A preprocessing layer which maps text features to integer sequences.
layer_text_vectorization(
object,
max_tokens = NULL,
standardize = "lower_and_strip_punctuation",
split = "whitespace",
ngrams = NULL,
output_mode = "int",
output_sequence_length = NULL,
pad_to_max_tokens = FALSE,
vocabulary = NULL,
...
)get_vocabulary(object, include_special_tokens = TRUE)
set_vocabulary(object, vocabulary, idf_weights = NULL, ...)
What to call the new Layer instance with. Typically a keras
Model, another Layer, or a tf.Tensor/KerasTensor. If object is
missing, the Layer instance is returned, otherwise, layer(object) is
returned.
The maximum size of the vocabulary for this layer. If NULL,
there is no cap on the size of the vocabulary. 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)).
Optional specification for standardization to apply to the
input text. Values can be NULL (no standardization),
"lower_and_strip_punctuation" (lowercase and remove punctuation) or a
Callable. Default is "lower_and_strip_punctuation".
Optional specification for splitting the input text. Values can be
NULL (no splitting), "whitespace" (split on ASCII whitespace), or a
Callable. The default is "whitespace".
Optional specification for ngrams to create from the possibly-split input text. Values can be NULL, an integer or list of integers; passing an integer will create ngrams up to that integer, and passing a list of integers will create ngrams for the specified values in the list. Passing NULL means that no ngrams will be created.
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.
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 NULL.
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.
Optional for layer_text_vectorization(). Either an array
of strings or a string path to a text file. If passing an array, can pass
an R list or character vector, 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 vocabulary is set (either by
passing layer_text_vectorization(vocabulary = ...) or by calling
set_vocabulary(layer, vocabulary = ...), there is no need to adapt()
the layer.
standard layer arguments.
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.
An R vector, 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.
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).
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:
Standardize each example (usually lowercasing + punctuation stripping)
Split each example into substrings (usually words)
Recombine substrings into tokens (usually ngrams)
Index tokens (associate a unique int value with each token)
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:
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$register_keras_serializable
for more details).
When using a custom callable for standardize, the data received
by the callable will be exactly as passed to this layer. The callable
should return a tensor of the same shape as the input.
When using a custom callable for split, the data received by the
callable will have the 1st dimension squeezed out - instead of
matrix(c("string to split", "another string to split")), the Callable will
see c("string to split", "another string to split"). The callable should
return a Tensor with the first dimension containing the split tokens -
in this example, we should see something like list(c("string", "to", "split"), c("another", "string", "to", "split")). This makes the callable
site natively compatible with tf$strings$split().
https://www.tensorflow.org/api_docs/python/tf/keras/layers/TextVectorization
https://keras.io/api/layers/preprocessing_layers/text/text_vectorization
Other preprocessing layers:
layer_category_encoding(),
layer_center_crop(),
layer_discretization(),
layer_hashing(),
layer_integer_lookup(),
layer_normalization(),
layer_random_contrast(),
layer_random_crop(),
layer_random_flip(),
layer_random_height(),
layer_random_rotation(),
layer_random_translation(),
layer_random_width(),
layer_random_zoom(),
layer_rescaling(),
layer_resizing(),
layer_string_lookup()