tf_tagging | 这是一个tensorflow使用的样例，改自于https

by Slyne Python Version: Current License: No License

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

kandi X-RAY | tf_tagging Summary

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

数据(原始数据和embedding)和模型文件链接: 链接: 密码: zvgk 解压到项目文件下即可另外, train.py是核心代码的摘要，忽略。.

Support

Quality

Security

License

Reuse

Support

tf_tagging has a low active ecosystem.

It has 39 star(s) with 26 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 0 have been closed. On average issues are closed in 853 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tf_tagging is current.

Quality

tf_tagging has 0 bugs and 0 code smells.

Security

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

tf_tagging code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tf_tagging 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

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

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

tf_tagging saves you 240 person hours of effort in developing the same functionality from scratch.

It has 586 lines of code, 38 functions and 7 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Run one epoch
Add a summary to the graph
Generate minibatches of data
Build the grammar
Add placeholder variables
Add loss op
Adds logits op
Builds a CoNLL Dataset
Exports the extracted embedding vectors
Builds a vocabulary from a given file
Returns a function for processing words
Runs an interactive shell
Print the sentence spacing
Get a logger
Extracts the embeddings from a file
Return a function for processing a word
Evaluate the model on test set

Get all kandi verified functions for this library.

tf_tagging Key Features

No Key Features are available at this moment for tf_tagging.

tf_tagging Examples and Code Snippets

No Code Snippets are available at this moment for tf_tagging.

Community Discussions

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

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

Vulnerabilities

No vulnerabilities reported

Install tf_tagging

You can download it from GitHub.
You can use tf_tagging 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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