PAPER-In-CODE | Mainly including ACL

by charlesXu86 Python Version: Current License: No License

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

kandi X-RAY | PAPER-In-CODE Summary

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

NLP-related paper code reproduction. Mainly including ACL, AAAI, EMNLP and other top conference papers.

Support

Quality

Security

License

Reuse

Support

PAPER-In-CODE has a low active ecosystem.

It has 76 star(s) with 29 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 93 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of PAPER-In-CODE is current.

Quality

PAPER-In-CODE has no bugs reported.

Security

PAPER-In-CODE has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

PAPER-In-CODE 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

PAPER-In-CODE releases are not available. You will need to build from source code and install.

PAPER-In-CODE has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

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

Train an LSTM model on the graph
Read data
Process multiple elements
Updates the values in the collection
Train and test LSTM
Return a generator of word data
Loads a data directory
Iterate through data
Normalize a vector
Returns True if any of the elements in the list are True
Train a ranker model
Extracts the gold graph from the gold graph
Parses the input text
Trains training data
Returns True if all elements in the list are True
Evaluate the model
Loads a dataset
Compute the Aacle F1 curve
Get valid actions
Convert a spade fade txt file
Loads the pre - trained data
Get pre - processed sentences
Argument parser
Test the model
Train the model
Get an optimizer

Get all kandi verified functions for this library.

PAPER-In-CODE Key Features

No Key Features are available at this moment for PAPER-In-CODE.

PAPER-In-CODE Examples and Code Snippets

No Code Snippets are available at this moment for PAPER-In-CODE.

Community Discussions

No Community Discussions are available at this moment for PAPER-In-CODE.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install PAPER-In-CODE

You can download it from GitHub.
You can use PAPER-In-CODE 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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