classifying-text | Classifying text with bag-of-words | Natural Language Processing library

by zygmuntz Python Version: Current License: BSD-2-Clause

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

kandi X-RAY | classifying-text Summary

classifying-text is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. classifying-text has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However classifying-text build file is not available. You can download it from GitHub.

Classifying text with bag-of-words

Support

Quality

Security

License

Reuse

Support

classifying-text has a low active ecosystem.

It has 115 star(s) with 48 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

classifying-text has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of classifying-text is current.

Quality

classifying-text has 0 bugs and 0 code smells.

Security

classifying-text has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

classifying-text code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

classifying-text is licensed under the BSD-2-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

classifying-text has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

classifying-text saves you 98 person hours of effort in developing the same functionality from scratch.

It has 249 lines of code, 8 functions and 8 files.

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

Top functions reviewed by kandi - BETA

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

Split a review string into sentences
Convert a review text to a list of words

Get all kandi verified functions for this library.

classifying-text Key Features

No Key Features are available at this moment for classifying-text.

classifying-text Examples and Code Snippets

No Code Snippets are available at this moment for classifying-text.

Community Discussions

Trending Discussions on classifying-text

NLP with less than 20 Words on google cloud

QUESTION

NLP with less than 20 Words on google cloud

Asked 2021-May-26 at 09:59

According to this documentation: the classifyText method requires at least 20 words.

https://cloud.google.com/natural-language/docs/classifying-text#language-classify-content-nodejs

If I send in less than 20 words I get this no matter how clear the content is:

...

ANSWER

Answered 2021-May-26 at 09:59

The problem with this is you're adding bias no matter what kind of text you send.

Your only chance is to fill up your string up to the minimum word limit with empty words that will be filtered out by the preprocessor and tokenizer before they go to the neural network.

I would try to add a string suffix at the end of the sentence with just stopwords from NLTK like this:

Source https://stackoverflow.com/questions/67626928

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

Vulnerabilities

No vulnerabilities reported

Install classifying-text

You can download it from GitHub.
You can use classifying-text 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 .

Find more information at: