This repo contains an implementation of the popular GloVe in Python with Keras/Tensorflow.

Introduction

Similarly to Word2Vec , GloVe is an unsupervised algorithm which learns vector representations for words. It is trained on aggregated word-word co-occurrence statistics and the resulting vectors expose linear substructures. For more detailed information the interested user should refer to the original web site

Implemented so far

  1. Model architecture in Keras with a custom loss function
  2. Helper functions for data loading and transformations

Pending

Setup

Datasets

You need to download a corpus in order to run the algorithm. Some suggestions:

The files should be unzipped and by convention are placed into the data folder

Output

All the generated files are stored in the output folder

Requirements

Start a new virtual environment as:

virtualenv --no-site-packages -p python3.6 venv

and then activate it with

. venv/bin/activate

You can install the library either with

python setup.py install

or 

python setup.py develop

if you want to stay up to date

Running the code

Currently there are two commands available. The first performs the actual training based on the given corpus. For example, the command:

kglove train data/my_corpus.txt -n 5000 -v 30 -e 3 -b 4098

will read the first 5000 lines from my_corpus.txt, it will train the model for 3 epochs with batch size 4098 producing 30-dimensional vectors. Look in orchestrator.py for the different options available

The second command returns the closest neighbours for a (comma separated) list of words:

kglove closest dirty,polite

will produce the following output:

Most similar words to dirty:
[('laid', 0.9819546), ('smelled', 0.98293394), ('dark', 0.98691344), ('worn', 0.99096316)]:

Most similar words to polite:
[('accomodating', 0.9964925), ('courteous', 0.9968112), ('professional', 0.9969903), ('accommodating', 0.99781287)]: