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

=================================
打开相机这
意图： LAUNCH
槽位： [('name', '相', 2, 2)]
=================================
=================================
国际象棋开局
意图： QUERY
槽位： [('name', '国际象棋', 0, 3)]
=================================
=================================
打开淘宝购物
意图： LAUNCH
槽位： [('name', '淘宝', 2, 3)]
=================================
=================================
搜狗
意图： LAUNCH
槽位： []
=================================
=================================
打开uc浏览器
意图： LAUNCH
槽位： [('name', 'uc浏', 2, 4)]
=================================
=================================
帮我打开人人
意图： LAUNCH
槽位： []
=================================
=================================
打开酷狗并随机播放
意图： LAUNCH
槽位： [('name', '酷狗', 2, 3)]
=================================
=================================
赶集
意图： LAUNCH
槽位： []
=================================
=================================
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)]
=================================
=================================
从台州到金华的汽车。
意图： QUERY
槽位： [('Src', '台州', 1, 2), ('Dest', '金华', 4, 5)]
=================================
=================================
从西安到石嘴山的汽车票。
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

Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome

This repository contains the reference implementation for our proposed Convolutional CRFs.

Example for AUAV 2022 with obstacle avoidance.

Generalized and Efficient Blackbox Optimization System.

mlpack: a scalable C++ machine learning library --

Yolo algorithm for detection + centroid tracker to track vehicles

Implementation of QuickDraw - an online game developed by Google, combined with AirGesture - a simple gesture recognition application

Volumetric Correspondence Networks for Optical Flow, NeurIPS 2019.

Computer vision - fun segmentation experience using classic and deep tools :)

(Personalized) Page-Rank computation using PyTorch

Malmo Collaborative AI Challenge - Team Pig Catcher

Retina blood vessel segmentation with a convolutional neural network

Attention for PyTorch with Linear Memory Footprint

Using contrastive learning and OpenAI's CLIP to find good embeddings for images with lossy transformations

Machine Learning Model deployment for Container (TensorFlow Serving)

PyTorch implementations of the beta divergence loss.

Blind visual quality assessment on 360° Video based on progressive learning

Pathdreamer: A World Model for Indoor Navigation

NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go

Multi-Objective Reinforced Active Learning