Crate lingua

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, Whatlang and 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, it does 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 methods but does not use 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, 75 languages are supported. They are listed as variants in the Language enum.

4. How good is it?

Lingua is able to report accuracy statistics for some bundled test data available for each supported language. The test data for each language is split into three parts:

1. a list of single words with a minimum length of 5 characters
2. a list of word pairs with a minimum length of 10 characters
3. a list of complete grammatical sentences of various lengths

Both the language models and the test data have been created from separate documents of the Wortschatz corpora offered by Leipzig University, Germany. Data crawled from various news websites have been used for training, each corpus comprising one million sentences. For testing, corpora made of arbitrarily chosen websites have been used, each comprising ten thousand sentences. From each test corpus, a random unsorted subset of 1000 single words, 1000 word pairs and 1000 sentences has been extracted, respectively.

Given the generated test data, I have compared the detection results of Lingua, CLD2, Whatlang and Whichlang running over the data of Lingua’s supported 75 languages. Languages that are not supported by the other classifiers are simply ignored for the respective library during the detection process.

The results of this comparison are available here.

5. Why is it better than other libraries?

Every language detector uses a probabilistic n-gram model trained on the character distribution in some training corpus. Most libraries only use n-grams of size 3 (trigrams) which is satisfactory for detecting the language of longer text fragments consisting of multiple sentences. For short phrases or single words, however, trigrams are not enough. The shorter the input text is, the less n-grams are available. The probabilities estimated from such few n-grams are not reliable. This is why Lingua makes use of n-grams of sizes 1 up to 5 which results in much more accurate prediction of the correct language.

A second important difference is that Lingua does not only use such a statistical model, but also a rule-based engine. This engine first determines the alphabet of the input text and searches for characters which are unique in one or more languages. If exactly one language can be reliably chosen this way, the statistical model is not necessary anymore. In any case, the rule-based engine filters out languages that do not satisfy the conditions of the input text. Only then, in a second step, the probabilistic n-gram model is taken into consideration. This makes sense because loading less language models means less memory consumption and better runtime performance.

In general, it is always a good idea to restrict the set of languages to be considered in the classification process using the respective api methods. If you know beforehand that certain languages are never to occur in an input text, do not let those take part in the classification process. The filtering mechanism of the rule-based engine is quite good, however, filtering based on your own knowledge of the input text is always preferable.

6. How to add it to your project?

Add Lingua to your Cargo.toml file like so:

[dependencies]
lingua = "1.6.2"

By default, this will download the language model dependencies for all 75 supported languages, a total of approximately 90 MB. If your bandwidth or hard drive space is limited, or you simply do not need all languages, you can specify a subset of the language models to be downloaded as separate features in your Cargo.toml:

[dependencies]
lingua = { version = "1.6.2", default-features = false, features = ["french", "italian", "spanish"] }

7. How to use?

7.1 Basic usage

use lingua::{Language, LanguageDetector, LanguageDetectorBuilder};
use lingua::Language::{English, French, German, Spanish};

let languages = vec![English, French, German, Spanish];
let detector: LanguageDetector = LanguageDetectorBuilder::from_languages(&languages).build();
let detected_language: Option<Language> = detector.detect_language_of("languages are awesome");

assert_eq!(detected_language, Some(English));

7.2 Minimum relative distance

By default, Lingua returns the most likely language for a given input text. However, there are certain words that are spelled the same in more than one language. The word prologue, for instance, is both a valid English and French word. Lingua would output either English or French which might be wrong in the given context. For cases like that, it is possible to specify a minimum relative distance that the logarithmized and summed up probabilities for each possible language have to satisfy. It can be stated in the following way:

use lingua::LanguageDetectorBuilder;
use lingua::Language::{English, French, German, Spanish};

let detector = LanguageDetectorBuilder::from_languages(&[English, French, German, Spanish])
    .with_minimum_relative_distance(0.9)
    .build();
let detected_language = detector.detect_language_of("languages are awesome");

assert_eq!(detected_language, None);

Be aware that the distance between the language probabilities is dependent on the length of the input text. The longer the input text, the larger the distance between the languages. So if you want to classify very short text phrases, do not set the minimum relative distance too high. Otherwise None will be returned most of the time as in the example above. This is the return value for cases where language detection is not reliably possible.

