NRTR | TensorFlow implementation of NRTR , a No-Recurrence Seq2Seq | Machine Learning library

by Belval Python Version: Current License: MIT

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

kandi X-RAY | NRTR Summary

NRTR is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Neural Network, Transformer applications. NRTR has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However NRTR build file is not available. You can download it from GitHub.

A TensorFlow implementation of NRTR, a No-Recurrence Seq2Seq Model for Scene Text Recognition. It is a Transformer-based model adapted to work with Images as input instead of text.

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NRTR has a low active ecosystem.

It has 27 star(s) with 8 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 651 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of NRTR is current.

Quality

NRTR has 0 bugs and 0 code smells.

Security

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

NRTR code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

NRTR 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

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

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

NRTR saves you 187 person hours of effort in developing the same functionality from scratch.

It has 461 lines of code, 26 functions and 5 files.

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

Top functions reviewed by kandi - BETA

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

Train the optimizer
Convert groundtruth to a word
Argument parser
Test the model

Get all kandi verified functions for this library.

NRTR Key Features

No Key Features are available at this moment for NRTR.

NRTR Examples and Code Snippets

No Code Snippets are available at this moment for NRTR.

Community Discussions

Trending Discussions on NRTR

Jinja2 syntax error: expected token 'end of print statement'

QUESTION

Jinja2 syntax error: expected token 'end of print statement'

Asked 2019-Mar-12 at 21:39

The below Jinja2 template is used by Ansible to generate a named zone configuration file. It fails during playbook execution (using template module) but the error is a bit cryptic to me so I didn't manage to fix it.

...

ANSWER

Answered 2019-Mar-09 at 23:12

{{ ansible_hostname }} IN A {{ ansible_{{net_int_dmz_untrusted}}['ipv4']['address'] }}

You can't use nested expressions like that, as Jinja does not do recursive evaluation. To do what you're trying to do, treat the vars as a dict and look up the key that way:

Source https://stackoverflow.com/questions/55081219

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

Vulnerabilities

No vulnerabilities reported

Install NRTR

You can download it from GitHub.
You can use NRTR 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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