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Top 10 Similar terms #3
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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 |
Hey @Madhu009 |
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]])
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