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Is it possible to choose between the Skip-gram and the CBOW model in Gensim when training a Word2Vec model?
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The word2vec algorithms include skip-gram and CBOW models, using either hierarchical softmax or negative sampling: Tomas Mikolov et al: Efficient Estimation of Word Representations in Vector Space, Tomas Mikolov et al: Distributed Representations of Words and Phrases and their Compositionality.
In the CBOW model, the distributed representations of context (or surrounding words) are combined to predict the word in the middle . While in the Skip-gram model, the distributed representation of the input word is used to predict the context .
According to the original paper, Mikolov et al., it is found that Skip-Gram works well with small datasets, and can better represent less frequent words. However, CBOW is found to train faster than Skip-Gram, and can better represent more frequent words.
The word2vec model has two different architectures to create the word embeddings. They are: Continuous bag of words(CBOW) Skip-gram model.
Yes. The initialization parameter sg controls the mode. If True-ish (sg=1), skip-gram is used; if False-ish (sg=0), CBOW is used.
The docs for gensim's Word2Vec class cover this.
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