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
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
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
- 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
Security
- 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
- 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
Reuse
- 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.
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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