Description Usage Arguments Value See Also Examples

Get either

the embedding of words

the nearest words which are similar to either a word or a word vector

1 2 3 4 5 6 7 8 9 |

`object` |
a word2vec model as returned by |

`newdata` |
for type 'embedding', |

`type` |
either 'embedding' or 'nearest'. Defaults to 'nearest'. |

`top_n` |
show only the top n nearest neighbours. Defaults to 10. |

`encoding` |
set the encoding of the text elements to the specified encoding. Defaults to 'UTF-8'. |

`...` |
not used |

depending on the type, you get a different result back:

for type nearest: a list of data.frames with columns term, similarity and rank indicating with words which are closest to the provided

`newdata`

words or word vectors. If`newdata`

is just one vector instead of a matrix, it returns a data.framefor type embedding: a matrix of word vectors of the words provided in

`newdata`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
path <- system.file(package = "word2vec", "models", "example.bin")
model <- read.word2vec(path)
emb <- predict(model, c("bus", "toilet", "unknownword"), type = "embedding")
emb
nn <- predict(model, c("bus", "toilet"), type = "nearest", top_n = 5)
nn
# Do some calculations with the vectors and find similar terms to these
emb <- as.matrix(model)
vector <- emb["buurt", ] - emb["rustige", ] + emb["restaurants", ]
predict(model, vector, type = "nearest", top_n = 10)
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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