Word embeddings with meaningful dimensions for better explainability.
Project description
nessvec
Install from Source (recommended)
Clone the repository with all the source code and data:
$ git clone git@gitlab.com:tangibleai/nessvec
$ cd nessvec
Create a conda environment and install the dependencies:
$ conda create -n nessvec 'python==3.9.7'
$ conda env update -n nessvec -f scripts/environment.yml
$ pip install -e .
Install from PyPi (only tested on Linux)
$ pip install nessvec
Get Started
>>> from nessvec.util import load_glove
>>> w2v = load_glove()
>>> seattle = w2v['seattle']
>>> seattle
array([-2.7303e-01, 8.5872e-01, 1.3546e-01, 8.3849e-01, ...
>>> portland = w2v['portland']
>>> portland
array([-0.78611 , 1.2758 , -0.0036066, 0.54873 , -0.31474 ,...
>>> len(portland)
50
>>> from numpy.linalg import norm
>>> norm(portland)
4.417...
>>> portland.std()
0.615...
>>> cosine_similarity(seattle, portland)
0.84...
>>> cosine_similarity(portland, seattle)
0.84...
>>> from nessvec.util import cosine_similarity
>>> cosine_similarity(w2v['los_angeles'], w2v['mumbai'])
.5
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