{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Recognizes and extracts numeric entities (cardinals, ordinals, percentages, ranges) from text in multiple languages, with support for English, Chinese, French, Spanish, Portuguese, German, Italian, Turkish, Hindi, and Dutch.","skillfed_tags":["nlp-entity-extraction","multilingual","alpha-stage"],"use_cases":["Extract numeric values from user input in chatbots or conversational AI systems for order quantities or date ranges.","Parse financial or scientific documents to identify amounts, percentages, and measurements in multiple languages.","Normalize spoken or written numbers in multilingual customer support tickets for downstream analysis.","Build NLP preprocessing pipelines that require numeric entity recognition before intent classification.","Validate or standardize numeric input in forms or APIs that accept natural-language number expressions."],"what_it_does":"recognizers-text-number is a language-aware numeric entity recognizer extracted from Microsoft's broader Recognizers-Text project. It identifies and extracts numbers expressed in natural language\u2014cardinals, ordinals, percentages, and ranges\u2014from text and normalizes them to machine-readable form. The package wraps the base recognizers-text library and the regex dependency to provide language-specific parsing rules.\n\nIt is designed for NLP pipelines where numeric entities must be identified before downstream processing. The package supports full recognition in English, Chinese, French, Spanish, Portuguese, German, Italian, Turkish, Hindi, and Dutch, with partial support in Japanese, Korean, Arabic, and Swedish. It powers entity extraction in Microsoft's LUIS, Power Virtual Agents, and Bot Framework, and is available as a standalone package for integration into custom applications.","worth_installing":"Yes, if you need multilingual numeric entity extraction and can accept that the package is in alpha status with no updates since 2019. The low install friction, active underlying repository, and MIT license make it a reasonable choice for NLP pipelines. However, verify that the specific language and entity types you need are supported, and be aware that the package itself is not actively maintained."},"id":"recognizers-text-number","links":{"html":"https://skillfed.io/packages/recognizers-text-number","md":"https://skillfed.io/packages/recognizers-text-number.md","pypi":"https://pypi.org/project/recognizers-text-number/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2019-11-12","license_spdx":null,"license_treatment":"permissive","name":"recognizers-text-number","python_support":"unspecified","summary":"recognizers-text-number README"},"popularity":{"monthly_downloads":100356,"position":12988,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.2a2"}