question-generation | sentence automatically generate reading | Natural Language Processing library

by sumehta Python Version: Current License: MIT

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

kandi X-RAY | question-generation Summary

question-generation is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. question-generation has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However question-generation build file is not available. You can download it from GitHub.

Given a statement of text such as,. Handheld devices find ways to bolster U.S. homeland defense and response.

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question-generation has a low active ecosystem.

It has 103 star(s) with 29 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 8 have been closed. On average issues are closed in 184 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of question-generation is current.

Quality

question-generation has 0 bugs and 1 code smells.

Security

question-generation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

question-generation code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

question-generation 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

question-generation releases are not available. You will need to build from source code and install.

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

Installation instructions, examples and code snippets are available.

question-generation saves you 19 person hours of effort in developing the same functionality from scratch.

It has 53 lines of code, 5 functions and 2 files.

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

Top functions reviewed by kandi - BETA

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

Generate a question
Get the raw output of the question
Generate sentence pairs
Add the arguments

Get all kandi verified functions for this library.

question-generation Key Features

No Key Features are available at this moment for question-generation.

question-generation Examples and Code Snippets

No Code Snippets are available at this moment for question-generation.

Community Discussions

Trending Discussions on question-generation

ValueError: You have to specify either decoder_input_ids or decoder_inputs_embeds

QUESTION

ValueError: You have to specify either decoder_input_ids or decoder_inputs_embeds

Asked 2020-Dec-06 at 22:12

Trying to convert a question-generation t5 model to torchscript model, while doing that Running into this error

ValueError: You have to specify either decoder_input_ids or decoder_inputs_embeds

here's the code that I ran on colab.

...

ANSWER

Answered 2020-Dec-06 at 22:12

I figured out what was causing the issue. Since the above model is sequential, it has both an encoder and a decoder. We need to pass the features into the encoder and labels (targets) into the decoder.

Source https://stackoverflow.com/questions/65140400

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

Vulnerabilities

No vulnerabilities reported

Install question-generation

To run the original code you need to have a JAVA Runtime Environment installed. JAVA downloads can be found on the Oracle website here. Version 1.6.0_07 of JAVA was used in developing the original system. The code is packaged for use on UNIX systems or for use in the Eclipse IDE. The following are the dependencies of the original code. -Apache Commons Lang (http://commons.apache.org/lang/). -Apahce Commons Logging (http://commons.apache.org/logging/). -Stanford NLP tools (http://www-nlp.stanford.edu/software/). -The sst-light-0.4 release of the SuperSenseTagger, from which we used the SemCor data for training the supersense tagger (http://sourceforge.net/projects/supersensetag/). -The Semcor corpus, used for training the supersense tagger (http://www.cse.unt.edu/~rada/downloads.html#semcor). -The WEKA toolkit, version 3.6.0 (http://www.cs.waikato.ac.nz/ml/weka/). The good news is that you don't need to download each one of those individually :grinning:. Everything is neatly packed in the QuestionGeneration.zip bundled with the code.

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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