sense2vec | 🦆 Contextually-keyed word vectors | Natural Language Processing library

by explosion Python Version: 2.0.2 License: MIT

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

kandi X-RAY | sense2vec Summary

sense2vec is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Natural Language Processing applications. sense2vec has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can install using 'pip install sense2vec' or download it from GitHub, PyPI.

sense2vec (Trask et. al, 2015) is a nice twist on word2vec that lets you learn more interesting and detailed word vectors. This library is a simple Python implementation for loading, querying and training sense2vec models. For more details, check out our blog post. To explore the semantic similarities across all Reddit comments of 2015 and 2019, see the interactive demo.

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Support

sense2vec has a highly active ecosystem.

It has 1510 star(s) with 238 fork(s). There are 50 watchers for this library.

It had no major release in the last 12 months.

There are 20 open issues and 91 have been closed. On average issues are closed in 64 days. There are no pull requests.

It has a negative sentiment in the developer community.

The latest version of sense2vec is 2.0.2

Quality

sense2vec has 0 bugs and 0 code smells.

Security

sense2vec has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

sense2vec code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

sense2vec is licensed under the MIT License. This license is Permissive.

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

Reuse

sense2vec releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

sense2vec saves you 805 person hours of effort in developing the same functionality from scratch.

It has 1970 lines of code, 115 functions and 19 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed sense2vec and discovered the below as its top functions. This is intended to give you an instant insight into sense2vec implemented functionality, and help decide if they suit your requirements.

Evaluate Sense2vec
Add a new key
Set frequency for key
Ensure key is an integer
Train a sentence embedding
Find the best sense for a given word
Return the frequency of the given key
Return the most similar key in s2v
Generate a key from an object
Evaluate a given dataset
Find the most similar keys in the table
Return the shape of the file
Evaluate the given keys
Returns True if obj is in s2v
Return a numpy array of s2v
The frequency of the s2v
A sorted list of the frequencies
Return the list of otherenses of an object
Make key from word
Reads a vocab file
Write data to disk
Merge all spans in a document
Return a list of keys that are min_ratio
Find redundant keys in vocab
Make a key from a token
Evaluate the most similar words
Dict key to row2key

Get all kandi verified functions for this library.

sense2vec Key Features

No Key Features are available at this moment for sense2vec.

sense2vec Examples and Code Snippets

Searching the vectors

HTML

Lines of Code : 3

License : Permissive (MIT)

Copy

judicial_review|NOUN
United_Kingdom|GPE
Lord_Pannick|PERSON

Leaf: Multiple-Choice Question Generation,Generating incorrect options (distractors)

Jupyter Notebook

Lines of Code : 2

License : Permissive (Apache-2.0)

Copy

'answer' + ' + 'question' + 'context' 

'distractor1' + ' + 'distractor2' + ' 'distractor3'

Community Discussions

Trending Discussions on sense2vec

sense2vec performance improvemets

QUESTION

sense2vec performance improvemets

Asked 2020-Jan-21 at 08:25

I am using Sense2Vec (builds upon Spacy, a fantastic NLP library/tool) and testing the similarity between various words. Unfortunately the performance is quite slow (execution is over a second on a regular MacBook Pro).

Does anyone have a way to speed this up?

Here is the code:

...

ANSWER

Answered 2020-Jan-21 at 08:25

There is indeed a way to speed up Sense2Vec.most_similar. There is a script available from https://github.com/explosion/sense2vec/blob/master/scripts/06_precompute_cache.py, which precomputes a cache of the nearest neighbors. It will then save that cache with the component on disk, making the data larger but the queries fast. If a query is not covered by the cache, most_similar falls back to using the normal computation.

Source https://stackoverflow.com/questions/59830899

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

Vulnerabilities

No vulnerabilities reported

Install sense2vec

sense2vec releases are available on pip:.

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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