Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

You got that right—arithmetic—really! Maybe you’ve seen this "equation"
somewhere else already:
KING - MAN + WOMAN = QUEEN
Awesome, right? We’ll try this "equation" out shortly, hang in there!
Pre-trained Word2Vec
Word2Vec is a simple model but it still requires a sizable amount of text data to
learn meaningful embeddings. Luckily for us, someone else had already done the
hard work of training these models, and we can use Gensim’s downloader to choose
from a variety of pre-trained word embeddings.
For a detailed list of the available models (embeddings), please
check Gensim-data’s repository [184] on GitHub.
"Why so many embeddings? How are they different from each other?"
Good question! It turns out, using different text corpora to train a Word2Vec
model produces different embeddings. On the one hand, this shouldn’t be a
surprise; after all, these are different datasets and it’s expected that they will
produce different results. On the other hand, if these datasets all contain
sentences in the same language (English, for example), how come the embeddings
are different?
The embeddings will be influenced by the kind of language used in the text: The
phrasing and wording used in novels are different from those used in news articles
and radically different from those used on Twitter, for example.
"Choose your word embeddings wisely."
Grail Knight
Moreover, not every word embedding is learned using a Word2Vec model
architecture. There are many different ways of learning word embeddings, one of
them being…
Word Embeddings | 927