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Text vectorization layer.
tf.compat.v1.keras.layers.experimental.preprocessing.TextVectorization( max_tokens=None, standardize=LOWER_AND_STRIP_PUNCTUATION, split=SPLIT_ON_WHITESPACE, ngrams=None, output_mode=INT, output_sequence_length=None, pad_to_max_tokens=True, **kwargs )
This layer has basic options for managing text in a Keras model. It transforms a batch of strings (one sample = one string) into either a list of token indices (one sample = 1D tensor of integer token indices) or a dense representation (one sample = 1D tensor of float values representing data about the sample's tokens).
The processing of each sample contains the following steps:
1) standardize each sample (usually lowercasing + punctuation stripping) 2) split each sample 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 sample using this index, either into a vector of ints or a dense float vector.
||The maximum size of the vocabulary for this layer. If None, there is no cap on the size of the vocabulary.|
||Optional specification for standardization to apply to the input text. Values can be None (no standardization), LOWER_AND_STRIP_PUNCTUATION (lowercase and remove punctuation) or a Callable.|
||Optional specification for splitting the input text. Values can be None (no splitting), SPLIT_ON_WHITESPACE (split on ASCII whitespace), or a Callable.|
||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.|
||Optional specification for the output of the layer. Values can be INT, BINARY, COUNT or TFIDF, which control the outputs as follows: INT: Outputs integer indices, one integer index per split string token. BINARY: 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: As BINARY, but the int array contains a count of the number of times the token at that index appeared in the batch item. TFIDF: As BINARY, but the TF-IDF algorithm is applied to find the value in each token slot.|
||Optional length for the output tensor. If set, the output will be padded or truncated to this value in INT mode.|
||If True, BINARY, COUNT, and TFIDF modes will have their outputs padded to max_tokens, even if the number of unique tokens in the vocabulary is less than max_tokens.|
adapt( data, reset_state=True )
Fits the state of the preprocessing layer to the dataset.
Overrides the default adapt method to apply relevant preprocessing to the inputs before passing to the combiner.
||The data to train on. It can be passed either as a tf.data Dataset, as a NumPy array, a string tensor, or as a list of texts.|
Optional argument specifying whether to clear the state of
the layer at the start of the call to
set_vocabulary( vocab, df_data=None, oov_df_value=None )
Sets vocabulary (and optionally document frequency) data for this layer.
This method sets the vocabulary and DF data 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.
||An array of string tokens.|
||An array of document frequency data. Only necessary if the layer output_mode is TFIDF.|
||The document frequency of the OOV token. Only necessary if output_mode is TFIDF.|
||If there are too many inputs, the inputs do not match, or input data is missing.|
||If the vocabulary cannot be set when this function is called. This happens when "binary", "count", and "tfidf" modes, if "pad_to_max_tokens" is False and the layer itself has already been called.|