ft_control: Default Control Settings

Description

A auxiliary function for defining the control variables.

Usage

ft_control(
  loss = c("softmax", "hs", "ns"),
  learning_rate = 0.05,
  learn_update = 100L,
  word_vec_size = 100L,
  window_size = 5L,
  epoch = 5L,
  min_count = 5L,
  min_count_label = 0L,
  neg = 5L,
  max_len_ngram = 1L,
  nbuckets = 2000000L,
  min_ngram = 3L,
  max_ngram = 6L,
  nthreads = 1L,
  threshold = 1e-04,
  label = "__label__",
  verbose = 0,
  pretrained_vectors = "",
  output = "",
  save_output = FALSE,
  seed = 0L,
  qnorm = FALSE,
  retrain = FALSE,
  qout = FALSE,
  cutoff = 0L,
  dsub = 2L,
  autotune_validation_file = "",
  autotune_metric = "f1",
  autotune_predictions = 1L,
  autotune_duration = 300L,
  autotune_model_size = ""
)

Value

a list with the control variables.

Arguments

loss: a character string giving the name of the loss function allowed values are 'softmax', 'hs' and 'ns'.
learning_rate: a numeric giving the learning rate, the default value is 0.05.
learn_update: an integer giving after how many tokens the learning rate should be updated. The default value is 100L, which means the learning rate is updated every 100 tokens.
word_vec_size: an integer giving the length (size) of the word vectors.
window_size: an integer giving the size of the context window.
epoch: an integer giving the number of epochs.
min_count: an integer giving the minimal number of word occurences.
min_count_label: and integer giving the minimal number of label occurences.
neg: an integer giving how many negatives are sampled (only used if loss is "ns").
max_len_ngram: an integer giving the maximum length of ngrams used.
nbuckets: an integer giving the number of buckets.
min_ngram: an integer giving the minimal ngram length.
max_ngram: an integer giving the maximal ngram length.
nthreads: an integer giving the number of threads.
threshold: a numeric giving the sampling threshold.
label: a character string specifying the label prefix (default is '__label__').
verbose: an integer giving the verbosity level, the default value is 0L and shouldn't be changed since Rcpp::Rcout cann't handle the traffic.
pretrained_vectors: a character string giving the file path to the pretrained word vectors which are used for the supervised learning.
output: a character string giving the output file path.
save_output: a logical (default is FALSE)
seed: an integer
qnorm: a logical (default is FALSE)
retrain: a logical (default is FALSE)
qout: a logical (default is FALSE)
cutoff: an integer (default is 0L)
dsub: an integer (default is 2L)
autotune_validation_file: a character string
autotune_metric: a character string (default is "f1")
autotune_predictions: an integer (default is 1L)
autotune_duration: an integer (default is 300L)
autotune_model_size: a character string

Examples

Run this code

ft_control(learning_rate=0.1)

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