Natural Language Processing in Rust with Python bidings

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  • License: Apache License 2.0
  • Requires: Python ~=3.5
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Project description

vtext

This is a Python wrapper for the Rust vtext crate.

This package aims to provide a high performance toolkit for ingesting textual data for machine learning applications.

The API is currently unstable.

Features

  • Tokenization: Regexp tokenizer, Unicode segmentation + language specific rules
  • 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), feature normalization.
  • Levenshtein edit distance; Sørensen-Dice, Jaro, Jaro Winkler string similarities

Installation

vtext requires Python 3.5+ and can be installed with,

pip install --pre vtext

Documentation

Project documentation: vtext.io/doc/latest/index.html

License

vtext is released under the Apache License, Version 2.0.

Project details

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  • License: Apache License 2.0
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0.2.0

0.1.0a3 pre-release

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