abstractive_summarization | Byte Cup 2018国际机器学习竞赛 23 名（水滴队）代码

by taoyafan Python Version: Current License: No License

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

kandi X-RAY | abstractive_summarization Summary

abstractive_summarization is a Python library. abstractive_summarization has no bugs, it has no vulnerabilities and it has low support. However abstractive_summarization build file is not available. You can download it from GitHub.

这个仓库是字节跳动比赛 Byte Cup 2018，自动生成新闻标题，水滴队（最终成绩23名）的代码。有关比赛的总结，欢迎移步博客：

Support

Quality

Security

License

Reuse

Support

abstractive_summarization has a low active ecosystem.

It has 43 star(s) with 10 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

abstractive_summarization has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of abstractive_summarization is current.

Quality

abstractive_summarization has 0 bugs and 0 code smells.

Security

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

abstractive_summarization code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

abstractive_summarization 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

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

abstractive_summarization 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.

abstractive_summarization saves you 1520 person hours of effort in developing the same functionality from scratch.

It has 3386 lines of code, 165 functions and 19 files.

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

Top functions reviewed by kandi - BETA

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

Build the graph
Creates a dictionary of workflow states
Creates a dict from a given model
Run the test steps
Run nlp
Preprocess a text
Detect encoding from a file
Remove non - ascii characters
Decode the ROUGE
Runs the beam search
Extend the hypothesis
Return True if there are a trigram
Setup the training graph
Convert a non - coverage model to a coverage model
Loads the word embedding
Build the word embedding
Fills the example queue
Decode ROUGE dataset
Builds the graph
Fills the batch of examples
Convert a list of abstract_words to ids
Create a new hypothesis
Given a list of article words return a list of ids
Train the model
Calculate RCS summary level
Run training steps
Create a directory

Get all kandi verified functions for this library.

abstractive_summarization Key Features

No Key Features are available at this moment for abstractive_summarization.

abstractive_summarization Examples and Code Snippets

No Code Snippets are available at this moment for abstractive_summarization.

Community Discussions

No Community Discussions are available at this moment for abstractive_summarization.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install abstractive_summarization

You can download it from GitHub.
You can use abstractive_summarization 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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