7.3 Confidence values

Knowing about the most likely language is nice but how reliable is the computed likelihood? And how less likely are the other examined languages in comparison to the most likely one? These questions can be answered as well:

use lingua::Language::{English, French, German, Spanish};
use lingua::{Language, LanguageDetectorBuilder};

let languages = vec![English, French, German, Spanish];
let detector = LanguageDetectorBuilder::from_languages(&languages).build();
let confidence_values: Vec<(Language, f64)> = detector
    .compute_language_confidence_values("languages are awesome")
    .into_iter()
    // Let's round the values to two decimal places for easier assertions
    .map(|(language, confidence)| (language, (confidence * 100.0).round() / 100.0))
    .collect();

assert_eq!(
    confidence_values,
    vec![(English, 0.93), (French, 0.04), (German, 0.02), (Spanish, 0.01)]
);

In the example above, a vector of two-element tuples is returned containing all possible languages sorted by their confidence value in descending order. Each value is a probability between 0.0 and 1.0. The probabilities of all languages will sum to 1.0. If the language is unambiguously identified by the rule engine, the value 1.0 will always be returned for this language. The other languages will receive a value of 0.0.

There is also a method for returning the confidence value for one specific language only:

use lingua::Language::{English, French, German, Spanish};
use lingua::LanguageDetectorBuilder;

let languages = vec![English, French, German, Spanish];
let detector = LanguageDetectorBuilder::from_languages(&languages).build();
let confidence = detector.compute_language_confidence("languages are awesome", French);
let rounded_confidence = (confidence * 100.0).round() / 100.0;

assert_eq!(rounded_confidence, 0.04);

The value that this method computes is a number between 0.0 and 1.0. If the language is unambiguously identified by the rule engine, the value 1.0 will always be returned. If the given language is not supported by this detector instance, the value 0.0 will always be returned.

7.4 Eager loading versus lazy loading

By default, Lingua uses lazy-loading to load only those language models on demand which are considered relevant by the rule-based filter engine. For web services, for instance, it is rather beneficial to preload all language models into memory to avoid unexpected latency while waiting for the service response. If you want to enable the eager-loading mode, you can do it like this:

use lingua::LanguageDetectorBuilder;

LanguageDetectorBuilder::from_all_languages().with_preloaded_language_models().build();

Multiple instances of LanguageDetector share the same language models in memory which are accessed asynchronously by the instances.

7.5 Low accuracy mode versus high accuracy mode

Lingua’s high detection accuracy comes at the cost of being noticeably slower than other language detectors. The large language models also consume significant amounts of memory. These requirements might not be feasible for systems running low on resources. If you want to classify mostly long texts or need to save resources, you can enable a low accuracy mode that loads only a small subset of the language models into memory:

use lingua::LanguageDetectorBuilder;

LanguageDetectorBuilder::from_all_languages().with_low_accuracy_mode().build();

The downside of this approach is that detection accuracy for short texts consisting of less than 120 characters will drop significantly. However, detection accuracy for texts which are longer than 120 characters will remain mostly unaffected.

In high accuracy mode (the default), the language detector consumes approximately 1,200 MB of memory if all language models are loaded. In low accuracy mode, memory consumption is reduced to approximately 90 MB. The goal is to further reduce memory consumption in later releases.

An alternative for a smaller memory footprint and faster performance is to reduce the set of languages when building the language detector. In most cases, it is not advisable to build the detector from all supported languages. When you have knowledge about the texts you want to classify you can almost always rule out certain languages as impossible or unlikely to occur.

7.6 Detection of multiple languages in mixed-language texts

In contrast to most other language detectors, Lingua is able to detect multiple languages in mixed-language texts. This feature can yield quite reasonable results, but it is still in an experimental state and therefore the detection result is highly dependent on the input text. It works best in high-accuracy mode with multiple long words for each language. The shorter the phrases and their words are, the less accurate are the results. Reducing the set of languages when building the language detector can also improve accuracy for this task if the languages occurring in the text are equal to the languages supported by the respective language detector instance.

