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A preprocessing layer which maps text features to integer sequences.

Source: R/layers-preprocessing.R
layer_text_vectorization.Rd

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 layer_string_lookup().

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 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 list("string to split", "another string to split"), the Callable will see c("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 list(c("string", "to", "split"), c("another", "string", "to", "split")).

Note: This layer uses TensorFlow internally. It cannot be used as part of the compiled computation graph of a model with any backend other than TensorFlow. It can however be used with any backend when running eagerly. It can also always be used as part of an input preprocessing pipeline with any backend (outside the model itself), which is how we recommend to use this layer.

Note: This layer is safe to use inside a tf.data pipeline (independently of which backend you're using).

Usage

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,
  idf_weights = NULL,
  sparse = FALSE,
  ragged = FALSE,
  encoding = "utf-8",
  name = NULL,
  ...
)

get_vocabulary(object, include_special_tokens = TRUE)

set_vocabulary(object, vocabulary, idf_weights = NULL, ...)

Arguments

object

Object to compose the layer with. A tensor, array, or sequential model.

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:

  • NULL: 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:

  • NULL: 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 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.

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 NULL. 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 list, 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

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.

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.

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.

encoding

Optional. The text encoding to use to interpret the input strings. Defaults to "utf-8".

name

String, name for the object

...

For forward/backward compatability.

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.

Value

The return value depends on the value provided for the first argument. If object is:

  • a keras_model_sequential(), then the layer is added to the sequential model (which is modified in place). To enable piping, the sequential model is also returned, invisibly.

  • a keras_input(), then the output tensor from calling layer(input) is returned.

  • NULL or missing, then a Layer instance is returned.

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 <- layer_text_vectorization(
    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(c("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 <- rbind("foo qux bar", "qux baz")
vectorize_layer(input_data)

## tf.Tensor(
## [[4 1 3 0]
##  [1 2 0 0]], shape=(2, 4), dtype=int64)

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

vocab_data <- c("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 <- layer_text_vectorization(
    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()

## [1] ""      "[UNK]" "earth" "wind"  "and"   "fire"


# ['', '[UNK]', 'earth', 'wind', 'and', 'fire']

See also

Other preprocessing layers:
layer_category_encoding()
layer_center_crop()
layer_discretization()
layer_feature_space()
layer_hashed_crossing()
layer_hashing()
layer_integer_lookup()
layer_mel_spectrogram()
layer_normalization()
layer_random_brightness()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_rotation()
layer_random_translation()
layer_random_zoom()
layer_rescaling()
layer_resizing()
layer_string_lookup()

Other layers:
Layer()
layer_activation()
layer_activation_elu()
layer_activation_leaky_relu()
layer_activation_parametric_relu()
layer_activation_relu()
layer_activation_softmax()
layer_activity_regularization()
layer_add()
layer_additive_attention()
layer_alpha_dropout()
layer_attention()
layer_average()
layer_average_pooling_1d()
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_batch_normalization()
layer_bidirectional()
layer_category_encoding()
layer_center_crop()
layer_concatenate()
layer_conv_1d()
layer_conv_1d_transpose()
layer_conv_2d()
layer_conv_2d_transpose()
layer_conv_3d()
layer_conv_3d_transpose()
layer_conv_lstm_1d()
layer_conv_lstm_2d()
layer_conv_lstm_3d()
layer_cropping_1d()
layer_cropping_2d()
layer_cropping_3d()
layer_dense()
layer_depthwise_conv_1d()
layer_depthwise_conv_2d()
layer_discretization()
layer_dot()
layer_dropout()
layer_einsum_dense()
layer_embedding()
layer_feature_space()
layer_flatten()
layer_flax_module_wrapper()
layer_gaussian_dropout()
layer_gaussian_noise()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
layer_global_average_pooling_3d()
layer_global_max_pooling_1d()
layer_global_max_pooling_2d()
layer_global_max_pooling_3d()
layer_group_normalization()
layer_group_query_attention()
layer_gru()
layer_hashed_crossing()
layer_hashing()
layer_identity()
layer_integer_lookup()
layer_jax_model_wrapper()
layer_lambda()
layer_layer_normalization()
layer_lstm()
layer_masking()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()
layer_maximum()
layer_mel_spectrogram()
layer_minimum()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_random_brightness()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_rotation()
layer_random_translation()
layer_random_zoom()
layer_repeat_vector()
layer_rescaling()
layer_reshape()
layer_resizing()
layer_rnn()
layer_separable_conv_1d()
layer_separable_conv_2d()
layer_simple_rnn()
layer_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
layer_string_lookup()
layer_subtract()
layer_tfsm()
layer_time_distributed()
layer_torch_module_wrapper()
layer_unit_normalization()
layer_upsampling_1d()
layer_upsampling_2d()
layer_upsampling_3d()
layer_zero_padding_1d()
layer_zero_padding_2d()
layer_zero_padding_3d()
rnn_cell_gru()
rnn_cell_lstm()
rnn_cell_simple()
rnn_cells_stack()