DocumentClassification | code implements a simple CNN model | Machine Learning library

by liu-nlper Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | DocumentClassification Summary

DocumentClassification is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. DocumentClassification has no vulnerabilities and it has low support. However DocumentClassification has 1 bugs and it build file is not available. You can download it from GitHub.

This code implements a simple CNN model for document classification with tensorflow.

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Quality

Security

License

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Support

DocumentClassification has a low active ecosystem.

It has 153 star(s) with 77 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

There are 5 open issues and 2 have been closed. On average issues are closed in 46 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of DocumentClassification is current.

Quality

DocumentClassification has 1 bugs (1 blocker, 0 critical, 0 major, 0 minor) and 11 code smells.

Security

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

DocumentClassification code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

DocumentClassification 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

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

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

DocumentClassification saves you 307 person hours of effort in developing the same functionality from scratch.

It has 740 lines of code, 60 functions and 17 files.

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

Top functions reviewed by kandi - BETA

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

Run the prediction
Returns the highest score for the given epoch
Clear the model
Read lines from file
Train the model
Load test data
Load training data
Load embedding
Loads word vocab
Use pre - trained prediction
Evaluate the model
Compute the similarity between two labels
Calculate sentence and post tags
Map item to ids
Example example
Perform a random over - sampling
Merge result data
Converts word2vec to pickle format

Get all kandi verified functions for this library.

DocumentClassification Key Features

No Key Features are available at this moment for DocumentClassification.

DocumentClassification Examples and Code Snippets

No Code Snippets are available at this moment for DocumentClassification.

Community Discussions

Trending Discussions on DocumentClassification

Input parameter for model as string in Text classification

Is it possible to have extra (ignored) properties in C#?

QUESTION

Input parameter for model as string in Text classification

Asked 2018-Feb-26 at 15:35

I am building document classification system using scikit-learn and it works fine. I am converting the model to Core ML model format. But the model format excepts the input parameter as multiArrayType. I want make it to excepts string or array of string so that I can easily predict from IOS application.I have tried following way:

...

ANSWER

Answered 2018-Feb-26 at 15:35

It sounds like that other mlmodel you found uses a DictVectorizer to turn the strings into indexes (possibly followed by a OneHotEncoder).

You can do this by making a pipeline in sklearn and converting that pipeline to Core ML.

Source https://stackoverflow.com/questions/48989629

QUESTION

Is it possible to have extra (ignored) properties in C#?

Asked 2017-Aug-03 at 07:39

I have a repository for a DocumentDb database. My documents all have a set of common properties so all documents implement the IDocumentEntity interface.

...

ANSWER

Answered 2017-Apr-15 at 15:10

Entity parameter passed to the UpsertDocument should explicitly implement IDocumentEntity in order do make the code works, it is not enough just have a Id property.

Some options:

1) Proxy may be applied:

Source https://stackoverflow.com/questions/43426511

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

Vulnerabilities

No vulnerabilities reported

Install DocumentClassification

You can download it from GitHub.
You can use DocumentClassification 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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