quantulum3 | Library for unit extraction - fork of quantulum for python3 | Natural Language Processing library

by nielstron Python Version: 0.9.1 License: MIT

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

kandi X-RAY | quantulum3 Summary

quantulum3 is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. quantulum3 has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install quantulum3' or download it from GitHub, PyPI.

Library for unit extraction - fork of quantulum for python3

Support

Quality

Security

License

Reuse

Support

quantulum3 has a low active ecosystem.

It has 112 star(s) with 52 fork(s). There are 5 watchers for this library.

It had no major release in the last 12 months.

There are 42 open issues and 61 have been closed. On average issues are closed in 166 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of quantulum3 is 0.9.1

Quality

quantulum3 has 0 bugs and 24 code smells.

Security

quantulum3 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

quantulum3 code analysis shows 0 unresolved vulnerabilities.

There are 2 security hotspots that need review.

License

quantulum3 is licensed under the MIT License. This license is Permissive.

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

Reuse

quantulum3 releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

It has 2238 lines of code, 157 functions and 30 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed quantulum3 and discovered the below as its top functions. This is intended to give you an instant insight into quantulum3 implemented functionality, and help decide if they suit your requirements.

Trains the classifier
Import a module
Return the subdirectory of the given language
Return stop words
Parse text and replace in text
Build a unit from dimensions
Build a quantity
Parse text
Load a unit
Return pluralize of singular
Convert value to integer if possible
Download wikipedia pages
Return a list of all available entities
Return ambiguous units
Loads a JSON object from a path or string
Load json file
Return a dictionary of derived units
Get a key from derived dimensions
Runs glove on a given magnitude
Load common words
Return a dictionary of all available languages
Infer the name of a unit
Yield the names of all units

Get all kandi verified functions for this library.

quantulum3 Key Features

No Key Features are available at this moment for quantulum3.

quantulum3 Examples and Code Snippets

Usage

Python

Lines of Code : 4

License : Permissive (MIT)

Copy

>>> from quantulum3 import parser
>>> quants = parser.parse('I want 2 liters of wine')
>>> quants
[Quantity(2, 'litre')]

Community Discussions

Trending Discussions on quantulum3

Regular Expression to extract quantity with dimensions from text in Python

QUESTION

Regular Expression to extract quantity with dimensions from text in Python

Asked 2021-Jan-15 at 13:15

I'm trying to extract dimensions and units from text.

The data could look like anything:

53 inch x 45 inch

10 in by 5 in

53" W x 74" L x 15" H

53 inch W x 74 inch L x 15 inch H

There are posts which cover the first two cases but I was not able to understand how to deal with case 3 and 4 here.

This is what I tried for the basics from this but somehow it doesn't work:

...

ANSWER

Answered 2021-Jan-15 at 13:15

You can use

Source https://stackoverflow.com/questions/65736389

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

Vulnerabilities

No vulnerabilities reported

Install quantulum3

First, install [numpy](https://pypi.org/project/numpy/), [scipy](https://www.scipy.org/install.html) and [sklearn](http://scikit-learn.org/stable/install.html). Quantulum would still work without those packages, but it wouldn't be able to disambiguate between units with the same name (e.g. pound as currency or as unit of mass).

Support

[![Travis dev build state](https://travis-ci.com/nielstron/quantulum3.svg?branch=dev "Travis dev build state")](https://travis-ci.com/nielstron/quantulum3) [![Coverage Status](https://coveralls.io/repos/github/nielstron/quantulum3/badge.svg?branch=dev)](https://coveralls.io/github/nielstron/quantulum3?branch=dev). If you’d like to contribute follow these steps: 1. Clone a fork of this project into your workspace 2. Run pip install -e . at the root of your development folder. 3. pip install pipenv and pipenv shell 4. Inside the project folder run pipenv install --dev 5. Make your changes 6. Run scripts/format.sh and scripts/build.py from the package root directory. 7. Test your changes with python3 setup.py test (Optional, will be done automatically after pushing) 8. Create a Pull Request when having commited and pushed your changes.

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