# tf.keras.preprocessing.text.Tokenizer

Text tokenization utility class.

This class allows to 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...

# Arguments

num_words: the maximum number of words to keep, based
on word frequency. Only the most common num_words-1 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: str. Separator for word splitting.
char_level: if True, every character will be treated as a token.
oov_token: if given, it will be added to word_index and used to
replace out-of-vocabulary words during text_to_sequence calls


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.

## Methods

### fit_on_sequences

Updates internal vocabulary based on a list of sequences.

Required before using sequences_to_matrix (if fit_on_texts was never called).

# Arguments

sequences: A list of sequence.
A "sequence" is a list of integer word indices.


### fit_on_texts

Updates internal vocabulary based on a list of texts.

In the case where texts contains lists, we assume each entry of the lists to be a token.

Required before using texts_to_sequences or texts_to_matrix.

# Arguments

texts: can be a list of strings,
a generator of strings (for memory-efficiency),
or a list of list of strings.


### get_config

Returns the tokenizer configuration as Python dictionary. The word count dictionaries used by the tokenizer get serialized into plain JSON, so that the configuration can be read by other projects.

# Returns

A Python dictionary with the tokenizer configuration.


### sequences_to_matrix

Converts a list of sequences into a Numpy matrix.

# Arguments

sequences: list of sequences
(a sequence is a list of integer word indices).
mode: one of "binary", "count", "tfidf", "freq"


# Returns

A Numpy matrix.


# Raises

ValueError: In case of invalid mode argument,
or if the Tokenizer requires to be fit to sample data.


### sequences_to_texts

Transforms each sequence into a list of text.

Only top num_words-1 most frequent words will be taken into account. Only words known by the tokenizer will be taken into account.

# Arguments

sequences: A list of sequences (list of integers).


# Returns

A list of texts (strings)


### sequences_to_texts_generator

Transforms each sequence in sequences to a list of texts(strings).

Each sequence has to a list of integers. In other words, sequences should be a list of sequences

Only top num_words-1 most frequent words will be taken into account. Only words known by the tokenizer will be taken into account.

# Arguments

sequences: A list of sequences.


# Yields

Yields individual texts.


### texts_to_matrix

Convert a list of texts to a Numpy matrix.

# Arguments

texts: list of strings.
mode: one of "binary", "count", "tfidf", "freq".


# Returns

A Numpy matrix.


### texts_to_sequences

Transforms each text in texts to a sequence of integers.

Only top num_words-1 most frequent words will be taken into account. Only words known by the tokenizer will be taken into account.

# Arguments

texts: A list of texts (strings).


# Returns

A list of sequences.


### texts_to_sequences_generator

Transforms each text in texts to a sequence of integers.

Each item in texts can also be a list, in which case we assume each item of that list to be a token.

Only top num_words-1 most frequent words will be taken into account. Only words known by the tokenizer will be taken into account.

# Arguments

texts: A list of texts (strings).


# Yields

Yields individual sequences.


### to_json

Returns a JSON string containing the tokenizer configuration. To load a tokenizer from a JSON string, use keras.preprocessing.text.tokenizer_from_json(json_string).

# Arguments

**kwargs: Additional keyword arguments
to be passed to json.dumps().


# Returns

A JSON string containing the tokenizer configuration.

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