{"categories":[{"label":"Linguistic","url":"https://skillfed.io/packages/category/text-processing-linguistic"}],"enrichment":{"capability":"Detects the language of text using FastText models, returning language codes and confidence scores with minimal dependencies and offline capability.","skillfed_tags":["nlp","language-detection","fasttext"],"use_cases":["Detect the language of user-submitted text in a web form or API to route it to the correct processing pipeline.","Identify languages in multilingual documents to split or segment content by language before translation or analysis.","Filter or categorize incoming messages by language in a chat or support system without external API calls.","Validate that text input matches an expected language in a localized application.","Build a language-aware search index that tags documents with their detected language for filtering or ranking."],"what_it_does":"fast-langdetect is a language detection library built on FastText that identifies the language of input text and returns a ranked list of language codes with confidence scores. It offers two model modes: a lite model (~45\u201360 MB) that works entirely offline, and a full model (~170\u2013210 MB) that downloads on first use and provides higher accuracy. The library handles text normalization automatically (replacing newlines with spaces), truncates input to 80 characters by default to optimize accuracy, and falls back from the full model to the lite model only if memory runs out when using model='auto'.\n\nThe package is designed for low-friction integration: it requires no NumPy, installs as a pure Python wheel, and caches loaded models in memory within each detector instance to avoid reloading. You can use the global detect() function for simple cases or create a LangDetector instance with custom configuration (cache directory, input length limits, proxy settings) for more control. Language codes follow BCP-47 style (e.g., en, zh-cn, pt-br, yue), and the library provides examples for mapping codes to display names using langcodes or pycountry.","worth_installing":"Yes. The package is actively maintained, has no known vulnerabilities, installs with low friction, and offers both offline and high-accuracy modes. Use it if you need fast, offline-capable language detection without heavy dependencies. The lite model is practical for memory-constrained environments; the full model is appropriate when accuracy matters more than footprint. MIT licensing poses no restrictions."},"id":"fast-langdetect","links":{"html":"https://skillfed.io/packages/fast-langdetect","md":"https://skillfed.io/packages/fast-langdetect.md","pypi":"https://pypi.org/project/fast-langdetect/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-06","license_spdx":null,"license_treatment":"permissive","name":"fast-langdetect","python_support":"supports_current","summary":"Quickly detect text language and segment language"},"popularity":{"monthly_downloads":4349178,"position":2324,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.0.1"}