO - __main__ - GOVERNMENT = Score(f1=0.823529, precision=0.775, recall=0.878543, tp=217, pred=280, gold=247)
  04/08/2022 10:53:34 - INFO - __main__ - MOVIE = Score(f1=0.810997, precision=0.842857, recall=0.781457, tp=118, pred=140, gold=151)
  04/08/2022 10:53:34 - INFO - __main__ - NAME = Score(f1=0.874042, precision=0.890625, recall=0.858065, tp=399, pred=448, gold=465)
  04/08/2022 10:53:34 - INFO - __main__ - ORGANIZATION = Score(f1=0.813986, precision=0.836207, recall=0.792916, tp=291, pred=348, gold=367)
  04/08/2022 10:53:34 - INFO - __main__ - POSITION = Score(f1=0.78478, precision=0.808824, recall=0.762125, tp=330, pred=408, gold=433)
  04/08/2022 10:53:34 - INFO - __main__ - SCENE = Score(f1=0.683805, precision=0.738889, recall=0.636364, tp=133, pred=180, gold=209)
  04/08/2022 10:53:34 - INFO - __main__ - micro_f1 = Score(f1=0.79175, precision=0.809524, recall=0.77474, tp=2380, pred=2940, gold=3072)
  04/08/2022 10:53:34 - INFO - __main__ - macro_f1 = Score(f1=0.788395, precision=0.808275, recall=0.771906, tp=0, pred=0, gold=0)
  04/08/2022 10:53:34 - INFO - __main__ - mean_f1 = 0.790072
  
  # flash base  + softmax
  04/08/2022 11:10:44 - INFO - __main__ - ADDRESS = Score(f1=0.568987, precision=0.522422, recall=0.624665, tp=233, pred=446, gold=373)
  04/08/2022 11:10:44 - INFO - __main__ - BOOK = Score(f1=0.750789, precision=0.730061, recall=0.772727, tp=119, pred=163, gold=154)
  04/08/2022 11:10:44 - INFO - __main__ - COMPANY = Score(f1=0.75528, precision=0.711944, recall=0.804233, tp=304, pred=427, gold=378)
  04/08/2022 11:10:44 - INFO - __main__ - GAME = Score(f1=0.811502, precision=0.767372, recall=0.861017, tp=254, pred=331, gold=295)
  04/08/2022 11:10:44 - INFO - __main__ - GOVERNMENT = Score(f1=0.738636, precision=0.69395, recall=0.789474, tp=195, pred=281, gold=247)
  04/08/2022 11:10:44 - INFO - __main__ - MOVIE = Score(f1=0.74359, precision=0.720497, recall=0.768212, tp=116, pred=161, gold=151)
  04/08/2022 11:10:44 - INFO - __main__ - NAME = Score(f1=0.831967, precision=0.794521, recall=0.873118, tp=406, pred=511, gold=465)
  04/08/2022 11:10:44 - INFO - __main__ - ORGANIZATION = Score(f1=0.754054, precision=0.747989, recall=0.760218, tp=279, pred=373, gold=367)
  04/08/2022 11:10:44 - INFO - __main__ - POSITION = Score(f1=0.742729, precision=0.720174, recall=0.766744, tp=332, pred=461, gold=433)
  04/08/2022 11:10:44 - INFO - __main__ - SCENE = Score(f1=0.628842, precision=0.621495, recall=0.636364, tp=133, pred=214, gold=209)
  04/08/2022 11:10:44 - INFO - __main__ - micro_f1 = Score(f1=0.736335, precision=0.703979, recall=0.77181, tp=2371, pred=3368, gold=3072)
  04/08/2022 11:10:44 - INFO - __main__ - macro_f1 = Score(f1=0.732638, precision=0.703043, recall=0.765677, tp=0, pred=0, gold=0)
  04/08/2022 11:10:44 - INFO - __main__ - mean_f1 = 0.734486
  
  
  # bert base + globalpointer
  04/08/2022 11:22:48 - INFO - __main__ - ADDRESS = Score(f1=0.641558, precision=0.622166, recall=0.662198, tp=247, pred=397, gold=373)
  04/08/2022 11:22:48 - INFO - __main__ - BOOK = Score(f1=0.813115, precision=0.821192, recall=0.805195, tp=124, pred=151, gold=154)
  04/08/2022 11:22:48 - INFO - __main__ - COMPANY = Score(f1=0.823684, precision=0.819372, recall=0.828042, tp=313, pred=382, gold=378)
  04/08/2022 11:22:48 - INFO - __main__ - GAME = Score(f1=0.841762, precision=0.811321, recall=0.874576, tp=258, pred=318, gold=295)
  04/08/2022 11:22:48 - INFO - __main__ - GOVERNMENT = Score(f1=0.827324, precision=0.778571, recall=0.882591, tp=218, pred=280, gold=247)
  04/08/2022 11:22:48 - INFO - __main__ - MOVIE = Score(f1=0.82392, precision=0.826667, recall=0.821192, tp=124, pred=150, gold=151)
  04/08/2022 11:22:48 - INFO - __main__ - NAME = Score(f1=0.861345, precision=0.840164, recall=0.883621, tp=410, pred=488, gold=464)
  04/08/2022 11:22:48 - INFO - __main__ - ORGANIZATION = Score(f1=0.804911, precision=0.806011, recall=0.803815, tp=295, pred=366, gold=367)
  04/08/2022 11:22:48 - INFO - __main__ - POSITION = Score(f1=0.805046, precision=0.799544, recall=0.810624, tp=351, pred=439, gold=433)
  04/08/2022 11:22:48 - INFO - __main__ - SCENE = Score(f1=0.702703, precision=0.722222, recall=0.684211, tp=143, pred=198, gold=209)
  04/08/2022 11:22:48 - INFO - __main__ - micro_f1 = Score(f1=0.795833, precision=0.783528, recall=0.808531, tp=2483, pred=31