pytorch bert intent classification and slot filling

Overview

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)]
=================================
Owner
西西嘛呦
西西嘛呦
“英特尔创新大师杯”深度学习挑战赛 赛道3:CCKS2021中文NLP地址相关性任务

ccks2021-track3 CCKS2021中文NLP地址相关性任务-赛道三-冠军方案 团队:我的加菲鱼- wodejiafeiyu 初赛第二/复赛第一/决赛第一 前言 19年开始,陆陆续续参加了一些比赛,拿到过一些top,比较懒一直都没分享过,这次比较幸运又拿了top1,打算分享下 分类的任务

shaochenjie 131 Dec 31, 2022
This project is a re-implementation of MASTER: Multi-Aspect Non-local Network for Scene Text Recognition by MMOCR

This project is a re-implementation of MASTER: Multi-Aspect Non-local Network for Scene Text Recognition by MMOCR,which is an open-source toolbox based on PyTorch. The overall architecture will be sh

Jianquan Ye 82 Nov 17, 2022
A PyTorch Reimplementation of TecoGAN: Temporally Coherent GAN for Video Super-Resolution

TecoGAN-PyTorch Introduction This is a PyTorch reimplementation of TecoGAN: Temporally Coherent GAN for Video Super-Resolution (VSR). Please refer to

165 Dec 17, 2022
An LSTM based GAN for Human motion synthesis

GAN-motion-Prediction An LSTM based GAN for motion synthesis has a few issues reading H3.6M data from A.Jain et al , will fix soon. Prediction of the

Amogh Adishesha 9 Jun 17, 2022
Deep-Learning-Image-Captioning - Implementing convolutional and recurrent neural networks in Keras to generate sentence descriptions of images

Deep Learning - Image Captioning with Convolutional and Recurrent Neural Nets ========================================================================

23 Apr 06, 2022
SemiNAS: Semi-Supervised Neural Architecture Search

SemiNAS: Semi-Supervised Neural Architecture Search This repository contains the code used for Semi-Supervised Neural Architecture Search, by Renqian

Renqian Luo 21 Aug 31, 2022
Simple STAC Catalogs discovery tool.

STAC Catalog Discovery Simple STAC discovery tool. Just paste the STAC Catalog link and press Enter. Details STAC Discovery tool enables discovering d

Mykola Kozyr 21 Oct 19, 2022
ChainerRL is a deep reinforcement learning library built on top of Chainer.

ChainerRL and PFRL ChainerRL (this repository) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement al

Chainer 1.1k Jan 01, 2023
Main Results on ImageNet with Pretrained Models

This repository contains Pytorch evaluation code, training code and pretrained models for the following projects: SPACH (A Battle of Network Structure

Microsoft 151 Dec 14, 2022
Open source annotation tool for machine learning practitioners.

doccano doccano is an open source text annotation tool for humans. It provides annotation features for text classification, sequence labeling and sequ

7.1k Jan 01, 2023
EPSANet:An Efficient Pyramid Split Attention Block on Convolutional Neural Network

EPSANet:An Efficient Pyramid Split Attention Block on Convolutional Neural Network This repo contains the official Pytorch implementaion code and conf

Hu Zhang 175 Jan 07, 2023
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image

Expressive Body Capture: 3D Hands, Face, and Body from a Single Image [Project Page] [Paper] [Supp. Mat.] Table of Contents License Description Fittin

Vassilis Choutas 1.3k Jan 07, 2023
Benchmarks for Object Detection in Aerial Images

Benchmarks for Object Detection in Aerial Images

Jian Ding 691 Dec 30, 2022
Politecnico of Turin Thesis: "Implementation and Evaluation of an Educational Chatbot based on NLP Techniques"

THESIS_CAIRONE_FIORENTINO Politecnico of Turin Thesis: "Implementation and Evaluation of an Educational Chatbot based on NLP Techniques" GENERATE TOKE

cairone_fiorentino97 1 Dec 10, 2021
PyTorch implementation for View-Guided Point Cloud Completion

PyTorch implementation for View-Guided Point Cloud Completion

22 Jan 04, 2023
TriMap: Large-scale Dimensionality Reduction Using Triplets

TriMap TriMap is a dimensionality reduction method that uses triplet constraints to form a low-dimensional embedding of a set of points. The triplet c

Ehsan Amid 235 Dec 24, 2022
STBP is a way to train SNN with datasets by Backward propagation.

Spiking neural network (SNN), compared with depth neural network (DNN), has faster processing speed, lower energy consumption and more biological interpretability, which is expected to approach Stron

Ling Zhang 18 Dec 09, 2022
PyTorch implementation of Federated Learning with Non-IID Data, and federated learning algorithms, including FedAvg, FedProx.

Federated Learning with Non-IID Data This is an implementation of the following paper: Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vik

Youngjoon Lee 48 Dec 29, 2022
根据midi文件演奏“风物之诗琴”的脚本 "Windsong Lyre" auto play

Genshin-lyre-auto-play 简体中文 | English 简介 根据midi文件演奏“风物之诗琴”的脚本。由Python驱动,在此承诺, ⚠️ 项目内绝不含任何能够引起安全问题的代码。 前排提示:所有键盘在动但是原神没反应的都是因为没有管理员权限,双击run.bat或者以管理员模式

御坂17032号 386 Jan 01, 2023
Fully Convlutional Neural Networks for state-of-the-art time series classification

Deep Learning for Time Series Classification As the simplest type of time series data, univariate time series provides a reasonably good starting poin

Stephen 572 Dec 23, 2022