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...

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. 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. boolean. Whether to convert the texts to lowercase. character or string to use for token splitting. if TRUE, every character will be treated as a 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:

• word_counts --- named list mapping words to the number of times they appeared on during fit. Only set after fit_text_tokenizer() is called on the tokenizer.

• word_docs --- named list mapping words to the number of documents/texts they appeared on during fit. Only set after fit_text_tokenizer() is called on the tokenizer.

• word_index --- named list mapping words to their rank/index (int). Only set after fit_text_tokenizer() is called on the tokenizer.

• document_count --- int. Number of documents (texts/sequences) the tokenizer was trained on. Only set after fit_text_tokenizer() is called on the tokenizer.

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