sentence2vec | arbitrary length to vector space | Genomics library

by klb3713 Python Version: Current License: No License

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

kandi X-RAY | sentence2vec Summary

sentence2vec is a Python library typically used in Artificial Intelligence, Genomics applications. sentence2vec has no bugs, it has no vulnerabilities and it has low support. However sentence2vec build file is not available. You can download it from GitHub.

Tools for mapping a sentence with arbitrary length to vector space. We provide an implementation of the Paragraph Vector in Quoc Le and Tomas Mikolov’s paper: Distributed representations of Sentences and Documents. This project is based on [gensim][1]. 2014-9-23 update: add test files for demo.

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Support

sentence2vec has a low active ecosystem.

It has 671 star(s) with 227 fork(s). There are 60 watchers for this library.

It had no major release in the last 6 months.

There are 15 open issues and 0 have been closed. On average issues are closed in 1730 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of sentence2vec is current.

Quality

sentence2vec has 0 bugs and 0 code smells.

Security

sentence2vec has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

sentence2vec code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

sentence2vec 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

sentence2vec releases are not available. You will need to build from source code and install.

sentence2vec 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.

Top functions reviewed by kandi - BETA

kandi has reviewed sentence2vec and discovered the below as its top functions. This is intended to give you an instant insight into sentence2vec implemented functionality, and help decide if they suit your requirements.

Train the model
Train a single sentence using cbow
Create a new array of zeros
Return the value of the term
Load a Word2vec from file
Makes a closing object
Attempt to open a file
Close the file
Calculate accuracy for each question
Saves the object to fname
Write a corpus to a file
Builds the vocabulary
Load data from a file
Save the word2vec to a file
Convert a corpus to dense format
Return the distance between the words in the corpus
Create a Pyro4 daemon
Compute the similarity between two documents
Compute similarity between two sentences
Save sentence weights to a file
Compute similarity between two vectors
Yield a file - like object
Convert a numpy array into a list of tuples
Create a Dictionary from a corpus
Tokenize a docstring
Convert a numpy array to a list of integers

Get all kandi verified functions for this library.

sentence2vec Key Features

No Key Features are available at this moment for sentence2vec.

sentence2vec Examples and Code Snippets

No Code Snippets are available at this moment for sentence2vec.

Community Discussions

Trending Discussions on sentence2vec

Sentence2vec and Word2vec involving stop words and Named Entities

QUESTION

Sentence2vec and Word2vec involving stop words and Named Entities

Asked 2018-Feb-28 at 09:53

I'm working on a NLP project, involving sentence2vec. I'm presuming I would be using pre-trained word embeddings for converting tokens into vectors and then proceeding to sentence embedding.

Since my sentence involves : stop words like can't, won't, aren't etc. which NLTK would reduce to {ca, wo, are} + not.
So I can't reduce them, and I don't want to remove them as stop words since sentences like mentioned below, should have different embedding.

My name is Priyank
My name is not Priyank

Another Important doubt is that how to incorporate Named entities such as the name of a person like Mark K. Hogg in my sentence vector.

...

ANSWER

Answered 2018-Feb-28 at 09:53

you can remove the ones you do not want to be as stop words from this list

Source https://stackoverflow.com/questions/49010792

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

Vulnerabilities

No vulnerabilities reported

Install sentence2vec

You can download it from GitHub.
You can use sentence2vec 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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