vtext

NLP in Rust with Python bindings

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

Usage

Usage in Python

vtext requires Python 3.5+ and can be installed with, pip install --pre vtext

Below is a simple tokenization example,

```python

from vtext.tokenize import VTextTokenizer VTextTokenizer("en").tokenize("Flights can't depart after 2:00 pm.") ["Flights", "ca", "n't", "depart" "after", "2:00", "pm", "."] ```

For more details see the project documentation: vtext.io/doc/latest/index.html

Usage in Rust

Add the following to Cargo.toml, toml [dependencies] vtext = "0.1.0-alpha.1"

For more details see rust documentation: docs.rs/vtext

Benchmarks

Tokenization

Following benchmarks illustrate the tokenization accuracy (F1 score) on UD treebanks ,

| lang | dataset |regexp | spacy 2.1 | vtext |
|-------|-----------|----------|-----------|----------| | en | EWT | 0.812 | 0.972 | 0.966 | | en | GUM | 0.881 | 0.989 | 0.996 | | de | GSD | 0.896 | 0.944 | 0.964 | | fr | Sequoia | 0.844 | 0.968 | 0.971 |

and the English tokenization speed,

| |regexp | spacy 2.1 | vtext | |--------------------------|-------|-----------|-------| | Speed (10⁶ tokens/s) | 3.1 | 0.14 | 2.1 |

Text vectorization

Below are benchmarks for converting textual data to a sparse document-term matrix using the 20 newsgroups dataset,

| Speed (MB/s) | scikit-learn 0.20.1 | vtext 0.1.0a1 | |--------------------|---------------------|---------------| | CountVectorizer | 14 | 35 | | HashingVectorizer | 19 | 68 |

see benchmarks/README.md for more details.

License

vtext is released under the Apache License, Version 2.0.