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// Copyright 2019 vtext developers
//
// Licensed under the Apache License, Version 2.0,
// <http://apache.org/licenses/LICENSE-2.0>. This file may not be copied,
// modified, or distributed except according to those terms.
/*!
# vtext
NLP in Rust with Python bindings
This package aims to provide a high performance toolkit for ingesting textual data for
machine learning applications.
## Features
- Tokenization: Regexp tokenizer, Unicode segmentation + language specific rules
- Token counting: converting token counts to sparse matrices for use
in machine learning libraries. Similar to `CountVectorizer` and
`HashingVectorizer` in scikit-learn but will less broad functionality.
- Levenshtein edit distance; Sørensen-Dice, Jaro, Jaro Winkler string similarities
# Example
A simple tokenization example can be found below,
```rust
extern crate vtext;
use vtext::tokenize::{VTextTokenizerParams,Tokenizer};
let tok = VTextTokenizerParams::default().lang("en").build().unwrap();
let tokens: Vec<&str> = tok.tokenize("Flights can't depart after 2:00 pm.").collect();
assert_eq!(tokens, vec!["Flights", "ca", "n't", "depart", "after", "2:00", "pm", "."])
```
*/