multi-label-text-classification | label text classification using ConvNet and graph | Machine Learning library

by xiaohan2012 Jupyter Notebook Version: Current License: No License

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kandi X-RAY | multi-label-text-classification Summary

multi-label-text-classification is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. multi-label-text-classification has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Mutli-label text classification using ConvNet and graph embedding (Tensorflow implementation)

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multi-label-text-classification has a low active ecosystem.

It has 44 star(s) with 15 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 0 have been closed. There are 14 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of multi-label-text-classification is current.

Quality

multi-label-text-classification has 0 bugs and 0 code smells.

Security

multi-label-text-classification has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

multi-label-text-classification code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

multi-label-text-classification 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.

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multi-label-text-classification releases are not available. You will need to build from source code and install.

It has 1450 lines of code, 53 functions and 23 files.

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

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multi-label-text-classification Key Features

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multi-label-text-classification Examples and Code Snippets

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Community Discussions

Trending Discussions on multi-label-text-classification

Saving a trained multi-input classification algorithm in Python

QUESTION

Saving a trained multi-input classification algorithm in Python

Asked 2020-Jul-14 at 12:02

I developed a script that predicts probable tags for some text, based on previously manually tagged feedback. I used several online articles to help me (namely: https://towardsdatascience.com/multi-label-text-classification-with-scikit-learn-30714b7819c5).

Because I want the probability for each tag, here's the code I used:

...

ANSWER

Answered 2020-Jul-14 at 12:02

You could always use pickle to serialize any python object including yours. So the simplest and fastest way to save your model is to just serialize it to a file, say model.pickle. This is done in the first part after you train your model. After that, all you have to do is to check if the file exists and deserialize it using pickle again.

This is a function that serializes python objects to files:

Source https://stackoverflow.com/questions/62893978

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

Vulnerabilities

No vulnerabilities reported

Install multi-label-text-classification

You can download it from GitHub.

Support

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