Vaporetto is a fast and lightweight pointwise prediction based tokenizer.

use std::fs::File;
use std::io::{prelude::*, stdin, BufReader};

use vaporetto::{Model, Predictor, Sentence};

let mut f = BufReader::new(File::open("model.raw").unwrap());
let model = Model::read(&mut f).unwrap();
let predictor = Predictor::new(model);

let s = Sentence::from_raw("火星猫の生態").unwrap();
let s = predictor.predict(s);

println!("{:?}", s.to_tokenized_vec().unwrap());
// ["火星", "猫", "の", "生態"]

The following features are disabled by default:

• kytea - Enables the reader for models generated by KyTea.
• train - Enables the trainer.
• portable-simd - Uses the portable SIMD API instead of our SIMD-conscious data layout. (Nightly Rust is required.)

The following features are enabled by default:

• cache-type-score - Enables caching type scores for faster processing. If disabled, type scores are calculated in a straightforward manner.
• fix-weight-length - Uses fixed-size arrays for storing scores to facilitate optimization. If disabled, vectors are used instead.
• tag-prediction - Enables tag prediction.

Licensed under either of

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.