GraphIE | A GCN-based NER framework

by zhijing-jin Python Version: Current License: No License

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

kandi X-RAY | GraphIE Summary

GraphIE is a Python library. GraphIE has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

To ensure the files are correct, I did the following checks on Apr 2, 2019.

Support

Quality

Security

License

Reuse

Support

GraphIE has a low active ecosystem.

It has 9 star(s) with 4 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 2 have been closed. On average issues are closed in 118 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of GraphIE is current.

Quality

GraphIE has no bugs reported.

Security

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

License

GraphIE 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

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

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Decode a MST structure into a cycle graph
Load embedding
Pad the given batch
Returns the next batch
Creates a vocabulary for each token
Return the index of an instance
Adds an instance to the list
Adds a singleton id
Compute the loss
Forward attention
Evaluate the model
Decode the energy of an input array
Compute the GCN layer
Get the next token from the source file
R reformats the text into a dictionary
Calculate learning rate
Forward computation
Forward Connect recursively
Performs the majority rule
Transform a file into a BIO format
Read data from source_path
Add end of text
Compute the loss function
Step through an RNN layer
Construct a SkipConnectRNN layer
Creates a layer - masked RNN layer

Get all kandi verified functions for this library.

GraphIE Key Features

No Key Features are available at this moment for GraphIE.

GraphIE Examples and Code Snippets

No Code Snippets are available at this moment for GraphIE.

Community Discussions

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

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

Vulnerabilities

No vulnerabilities reported

Install GraphIE

Python>=3.6
PyTorch 0.4.0 # install it according to your cuda version. e.g. conda install pytorch=0.4.0 torchvision cuda80 -c pytorch
Note: If you need to preprocess new data sources, please see.
Small data samples are provided.
If have any questions regarding preprocess.py, you can contact the author by email.

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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