#Lingua

The lingua-py library provides accurate language detection for both long and short texts, including single words, which is useful in natural language processing tasks such as text classification and email sorting. It is a lightweight tool that operates offline without the need for external services. By using a combination of rule-based and statistical methods, it effectively identifies languages from minimal data. The library supports a range of 75 languages and is optimized for performance and memory efficiency through its integration with a Rust implementation.

Lingua-go is an efficient and standalone language detection library suitable for NLP applications such as text classification and spell checking. It addresses common limitations by providing reliable results for both long and short texts without the need for extensive setup or external API connections. Supporting 75 languages, it focuses on delivering high-quality detection through a combination of rule-based and statistical approaches, setting itself apart within the Go programming environment.

This library specializes in determining the language of textual data, making it suitable for preprocessing in NLP applications such as text classification and spell checking. It provides a streamlined alternative to larger machine learning systems, supporting 75 languages with a focus on high-quality detection. Lingua is particularly adept at recognizing languages in short text, including individual words and phrases, without needing configuration or external APIs, thereby enhancing its utility in various text-based scenarios.

Explore a versatile language detection library designed for accurate identification of text languages, from long documents to single words, without the complexity of large machine learning systems. This project overcomes challenges found in other Rust libraries by providing high accuracy across 75 languages, supporting NLP tasks offline with minimal setup. Suitable for various applications like text classification and email routing, it combines rule-based and statistical techniques for reliable language detection.