sentence-similarity | repository contains various ways to calculate | Natural Language Processing library

by Huffon Python Version: Current License: No License

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kandi X-RAY | sentence-similarity Summary

sentence-similarity is a Python library typically used in Artificial Intelligence, Natural Language Processing, Tensorflow, Bert applications. sentence-similarity has no bugs, it has no vulnerabilities, it has build file available and it has high support. You can download it from GitHub.

This repo contains various ways to calculate the similarity between source and target sentences. You can choose the pre-trained models you want to use such as ELMo, BERT and Universal Sentence Encoder (USE).

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Support

sentence-similarity has a highly active ecosystem.

It has 185 star(s) with 33 fork(s). There are 11 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 1 have been closed. On average issues are closed in 78 days. There are no pull requests.

It has a positive sentiment in the developer community.

The latest version of sentence-similarity is current.

Quality

sentence-similarity has 0 bugs and 0 code smells.

Security

sentence-similarity has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

sentence-similarity code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

sentence-similarity 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

sentence-similarity 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.

sentence-similarity saves you 103 person hours of effort in developing the same functionality from scratch.

It has 262 lines of code, 20 functions and 7 files.

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

Top functions reviewed by kandi - BETA

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

Compute the similarity between two triangles
Calculate the similarity between two sectors
Compute the magnitude difference between two vectors
Compute similarity between two triangles
Calculate theta of src and tgt
Euclidean distance between src and target
Calculate cosine similarity
Calculate similarity
Plots the similarity plot
Compute pairwise cosine cosine cos similarity
Calculate the similarity score
Sum a vector of sentences
Compute similarity between sentences

Get all kandi verified functions for this library.

sentence-similarity Key Features

No Key Features are available at this moment for sentence-similarity.

sentence-similarity Examples and Code Snippets

No Code Snippets are available at this moment for sentence-similarity.

Community Discussions

Trending Discussions on sentence-similarity

Output shape of lambda layer not right in Neural Net. How change it?

QUESTION

Output shape of lambda layer not right in Neural Net. How change it?

Asked 2020-Apr-02 at 07:39

this is my first question on Stackoverflow, so if I missed somehting please point it out to me. I have a Problem with my Lambda layer using keras and tensorflow 1. In this Lambda layer I am taking a 100-dimensional glove Vector as Input and compute cosine similarity to 8 other vectors (I converted to Tensors previously). As ouput I want the eight resulting cosine similarities as a Tensor (I thought this is necessary in tensorflow?).

My Problem now is that the shape of the resulting Tensor obviously is (8, 1), but actually I think I Need the Output shape (None, 8). Otherwise it will not match the subsequent layer in my Network which is the Output layer and should Output six class probabilities.

This is the Code for my custom function I feed into the Lambda layer and took from Sentence similarity using keras:

...

ANSWER

Answered 2020-Apr-02 at 00:00

Simply change your cosine computation to a vectorized operation,

Source https://stackoverflow.com/questions/60967619

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

Vulnerabilities

No vulnerabilities reported

Install sentence-similarity

This project is developed under conda enviroment
After cloning this repository, you can simply install all the dependent libraries described in requirements.txt with bash install.sh

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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