vtext

Fast NLP in Rust with Python bindings

This package aims to provide a convenient and high performance toolkit for ingesting textual data for machine learning applications. Making existing NLP Rust crates accesible in Python with a common API is another goal of this project.

The API is currently unstable.

Features

• Tokenization: Regexp tokenizer, Unicode segmentation
• Stemming: Snowball (in Python 15-20x faster than NLTK)
• Analyzers (planned): word and character n-grams, skip grams
• Token counting: converting token counts to sparse matrices for use in machine learning libraries. Similar to CountVectorizer and HashingVectorizer in scikit-learn.
• Feature weighting (planned): feature weighting based on document frequency (TF-IDF), supervised term weighting (e.g. TF-IGM), feature normalization

Usage

Usage in Rust

Add the following to Cargo.toml,

[dependencies]
text-vectorize = {"git" = "https://github.com/rth/vtext"}

A simple example can be found below,

extern crate vtext;

use vtext::CountVectorizer;

let documents = vec![
    String::from("Some text input"),
    String::from("Another line"),
];

let mut vect = CountVectorizer::new();
let X = vect.fit_transform(&documents);

where X is a CSRArray struct with the following attributes X.indptr, X.indices, X.values.

Usage in Python

The API aims to be compatible with scikit-learn's CountVectorizer and HashingVectorizer though only a subset of features will be implemented.

Benchmarks

Vectorization

Below are some very preliminary benchmarks on the 20 newsgroups dataset of 19924 documents (~91 MB in total),

	CountVectorizer	HashingVectorizer
CountVectorizer	scikit-learn 0.20.1	14 MB/s
CountVectorizer	vtext 0.1.0-a1	33 MB/s
HashingVectorizer	scikit-learn 0.20.1	18 MB/s
HashingVectorizer	vtext 0.1.0-a1	68 MB/s

see benchmarks/README.md for more details. Note that these are not strictly equivalent, and are meant as a rough estimate for the possible performance improvements.

License

text-vectorize is released under the BSD 3-clause license.