nlppreprocess | NLPPREPROCESS is a preprocessing package for NLP task | Machine Learning library

by gaganmanku96 Python Version: 1.0.2 License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | nlppreprocess Summary

nlppreprocess is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. nlppreprocess 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 nlppreprocess' or download it from GitHub, PyPI.

NLPPREPROCESS is a preprocessing package for NLP task. The main objective of the package is to reduce time consumed for preprocessing by using ready made functions.

Support

Quality

Security

License

Reuse

Support

nlppreprocess has a low active ecosystem.

It has 14 star(s) with 3 fork(s). There are no watchers for this library.

It had no major release in the last 12 months.

There are 1 open issues and 1 have been closed. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of nlppreprocess is 1.0.2

Quality

nlppreprocess has no bugs reported.

Security

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

License

nlppreprocess 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

nlppreprocess 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 are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Process the given doc
Lemmatize the document
Replace the replacement_words function
Removes stopwords from the docstring
Remove HTML tags from the document
Removes numbers from the docstring
Removes punctations from the docstring

Get all kandi verified functions for this library.

nlppreprocess Key Features

No Key Features are available at this moment for nlppreprocess.

nlppreprocess Examples and Code Snippets

Usage,Parameters

Python

Lines of Code : 9

License : Permissive (MIT)

Copy

>>> obj = NLP(
       replace_words=True,
       remove_stopwords=True,
       remove_numbers=True,
       remove_HTML_tags=True,
       remove_punctation=True,
       lemmatize=False,
       lemmatize_method='wordnet'
      )

Manually via GIT

Python

Lines of Code : 3

License : Permissive (MIT)

Copy

$ git clone git://github.com/gaganmanku96/nlppreprocess
$ cd nlppreprocess
$ python setup.py install

Usage

Python

Lines of Code : 2

License : Permissive (MIT)

Copy

>>> from nlpuitls import NLP
>>> obj = NLP()

Community Discussions

Trending Discussions on nlppreprocess

ValueError: operands could not be broadcast together with shapes (1,55) (42,)

QUESTION

ValueError: operands could not be broadcast together with shapes (1,55) (42,)

Asked 2020-Sep-26 at 09:34

To Download Dataset click link

I am trying to find out disease type based on the symptoms, by using a machine learning model. All are going well but when I trying to predict the disease type based on given symptoms it gives me "ValueError: operands could not be broadcast together with shapes (1,55) (42,) " that error. to solve this i have seen many of the similar post but not able to solve it.

...

ANSWER

Answered 2020-Sep-26 at 09:34

Okay, finally I solved it. Actually, there is a dimensional problem. Problems come because I was given an input data dimension of the model is X=(10 rows × 42 columns) and y = (10 rows × 1 column). and when use the model for prediction then I was given a test data dimension of = (1 rows × 55 columns). That's the problem of dimension. Now I changed my input data shape of X = (10 rows × 55 columns). So now it works fine and predicted well.

Source https://stackoverflow.com/questions/64059739

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

Vulnerabilities

No vulnerabilities reported

Install nlppreprocess

You can install using 'pip install nlppreprocess' or download it from GitHub, PyPI.
You can use nlppreprocess 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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