pytorch_bert_intent_classification_and_slot_filling

基于pytorch的中文意图识别和槽位填充

说明

基本思路就是：分类+序列标注（命名实体识别）同时训练。使用的预训练模型：hugging face上的chinese-bert-wwm-ext 依赖：

pytorch==1.6+
transformers==4.x+

运行指令：

python main.py

可在config.py里面修改相关的参数，训练、验证、测试、还有预测。

结果

意图识别：
accuracy:0.9767441860465116
precision:0.9767441860465116
recall:0.9767441860465116
f1:0.9767441860465116
              precision    recall  f1-score   support

           0       1.00      0.94      0.97        16
           2       1.00      1.00      1.00         1
           3       1.00      1.00      1.00         4
           4       1.00      1.00      1.00        16
           5       0.00      0.00      0.00         1
           6       1.00      1.00      1.00        22
           7       0.84      0.89      0.86        18
           8       0.98      0.95      0.96        57
           9       1.00      1.00      1.00         2
          10       0.00      0.00      0.00         0
          11       0.00      0.00      0.00         1
          12       0.98      0.99      0.99       327
          13       1.00      1.00      1.00         1
          14       1.00      1.00      1.00         3
          15       1.00      1.00      1.00         1
          17       1.00      1.00      1.00         4
          18       1.00      0.80      0.89         5
          19       1.00      1.00      1.00        14
          21       0.00      0.00      0.00         1
          22       1.00      1.00      1.00        13
          23       1.00      1.00      1.00         9

    accuracy                           0.98       516
   macro avg       0.80      0.79      0.79       516
weighted avg       0.97      0.98      0.97       516

槽位填充：
accuracy:0.9366942909760589
precision:0.8052708638360175
recall:0.8461538461538461
f1:0.8252063015753938
                   precision    recall  f1-score   support

             Dest       1.00      1.00      1.00         7
              Src       1.00      0.86      0.92         7
             area       1.00      0.25      0.40         4
           artist       0.89      1.00      0.94         8
       artistRole       1.00      1.00      1.00         2
           author       1.00      1.00      1.00        13
         category       0.73      0.90      0.81        42
             code       0.71      0.83      0.77         6
          content       0.89      0.94      0.91        17
    datetime_date       0.72      0.95      0.82        19
    datetime_time       0.58      0.64      0.61        11
         dishName       0.84      0.88      0.86        74
        dishNamet       0.00      0.00      0.00         1
          dynasty       1.00      1.00      1.00        11
      endLoc_area       0.00      0.00      0.00         2
      endLoc_city       0.96      1.00      0.98        43
       endLoc_poi       0.62      0.73      0.67        11
  endLoc_province       0.00      0.00      0.00         1
          episode       1.00      1.00      1.00         1
             film       0.00      0.00      0.00         1
       ingredient       0.53      0.62      0.57        16
          keyword       0.88      0.88      0.88        25
    location_area       0.00      0.00      0.00         2
    location_city       0.40      1.00      0.57         4
     location_poi       0.36      0.57      0.44         7
location_province       0.00      0.00      0.00         3
             name       0.80      0.88      0.84       182
       popularity       0.00      0.00      0.00         5
       queryField       1.00      1.00      1.00         2
     questionWord       0.00      0.00      0.00         1
         receiver       1.00      1.00      1.00         4
         relIssue       0.00      0.00      0.00         1
       scoreDescr       0.00      0.00      0.00         1
             song       0.86      0.80      0.83        15
   startDate_date       0.93      0.93      0.93        15
   startDate_time       0.00      0.00      0.00         1
    startLoc_area       0.00      0.00      0.00         1
    startLoc_city       0.95      0.97      0.96        38
     startLoc_poi       0.00      0.00      0.00         1
         subfocus       0.00      0.00      0.00         1
              tag       0.40      0.40      0.40         5
           target       1.00      1.00      1.00        12
     teleOperator       0.00      0.00      0.00         1
          theatre       0.50      0.50      0.50         2
        timeDescr       0.00      0.00      0.00         2
        tvchannel       0.74      0.81      0.77        21
        yesterday       0.00      0.00      0.00         1

        micro avg       0.81      0.85      0.83       650
        macro avg       0.52      0.54      0.52       650
     weighted avg       0.79      0.85      0.81       650

=================================
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意图： LAUNCH
槽位： [('name', '相', 2, 2)]
=================================
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意图： QUERY
槽位： [('name', '国际象棋', 0, 3)]
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槽位： [('name', 'uc浏', 2, 4)]
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意图： LAUNCH
槽位： [('name', '酷狗', 2, 3)]
=================================
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意图： LAUNCH
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=================================
从合肥到上海可以到哪坐车？
意图： QUERY
槽位： [('Src', '合肥', 1, 2), ('Dest', '上海', 4, 5)]
=================================
=================================
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意图： QUERY
槽位： [('Src', '台州', 1, 2), ('Dest', '金华', 4, 5)]
=================================
=================================
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意图： QUERY
槽位： [('Src', '西安', 1, 2), ('Dest', '石嘴山', 4, 6)]
=================================

pytorch bert intent classification and slot filling

Related tags

Overview

pytorch_bert_intent_classification_and_slot_filling

说明

结果

Owner

西西嘛呦

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