Natural Language Processing with Python Quick Start Guide by Nirant Kasliwal

Author:Nirant Kasliwal [Nirant Kasliwal]
Language: eng
Format: epub
Tags: COM042000 - COMPUTERS / Natural Language Processing, COM051360 - COMPUTERS / Programming Languages / Python, COM004000 - COMPUTERS / Intelligence (AI) and Semantics
Publisher: Packt
Published: 2018-11-30T14:01:43+00:00

Training word2vec embeddings

Importing the model is simple, simply use the following command. By now, you should have an intuitive feel of how the Gensim model's API is structured:

from gensim.models.word2vec import Word2Vec

Here, we are using an identical configuration for the word2vec model as we did for fastText. This helps to reduce bias in the comparison.

You are encouraged to compare the best fastText model to the best word2vec model with the following:

word2vec_ted_model = Word2Vec(sentences=sentences_ted, size=100, window=5, min_count=5, workers=-1, sg=1)

Right, let's now look at the words most similar to india, as follows:

word2vec_ted_model.wv.most_similar("india")

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