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recognizers-text-choice

recognizers-text-choice README

With conditionsPyPI Artificial IntelligenceReleased Nov 201998.3K downloads / moMITPure Python

Decision gist · record as of 2026-08-14

pure-Python wheel — recognizers_text_choice-1.0.2a2-py3-none-any.whl
v1.0.2a2 · released 2019-11-12 · 3 runtime deps: recognizers-text, regex, grapheme

Yes, with conditions. The package has low install friction, permissive licensing, and active maintenance. However, its alpha status and lack of recent releases (latest from November 2019) suggest it may not be actively evolving. Install it if you need multilingual choice entity recognition as part of a Microsoft Recognizers-Text integration; verify that the alpha designation doesn't conflict with your production requirements.AI-flagged interpretation of the facts on this page — verify before relying

Before you install

  • Low install friction with a pure-Python wheel.
  • The package is actively maintained with recent commits and has been in active development since its initial release.
  • It depends on recognizers-text, regex, and grapheme—all stable, widely-used libraries.

License · maintenance · safety

MIT (permissive) — MIT license permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.

last release 2019-11-12 (2467 days) · last repo commit 2026-04-17 · 1,793 stars

0 known vulnerabilities (OSV.dev, 2026-08-14) · 98,336 downloads/mo, #13,091 on PyPI

Verify before relying

pip install recognizers-text-choice

from recognizers_text_choice import recognize_choice

result = recognize_choice("yes", "en-us")
  • Whether the package is production-ready despite alpha classification (Development Status :: 3 - Alpha)
  • Specific performance characteristics or accuracy metrics for entity recognition across supported languages
  • Whether Python 3.6 support (listed in classifiers) is still maintained or if newer versions are required
Same gist for agents: .md · .json

What it is and what it does

recognizers-text-choice is a Python binding for Microsoft's Recognizers-Text library, specializing in extracting and normalizing structured entities from unstructured text. It focuses on choice/boolean entity recognition as part of the broader Recognizers-Text ecosystem, which powers LUIS, Power Virtual Agents, and Microsoft Bot Framework. The package depends on recognizers-text for core recognition infrastructure, regex for pattern matching, and grapheme for proper handling of complex Unicode characters.

The library is designed for natural language processing pipelines where you need to identify and resolve boolean choices or similar categorical entities in multiple languages. It's particularly useful in conversational AI, chatbot development, and text analytics applications where standardized entity extraction across languages is required. The package remains in alpha status, indicating it may still be evolving, though the underlying Recognizers-Text project is mature and widely deployed in Microsoft services.

Use it for

  • Extract yes/no or true/false answers from user input in chatbots and conversational agents
  • Normalize boolean choice entities in multilingual text analytics pipelines
  • Preprocess user responses for LUIS or other language understanding services
  • Parse choice-based entities in customer support or survey response automation
  • Build language-agnostic entity extraction for international chatbot applications

Worth the install?

AI-flagged interpretation of the facts on this page. Verify before relying on it.

With conditions

Yes, with conditions.

The package has low install friction, permissive licensing, and active maintenance. However, its alpha status and lack of recent releases (latest from November 2019) suggest it may not be actively evolving. Install it if you need multilingual choice entity recognition as part of a Microsoft Recognizers-Text integration; verify that the alpha designation doesn't conflict with your production requirements.

Install

recognizers-text-choice on PyPI

Before you install

Low install friction with a pure-Python wheel. The package is actively maintained with recent commits and has been in active development since its initial release. It depends on recognizers-text, regex, and grapheme—all stable, widely-used libraries.

License in practice

MIT license permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.

Quickstart

pip install recognizers-text-choice

from recognizers_text_choice import recognize_choice

result = recognize_choice("yes", "en-us")

Verify before relying

  • Whether the package is production-ready despite alpha classification (Development Status :: 3 - Alpha)
  • Specific performance characteristics or accuracy metrics for entity recognition across supported languages
  • Whether Python 3.6 support (listed in classifiers) is still maintained or if newer versions are required

Package facts

LicenseMIT permissive
Python supportNot specified
Install frictionLow. Pure-Python wheel
Runtime dependencies
3 packages
recognizers-textregexgrapheme
MaintenanceActively maintained 2,467 days since the last release
Last repo commit
First released
Downloads98,336 / month, #13,091 on PyPI 30-day window, as of 2026-08-14
Known vulnerabilitiesNone known OSV.dev, checked 2026-08-14
Classifiers
Development Status :: 3 - AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3.6Topic :: Scientific/Engineering :: Artificial Intelligence

Evidence: recognizers_text_choice-1.0.2a2-py3-none-any.whl

Tags

Capabilities
entity recognition multilingualdate time number extractionnlp entity recognizertext parsing language understandingcurrency unit recognitionnamed entity extraction
Topics
entity-extractionmultilingual-nlpconversational-ai
PyPI keywords
nlpnlp-entity-extractionentity-extractionparser-library

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See also recognizers-text · recognizers-text-number · recognizers-text-date-time · recognizers-text-number-with-unit · presidio-analyzer · quantulum3 · num2words · number-parser · date-spacy · ctparse