tf.keras.layers.TextVectorization

A preprocessing layer which maps text features to integer sequences.

Inherits From: Layer, Operation

tf.keras.layers.TextVectorization(
    max_tokens=None,
    standardize='lower_and_strip_punctuation',
    split='whitespace',
    ngrams=None,
    output_mode='int',
    output_sequence_length=None,
    pad_to_max_tokens=False,
    vocabulary=None,
    idf_weights=None,
    sparse=False,
    ragged=False,
    encoding='utf-8',
    name=None,
    **kwargs
)

Used in the notebooks

Used in the guide Used in the tutorials

This layer has basic options for managing text in a Keras model. It transforms a batch of strings (one example = one string) into either a list of token indices (one example = 1D tensor of integer token indices) or a dense representation (one example = 1D tensor of float values representing data about the example's tokens). This layer is meant to handle natural language inputs. To handle simple string inputs (categorical strings or pre-tokenized strings) see kers_core.layers.StringLookup.

The vocabulary for the layer must be either supplied on construction or learned via adapt(). When this layer is adapted, it will analyze the dataset, determine the frequency of individual string values, and create a vocabulary from them. This vocabulary can have unlimited size or be capped, depending on the configuration options for this layer; if there are more unique values in the input than the maximum vocabulary size, the most frequent terms will be used to create the vocabulary.

The processing of each example contains the following steps:

  1. Standardize each example (usually lowercasing + punctuation stripping)
  2. Split each example into substrings (usually words)
  3. Recombine substrings into tokens (usually ngrams)
  4. Index tokens (associate a unique int value with each token)
  5. Transform each example using this index, either into a vector of ints or a dense float vector.

Some notes on passing callables to customize splitting and normalization for this layer:

  1. Any callable can be passed to this Layer, but if you want to serialize this object you should only pass functions that are registered Keras serializables (see keras.saving.register_keras_serializable for more details).
  2. When using a custom callable for standardize, the data received by the callable will be exactly as passed to this layer. The callable should return a tensor of the same shape as the input.
  3. When using a custom callable for split, the data received by the callable will have the 1st dimension squeezed out - instead of [["string to split"], ["another string to split"]], the Callable will see ["string to split", "another string to split"]. The callable should return a tf.Tensor of dtype string with the first dimension containing the split tokens - in this example, we should see something like [["string", "to", "split"], ["another", "string", "to", "split"]].

max_tokens Maximum size of the vocabulary for this layer. This should only be specified when adapting a vocabulary or when setting pad_to_max_tokens=True. Note that this vocabulary contains 1 OOV token, so the effective number of tokens is (max_tokens - 1 - (1 if output_mode == "int" else 0)).
standardize Optional specification for standardization to apply to the input text. Values can be:

  • None: No standardization.
  • "lower_and_strip_punctuation": Text will be lowercased and all punctuation removed.
  • "lower": Text will be lowercased.
  • "strip_punctuation": All punctuation will be removed.
  • Callable: Inputs will passed to the callable function, which should be standardized and returned.
split Optional specification for splitting the input text. Values can be:
  • None: No splitting.
  • "whitespace": Split on whitespace.
  • "character": Split on each unicode character.
  • Callable: Standardized inputs will passed to the callable function, which should be split and returned.
    • ngrams Optional specification for ngrams to create from the possibly-split input text. Values can be None, an integer or tuple of integers; passing an integer will create ngrams up to that integer, and passing a tuple of integers will create ngrams for the specified values in the tuple. Passing None means that no ngrams will be created.
    output_mode Optional specification for the output of the layer. Values can be "int", "multi_hot", "count" or "tf_idf", configuring the layer as follows:
  • "int": Outputs integer indices, one integer index per split string token. When output_mode == "int", 0 is reserved for masked locations; this reduces the vocab size to max_tokens - 2 instead of max_tokens - 1.
  • "multi_hot": Outputs a single int array per batch, of either vocab_size or max_tokens size, containing 1s in all elements where the token mapped to that index exists at least once in the batch item.
  • "count": Like "multi_hot", but the int array contains a count of the number of times the token at that index appeared in the batch item.
  • "tf_idf": Like "multi_hot", but the TF-IDF algorithm is applied to find the value in each token slot. For "int" output, any shape of input and output is supported. For all other output modes, currently only rank 1 inputs (and rank 2 outputs after splitting) are supported.
    • output_sequence_length Only valid in INT mode. If set, the output will have its time dimension padded or truncated to exactly output_sequence_length values, resulting in a tensor of shape (batch_size, output_sequence_length) regardless of how many tokens resulted from the splitting step. Defaults to None. If ragged is True then output_sequence_length may still truncate the output.
    pad_to_max_tokens Only valid in "multi_hot", "count", and "tf_idf" modes. If True, the output will have its feature axis padded to max_tokens even if the number of unique tokens in the vocabulary is less than max_tokens, resulting in a tensor of shape (batch_size, max_tokens) regardless of vocabulary size. Defaults to False.
    vocabulary Optional. Either an array of strings or a string path to a text file. If passing an array, can pass a tuple, list, 1D NumPy array, or 1D tensor containing the string vocabulary terms. If passing a file path, the file should contain one line per term in the vocabulary. If this argument is set, there is no need to adapt() the layer.
    idf_weights Only valid when output_mode is "tf_idf". A tuple, list, 1D NumPy array, or 1D tensor of the same length as the vocabulary, containing the floating point inverse document frequency weights, which will be multiplied by per sample term counts for the final tf_idf weight. If the vocabulary argument is set, and output_mode is "tf_idf", this argument must be supplied.
    ragged Boolean. Only applicable to "int" output mode. Only supported with TensorFlow backend. If True, returns a RaggedTensor instead of a dense Tensor, where each sequence may have a different length after string splitting. Defaults to False.
    sparse Boolean. Only applicable to "multi_hot", "count", and "tf_idf" output modes. Only supported with TensorFlow backend. If True, returns a SparseTensor instead of a dense Tensor. Defaults to False.
    encoding Optional. The text encoding to use to interpret the input strings. Defaults to "utf-8".

