CODEWITHSUNDEEP
pythonnlpgensimword2vecdoc2vec
RSB
RSB

Reputation: 49

AttributeError: 'Word2Vec' object has no attribute 'most_similar' (Word2Vec)

I am using Word2Vec and using a wiki trained model that gives out the most similar words. I ran this before and it worked but now it gives me this error even after rerunning the whole program. I tried to take off return_path=True but im still getting the same error

print(api.load('glove-wiki-gigaword-50', return_path=True))
model.most_similar("glass")

#ERROR:

/Users/me/gensim-data/glove-wiki-gigaword-50/glove-wiki-gigaword-50.gz
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-153-3bf32168d154> in <module>
      1 print(api.load('glove-wiki-gigaword-50', return_path=True))
----> 2 model.most_similar("glass") 

AttributeError: 'Word2Vec' object has no attribute 'most_similar'

#MODEL this is the model I used

    print(
        '%s (%d records): %s' % (
            model_name,
            model_data.get('num_records', -1),
            model_data['description'][:40] + '...',
        )
    )

Edit: here is my gensim download & output

!python -m pip install -U gensim

OUTPUT:

Requirement already satisfied: gensim in ./opt/anaconda3/lib/python3.8/site-packages (4.0.1)

Requirement already satisfied: numpy>=1.11.3 in ./opt/anaconda3/lib/python3.8/site-packages (from gensim) (1.20.1)

Requirement already satisfied: smart-open>=1.8.1 in ./opt/anaconda3/lib/python3.8/site-packages (from gensim) (5.1.0)

Requirement already satisfied: scipy>=0.18.1 in ./opt/anaconda3/lib/python3.8/site-packages (from gensim) (1.6.2)

Upvotes: 5

Views: 17497

Answers (2)

gojomo
gojomo

Reputation: 54243

Your shown code...

print(api.load('glove-wiki-gigaword-50', return_path=True))
model.most_similar("glass")

...doesn't assign anything into model. (Was it assigned earlier?)

And, using return_path=True there means the api.load() will only return a string path to the datafile. That'd only be interesting if you were going to use that string to then do your own loading of the data into a model.

That api.load() call without return_path=True likely returns an instance of KeyedVectors, which is a set of vectors. That's different from a full Word2Vec model, but would still support a .most_similar() method. However, if you're just print()ing that returned path, or returned model, it's not going to be in the model variable for your later .most_similar() operation.

So you may want:

kv_model = api.load('glove-wiki-gigaword-50')
similars = kv_model.most_similar('glass')
print(similars)

(Personally, I don't like the opaque magic, & running of new downloaded code, that api.load() does. I think it's a better habit to download the raw data files yourself, from a known source, so that you know what files have arrived, to which directories, on your own machine. Then use a dataset-specific load method to load that data, so that you learn what library methods work with which kinds of files.)

If your model variable does in fact include a full Word2Vec model, from some unshown other code, then it will also contain a set of vectors in its .wv (for word-vectors) property:

similars = model.wv.most_similar('glass')
print(similars)

Upvotes: 0

sophros
sophros

Reputation: 16738

You are probably looking for <MODEL>.wv.most_similar, so please try:

model.wv.most_similar("glass")

Upvotes: 18

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