CrimeKgAssitant | Crime assistant including crime type prediction and crime

by liuhuanyong Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | CrimeKgAssitant Summary

CrimeKgAssitant is a Python library. CrimeKgAssitant has no bugs, it has no vulnerabilities and it has medium support. However CrimeKgAssitant build file is not available. You can download it from GitHub.

Crime assistant including crime type prediction and crime consult service based on nlp methods and crime kg,罪名法务智能项目,内容包括856项罪名知识图谱, 基于280万罪名训练库的罪名预测,基于20W法务问答对的13类问题分类与法律资讯问答功能.

Support

Quality

Security

License

Reuse

Support

CrimeKgAssitant has a medium active ecosystem.

It has 1066 star(s) with 339 fork(s). There are 37 watchers for this library.

It had no major release in the last 6 months.

There are 14 open issues and 3 have been closed. On average issues are closed in 225 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of CrimeKgAssitant is current.

Quality

CrimeKgAssitant has 0 bugs and 27 code smells.

Security

CrimeKgAssitant has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

CrimeKgAssitant code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

CrimeKgAssitant does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

CrimeKgAssitant releases are not available. You will need to build from source code and install.

CrimeKgAssitant has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

CrimeKgAssitant saves you 291 person hours of effort in developing the same functionality from scratch.

It has 703 lines of code, 53 functions and 6 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed CrimeKgAssitant and discovered the below as its top functions. This is intended to give you an instant insight into CrimeKgAssitant implemented functionality, and help decide if they suit your requirements.

Compute the embedding of a sentence
Train the classifer
Load train data
Compute embedding of a sentence
Check precision
Searches a given question
Search for a specific search
Compute the similarity between two vectors
Test the crime classification
Predict label for given text
Predict label
Modify embedding matrix
Returns embedding matrix
Train the neural network
Loads train data from file
Build a CNN
Split the train data into train and test sets
Return a list of one - hot labels for a given label
Train LSTM model
Build LSTM model

Get all kandi verified functions for this library.

CrimeKgAssitant Key Features

No Key Features are available at this moment for CrimeKgAssitant.

CrimeKgAssitant Examples and Code Snippets

No Code Snippets are available at this moment for CrimeKgAssitant.

Community Discussions

No Community Discussions are available at this moment for CrimeKgAssitant.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install CrimeKgAssitant

You can download it from GitHub.
You can use CrimeKgAssitant like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

如有自然语言处理、知识图谱、事理图谱、社会计算、语言资源建设等问题或合作，请联系我: 邮箱:lhy_in_blcu@126.com csdn:https://blog.csdn.net/lhy2014 我的自然语言处理项目: https://liuhuanyong.github.io/ 刘焕勇，中国科学院软件研究所.

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