Usage: w2v = KeyedVectors.load_word2vec_format("", binary=True) It rewrites all of the variables which are related to the words based on the Word2VecKeyedVectors. def restrict_w2v(w2v, restricted_word_set): I wrote a function which removes words from KeyedVectors which aren't in a predefined word list. most_similar() words won't return words outside of the specific domain. It would be better if the word is totally removed from the word vectors then the. most_similar() then filter the words but lets say the space of the specific domain is small, I might be looking for a word that's ranked 1000th most similar which is inefficient. It's possible to generate more than enough from. Lets say my universe of words in all words in the corpus to learn the dense relations between all words.īut when I want to generate the similar words, it should only come from a subset of domain specific word. How to remove a word completely from a Word2Vec model in gensim?Ĭould you give some details as to why you want to delete a word KeyError: "word 'graph' not in vocabulary"īut when we do a similarity on other words after deleting graph, we see the word graph popping up, e.g.
It's possible to remove the word from the w2v vocabulary, e.g. W2v_model = Word2Vec(texts, size=5, window=5, min_count=1, workers=10) "Graph minors IV Widths of trees and well quasi ordering", "The intersection graph of paths in trees", "The generation of random binary unordered trees", "Relation of user perceived response time to error measurement", "System and human system engineering testing of EPS", "The EPS user interface management system", "A survey of user opinion of computer system response time", from 2vec import Word2Vecĭocuments = ["Human machine interface for lab abc computer applications", To review, open the file in an editor that reveals hidden Unicode characters.Given a model, e.g. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below.
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