{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Recognizes and extracts entities like numbers, units, dates, and times from text in multiple languages, returning structured resolution data for each match.","skillfed_tags":["entity-extraction","multilingual-nlp","alpha-stage"],"use_cases":["Extract structured date and time expressions from user messages in chatbots or voice assistants.","Parse currency amounts and unit measurements from product descriptions or user input.","Identify and normalize phone numbers, email addresses, and URLs in text for data cleaning.","Build multilingual NLP pipelines that need to recognize numbers, ordinals, and percentages across supported languages.","Pre-process text for downstream ML models by extracting and normalizing temporal and numeric entities."],"what_it_does":"Recognizers-Text is a multilingual entity recognition library that identifies and resolves structured entities\u2014numbers, ordinals, percentages, units (currency, temperature, dimensions, age), dates, times, email addresses, phone numbers, URLs, and more\u2014from natural language text. It powers Microsoft's LUIS, Power Virtual Agents, and Bot Framework, and is also available as a standalone package.\n\nThe library targets multiple languages with varying levels of support: full support for English, Chinese, French, Spanish, Portuguese, German, Italian, Turkish, Hindi, and Dutch; partial support for Japanese, Korean, Arabic, and Swedish. It depends on emoji and multipledispatch for runtime operation. The Python package is currently in alpha status and has not been updated since November 2019, though the upstream repository remains active.","worth_installing":"Yes, with conditions. The package is permissively licensed and has low install friction, making it suitable for prototyping and integration into Microsoft ecosystem tools. However, it is alpha-stage and has not been updated since 2019; use it for production only if you can accept the maintenance risk and have validated that entity recognition quality meets your requirements for your target languages."},"id":"recognizers-text","links":{"html":"https://skillfed.io/packages/recognizers-text","md":"https://skillfed.io/packages/recognizers-text.md","pypi":"https://pypi.org/project/recognizers-text/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2019-11-12","license_spdx":null,"license_treatment":"permissive","name":"recognizers-text","python_support":"unspecified","summary":"recognizers-text README"},"popularity":{"monthly_downloads":101349,"position":12943,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.2a2"}