Doc2vec model

Train a doc2vec model (Le & Mikolov, 2014) using a quanteda::tokens object.

textmodel_doc2vec(
  x,
  dim = 50,
  type = c("dm", "dbow"),
  min_count = 5,
  window = 5,
  iter = 10,
  alpha = 0.05,
  model = NULL,
  use_ns = TRUE,
  ns_size = 5,
  sample = 0.001,
  tolower = TRUE,
  include_data = FALSE,
  verbose = FALSE,
  ...
)

Arguments

x

a quanteda::tokens or quanteda::tokens_xptr object.

dim

the size of the word vectors.

type

the architecture of the model; either "dm" (distributed memory) or "dbow" (distributed bag-of-words).

min_count

the minimum frequency of the words. Words less frequent than this in x are removed before training.

window

the size of the window for context words. Ignored when type = "dbow" as its context window is the entire document (sentence or paragraph).

iter

the number of iterations in model training.

alpha

the initial learning rate.

model

a trained Word2vec model; if provided, its word vectors are updated for x.

use_ns

if TRUE, negative sampling is used. Otherwise, hierarchical softmax is used.

ns_size

the size of negative samples. Only used when use_ns = TRUE.

sample

the rate of sampling of words based on their frequency. Sampling is disabled when sample = 1.0

tolower

lower-case all the tokens before fitting the model.

include_data

if TRUE, the resulting object includes the data supplied as x.

verbose

if TRUE, print the progress of training.

...

additional arguments.

Value

Returns a textmodel_doc2vec object with matrices for word and document vector values in values. Other elements are the same as textmodel_word2vec.

References

Le, Q. V., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents (No. arXiv:1405.4053). arXiv. https://doi.org/10.48550/arXiv.1405.4053