text_tokenizer: Text tokenization utility

Description Usage Arguments Details Attributes See Also

View source: R/preprocessing.R

Description

Vectorize a text corpus, by turning each text into either a sequence of integers (each integer being the index of a token in a dictionary) or into a vector where the coefficient for each token could be binary, based on word count, based on tf-idf...

Usage

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text_tokenizer(
  num_words = NULL,
  filters = "!\"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n",
  lower = TRUE,
  split = " ",
  char_level = FALSE,
  oov_token = NULL
)

Arguments

num_words

the maximum number of words to keep, based on word frequency. Only the most common num_words words will be kept.

filters

a string where each element is a character that will be filtered from the texts. The default is all punctuation, plus tabs and line breaks, minus the ' character.

lower

boolean. Whether to convert the texts to lowercase.

split

character or string to use for token splitting.

char_level

if TRUE, every character will be treated as a token

oov_token

NULL or string If given, it will be added to 'word_index“ and used to replace out-of-vocabulary words during text_to_sequence calls.

Details

By default, all punctuation is removed, turning the texts into space-separated sequences of words (words maybe include the ' character). These sequences are then split into lists of tokens. They will then be indexed or vectorized. 0 is a reserved index that won't be assigned to any word.

Attributes

The tokenizer object has the following attributes:

See Also

Other text tokenization: fit_text_tokenizer(), save_text_tokenizer(), sequences_to_matrix(), texts_to_matrix(), texts_to_sequences_generator(), texts_to_sequences()


dfalbel/keras documentation built on Nov. 27, 2019, 8:16 p.m.