sodner | The repo includes the code for the following paper

by foxlf823 Python Version: Current License: No License

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

kandi X-RAY | sodner Summary

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

The repo includes the code for the following paper:.

Support

Quality

Security

License

Reuse

Support

sodner has a low active ecosystem.

It has 29 star(s) with 3 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 5 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of sodner is current.

Quality

sodner has no bugs reported.

Security

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

License

sodner 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

sodner 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.

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sodner Key Features

No Key Features are available at this moment for sodner.

sodner Examples and Code Snippets

No Code Snippets are available at this moment for sodner.

Community Discussions

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

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

Vulnerabilities

No vulnerabilities reported

Install sodner

Download the PyTorch AllenNLP version of SciBERT from here. Put it into the current directory. Put the preprocessed data into "data" directory. There is a sample directory for your reference to preprocess original datasets.
Use "conda" or "virtualenv" to create a virtual python3 environment. Take "conda" as example, run:
Activate the environment.
Run the following command to install necessary packages.
Download the PyTorch AllenNLP version of SciBERT from here. Put it into the current directory.
Put the preprocessed data into "data" directory. There is a sample directory for your reference to preprocess original datasets.

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 .

Find more information at: