pytorch-relation-extraction | distant supervised relation extraction models : PCNN MIL

by ShomyLiu Python Version: Current License: No License

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kandi X-RAY | pytorch-relation-extraction Summary

pytorch-relation-extraction is a Python library. pytorch-relation-extraction has no bugs, it has no vulnerabilities and it has low support. However pytorch-relation-extraction build file is not available. You can download it from GitHub.

使用Pytorch 复现 PCNN+MIL (Zeng 2015) 与 PCNN+ATT (Lin 2016), 以及两个模型在两个大小版本的数据集上(27类关系/53类关系)的表现对比。. 在代码的组织,结构设计上, 主要参考陈云Pytorch实战指南 (个人推荐)。因此一些实现细节就不再赘述了，可以参考陈云的实战指南。.

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Support

pytorch-relation-extraction has a low active ecosystem.

It has 473 star(s) with 117 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 35 open issues and 33 have been closed. On average issues are closed in 34 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of pytorch-relation-extraction is current.

Quality

pytorch-relation-extraction has 0 bugs and 0 code smells.

Security

pytorch-relation-extraction has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pytorch-relation-extraction code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pytorch-relation-extraction 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

pytorch-relation-extraction releases are not available. You will need to build from source code and install.

pytorch-relation-extraction 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.

Top functions reviewed by kandi - BETA

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

Forward embedding
Get embedding embedding
Evaluate the loss function
Calculate the weights for the given labels
Returns a list of tensors embedded in a bag
Compute cross entropy of a label
Mask the input tensor
Parse asen file
Calculate a feature feature
Calculates the sentence feature from a bag
Return the padded representation of the token
Parse thesen file
Compute the padding for a given position
Calculate the sentence feature
Forward computation
Mask the piece pooling

Get all kandi verified functions for this library.

pytorch-relation-extraction Key Features

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pytorch-relation-extraction Examples and Code Snippets

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Community Discussions

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

Vulnerabilities

No vulnerabilities reported

Install pytorch-relation-extraction

You can download it from GitHub.
You can use pytorch-relation-extraction 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

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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