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wordfreq | word frequencies, in various natural languages | Natural Language Processing library

 by   LuminosoInsight Python Version: v2.2 License: MIT

 by   LuminosoInsight Python Version: v2.2 License: MIT

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kandi X-RAY | wordfreq Summary

wordfreq is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. wordfreq has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install wordfreq' or download it from GitHub, PyPI.
wordfreq is a Python library for looking up the frequencies of words in many languages, based on many sources of data.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • wordfreq has a low active ecosystem.
  • It has 424 star(s) with 34 fork(s). There are 45 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 6 open issues and 13 have been closed. On average issues are closed in 73 days. There are 1 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of wordfreq is v2.2
wordfreq Support
Best in #Natural Language Processing
Average in #Natural Language Processing
wordfreq Support
Best in #Natural Language Processing
Average in #Natural Language Processing

quality kandi Quality

  • wordfreq has 0 bugs and 0 code smells.
wordfreq Quality
Best in #Natural Language Processing
Average in #Natural Language Processing
wordfreq Quality
Best in #Natural Language Processing
Average in #Natural Language Processing

securitySecurity

  • wordfreq has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • wordfreq code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
wordfreq Security
Best in #Natural Language Processing
Average in #Natural Language Processing
wordfreq Security
Best in #Natural Language Processing
Average in #Natural Language Processing

license License

  • wordfreq is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
wordfreq License
Best in #Natural Language Processing
Average in #Natural Language Processing
wordfreq License
Best in #Natural Language Processing
Average in #Natural Language Processing

buildReuse

  • wordfreq 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.
  • wordfreq saves you 413 person hours of effort in developing the same functionality from scratch.
  • It has 980 lines of code, 71 functions and 17 files.
  • It has low code complexity. Code complexity directly impacts maintainability of the code.
wordfreq Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
wordfreq Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
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wordfreq Key Features

Access a database of word frequencies, in various natural languages.

wordfreq Examples and Code Snippets

Community Discussions

Vulnerabilities

No vulnerabilities reported

Install wordfreq

wordfreq requires Python 3 and depends on a few other Python modules (msgpack, langcodes, and regex). You can install it and its dependencies in the usual way, either by getting it from pip:.
Chinese, Japanese, and Korean have additional external dependencies so that they can be tokenized correctly. They can all be installed at once by requesting the 'cjk' feature:. Tokenizing Chinese depends on the jieba package, tokenizing Japanese depends on mecab-python3 and ipadic, and tokenizing Korean depends on mecab-python3 and mecab-ko-dic. As of version 2.4.2, you no longer have to install dictionaries separately.

Support

This data comes from a Luminoso project called Exquisite Corpus, whose goal is to download good, varied, multilingual corpus data, process it appropriately, and combine it into unified resources such as wordfreq.

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