Lingua Rs Versions Save

Features

• The new method LanguageDetector.detect_multiple_languages_of() has been introduced. It allows to detect multiple languages in mixed-language text. (#1)

• The new method LanguageDetectorBuilder.with_low_accuracy_mode() has been introduced. By activating it, detection accuracy for short text is reduced in favor of a smaller memory footprint and faster detection performance. (#119)

• The new method LanguageDetector.compute_language_confidence() has been introduced. It allows to retrieve the confidence value for one specific language only, given the input text. (#102)

Improvements

• The computation of the confidence values has been revised and the softmax function is now applied to the values, making them better comparable by behaving more like real probabilities. (#120)

• The WASM API has been revised. Now it makes use of the same builder pattern as the Rust API. (#122)

• The language model files are now compressed with the Brotli algorithm which reduces the file size by 15 %, on average. (#189)

• The language model ngrams are now stored in a CompactString type which reduces the amount of consumed memory by 20 %. (#198)

• Several performance optimizations have been applied which makes the library nearly twice as fast as the previous version. Big thanks go out to @serega and @koute for their help. (#82, #148, #177)

• The enums IsoCode639_1 and IsoCode639_3 now implement some new traits such as Copy, Hash and Serde's Serialize and Deserialize. The enum Language now implements Copy as well. (#175)

Features

• The library can now be compiled to WebAssembly and be used in any JavaScript project. Big thanks to @martindisch for bringing this forward. (#14)

Improvements

• Some minor performance tweaks have been applied to the rule engine.

Bug Fixes

• This release updates outdated dependencies and fixes an incompatibility between different versions of the include_dir crate which are used in the main lingua crate and the language model crates.

Bug Fixes

• Another compilation error has been fixed which occurred when the Latin language was left out as Cargo feature.

Bug Fixes

• When Chinese, Japanese or Korean were left out as Cargo features, there were compilation errors. This has been fixed.

Features

• The language model dependencies are separate Cargo features now. Users can decide which languages shall be downloaded and used in the library. (#12)

Improvements

• The code that does the lazy-loading of the language models has been refactored significantly, making the code more stable and less error-prone.

Bug Fixes

• In very rare cases, the language returned by the detector was non-deterministic. This has been fixed. Big thanks to @asg0451 for identifying this problem. (#17)

Features

• The enums Language, IsoCode639_1 and IsoCode639_3 now implement std::str::FromStr in order to instantiate enum variants by string values. This comes in handy for JavaScript bindings and the like. (#15)

Improvements

• The performance of preloading the language models has been improved.

Bug Fixes

• Language detection for sentences with more than 120 characters was supposed to be done by iterating through trigrams only but this was never the case. This has been corrected.

Improvements

• Language detection for sentences with more than 120 characters now performs more quickly by iterating through trigrams only which is enough to achieve high detection accuracy.
• Textual input that includes logograms from Chinese, Japanese or Korean is now split at each logogram and not only at whitespace. This provides for more reliable language detection for sentences that include multi-language content.

Bug Fixes

• Errors in the rule engine for the Latvian language have been resolved.
• Corrupted characters in the Latvian test data have been corrected.

Features

• A LanguageDetector can now be built with lazy-loading required language models on demand (default) or with preloading all language models at once by calling LanguageDetectorBuilder.with_preloaded_language_models(). (#10)

Languages

• The Maori language is now supported. Thanks to @eekkaiia for the contribution. (#5)

Performance

• Loading and searching the language models has been quite slow so far. Using parallel iterators from the Rayon library, this process is now at least 50% faster, depending on how many CPU cores are available. (#8)

Accuracy Reports

• Accuracy reports are now also generated for the CLD2 library and included in the language detector comparison plots. (#6)