Training a Word2vec model

Now that we know how the pretrained Word2vec model can be leveraged and we have looked at and understood the Word2vec model architecture, let's try to actually train a Word2vec model. We can create a custom implementation for this; however, for the sake of this exercise, we will leverage the functionalities provided by the gensim library.

The gensim library provides a convenient interface for building a Word2vec model. We will start by building a very simple model using the fewest possible parameters and then we will build on it.

Building a basic Word2vec model

Let's build a basic Word2vec model by executing the following steps:

1. We will start by importing the Word2vec module from gensim, define a few sentences as our data, and then build a model using the following code:

from gensim.models import Word2Vec
sentences = [["I", "am", "trying", "to", "understand", "Natural", 
              &quot...