    Examples:

    This example instantiates a TextVectorization layer that lowercases text, splits on whitespace, strips punctuation, and outputs integer vocab indices.

    max_tokens = 5000  # Maximum vocab size.
max_len = 4  # Sequence length to pad the outputs to.
# Create the layer.
vectorize_layer = TextVectorization(
    max_tokens=max_tokens,
    output_mode='int',
    output_sequence_length=max_len)
     
    # Now that the vocab layer has been created, call `adapt` on the
# list of strings to create the vocabulary.
vectorize_layer.adapt(["foo bar", "bar baz", "baz bada boom"])
     
    # Now, the layer can map strings to integers -- you can use an
# embedding layer to map these integers to learned embeddings.
input_data = [["foo qux bar"], ["qux baz"]]
vectorize_layer(input_data)
array([[4, 1, 3, 0],
       [1, 2, 0, 0]])

    This example instantiates a TextVectorization layer by passing a list of vocabulary terms to the layer's __init__() method.

    vocab_data = ["earth", "wind", "and", "fire"]
max_len = 4  # Sequence length to pad the outputs to.
# Create the layer, passing the vocab directly. You can also pass the
# vocabulary arg a path to a file containing one vocabulary word per
# line.
vectorize_layer = keras.layers.TextVectorization(
    max_tokens=max_tokens,
    output_mode='int',
    output_sequence_length=max_len,
    vocabulary=vocab_data)
     
    # Because we've passed the vocabulary directly, we don't need to adapt
# the layer - the vocabulary is already set. The vocabulary contains the
# padding token ('') and OOV token ('[UNK]')
# as well as the passed tokens.
vectorize_layer.get_vocabulary()
['', '[UNK]', 'earth', 'wind', 'and', 'fire']

    input Retrieves the input tensor(s) of a symbolic operation.

    Only returns the tensor(s) corresponding to the first time the operation was called.

    output Retrieves the output tensor(s) of a layer.

    Only returns the tensor(s) corresponding to the first time the operation was called.

    Methods

    adapt

    View source

    adapt(
    data, batch_size=None, steps=None
)

    Computes a vocabulary of string terms from tokens in a dataset.

    Calling adapt() on a TextVectorization layer is an alternative to passing in a precomputed vocabulary on construction via the vocabulary argument. A TextVectorization layer should always be either adapted over a dataset or supplied with a vocabulary.

    During adapt(), the layer will build a vocabulary of all string tokens seen in the dataset, sorted by occurrence count, with ties broken by sort order of the tokens (high to low). At the end of adapt(), if max_tokens is set, the vocabulary will be truncated to max_tokens size. For example, adapting a layer with max_tokens=1000 will compute the 1000 most frequent tokens occurring in the input dataset. If output_mode='tf-idf', adapt() will also learn the document frequencies of each token in the input dataset.

    Arguments
    data The data to train on. It can be passed either as a batched tf.data.Dataset, as a list of strings, or as a NumPy array.
    steps Integer or None. Total number of steps (batches of samples) to process. If data is a tf.data.Dataset, and steps is None, adapt() will run until the input dataset is exhausted. When passing an infinitely repeating dataset, you must specify the steps argument. This argument is not supported with array inputs or list inputs.

    finalize_state

    View source

    finalize_state()

    from_config

    View source

    @classmethod
from_config(
    config
)

    Creates a layer from its config.

    This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

    Args
    config A Python dictionary, typically the output of get_config.

    Returns
    A layer instance.

    get_vocabulary

    View source

    get_vocabulary(
    include_special_tokens=True
)

    Returns the current vocabulary of the layer.

    Args
    include_special_tokens If True, the returned vocabulary will include the padding and OOV tokens, and a term's index in the vocabulary will equal the term's index when calling the layer. If False, the returned vocabulary will not include any padding or OOV tokens.

    load_assets

    View source

    load_assets(
    dir_path
)

    reset_state

    View source

    reset_state()

    save_assets

    View source

    save_assets(
    dir_path
)

    set_vocabulary

    View source

    set_vocabulary(
    vocabulary, idf_weights=None
)

    Sets vocabulary (and optionally document frequency) for this layer.

    This method sets the vocabulary and IDF weights for this layer directly, instead of analyzing a dataset through adapt(). It should be used whenever the vocab (and optionally document frequency) information is already known. If vocabulary data is already present in the layer, this method will replace it.

    Args
    vocabulary Either an array or a string path to a text file. If passing an array, can pass a tuple, list, 1D NumPy array, or 1D tensor containing the vocbulary terms. If passing a file path, the file should contain one line per term in the vocabulary.
    idf_weights A tuple, list, 1D NumPy array, or 1D tensor of inverse document frequency weights with equal length to vocabulary. Must be set if output_mode is "tf_idf". Should not be set otherwise.

    symbolic_call

    View source

    symbolic_call(
    *args, **kwargs
)

    update_state

    View source

    update_state(
    data
)

    vocabulary_size

    View source

    vocabulary_size()

    Gets the current size of the layer's vocabulary.

    Returns
    The integer size of the vocabulary, including optional mask and OOV indices.