For anybody interested in why this comment says "cosine similarity _or_ dot product", its because the vectors in word embedding models are typically scaled to u

For anybody interested in why this comment says "cosine similarity _or_ dot product", its because the vectors in word embedding models are typically scaled to unit length. If cos(theta) := A.B / (|A|^2 * |B|^2) And A and B are normalised, then the denominator is 1, and the RHS is equal to the dot product.