Multi-label-Text-Classification

by RingBDStack Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | Multi-label-Text-Classification Summary

Multi-label-Text-Classification is a Python library. Multi-label-Text-Classification has no bugs, it has no vulnerabilities and it has low support. However Multi-label-Text-Classification build file is not available. You can download it from GitHub.

Multi-label-Text-Classification

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Quality

Security

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Support

Multi-label-Text-Classification has a low active ecosystem.

It has 12 star(s) with 3 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

Multi-label-Text-Classification has no issues reported. There are no pull 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.

Reuse

Multi-label-Text-Classification releases are not available. You will need to build from source code and install.

Multi-label-Text-Classification has no build file. You will be need to create the build yourself to build the component from source.

It has 11243 lines of code, 320 functions and 62 files.

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

Top functions reviewed by kandi - BETA

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

Evaluate the given checkpoint
Generate next batch
Estimate logits for a given data
Create a Vocabulary from a JSON file
Generates the hierarchy data for the hierarchy
Inference function for LSHTCT
Inverse inference
End of LSHTC
Implementation of the convolution layer
Implements LSHTCT
Performs inference
Train the model
Calculate the loss between logits and labels
Add summaries for all losses
Generate the hierarchy of the hierarchy
Generate evaluation data
Implements fc1conv
Inverse layer
Generates the evaluation result file
Generate the root of the tree
Generate a subfunc for each node
Generate a hierarchy for the hierarchy of leaf2 nodes
Generate example labels
Generate hierarchy of hierarchy data
Implements LSHTC CNN
Implementation of inference1conv

Get all kandi verified functions for this library.

Multi-label-Text-Classification Key Features

No Key Features are available at this moment for Multi-label-Text-Classification.

Multi-label-Text-Classification Examples and Code Snippets

No Code Snippets are available at this moment for Multi-label-Text-Classification.

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.
You can use Multi-label-Text-Classification 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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