vhash

############################## VHash: Vectorizing Hash Tables ##############################

If you have documents with class labels, and you want to create numeric representations of those documents that maximize inter-class differences, then this package is for you. This package provides vectorizing hash tables that quickly transform your text, optimizing for the maximum distance between document-class vectors.

This project has a C++ backend with a python interface, allowing for maximum speed and maximum interopability.

Installation

To install, use :code:pip:

.. code-block:: bash

pip install vhash

Minimum Working Example

This package follows the conventions of :code:scikit-learn to provide an intuitive and familiar interface.

.. code-block:: python

from typing import Any

from nptyping import NDArray

from vhash import VHash

# sample documents: each item in the list is its own document
docs = [
    'hi, my name is Mike',
    'hi, my name is George',
    'hello, my name is Mike',
]

# class labels for each document
labels = [1, 0, 1]

# create & train model
vhash = VHash().fit(docs, labels)

# create numeric representation (2D float array)
numeric: NDArray[(Any, Any), float] = vhash.transform(docs)

Metrics

To see how this text transformer compares to BERT, check out the sample notebook <https://github.com/lakes-legendaries/vhash/blob/main/stats/bert.ipynb>_, where we show how :code:vhash outperforms :code:sBERT on a sentiment analysis task.

.. docs-links

Documentation

To get started, check out the docs <https://lakes-legendaries.github.io/vhash/>_!

If you will be contributing to this repo, checkout the developer guide <https://lakes-legendaries.github.io/vhash/dev.html>_.