https://github.com/lopuhin/python-adagram

AdaGram (adaptive skip-gram) for Python
https://github.com/lopuhin/python-adagram

Last synced: 17 days ago
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AdaGram (adaptive skip-gram) for Python

• Host: GitHub
• URL: https://github.com/lopuhin/python-adagram
• Owner: lopuhin
• Created: 2016-07-27T22:57:10.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2017-05-09T08:33:42.000Z (over 7 years ago)
• Last Synced: 2024-10-14T12:51:28.616Z (about 1 month ago)
• Language: Python
• Size: 87.9 KB
• Stars: 74
• Watchers: 9
• Forks: 16
• Open Issues: 8
• Metadata Files:
  • Readme: README.rst

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README

        
Python-AdaGram

==============

Python-AdaGram is an implementation of AdaGram (adaptive skip-gram) for Python.

It borrows a lot of C code from the original AdaGram implementation in Julia

(https://github.com/sbos/AdaGram.jl). AdaGram was introduced in a paper by

Sergey Bartunov, Dmitry Kondrashkin, Anton Osokin and Dmitry Vetrov

at http://arxiv.org/abs/1502.07257.

**Note**: this is a work in progress: it lacks tests,

and training is not working correctly yet.

But it can already load AdaGram.jl models, perform disambiguation,

search for nearest neighbours, etc.

If you have a more mature implementation or want to help,

please get in touch.

Install

-------

The package is not on PyPI yet, please install it from source in the meantime::

    $ pip install Cython numpy

    $ pip install git+https://github.com/lopuhin/python-adagram.git

Usage

-----

Train a model from command line::

    $ adagram-train tokenized.txt out.pkl

Input corpus must be already tokenized, with tokens (usually words)

separated by spaces.

There are many options available, see ``adagram-train --help``.

Load model::

    >>> import adagram

    >>> vm = adagram.VectorModel.load('out.pkl')

Get sense probabilities for some word::

    >>> vm.word_sense_probs('apple')

    [0.341832, 0.658164]

Get sense neighbors::

    >>> vm.sense_neighbors('apple', 0)

    [('almond', 0, 0.70396507),

     ('cherry', 1, 0.69193166),

     ('plum', 0, 0.690269),

     ('apricot', 0, 0.6882005),

     ('orange', 3, 0.6739181),

     ('pecan', 0, 0.6662803),

     ('pomegranate', 0, 0.6580653)

     ('blueberry', 0, 0.6509351),

     ('pear', 0, 0.6484747),

     ('peach', 0, 0.6313036)]

    >>> vm.sense_neighbors('apple',  1)

    [('macintosh', 0, 0.79053026),

     ('iifx', 0, 0.71349466),

     ('iigs', 0, 0.7030192),

     ('computers', 0, 0.6952761),

     ('kaypro', 0, 0.6938647),

     ('ipad', 0, 0.6914306),

     ('pc', 3, 0.6801078),

     ('ibm', 0, 0.66797054),

     ('powerpc-based', 0, 0.66319686),

     ('ibm-compatible', 0, 0.66120595)]

Get sense vector::

    >>> vm.sense_vector('apple', 1)

    array([...], dtype=float32)

Converting models built with AdaGram.jl

---------------------------------------

First, install AdaGram.jl as described here https://github.com/sbos/AdaGram.jl.

Install JSON package::

    $ julia

    julia> Pkg.add("JSON")

Run the script that converts a julia model to JSON::

    $ julia adagram/dump_julia.jl julia-model out-directory

This will save two JSON files to ``out-directory``.

Next, to convert model to python format, run::

    $ ./adagram/load_julia.py out-directory model.joblib