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The most accurate natural language detection library for Rust, suitable for short text and mixed-language text
| Date | Stars |
|---|---|
| 2026-07-24 | 1107 |
| 2026-07-25 | 1107 |
| 2026-07-28 | 1107 |
| 2026-07-30 | 1107 |
| 2026-08-06 | 1107 |
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<div align="center">  [](https://github.com/pemistahl/lingua-rs/actions/workflows/rust-build.yml) [](https://github.com/pemistahl/lingua-rs/actions/workflows/python-build.yml) [](https://docs.rs/lingua) [](https://codecov.io/gh/pemistahl/lingua-rs) [](#3-which-languages-are-supported) [](https://deps.rs/crate/lingua/1.8.0) [](https://crates.io/crates/lingua) [](https://crates.io/crates/lingua) [](https://lib.rs/crates/lingua) [](https://www.apache.org/licenses/LICENSE-2.0) </div> <br> ## 1. What does this library do? Its task is simple: It tells you which language some text is written in. This is very useful as a preprocessing step for linguistic data in natural language processing applications such as text classification and spell checking. Other use cases, for instance, might include routing e-mails to the right geographically located customer service department, based on the e-mails' languages. ## 2. Why does this library exist? Language detection is often done as part of large machine learning frameworks or natural language processing applications. In cases where you don't need the full-fledged functionality of those systems or don't want to learn the ropes of those, a small flexible library comes in handy. So far, other comprehensive open source libraries in the Rust ecosystem for this task are [*CLD2*](https://github.com/emk/rust-cld2), [*Whatlang*](https://github.com/greyblake/whatlang-rs) and [*Whichlang*](https://github.com/quickwit-oss/whichlang). Unfortunately, most of them have two major drawbacks: 1. Detection only works with quite lengthy text fragments. For very short text snippets such as Twitter messages, they do not provide adequate results. 2. The more languages take part in the decision process, the less accurate are the detection results. *Lingua* aims at eliminating these problems. She nearly does not need any configuration and yields pretty accurate results on both long and short text, even on single words and phrases. She draws on both rule-based and statistical Naive Bayes methods but does not use neural networks or any dictionaries of words. She does not need a connection to any external API or service either. Once the library has been downloaded, it can be used completely offline. ## 3. Which languages are supported? Compared to other language detection libraries, *Lingua's* focus is on *quality over quantity*, that is, getting detection right for a small set of languages first before adding new ones. Currently, the following 75 languages are supported: - A - Afrikaans - Albanian - Arabic - Armenian - Azerbaijani - B - Basque - Belarusian - Bengali - Norwegian Bokmal - Bosnian - Bulgarian - C - Catalan - Chinese - Croatian - Czech - D - Danish - Dutch - E - English - Esperanto - Estonian - F - Finnish - French - G - Ganda - Georgian - German - Greek - Gujarati - H - Hebrew - Hindi - Hungarian - I - Icelandic - Indonesian - Irish - Italian - J - Japanese - K - Kazakh - Korean - L - Latin - Latvian - Lithuanian - M - Macedonian - Malay - Maori - Marathi - Mongolian - N - Norwegian Nynorsk - P - P
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matched fp:3743b7ecd3efaffe, topic:nlp, topic:natural-language-processing, readme:natural language processing