Top 10 Similar terms

[Mustyy]

Thank you firstly for the tutorial
I wanted to ask if it is possible to use the final embeddings to test out a word and return top 10 similar terms.

e.g

Top 10 Similar words given an input word

word="external"
word_vec = final_embeddings[dictionary[word]]
sim = np.dot(word_vec,-final_embeddings.T).argsort()[0:8]
for idx in range(8):
print (reverse_dictionary[sim[idx]])

[Madhu009]

a emdeding vector represents the word. so similar words have similar emebeding (we can use a distance metric to find out the distance ) .

This thread has different ways to handle please check it out
https://stackoverflow.com/questions/40074412/word2vec-get-nearest-words

[Mustyy]

Hey @Madhu009
Thanks for the reply
I understand embedding vectors, I was just wondering if there was a quick workaround for the code so that I can plug in a word and return top 10 similar terms.
I tried using the tensorflow board but it wasnt successful either.
Currently researching other methods too