Library API Overview

The litsea crate provides a Rust API for word segmentation, model training, and feature extraction.

Installation

[dependencies]
litsea = "0.13.0"

Loading models from local files is synchronous and needs no async runtime. An async runtime such as tokio is only required when loading models over HTTP/HTTPS with the async load_model method (for example TwoStageLearner::load_model, which always resolves the model URI through the same async path).

Module Map

graph LR
    A["litsea::segmenter"] --- B["Segmenter"]
    C["litsea::adaboost"] --- D["AdaBoost"]
    E["litsea::language"] --- F["Language"]
    G["litsea::extractor"] --- H["Extractor"]
    I["litsea::trainer"] --- J["Trainer, PerceptronTrainer, TwoStageTrainer, TwoStageMetrics"]
    K["litsea::error"] --- L["LitseaError, Result"]
    M["litsea::perceptron"] --- N["AveragedPerceptron"]
    O["litsea::upos"] --- P["Upos, SegmentLabel"]
    Q["litsea::metrics"] --- R["BinaryMetrics, MulticlassMetrics"]
    S["litsea::evaluation"] --- T["PosMetrics, SegmentationMetrics"]
    U["litsea::two_stage"] --- V["ModelKind, TwoStageFeatureSet, TwoStageLearner"]

All primary types are also re-exported at the crate root, so use litsea::Segmenter; works as a shorthand for use litsea::segmenter::Segmenter;.

The learners resolve their own URIs, so model_io::read_model_bytes is rarely needed directly. It is public for callers that must inspect a model before choosing a learner — litsea-binding-core reads the bytes once, detects the kind with ModelKind::detect, and feeds the same bytes to load_model_from_reader, which avoids downloading a remote model twice.

Quick Example

use std::path::Path;

use litsea::adaboost::AdaBoost;
use litsea::language::Language;
use litsea::segmenter::Segmenter;

fn main() -> litsea::Result<()> {
    let mut learner = AdaBoost::new(0.01, 100);
    learner.load_model_from_path(Path::new("./models/RWCP.model"))?;

    let segmenter = Segmenter::with_learner(Language::Japanese, learner);
    let tokens = segmenter.segment("これはテストです。");

    assert_eq!(tokens, vec!["これ", "は", "テスト", "です", "。"]);
    Ok(())
}

Quick Example (POS Tagging)

use std::path::Path;

use litsea::language::Language;
use litsea::segmenter::Segmenter;
use litsea::two_stage::TwoStageLearner;

fn main() -> litsea::Result<()> {
    let mut learner = TwoStageLearner::new();
    learner.load_model_from_path(Path::new("./models/japanese_pos.model"))?;

    let segmenter = Segmenter::with_two_stage_learner(Language::Japanese, learner);
    let tokens = segmenter.segment_with_pos("これはテストです。")?;

    for (word, pos) in &tokens {
        print!("{}/{} ", word, pos);
    }
    println!();

    Ok(())
}

Full API documentation is available on docs.rs/litsea.