use lingua::DetectionResult;
use lingua::Language::{English, French, German};
use lingua::LanguageDetectorBuilder;

let languages = vec![English, French, German];
let detector = LanguageDetectorBuilder::from_languages(&languages).build();
let sentence = "Parlez-vous français? \
    Ich spreche Französisch nur ein bisschen. \
    A little bit is better than nothing.";

let results: Vec<DetectionResult> = detector.detect_multiple_languages_of(sentence);

if let [first, second, third] = &results[..] {
    assert_eq!(first.language(), French);
    assert_eq!(
        &sentence[first.start_index()..first.end_index()],
        "Parlez-vous français? "
    );

    assert_eq!(second.language(), German);
    assert_eq!(
        &sentence[second.start_index()..second.end_index()],
        "Ich spreche Französisch nur ein bisschen. "
    );

    assert_eq!(third.language(), English);
    assert_eq!(
        &sentence[third.start_index()..third.end_index()],
        "A little bit is better than nothing."
    );
}

In the example above, a vector of DetectionResult is returned. Each entry in the vector describes a contiguous single-language text section, providing start and end indices of the respective substring.

7.7 Single-threaded versus multi-threaded language detection

The LanguageDetector methods explained above all operate in a single thread. If you want to classify a very large set of texts, you will probably want to use all available CPU cores efficiently in multiple threads for maximum performance.

Every single-threaded method has a multi-threaded equivalent that accepts a list of texts and returns a list of results.

Single-threaded	Multi-threaded
detect_language_of	detect_languages_in_parallel_of
detect_multiple_languages_of	detect_multiple_languages_in_parallel_of
compute_language_confidence_values	compute_language_confidence_values_in_parallel
compute_language_confidence	compute_language_confidence_in_parallel

7.8 Methods to build the LanguageDetector

There might be classification tasks where you know beforehand that your language data is definitely not written in Latin, for instance (what a surprise :-). The detection accuracy can become better in such cases if you exclude certain languages from the decision process or just explicitly include relevant languages:

use lingua::{LanguageDetectorBuilder, Language, IsoCode639_1, IsoCode639_3};

// Include all languages available in the library.
LanguageDetectorBuilder::from_all_languages();

// Include only languages that are not yet extinct (= currently excludes Latin).
LanguageDetectorBuilder::from_all_spoken_languages();

// Include only languages written with Cyrillic script.
LanguageDetectorBuilder::from_all_languages_with_cyrillic_script();

// Exclude only the Spanish language from the decision algorithm.
LanguageDetectorBuilder::from_all_languages_without(&[Language::Spanish]);

// Only decide between English and German.
LanguageDetectorBuilder::from_languages(&[Language::English, Language::German]);

// Select languages by ISO 639-1 code.
LanguageDetectorBuilder::from_iso_codes_639_1(&[IsoCode639_1::EN, IsoCode639_1::DE]);

// Select languages by ISO 639-3 code.
LanguageDetectorBuilder::from_iso_codes_639_3(&[IsoCode639_3::ENG, IsoCode639_3::DEU]);

8. WebAssembly support

This library can be compiled to WebAssembly (WASM) which allows to use Lingua in any JavaScript-based project, be it in the browser or in the back end running on Node.js.

The easiest way to compile is to use wasm-pack. After the installation, you can, for instance, build the library with the web target so that it can be directly used in the browser:

wasm-pack build --target web

By default, all 75 supported languages are included in the compiled wasm file which has a size of 74 MB, approximately. If you only need a subset of certain languages, you can tell wasm-pack which ones to include:

wasm-pack build --target web -- --no-default-features --features "french,italian,spanish"

The output of wasm-pack will be hosted in a separate repository which allows to add further JavaScript-related configuration, tests and documentation. Lingua will then be added to the npm registry as well, allowing for an easy download and installation within every JavaScript or TypeScript project.

Structs

• DetectionResult
  This struct describes a contiguous single-language text section within a possibly mixed-language text.
• LanguageDetector
  This struct detects the language of given input text.
• LanguageDetectorBuilder
  This struct configures and creates an instance of LanguageDetector.
• LanguageModelFilesWriter
  This struct creates language model files and writes them to a directory.
• TestDataFilesWriter
  This struct creates test data files for accuracy report generation and writes them to a directory.

Enums

• IsoCode639_1
  This enum specifies the ISO 639-1 code representations for the supported languages.
• IsoCode639_3
  This enum specifies the ISO 639-3 code representations for the supported languages.
• Language
  This enum specifies the so far 75 supported languages which can be detected by Lingua.