DeepQA | My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot | Machine Learning library

by Conchylicultor Python Version: Current License: Apache-2.0

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

kandi X-RAY | DeepQA Summary

DeepQA is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Neural Network applications. DeepQA has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However DeepQA has 1 bugs. You can download it from GitHub.

My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot

Support

Quality

Security

License

Reuse

Support

DeepQA has a medium active ecosystem.

It has 2910 star(s) with 1200 fork(s). There are 213 watchers for this library.

It had no major release in the last 6 months.

There are 90 open issues and 91 have been closed. On average issues are closed in 135 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of DeepQA is current.

Quality

DeepQA has 1 bugs (0 blocker, 0 critical, 1 major, 0 minor) and 200 code smells.

Security

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

DeepQA code analysis shows 0 unresolved vulnerabilities.

There are 3 security hotspots that need review.

License

DeepQA 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

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

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

DeepQA saves you 1496 person hours of effort in developing the same functionality from scratch.

It has 3336 lines of code, 93 functions and 54 files.

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

Top functions reviewed by kandi - BETA

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

Loads the corpus
Extract examples from a given conversation
Extracts text from a given string
Create the full corpus
Load all open subtitles from a directory
Returns all files in a directory
Returns True if the question is in question
Generate a list of Time objects
Main function for DeepQA
Returns the name of the summary
Get device name
Load embeddings
Builds the network
Returns the length of the vocabulary
Get weights and b
Receive a message from the chatroom
Performs a daemon prediction
Call bot
Handle a new client
Return the name for a client
Convert a vector file into a binary vector
Prints a batch
Play a random dataset

Get all kandi verified functions for this library.

DeepQA Key Features

No Key Features are available at this moment for DeepQA.

DeepQA Examples and Code Snippets

DeepQA,Training DeepQA

Python

Lines of Code : 1

License : No License

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python example.py

Community Discussions

Trending Discussions on DeepQA

Load pretrained word embedding into Tensorflow model

QUESTION

Load pretrained word embedding into Tensorflow model

Asked 2017-Sep-18 at 07:22

I'm trying to modify this Tensorflow LSTM model to load this pre-trained GoogleNews word ebmedding GoogleNews-vectors-negative300.bin (or a tensorflow Word2Vec embedding would be just as good).

I've been reading examples on how to load a pre-trained word embedding into tensorflow (eg. 1: here, 2: here, 3: here and 4: here).

In the first linked example they can easily assign the embedding to the graph:

...

ANSWER

Answered 2017-Mar-28 at 21:04

Correct me if I am wrong, trying to answer with my limited understanding of tensorflow.

Source https://stackoverflow.com/questions/42944787

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

Vulnerabilities

No vulnerabilities reported

Install DeepQA

The program requires the following dependencies (easy to install using pip: pip3 install -r requirements.txt):. You might also need to download additional data to make nltk work. The Cornell dataset is already included. For the other datasets, look at the readme files into their respective folders (inside data/).
python 3.5
tensorflow (tested with v1.0)
numpy
CUDA (for using GPU)
nltk (natural language toolkit for tokenized the sentences)
tqdm (for the nice progression bars)
django (tested with 1.10)
channels
Redis (see here)
asgi_redis (at least 1.0)

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 .

Find more information at: