How to find similar words with FastText?

Question

I am playing around with FastText, https://pypi.python.org/pypi/fasttext,which is quite similar to Word2Vec. Since it seems to be a pretty new library

Answer 1

Use gensim,

from gensim.models import FastText

model = FastText.load(PATH_TO_MODEL)
model.wv.most_similar(positive=['dog'])

More info here

Answer 2

You can install and import gensim library and then use gensim library to extract most similar words from the model that you downloaded from FastText.

Use this:

import gensim
model = gensim.models.KeyedVectors.load_word2vec_format('model.vec')
similar = model.most_similar(positive=['man'],topn=10)

And by topn parameter you get the top 10 most similar words.

Answer 3

You should use gensim to load the model.vec and then get similar words:

m = gensim.models.Word2Vec.load_word2vec_format('model.vec')
m.most_similar(...)

Answer 4

Use Gensim, load fastText trained .vec file with load.word2vec models and use most_similiar() method to find similar words!

Answer 5

You can install pyfasttext library to extract the most similar or nearest words to a particualr word.

from pyfasttext import FastText
model = FastText('model.bin')
model.nearest_neighbors('dog', k=2000)

Or you can get the latest development version of fasttext, you can install from the github repository :

import fasttext
model = fasttext.load_model('model.bin')
model.get_nearest_neighbors('dog', k=100)