question-classification-cnn-rnn-attention | 针对百度知道电影问答数据的问题分类 question classification

by sefira Python Version: Current License: Apache-2.0

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

kandi X-RAY | question-classification-cnn-rnn-attention Summary

question-classification-cnn-rnn-attention is a Python library. question-classification-cnn-rnn-attention has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However question-classification-cnn-rnn-attention build file is not available. You can download it from GitHub.

针对百度知道电影问答数据的问题分类 question classification for zhidao.baidu.com in movie domain

Support

Quality

Security

License

Reuse

Support

question-classification-cnn-rnn-attention has a low active ecosystem.

It has 26 star(s) with 9 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 1 have been closed. On average issues are closed in 87 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of question-classification-cnn-rnn-attention is current.

Quality

question-classification-cnn-rnn-attention has 0 bugs and 0 code smells.

Security

question-classification-cnn-rnn-attention has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

question-classification-cnn-rnn-attention code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

question-classification-cnn-rnn-attention is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

question-classification-cnn-rnn-attention releases are not available. You will need to build from source code and install.

question-classification-cnn-rnn-attention 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.

question-classification-cnn-rnn-attention saves you 707 person hours of effort in developing the same functionality from scratch.

It has 1634 lines of code, 60 functions and 20 files.

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

Top functions reviewed by kandi - BETA

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

Evaluate predictions
Load data
Overrides the oversampling
Read data and labels from training data
Process data block
Index a list of sentences
Parses the query and returns the prediction
Predict a sentence
Export the current checkpoint
Loads training data
Removes words from a file
Generate batches of data
Run WordBeark
Count the maximum length of the words
Convert to simple

Get all kandi verified functions for this library.

question-classification-cnn-rnn-attention Key Features

No Key Features are available at this moment for question-classification-cnn-rnn-attention.

question-classification-cnn-rnn-attention Examples and Code Snippets

No Code Snippets are available at this moment for question-classification-cnn-rnn-attention.

Community Discussions

No Community Discussions are available at this moment for question-classification-cnn-rnn-attention.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install question-classification-cnn-rnn-attention

You can download it from GitHub.
You can use question-classification-cnn-rnn-attention 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: