nlp_primitives.
UniversalSentenceEncoder
Transforms a sentence or short paragraph to a vector using [tfhub model](https://tfhub.dev/google/universal-sentence-encoder/2)
None –
Examples
>>> sentences = ["I like to eat pizza", "The roller coaster was built in 1885.", ""] >>> universal_sentence_encoder = UniversalSentenceEncoder() >>> output = universal_sentence_encoder(sentences) >>> len(output) 512 >>> len(output[0]) 3 >>> values = output[:3, 0] >>> [round(x, 4) for x in values] [0.0178, 0.0616, -0.0089]
__init__
Initialize self. See help(type(self)) for accurate signature.
Methods
__init__()
Initialize self.
generate_name(base_feature_names)
generate_name
generate_names(base_feature_names)
generate_names
get_args_string()
get_args_string
get_arguments()
get_arguments
get_description(input_column_descriptions[, …])
get_description
get_filepath(filename)
get_filepath
get_function()
get_function
Attributes
base_of
base_of_exclude
commutative
compatibility
default_value
description_template
input_types
max_stack_depth
name
number_output_features
uses_calc_time
uses_full_entity