I have run a word2vec model on my data list_of_sentance
:
from gensim.models import Word2Vec
w2v_model=Word2Vec(list_of_sentance,min_count=5, workers=4)
print(type(w2v_model))
<class 'gensim.models.word2vec.Word2Vec'>
I would like to know the dimensionality of w2v_model. How can i check it?
CodePudding user response:
The vector dimensionality is included as an argument in Word2Vec
:
- In gensim versions up to 3.8.3, the argument was called
size
(docs) - In the latest gensim versions (4.0 onwards), the relevant argument is renamed to
vector_size
(docs)
In both cases, the argument has a default value of 100; this means that, if you do not specify it explicitly (as you do here), the dimensionality will be 100.
Here is a reproducible example using gensim 3.6:
import gensim
gensim.__version__
# 3.6.0
from gensim.test.utils import common_texts
from gensim.models import Word2Vec
model = Word2Vec(sentences=common_texts, window=5, min_count=1, workers=4) # do not specify size, leave the default 100
wv = model.wv['computer'] # get numpy vector of a word in the corpus
wv.shape # verify the dimension of a single vector is 100
# (100,)
If you want to change this dimensionality to, say, 256, you should call Word2Vec
with the argument size=256
(for gensim versions up to 3.8.3) or vector_size=256
(for gensim versions 4.0 or later).