Robust and Fast tokenizations alignment library for Rust and Python

Demo: demo
Rust document: docs.rs
Blog post: How to calculate the alignment between BERT and spaCy tokens effectively and robustly

Usage (Python)

• Installation

$ pip install -U pip # update pip
$ pip install pytokenizations

• Install from source

This library uses maturin to build the wheel.

$ git clone https://github.com/tamuhey/tokenizations
$ cd tokenizations/python
$ pip install maturin
$ maturin build

Now the wheel is created in python/target/wheels directory, and you can install it with pip install *whl.

get_alignments

def get_alignments(a: Sequence[str], b: Sequence[str]) -> Tuple[List[List[int]], List[List[int]]]: ...

Returns alignment mappings for two different tokenizations:

>>> tokens_a = ["å", "BC"]
>>> tokens_b = ["abc"] # the accent is dropped (å -> a) and the letters are lowercased(BC -> bc)
>>> a2b, b2a = tokenizations.get_alignments(tokens_a, tokens_b)
>>> print(a2b)
[[0], [0]]
>>> print(b2a)
[[0, 1]]

a2b[i] is a list representing the alignment from tokens_a to tokens_b.

Usage (Rust)

See here: docs.rs

Related

• Algorithm overview
• Blog post
• seqdiff is used for the diff process.
• textspan
• explosion/spacy-alignments: 💫 A spaCy package for Yohei Tamura's Rust tokenizations library
  • Python bindings for this library, maintained by Explosion, author of spaCy. If you feel difficult to install pytokenizations, please try this.