Description Usage Arguments Value Examples

Read a binary word2vec model from disk

1 | ```
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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
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)
``` |

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