Text-Similarity | Text-Similarity Method in Pytorch | Natural Language Processing library

by pengshuang Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(5)Vulnerabilities Install Support

kandi X-RAY | Text-Similarity Summary

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

Text-Similarity Method in Pytorch

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Security

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Support

Text-Similarity has a low active ecosystem.

It has 381 star(s) with 87 fork(s). There are 12 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 0 have been closed. On average issues are closed in 498 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Text-Similarity is current.

Quality

Text-Similarity has 0 bugs and 0 code smells.

Security

Text-Similarity has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Text-Similarity code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Text-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

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

Text-Similarity 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.

Text-Similarity saves you 422 person hours of effort in developing the same functionality from scratch.

It has 1000 lines of code, 89 functions and 10 files.

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

Top functions reviewed by kandi - BETA

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

Performs the forward attention
Performs soft attention
Apply multiple layers
Submulative multiplication
Computes the bleu score for a hypothesis
Calculate reference length
Compute the bleu score
Compute the score for each candidate
Compute the model
Fill the context mask with v_unmask
Compute the score for a given q and c
Forward computation
Calculate average pooling
Compute the score between two sequences
Beam search
Given a list of siblings of the best k best match
Compute the attention
Compute the attention matrix
Manhattan distance between two vectors
Rescore the test score
Forwarding layer
Compute the output
Compute the match
Compute the bleu score for a single reference
Compute the attention layer
Generate a GRU

Get all kandi verified functions for this library.

Text-Similarity Key Features

No Key Features are available at this moment for Text-Similarity.

Text-Similarity Examples and Code Snippets

No Code Snippets are available at this moment for Text-Similarity.

Community Discussions

Trending Discussions on Text-Similarity

Convert doubles array to a dense vector in ElasticSearch

Python string similarity (with complexity)

How to loop a particular code for each populated cell in a column

TypeError: Fetch argument array has invalid type numpy.ndarray, must be a string or Tensor. (Can not convert a ndarray into a Tensor or Operation.)

Grouping bulk text as into group based on the given similarity percentage

QUESTION

Convert doubles array to a dense vector in ElasticSearch

Asked 2019-Dec-02 at 07:24

I'm attempting to run a relatively simple query based on the dense vector examples:

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ANSWER

Answered 2019-Sep-26 at 19:35

I had the same problem, and I wasn't properly defining the vector field in my index mapping. Have you explicitly defined the field in your mapping, and is the type right? Should be something like

Source https://stackoverflow.com/questions/57895330

QUESTION

Python string similarity (with complexity)

Asked 2019-Nov-23 at 21:21

I have a string I would like to match against a list of candidates. Here is an example:

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ANSWER

Answered 2019-Nov-23 at 21:21

You're looking for the gensim or fuzzywuzzy package.

In this specific case, you're probably leaning towards fuzzywuzzy since you are just trying to do a string match.

gensim is more for calculating similarity scores and vector representations for documents, paragraphs, sentences, words, corpora, etc... with the goal of capturing semantic/topical meaning rather than literal string matching.

So in your case, using fuzzy string matching, you might do:

Source https://stackoverflow.com/questions/59011972

QUESTION

How to loop a particular code for each populated cell in a column

Asked 2019-Sep-03 at 08:11

I am automating the search process and downloading the results .

My code runs for one row, i want to loop the same for each populated cell in 1st column .I have tried the below code but is throws an error

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ANSWER

Answered 2019-Sep-03 at 07:13

You are using the Range property the wrong way.

Change:

Source https://stackoverflow.com/questions/57766325

QUESTION

TypeError: Fetch argument array has invalid type numpy.ndarray, must be a string or Tensor. (Can not convert a ndarray into a Tensor or Operation.)

Asked 2017-Dec-04 at 11:30

I am trying to reproduce the results in siaseme LSTM to compare the semantic similarity of two sentences from here :- https://github.com/dhwajraj/deep-siamese-text-similarity

I am using tensorflow 1.4 & python 2.7

The train.py is working properly. For evaluating the model, I created a match_valid.tsv file which is a subset of "train_snli.txt" available there. I have modified the getTsvTestData function present in the input_helpers.py file.

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ANSWER

Answered 2017-Dec-04 at 11:30

The problem is that you are replacing the value of sim, which (I suppose) initially contains a reference to a TensorFlow tensor or operation, with the result of evaluating it (which is a NumPy array), so the second iteration fails because sim is not a TensorFlow tensor or operation anymore.

You can try something like this:

Source https://stackoverflow.com/questions/47632262

QUESTION

Grouping bulk text as into group based on the given similarity percentage

Asked 2017-Jan-28 at 23:47

I went through the following NLP gems available in GitHub NLP but not able to find the right solution.

Is there any gem or library available for grouping text based on a given similar percentage. All the above gems are helps to find similarity between two string but grouping a bulk array of data taking a lot of time complete.

...

ANSWER

Answered 2017-Jan-28 at 23:47

You can do it by using just Ruby plus one of the listed gems.

I chose fuzzy-string-match because I liked the name

Here's how you use the gem:

Source https://stackoverflow.com/questions/41912851

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

Vulnerabilities

No vulnerabilities reported

Install Text-Similarity

You can download it from GitHub.
You can use Text-Similarity 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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