word2vec

word2vec

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About

Coverage.py is a tool for measuring code coverage of Python programs. It monitors your program, noting which parts of the code have been executed, then analyzes the source to identify code that could have been executed but was not. Coverage measurement is typically used to gauge the effectiveness of tests. It can show which parts of your code are being exercised by tests, and which are not. Use coverage run to run your test suite and gather data. However you normally run your test suite, and you can run your test runner under coverage. If your test runner command starts with “python”, just replace the initial “python” with “coverage run”. To limit coverage measurement to code in the current directory, and also find files that weren’t executed at all, add the source argument to your coverage command line. By default, it will measure line (statement) coverage. It can also measure branch coverage. It can tell you what tests ran which lines.

About

The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. Whether you're new to programming or an experienced developer, it's easy to learn and use Python. Python can be easy to pick up whether you're a first-time programmer or you're experienced with other languages. The following pages are a useful first step to get on your way to writing programs with Python! The community hosts conferences and meetups to collaborate on code, and much more. Python's documentation will help you along the way, and the mailing lists will keep you in touch. The Python Package Index (PyPI) hosts thousands of third-party modules for Python. Both Python's standard library and the community-contributed modules allow for endless possibilities.

About

Word2Vec is a neural network-based technique for learning word embeddings, developed by researchers at Google. It transforms words into continuous vector representations in a multi-dimensional space, capturing semantic relationships based on context. Word2Vec uses two main architectures: Skip-gram, which predicts surrounding words given a target word, and Continuous Bag-of-Words (CBOW), which predicts a target word based on surrounding words. By training on large text corpora, Word2Vec generates word embeddings where similar words are positioned closely, enabling tasks like semantic similarity, analogy solving, and text clustering. The model was influential in advancing NLP by introducing efficient training techniques such as hierarchical softmax and negative sampling. Though newer embedding models like BERT and Transformer-based methods have surpassed it in complexity and performance, Word2Vec remains a foundational method in natural language processing and machine learning research.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Any user looking for a solution to measure line and branch coverage to produce test reports

Audience

Developers interested in a beautiful but advanced programming language

Audience

Researchers, data scientists, and developers working in natural language processing (NLP) and machine learning who need efficient word embeddings for text analysis and semantic understanding

Support

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Support

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24/7 Live Support
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API

Offers API

API

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API

Offers API

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Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
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support 0.0 / 5

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Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5
design 5.0 / 5
support 5.0 / 5

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Coverage.py
United States
coverage.readthedocs.io/en/7.0.0/

Company Information

Python
Founded: 1991
www.python.org

Company Information

Google
Founded: 1998
United States
code.google.com/archive/p/word2vec/

Alternatives

Alternatives

Alternatives

JCov

JCov

OpenJDK
Gensim

Gensim

Radim Řehůřek
GloVe

GloVe

Stanford NLP
blanket.js

blanket.js

Blanket.js
Devel::Cover

Devel::Cover

metacpan
LexVec

LexVec

Alexandre Salle

Categories

Categories

Categories

Integrations

AiXcoder
Bottle
Convex
CoppeliaSim
DeepSeek R1
FOSSA
Gemini 2.5 Flash-Lite
IPLocate
JSON
Lilac
Massive
ModelOp
Navie AI
Neovim
Rainforest
Sensible
SuperNova Proxies
Svix
Workona
jEdit

Integrations

AiXcoder
Bottle
Convex
CoppeliaSim
DeepSeek R1
FOSSA
Gemini 2.5 Flash-Lite
IPLocate
JSON
Lilac
Massive
ModelOp
Navie AI
Neovim
Rainforest
Sensible
SuperNova Proxies
Svix
Workona
jEdit

Integrations

AiXcoder
Bottle
Convex
CoppeliaSim
DeepSeek R1
FOSSA
Gemini 2.5 Flash-Lite
IPLocate
JSON
Lilac
Massive
ModelOp
Navie AI
Neovim
Rainforest
Sensible
SuperNova Proxies
Svix
Workona
jEdit
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