tf.keras.layers.TextVectorization

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A preprocessing layer which maps text features to integer sequences.

Inherits From: PreprocessingLayer, Layer, Module

View aliases

Main aliases

tf.keras.layers.experimental.preprocessing.TextVectorization

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,
    **kwargs
)

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 tf.keras.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 tf.keras.utils.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 Tensor with the first dimension containing the split tokens - in this example, we should see something like [["string", "to", "split"], ["another", "string", "to", "split"]]. This makes the callable site natively compatible with tf.strings.split().

For an overview and full list of preprocessing layers, see the preprocessing guide.

Args
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 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 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.
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 vocbulary 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 or 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. 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. If True, returns a SparseTensor instead of a dense Tensor. Defaults to False.

Example:

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

text_dataset = tf.data.Dataset.from_tensor_slices(["foo", "bar", "baz"])
max_features = 5000  # Maximum vocab size.
max_len = 4  # Sequence length to pad the outputs to.

# Create the layer.
vectorize_layer = tf.keras.layers.TextVectorization(
 max_tokens=max_features,
 output_mode='int',
 output_sequence_length=max_len)

# Now that the vocab layer has been created, call `adapt` on the
# text-only dataset to create the vocabulary. You don't have to batch,
# but for large datasets this means we're not keeping spare copies of
# the dataset.
vectorize_layer.adapt(text_dataset.batch(64))

# Create the model that uses the vectorize text layer
model = tf.keras.models.Sequential()

# Start by creating an explicit input layer. It needs to have a shape of
# (1,) (because we need to guarantee that there is exactly one string
# input per batch), and the dtype needs to be 'string'.
model.add(tf.keras.Input(shape=(1,), dtype=tf.string))

# The first layer in our model is the vectorization layer. After this
# layer, we have a tensor of shape (batch_size, max_len) containing
# vocab indices.
model.add(vectorize_layer)

# Now, the model can map strings to integers, and you can add an
# embedding layer to map these integers to learned embeddings.
input_data = [["foo qux bar"], ["qux baz"]]
model.predict(input_data)
array([[2, 1, 4, 0],
       [1, 3, 0, 0]])

Example:

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 = tf.keras.layers.TextVectorization(
 max_tokens=max_features,
 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']

Attributes
is_adapted	Whether the layer has been fit to data already.

Methods

adapt

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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 wil 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.

In order to make TextVectorization efficient in any distribution context, the vocabulary is kept static with respect to any compiled tf.Graphs that call the layer. As a consequence, if the layer is adapted a second time, any models using the layer should be re-compiled. For more information see tf.keras.layers.experimental.preprocessing.PreprocessingLayer.adapt.

adapt() is meant only as a single machine utility to compute layer state. To analyze a dataset that cannot fit on a single machine, see Tensorflow Transform for a multi-machine, map-reduce solution.

Arguments
data	The data to train on. It can be passed either as a tf.data.Dataset, or as a numpy array.
batch_size	Integer or None. Number of samples per state update. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of datasets, generators, or keras.utils.Sequence instances (since they generate batches).
steps	Integer or None. Total number of steps (batches of samples) When training with input tensors such as TensorFlow data tensors, the default None is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined. If x is a tf.data dataset, and 'steps' is None, the epoch 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.

compile

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compile(
    run_eagerly=None, steps_per_execution=None
)

Configures the layer for adapt.

Arguments
run_eagerly	Bool. Defaults to False. If True, this Model's logic will not be wrapped in a tf.function. Recommended to leave this as None unless your Model cannot be run inside a tf.function.
steps_per_execution	Int. Defaults to 1. The number of batches to run during each tf.function call. Running multiple batches inside a single tf.function call can greatly improve performance on TPUs or small models with a large Python overhead.

get_vocabulary

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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.

reset_state

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reset_state()

Resets the statistics of the preprocessing layer.

set_vocabulary

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set_vocabulary(
    vocabulary, idf_weights=None
)

Sets vocabulary (and optionally document frequency) data 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.

Raises
ValueError	If there are too many inputs, the inputs do not match, or input data is missing.
RuntimeError	If the vocabulary cannot be set when this function is called. This happens when "multi_hot", "count", and "tf_idf" modes, if pad_to_max_tokens is False and the layer itself has already been called.

update_state

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update_state(
    data
)

Accumulates statistics for the preprocessing layer.

Arguments
data	A mini-batch of inputs to the layer.

vocabulary_size

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vocabulary_size()

Gets the current size of the layer's vocabulary.

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