dl-models-for-qa | Keras DL models to answer 8th grade science | Machine Learning library

by sujitpal Python Version: Current License: Apache-2.0

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

kandi X-RAY | dl-models-for-qa Summary

dl-models-for-qa is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Keras applications. dl-models-for-qa has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However dl-models-for-qa build file is not available. You can download it from GitHub.

This repository contains Python code to train and deploy Deep Learning (DL) models for Question Answering (QA). The code accompanies a talk I gave at the Question Answering Workshop organized by the Elsevier Search Guild. You can find the slides for this talk on Slideshare. Code is in Python. All the models are built using the awesome Keras library. Supporting code also uses gensim, NLTK and Spacy. Objective of the code was to build DL model(s) to answer 8th grade multiple-choice science questions, provided as part of this AllenAI competition on Kaggle.

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dl-models-for-qa has a low active ecosystem.

It has 241 star(s) with 74 fork(s). There are 18 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 3 have been closed. On average issues are closed in 28 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of dl-models-for-qa is current.

Quality

dl-models-for-qa has 0 bugs and 12 code smells.

Security

dl-models-for-qa has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

dl-models-for-qa code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

dl-models-for-qa 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

dl-models-for-qa releases are not available. You will need to build from source code and install.

dl-models-for-qa 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.

dl-models-for-qa saves you 668 person hours of effort in developing the same functionality from scratch.

It has 1548 lines of code, 21 functions and 22 files.

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

Top functions reviewed by kandi - BETA

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

Find the most stories for a question .
Get all the stories from a taskfile .
Returns a list of question pairs from a file .
Build vocabulary .
Parse a story question question question question answer .
get weights from word2vec
Vectorize data .
Vectorize a sequence of sequences .
Builds a vocabulary from a sqa triples .
Vectorize a sequence of qapairs .

Get all kandi verified functions for this library.

dl-models-for-qa Key Features

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dl-models-for-qa Examples and Code Snippets

No Code Snippets are available at this moment for dl-models-for-qa.

Community Discussions

Trending Discussions on dl-models-for-qa

Keras: compute cosine distance between two flattened outputs

QUESTION

Keras: compute cosine distance between two flattened outputs

Asked 2018-Apr-24 at 20:27

EDIT2: my code https://github.com/hcl14/my_simple_LSTM

I have the model of the following structure: Two LSTMs (question and answer) and additional attention layer which can be considered on top of answer. Here is the version which uses sum and softmax to compare two outputs:

...

ANSWER

Answered 2018-Apr-24 at 20:27

You're working with branches. Don't use sequential models with branches.

You can use qenc and aenc as Sequential models, no problem, because they're a single path, no ramifications.

I'm taking examples here from the first part of your code.

Updating the calls that are using keras 1:

Source https://stackoverflow.com/questions/49941903

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

Vulnerabilities

No vulnerabilities reported

Install dl-models-for-qa

You can download it from GitHub.
You can use dl-models-for-qa 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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