InferSent | Supervised Learning of Universal Sentence Representations | Machine Learning library

by triplemeng Python Version: Current License: No License

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kandi X-RAY | InferSent Summary

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

The repo is an implementation of the paper "Supervised Learning of Universal Sentence Representations from Natural Language Inference Data" (a.k.a. InferSent) by Alexis Conneau et. al.

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InferSent has a low active ecosystem.

It has 14 star(s) with 2 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 7 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of InferSent is current.

Quality

InferSent has 0 bugs and 0 code smells.

Security

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

InferSent code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

InferSent 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

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

InferSent has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

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

Calculate accuracy
Fetch next batch of data
Shuffle the dataframe
Builds the graph
Builds a BSTM as encoder
Builds a vocabulary from data
Read vectors from file
Applies a function to the data
Get next batch of data
Build embedding matrix
Replace tokenized data
Pads the given rep
Find a list of items that match a dictionary

Get all kandi verified functions for this library.

InferSent Key Features

No Key Features are available at this moment for InferSent.

InferSent Examples and Code Snippets

No Code Snippets are available at this moment for InferSent.

Community Discussions

Trending Discussions on InferSent

Create word embeddings without keeping fastText Vector file in the repository

QUESTION

Create word embeddings without keeping fastText Vector file in the repository

Asked 2019-Mar-05 at 21:52

I am trying to embed a sentence with the help of Infersent, and Infersent uses fastText vectors for word embedding. The fastText vector file is close to 5 GiB.

When we keep the fastText vector file along with the code repository it makes the repository size huge, and makes the code difficult to share/deploy (even creating a docker container).

Is there any method to avoid keeping the vector file along with the repository, but reuse it for embedding new sentences?

...

ANSWER

Answered 2019-Mar-05 at 21:52

What kind of sentences are you embedding, is it the same domain as the one on which fastText embeddings were generated?

Try to get a representation of your data in tokens i.e, a set of all tokens, or some representations of the most common tokens that appear in the sentences you want to embed using fastText.

Compute the overlap of your tokens with the tokens in fastText, remove the ones from fastText which don't appear in your data representation.

I did that recently and went from a 1.4GB file with some pre-trained word embeddings to 200 MB, mainly because the overlap with my corpus was around 10%.

Source https://stackoverflow.com/questions/55008804

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

Vulnerabilities

No vulnerabilities reported

Install InferSent

You can download it from GitHub.
You can use InferSent 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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