Expand description
§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
CountVectorizerandHashingVectorizerin 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,
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", "."])Modules§
- errors
- metrics
- Metrics module
- tokenize
- Tokenization module
- tokenize_
sentence - Sentence tokenization module
- vectorize
- Vectorization module