Read a binary word2vec model from disk
Read a binary word2vec model from disk
read.word2vec(file, normalize = FALSE)
file |
the path to the model file |
normalize |
logical indicating to normalize the embeddings by dividing by the factor (sqrt(sum(x . x) / length(x))). Defaults to FALSE. |
an object of class w2v which is a list with elements
model: a Rcpp pointer to the model
model_path: the path to the model on disk
dim: the dimension of the embedding matrix
n: the number of words in the vocabulary
path <- system.file(package = "word2vec", "models", "example.bin")
model <- read.word2vec(path)
vocab <- summary(model, type = "vocabulary")
emb <- predict(model, c("bus", "naar", "unknownword"), type = "embedding")
emb
nn <- predict(model, c("bus", "toilet"), type = "nearest")
nn
# Do some calculations with the vectors and find similar terms to these
emb <- as.matrix(model)
vector <- emb["gastvrouw", ] - emb["gastvrij", ]
predict(model, vector, type = "nearest", top_n = 5)
vectors <- emb[c("gastheer", "gastvrouw"), ]
vectors <- rbind(vectors, avg = colMeans(vectors))
predict(model, vectors, type = "nearest", top_n = 10)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.