laserembeddings | LASER multilingual sentence embeddings as a pip package | Natural Language Processing library

by yannvgn Python Version: 1.1.2 License: BSD-3-Clause

X-Ray Key Features Code Snippets(1)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | laserembeddings Summary

laserembeddings is a Python library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning, Pytorch, Bert, Transformer applications. laserembeddings has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However laserembeddings build file is not available. You can install using 'pip install laserembeddings' or download it from GitHub, PyPI.

laserembeddings is a pip-packaged, production-ready port of Facebook Research's LASER (Language-Agnostic SEntence Representations) to compute multilingual sentence embeddings. Version 1.1.2 is here! What's new?.

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Support

laserembeddings has a low active ecosystem.

It has 163 star(s) with 20 fork(s). There are 6 watchers for this library.

It had no major release in the last 12 months.

There are 3 open issues and 16 have been closed. On average issues are closed in 83 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of laserembeddings is 1.1.2

Quality

laserembeddings has 0 bugs and 6 code smells.

Security

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

laserembeddings code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

laserembeddings is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

Deployable package is available in PyPI.

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

Installation instructions, examples and code snippets are available.

laserembeddings saves you 252 person hours of effort in developing the same functionality from scratch.

It has 614 lines of code, 41 functions and 13 files.

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

Top functions reviewed by kandi - BETA

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

Forward embedding
Convert a padding direction
Returns a torch arange
Prints help message to stdout
Download models
Download a file
Download and extract the test data
Extract a tar file
Return a non - Windows string

Get all kandi verified functions for this library.

laserembeddings Key Features

No Key Features are available at this moment for laserembeddings.

laserembeddings Examples and Code Snippets

Optimize a nested for loop in Python

Python

Lines of Code : 6

License : Strong Copyleft (CC BY-SA 4.0)

Copy

avg = np.array(laser.embed_sentences([key_list], lang='si')[0]) 
for key, value in si_data_vec:
    bio = io.BytesIO(value)
    vec = np.load(bio)
    dist = np.linalg.norm(avg-vec)

Community Discussions

Trending Discussions on laserembeddings

How to run LASER sentence embeddings on GPU?

Optimize a nested for loop in Python

QUESTION

How to run LASER sentence embeddings on GPU?

Asked 2021-Apr-19 at 17:05

I have a 11 million sentences corpus that I need to vectorize to do further comparisons. Everything works just fine, with the exception that it is incredibly slow on a CPU (~6 sentences per second). The call to LASER library is very simple and it doesn't have more parameters to tune-up.

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ANSWER

Answered 2021-Apr-19 at 16:41

You are using this library I assume? What GPU do you have? Is it cuda supported?

From this source, it looks like GPU support is enabled by default.

Can you check if pytorch can reach your GPU?

Source https://stackoverflow.com/questions/67165417

QUESTION

Optimize a nested for loop in Python

Asked 2020-Feb-20 at 11:53

I have data stored as key value pairs in a leveldb database. The values are the laser vector embedding of sentences and keys are intents of those sentences. When a new sentence is inputted, I compare the vector embedding of that sentence against the values in the leveldb database in order to identify the intent. Here, I have used a nested for loop and this takes more than 5 seconds to execute. Can someone suggest a way to optimize this loop/ code segment?

expose.py

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ANSWER

Answered 2020-Feb-20 at 11:53

The only thing I can think of is using numpy for distance calculation (as you already import numpy anyway); I am not sure if this will give you much speedup though.

Source https://stackoverflow.com/questions/60316406

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

Vulnerabilities

No vulnerabilities reported

Install laserembeddings

Chinese is not supported by default. If you need to embed Chinese sentences, please install laserembeddings with the "zh" extra. This extra includes jieba. Japanese is not supported by default. If you need to embed Japanese sentences, please install laserembeddings with the "ja" extra. This extra includes mecab-python3 and the ipadic dictionary, which is used in the original LASER project. If you have issues running laserembeddings on Japanese sentences, please refer to mecab-python3 documentation for troubleshooting.

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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